Collaborative signal processing for target tracking in distributed wireless sensor networks

J. Parallel Distrib. Comput. 67 (2007) 501 – 515 www.elsevier.com/locate/jpdc

Collaborative signal processing for target tracking in distributed wireless sensor networks Xue Wang ∗ , Sheng Wang State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University, Beijing 100084, PR China Received 24 May 2006; received in revised form 22 December 2006; accepted 1 February 2007 Available online 17 February 2007

Abstract Target tracking, especially visual target tracking, in complex situations is challenging, which is always performed in single-view system. Because of the conflict between resolution and tracking range, however, single-view tracking is not robust and accurate. This paper presents a distributed multi-view tracking system using collaborative signal processing (CSP) in distributed wireless sensor networks (DWSNs). In the proposed tracking system, target detection and classification algorithms are based on single-node processing and target tracking is performed in sink node, whereas target localization algorithm is carried out by CSP between multisensor. For conquering the disadvantages of client/server based centralized data fusion, a progressive distributed data fusion are proposed. Finally, an indoor target tracking experiment is illustrated, and then tracking performance, execution time and energy consumption of progressive distributed data fusion are compared with client/server based centralized data fusion. Experimental results demonstrate that the CSP based distributed multi-view tracking system in DWSNs can accomplish multi-target extraction, classification, localization, tracking and association quickly and accurately with little congestion, energy consumption and execution time. © 2007 Elsevier Inc. All rights reserved. Keywords: Distributed wireless sensor networks; Collaborative signal processing; Target tracking; Distributed data fusion

1. Introduction Visual target tracking is a very important problem in surveillance, control and guidance of real-world and it is always implemented in single-view system for surveillance of people [10,25] and traffic [12]. But single-view system has several disadvantages in: limited observation window, ambient noise, interference, processing limitations at the sensor in terms of power and memory, and sensor reliability issues [17]. The use of multiview tracking and data fusion provides an eloquent mechanism for handling occlusion, articulated motion, and multiple moving objects in video sequences [6]. Several distributed and multisensor systems which place co-operating multiple cameras in a strategic way are introduced to overcome the limits of traditional monitoring systems [6,4,16,18]. Some of the technical challenges within multi-view tracking are to: (1) actively

∗ Corresponding author.

E-mail address: [email protected] (X. Wang). 0743-7315/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.jpdc.2007.02.001

control sensors to cooperatively track multiple moving objects; (2) fuse information from multiple sensors into scene-level object representations; (3) monitor the scene for events and activities [4]. Distributed wireless sensor networks are the key technology for the future world [14]. Because of the advantages on powerful sensing ability, intelligent information processing and low-cost manufacturing, DWSNs are suitable for many new applications, ranging from surveillance to environmental monitoring, especially in target tracking [3]. DWSNs are the typical distributed and multisensor systems, which allow for distributed sensing and collaborative signal processing (CSP), and DWSNs are desirable because the sensors are battery-powered and have limited wireless communication bandwidth [8]. Normally, in DWSNs, sensors will locally process the data in desired level to reduce its dimensionality and then transmit the processed data or decisions to a processing center, where the final result is carried out. For balancing the performance and consumption, CSP are desired to aggregate the distributed data from among the nodes in the network, and to make decisions

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in a reliable and efficient manner. Consequently, development of theories and methods for CSP of the data collected by different nodes is a key research area for improving the vision of DWSNs [5]. The CSP algorithms have to be developed under the constraints imposed by the limited communication and computational abilities of the nodes as well as their finite battery life. The key goal of CSP algorithms in sensor networks is to exchange the least amount of data between nodes to attain a desired level of performance. Client/server is one of the most popular CSP model in DWSNs. In this model, each node transmits data to the processing center, where the further data processing is performed. For reducing the amount of information transmission and calculation, the client/server mechanism always fuses the information from a fixed number of sensor nodes and acquires the final result. At each time instant, the sink node makes a decision of sensor node selection with some metrics, such as node location, environment and so on. Then sink node sends out the commands to relative sensor nodes to acquire the information, and offers services about data fusion and memory. Although it is widely used, however, the client/server model is not appropriate for data integration in DWSNs. The reason is that it consumes much scarce resources such as battery power and network bandwidth and it cannot respond to the load changing in real time because it uses a fixed set of sensor nodes for data fusion [19]. For improving the collaboration between nodes, Qi [19] proposed the concept of mobile agent-based distributed sensor networks (MADSNs) wherein a mobile agent selectively visits the sensors and incrementally fuses the appropriate measurement data, and Wu [22] discuss the routing problem of the data fusion using such a paradigm, and presented a routing algorithm based on the consideration of energy consumption, path loss, and signal energy. Zhao [24] presented an information-driven approach to sensor collaboration in ad hoc sensor networks which considered the information utility of each node and developed several approximate measures of the information utility. And Xu [23] introduced a distributed computing paradigm to carry out CSP in sensor networks using mobile agents. However, given the myriad of effects that can compromise the performance of signal processing algorithms in a sensor network, various forms of CSP may be necessary [27]. In this paper, we direct our research efforts to the multi-view target tracking in DWSNs with a focus on the CSP mechanism, especially in sensor node selection. Because the complexity of visual target detection and classification algorithm, the overhead of mobile agent is so large that MADSN is not better than DSN in this application [19]. Here, we adopt a progressive distributed data fusion mechanism which is similar to the mobile agent. This mechanism also incrementally fuses the information from selected sensor node, but it transfers the partially integrated results while the mobile agent carries a partially integrated overlap function in MADSN. This paper also presents a CSP based distributed multi-view tracking system in DWSNs. In this system, target detection and classification algorithms are based on single-node processing and target tracking is performed in sink node, whereas tar-

get localization algorithm requires multisensor fusion, where the progressive distributed data fusion mechanism is used. For improving the sensor node selection process, some practically feasible metrics are developed which refer to the lifetime of network and the impact of environment. The structure of this paper is as follows. Section 2 introduces the progressive distributed data fusion mechanism. Section 3 presents the CSP tracking system in DWSNs which contains five steps: background subtraction based target detection, principle components analysis (PCA) and support vector machine (SVM) based target classification, progressive distributed data fusion, particle filterradial basis function (PF-RBF) based target tracking, and classification-aided data association. Section 4 analyzes the results of indoor target tracking experiment and compares the tracking performance, energy consumption and execution time of progressive distributed data fusion and client/server based centralized data fusion. Section 5 has a discussion about unmentioned additional issues. And finally, Section 6 presents the conclusions. 2. Progressive distributed data fusion mechanism in distributed wireless sensor networks Similar to MADSN, the fusion procedure is performed progressively from one node/cluster to another node/cluster in progressive distributed data fusion mechanism. That is, local fusion method is performed in each selected node/cluster for integrating the new results with previous results to increase accuracy potentially, and then current node/cluster searches a new optimal node/cluster and transmits the partially fused results to it. The partially fusion and node/cluster selection are looped in other selected sensor nodes one by one. Once the desired accuracy is achieved, last node/cluster terminates migration and returns the final result to sink node to conserve energy. Because the progressive distributed data fusion dynamically chooses a proper set of sensor nodes/clusters according to the current predictions of contributions and transmits the information in order, the congestion, energy consumption and execution time of DWSNs can be decreased. This mechanism can develop an energy-efficient signal processing and communication methods to provide progressive accuracy in DWSNs. The order and number of nodes on the route traversed by the information package have a significant impact on the overall performance of the progressive distributed data fusion. In practice, the routing algorithm involves a trade off between the cost and performance. The routing objective is to find a path for a mobile agent that satisfies the desired detection accuracy while minimizing the energy consumption. For evaluating the efficiency of sensor node selection, the strategy based on energy consumption and information utility is considered. A routing algorithm based on the consideration of energy consumption, path loss, and signal energy is introduced in [22]. But the proposed algorithm just aims to the amount of energy consumption without consideration of evenness of the reserved energy in DWSNs which is also a very important metric for the lifetime. And the signal energy is not sufficiently reasonable for eval-

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uating the sensing performance, because the information utility of each sensor is determined by the characteristic of sensor node and environment. As presented in [24], the sensor node selection can be considered as an optimization problem with the objective function: M (Si ) = − · Cost (Si ) + (1 − ) · Utility (Si ) ,

i∈A

(2)

Depending on applications and assumptions, Cost and Utility have various forms which will be discussed below. 2.1. Resource consumption metric The resource consumption metric Cost is characterized by link bandwidth, transmission latency, node battery power reserve, etc. In this case, Cost contains three basic consumption types: sensing cost s , communication cost t and information fusion cost f , where communication cost is most important in DWSNs. The energy Pj,r received by sensor Sj has following relation with the energy Pi,t transmitted by sensor Si according to the Friis free space propagation model [20]: 2  (4)2 di,j Pi,t  = , Pj,r di,j Gi,t Gj,r 2

(3)

where Gi,t is the gain of sensor Si as a transmitter, Gj,r is the gain of sensor Sj as a receiver,  is the wavelength,  is the system loss factor. The physical distance di,j between Si and Sj is computed from their spatial locations. It means that the communication cost is proportional to the square of Euclidean distance between source and destination, so we can use

2 di,j

d02

as a crude measure of the consumed energy, where d0 is the predefined standard distance. Furthermore, the lifetime of DWSNs is always considered as the shortest lifetime of individual node. For prolonging the lifetime, we should consume the energy evenly among all nodes. So, here, we use entropy theory which can measure the randomness of a given random variable to score the equality of reserved energy in each node. We define the amount of energy reserved in ith sensor node after observation and transmission at instant t as Eit , and then define the proportion of its reserved energy p (Eit ) as the arbitrary logarithmic function:   1 (4) = − log (p (Eit )) . I (si ) = log p (Eit ) For the whole networks, the mean value of I (sk ) is  p (Eit ) log p (Eit ) , H (Sit ) = −

so we use H (S1 it ) to score the impact of energy consumption. The combined resource consumption metric is as follows: Cost (Si ) =

2 di,j

H (Sit )

.

(6)

(1)

where Si is the evaluated node, Cost is the resource consumption metric, Utility is the information utility metric,  is the relative weight. With this function, the sensor node selection problem can be defined as iˆ = arg max (M (Si )) .

503

(5)

it∈Sit

where H (Sit ) is the entropy of energy reserve in DWSNs. The bigger the entropy is, the more evenly the reserved energy is,

2.2. Information utility metric In order to measure the information utility of selected nodes, we should make use of the predicted contribution of selected sensor nodes, which can be called Predicted . This metric can be carried out by analyzing the theoretical model of specific application. Except predicted contribution, the characteristics of sensor nodes, such as sensibility, sensing range, sensing reliability, also have great effect on information acquisition. These factors should also be taken into account. Here, the impacts of these characteristics are evaluated as a characteristic coefficient Char in the information utility metric. Besides, the information utility is not only determined by sensor nodes but also by environment, such as obstacles, noise and so on. But the impact of environment is difficult to be determined and measured in practice. A more practical alternative is to estimate the confidence degree Confidence of the wireless sensor nodes, which can be measured by the match degree between tracking result of the wireless sensor node and the final fusion result at last instant. In conclusion, the information utility metric function can be defined as follows:         Utility Sj = Predicted Sj · Char Sj · Confidence Sj . (7)

And then the objective function of sensor node selection during information fusion is         M Sj = (1 − ) · Predicted Sj · Char Sj · Confidence Sj   2 di,j . (8) − · d02 H (Si ) The sensor node selection and progressive distributed data fusion process are repeated until some criterion is met. 3. CSP based distributed multi-view tracking system in distributed wireless sensor networks Compared with centralized tracking, CSP based multi-view tracking system can improve the tracking performance by purposefully fusing the results from specific sensor nodes, it also decreasing the resource consumption and execution time by the collaboration between nodes. With distributed tracking systems, however, additional issues are brought in, such as stereo

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matching; pose estimation; automated camera switching; system bandwidth and processing capabilities [6]. Generally, target tracking involves the following steps: target detection, classification, localization and tracking. Considering the tradeoff between the accuracy and resource consumption, target detection and classification are performed in single node, target localization is considered as a multi-view fusion based on progressive distributed data fusion mechanism, whereas the target tracking is operated in sink node for ensuring the entirety of tracking performance.

since the last time that the pixel was classified as a foreground pixel, the definitions are as follows:  gS (x, t) =



3.1. Single-node target detection and classification

⎡

⎤

  ⎤ mini V i (x) ⎢ ⎥ ⎢   ⎥ ⎢ n (x) ⎥ = ⎣ ⎦ maxi V i (x) ⎣ ⎦   i  i−1   maxi V (x) − V (x) d (x) m (x)

⎡

    where V i (x) −  (x) < 2 ∗  (x) .

(9)

Unfortunately, background model is not stable, because of illumination changes or physical changes. Two different methods to update the background have been used. The one is pixel-based update method which updates the background model periodically to adapt to illumination changes. The other is object-based update method which updates the background model to adapt to physical changes. During tracking, a change map is dynamically constructed to determine whether a pixelbased or an object based update method applies. The change map consists of three main components: detection support map (gS), motion support map (mS) and change history map (hS) which represents the number of times a pixel location is classified as a background pixel, moving pixel, and the elapsed time

gS (x, t − 1)

if x is foreground pixel, (10)

mS (x, t) = 3.1.1. Target detection based on background subtraction The limited data processing ability of each node constrains the selection of target detection method, here, we adopt background subtraction method which learns and models background scenes statistically to detect foreground objects. This method is demonstrated as a low-cost simple but efficient method for target detection, which is successfully adopted in W 4 real time visual surveillance system for detecting and tracking multiple people and monitoring their activities in an outdoor environment [10]. The output result is minimum boundary rectangle (MBR) of target. This algorithm contains two steps: initialize background model and update background model parameters. First, a pixelwise median filter over time is applied to several seconds of video to distinguish moving pixels from stationary pixels. Second, only stationary pixels are processed to construct the initial background model. Let V i (x) be the intensity of the location in the ith image of N consecutive images array V .  (x) and  (x) are the standard deviation and median value of intensities at pixel location x in all images in V . The initial background model for a stationary pixel location x, [m (x) , n (x) , d (x)], is obtained as follows:

gS (x, t − 1) + 1 if x is background pixel,

mS (x, t − 1) + 1 if M (x, t) = 1, mS (x, t − 1)

if M (x, t) = 0,

(11)

where

⎧ 1 if (|I (x, t) − I (x, t + 1)| > 2 ∗ ) ⎪ ⎪ ⎨ ∧ (|I (x, t − 1) − I (x, t)| > 2 ∗ ) , M (x, t) = ⎪ ⎪ ⎩ 0 otherwise, ⎧ 255 if x is foreground ⎪ ⎪ ⎪ ⎨ pixel, hS (x, t) = (12) ⎪ ⎪ 255 ⎪ ⎩ hS (x, t − 1) − otherwise. N And the change-maps are set to zero after the background model is updated. During tracking, the background model is computed separately for all pixels which are classified as fore ground pixels mf (x) , nf (x) , d f (x) and background pixels   b b (x) , nb (x) , d b (x) . Let mc (x), nc (x), d c (x) be the background model parameters currently being used; the new background model parameters m (x), n (x), d (x) are determined as follows: [m (x) , n (x) , d (x)] =  ⎧ b m (x) , nb (x) , d b (x) if (gS (x) > k ∗ N ) ⎪ ⎪ ⎪ ⎪ (pixel-based) , ⎪ ⎪ ⎨  f f f m (x) , n (x) , d (x) if (gS (x) < k ∗ N ∧ mS (x) ⎪ ⎪ ⎪ < r ∗ N ) (object-based) , ⎪ ⎪ ⎪  ⎩ c m (x) , nc (x) , d c (x) otherwise. (13) As presented in [10], k and r are scale factor to adjust the effect of number N, they are typically 0.8 and 0.1, respectively. Then each pixel is classified using the constructed background model. Giving the minimum m (x), maximum n (x), and the median of the largest interframe absolute difference d images, pixel x from image I t is a foreground pixel if: ⎧   t I (x) − m (x) < kd ⎪ ⎨ 0 background   B (x) = ∨ n (x) − I t (x) < kd , ⎪ ⎩ 1 foreground otherwise. (14)

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505

Q-dimensional feature space which is relatively computationintensive. An equivalent kernel function can be used to perform this transformation which can help in reducing the computational load and retaining the effect of transformation. The kernel function K (x · y) is defined as follows: Fig. 1. Example of the results of target detection: (a) background image; (b) foreground image, and (c) foreground region detection result.

Here, we consistently use k = 2 in our system as presented in [10]. With the presented method, one example of target detection is shown in Fig. 1. According to the complexity of calculation, the computational cost of this algorithm is low. The detail discussion about the computational cost of this algorithm can be found in [10]. It means that this algorithm is practically feasible in DWSNs. 3.1.2. Target classification based on PCA and SVM Target classification contains two key elements: features extraction and division. Features extraction refers to a process whereby a data space is transformed into a feature space. The transformation is designed in such a way that the data set may be represented by a reduced number of “effective” features by retaining most of the intrinsic information of the data. PCA maximizes the rate of decrease of variance and is therefore the right choice. Some theorems verify that the closer the eigenvalues are to zero, the more effective the dimensionality reduction will be in preserving the information content of the original input data. So the eigen-image can be extracted as follows [15]. First, calculate the difference between the average image of all M images and each image: i = i −

M 1  i . M

(15)

i=1

Then, form the difference matrix A = [1 2 . . . M ] and compute the covariance matrix C: C = AAT =

M 1  i Ti . M

(16)

i=1

Next, calculate the eigenvectors of the covariance matrix, and construct the eigen-subspace by selecting a subset of K eigenvectors with the largest eigenvalues. Here, K is set to 50 for decreasing the computational complexity in the polynomial computation in each node and ensuring the eigen-subspace can retain enough intrinsic information of the original space. For each new image, the eigen-image is calculated by projecting its difference image  to the eigen-subspace for providing the compact representation. After features extraction, SVM classifier is used to find an optimal hyperplane to separate the feature vectors that belong to two classes while the distance from the hyperplane to either class is maximized. A SVM is essentially a linear classifier operating in a higher dimensional space. In SVMs, a transformation (x) is used to convert the data from an N-dimensional input space to a

K (x · y) = (x) · (y) .

(17)

With the kernel functions, the basic form of SVM is as follows: f (x) = sign

 l 

 vi ( (x) · (xi )) + b .

(18)

i=1

The parameter vi is used as weighting factors to determine which input vectors are actually support vectors (0 < vi < ∞). In the numerical examples presented in this paper, we use a third degree polynomial kernel: 3    k xi , xj = xiT xj + 1 .

(19)

And b is a scalar threshold for adjusting the results of classification, here, it is set to 0. Similar to neural networks, the training phase can take a long time. However, once the classifier is trained, its application is relatively easy. In general, a different SVM is trained for each class. The output of each SVM can then be regarded as an estimate of the posterior probability for that class. Because this system is used for surveillance of people, the SVM classifier is train for human and non-human classification. For other applications, different classifiers can be trained and combined for more complicated and particular classification. In order to train the classifier, a total of 3000 binary images of human and 1000 binary images of non-human, which were produced in different dates with different groups of people and scaled to have a resolution of 10 × 24 pixels, are used for training, and 1000 binary images of human and 500 binary images of non-human are used for testing. Fig. 2 shows some examples of used human and non-human images. One example of target classification is shown in Fig. 3. Although the computational cost of carrying out PCA and training SVM is too high for constrained sensor nodes, on-node processing does not need to perform such a complex computation. The preprocessing computation can be performed by sink node which has more powerful computational ability, or even by other remote hyper-node, such as powerful PC. Then the prerequisite parameters are transferred into each node for features extraction and division, for example, the K eigenvectors in PCA and parameters of kernel functions in SVM. Each node just needs to perform some simple matrix or polynomial operation, which is reasonable for the processing ability of wireless sensor nodes. As mentioned before, each node just needs to store a human/non-human classifier, so the storage space of wireless sensor node is enough for the application.

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Fig. 4. The uncertainty of position of the target can be effectively approximated by a 2-D Gaussian distribution in MBR area.

Fig. 2. Reduced resolution training images of (a) human and (b) non-human.

Fig. 5. Multi-view fusion method fuses the bearing information of multiple sensor nodes and acquires the probability distribution of the target’s position.

Fig. 3. Example of target classification where the solid rectangle represents a human target, the dashed rectangle represents a non-human target.

3.2. Collaborative target localization Because each sensor node can only acquire the bearings of targets for visual tracking, 3-D locations of targets can only be carried out by fusing the information from multiple sensor nodes. Here, we present a multi-view fusion method for target localization which is based on progressive distributed data fusion mechanism. 3.2.1. Multi-view fusion localization method Environmental conditions such as dynamic ambient lighting, object occlusions, insufficient or incomplete scene knowledge, and other moving objects and people are just some of the naturally interfering factors [6]. To combat the negative effects of occlusion and articulated motion we use a multi-view fusion method for target localization, where each view is first independently processed by wireless sensor nodes. The proposed multi-view fusion is a simple method which integrates MBR results of several sensor nodes for acquiring 3-D locations of targets and improving accuracy and robustness.

It is assumed that measuring uncertainty of target position can be effectively approximated by a 2-D Gaussian distribution in MBR, as illustrated in Fig. 4. Multi-view fusion method merges Gaussian distribution results from multiple sensor nodes as follows:  n   E (X |Z1 , . . . , Zn ) =

P (X |Zi ) × P (Zi ) , (20) i=1

where P (X |Zi ) denotes the Gaussian distribution result acquired by ith wireless sensor node, P (Zi ) denotes the relative parameter of ith wireless sensor node to target position, here, it is given by the distance between wireless sensor node and predicted target position. presents the normalizing operator. X is the fused result. As illustrated in Fig. 5, multi-view fusion method fuses the bearing information of multiple sensor nodes and acquires the probability distribution of the target’s position. After that, the approximate position of moving target can be calculated as expectation of the distribution X: E (x) =

S 

xi · p (xi ).

(21)

i=1

If all sensor nodes work normally, the fused results are at most as large as the uncertainty of the most accurate individual node. But if some sensor nodes are broken or the targets are blocked in their sights (as node IV), their results are not useable. Generally, these nodes would not give any MBR result or give some MBR results which are apart from the former partial

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fused results acquired by other nodes. For justifying the validity of each node, we give the deduction as follows: if some nodes do not give any MBR result, or if the fused probability distribution becomes zero, or if the fused probability of each potential position becomes low, the relative nodes will be considered as useless ones, then the results will be reverted to the former partial fused results. Because the MBR results are locally extracted in parallel and only integrated results are needed to be transmitted, the detection time and transmission consumption can be sharply decreased. Furthermore, like most indoor surveillance video sequences, we assume that the targets can be correctly classified in most wireless sensor nodes and the type will not change frequently within a few frames. It means that in most cases the target class should be the same in all wireless sensor nodes and neighboring frames. A simple voting mechanism, utilizing the status consistency and the target tracking information, is applied to further improve the accuracy of the moving target classification. For each node, one vote is cast for the resulting target class, and votes are counted for five neighboring frames at each step, the target class receiving the largest number of votes decides the target’s class in this node. Moreover, after the result is sent back to the sink node, votes are counted and the final voted result determines the target’s class. This voting mechanism reduces the number of random misclassifications in the video sequence caused by the temporal occlusion such as imperfection in motion detection. Although this voting mechanism is very simple, it performs well in this system, because the classification results of wireless sensor nodes are always correct, which will be illustrated in Section 4.

3.2.2. Progressive distributed data fusion based multi-view fusion localization Because of the inherent characteristic of the proposed multiview fusion localization method, it can be incrementally carried out by integrating the Gaussian distribution results of wireless sensor nodes. During progressive distributed data fusion, the partially integrated Gaussian distribution result is transported and updated from one node to another until the predefined criterion is met. In Section 2, resource consumption metric is given without loss of generality, but the application-specific information utility metric is not given, which will be discussed below. First, the Mahalanobis distance is used as predicted contribution metric Predicted . Mahalanobis distance is a generalized distance, which can be considered as a single measure of the divergence degree in the mean values of different characteristics of a population by considering the correlations between the variables. The Mahalanobis distance is a useful way of determining the similarity of a set of unknown samples to a set of known samples. In DWSNs, it is useful that combine the measurement with the current belief state using Bayes rule to form new belief state for verifying the potential of the nodes to improve the current belief state. Experimental results verify that if the current belief can be well approximated by a Gaussian distribution or the distribution is very elongated, the

507

Mahalanobis distance-based utility can evaluate the new sensor’s contribution to the evolution of current belief state [23]. The utility function is T −1      (22) Psj − Xˆ , Predicted Sj = − Psj − Xˆ ˆ where Psj is the position of sensor sj , Xˆ and ˆ is the mean and covariance of the belief of partially fused target position, respectively. Second, in visual tracking, each node can only cover a part of tracking area. During tracking, if target is moving towards one sensor node, the sensor node has little contribution on analyzing the target’s speed, that is, the bigger the angle between target’s moving direction and lens axis is, the more accurate the information is. Here, the angle is defined as . So the bearings and positions of sensor nodes should be considered in selecting sensor nodes, the characteristic coefficient Char is defined as  0 , where 0 is the predefined standard angle. Third, the confidence degree Confidence of the wireless sensor nodes can be also evaluated by the Mahalanobis distance between tracking result of the wireless sensor node and the final fusion result at last instant, because Mahalanobis distance can easily determine the similarity between two probability distribution sets. The confidence degree can be defined as    Confidence Xs j , Xˆ  , ˆ  T −1   = − Xs j − Xˆ  ˆ Xs j − Xˆ  ,

(23)

where Xs j is the probability distribution of sensor j’s measureˆ  is the mean and covariance of ment at last instant, Xˆ  and  probability distribution of final fusion result at last instant, respectively. And then the objective function of sensor node selection during information fusion is as follows:  T −1      M Sj = (1 − ) · Psj − Xˆ ˆ Psj − Xˆ · 0  T −1   · Xs j − Xˆ  ˆ Xs j −Xˆ   − ·



2 di,j

d02 H (Si )

.

(24)

And the criterion for finalizing the fusion can be carried out by using a sufficiently good metric of probability distribution of target position. Here, we use the entropy to evaluate the performance of probability distribution: Hp (X) = −

S 

p (xi ) log p (xi ).

(25)

i=1

If the entropy of integrated probability distribution exceeds the predefined value, the progressive distributed data fusion is

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considered to be finished, and the final wireless sensor node will transport the fused result back to the sink node. For multi-target tracking, the sink node will acquire the positions of multi-target, searches the optimal node for each target and transmits the predicted result of each target to the correlative nodes. Then the correlative nodes operate the loops of target extracting, classifying, information fusing and node searching separately. While all analyzing loops are finished, the sink node fuses the final results together to acquire the positions of all targets. Compared with centralized data fusion, the proposed progressive distributed data fusion transmits the integrated result in order and completes the information fusion progressively. Because energy consumption metrics and information utility metrics are both considered in sensor node selection, the progressive distributed data fusion can realize the tradeoff between accuracy and consumption. Moreover, ordinal transmission mitigates the congestion in wireless link, which decreases the communication latency and reduces the energy consumption. Because the communication latency is low, we can consider the fusion as a synchronous procedure, which can be realized without the time stamp information.

x and y directions. Z (k) is measurement vector, and V (k) is process noise, W (k) is measurement noise; both sequences are assumed to be uncorrelated with white Gaussian noise sequence with zero means and the variance matrices are Q (k) and R (k), respectively. F is the model state transition matrix, G1 (k) is the coupling matrix for maneuver inputs, G2 (k) is the process noise input matrix, H is the model output matrix. The PF-RBF algorithm uses RBFNs to approximate the moving trajectory according to the previous observations and current prediction, and construct dynamic process model to replace standard process model for sampling/predicting the new value of each particle. Radial basis functions are a special class of function. The center, the distance scale, and the precise shape of the radial function are parameters of the model. In principle, they could be employed in any sort of linear or nonlinear model and single-layer or multi-layer network. RBFN is a three-layer feed-forward neural network which is embedded with several radial-basis functions. Such network is characterized by an input layer, a single layer of nonlinear processing neurons, and an output layer. The output of the RBFN is calculated according to yi = fi (x) =

N 

ik k ( x − ck 2 ),

i = 1, 2, . . . , m,

k=1

3.3. Target tracking and estimation

(30) At each time instant, sink node acquires the 3-D positions of targets from multi-view fusion localization method. Then target tracking and estimation is executed in sink node. Target tracking is always performed by some state estimator like, for example, Kalman filter or particle filter. In target tracking, acceleration increases the process noise and accordingly increases the difficulty of self-adaptive filter tracking. Even a short-term acceleration will cause a bias in the measurement sequence and result in divergence, if no compensation is used in time. Although standard PF can handle multimodal probability density functions (PDFs) [9] and solve nonlinear non-Gaussian problem [7], it cannot solve the estimation error cumulating problems when maneuver occurs. We propose a new particle filtering method called PF-RBF which is based on radial-basis function network (RBFN). Assume process model and observation model are as follows: Process model: X (k + 1) = F X (k) + G1 U (k) + G2 V (k) ,    1 k = j, T E V (k) V (j ) = Q (k) kj , kj = 0 k = j .

(26) (27)

Observation model: Z (k + 1) = H X (k + 1) + W (k + 1) ,   E w (k) w (j )T = R (k) kj ,

(28) (29)

where k is time index, X (k) = [x (k) , x˙ (k) , y (k) , y˙ (k)]T is the state vector which represents positions and velocities of the  T target in the two-dimension plane, U (k) = ux (k) , uy (k) is the input vector consisting of acceleration components in the

where x is an input vector, k is a basis function, · 2 denotes the Euclidean norm, ik are the weights in the output layer, N is the number of neurons in the hidden layer, and ck are the RBF centers in the input vector space. The functional form of k is assumed to have been given, the typical choice is Gaussian function which is as follows: (x) = exp −x 2 /2 . In standard PF algorithm, in order to compute a sequential estimate of the posterior distribution at time t without modifying the previously simulated states X0:t−1 , the proposal distribution q of the following form can be used: q (X0:t |Z1:t ) = q (X0:t−1 |Z1:t−1 ) q (Xt |X0:t−1 , Z1:t ) , (31) where the proposal distribution q is q (Xt |X0:t−1 , Z1:t ) p (Xt |Xt−1 ) ,

(32)

where p (Xt |Xt−1 ) is determined by the process model. The process model of standard PF algorithm is always single motion model which is static and cannot offer the consistent and precise dynamic representation of the environment. The improved PF algorithm based on interacting multi-model (IMM) filter can partially solve the problem which allows for several parallel modes which are combined to a weighted estimate [1]. But each model has to estimate and update the state separately, so the execution time will increase sharply. In PFRBF algorithm, the RBFN is trained as process model. The input of trained RBFN contains the previously simulated states X0:t−1 of each node, and the output is the wanted estimated current state Xt of corresponding node. For approximating the target’s real state, we use the previous observations and current prediction based on observations as the samples to train the

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Fig. 6. Data association based on predicted results.

RBFN. With the guidance of RBFN, each particle can estimate and update the probability distribution more effectively, and also keep the multimodality. The improvement of estimation accuracy decreases the cumulated effect of errors and makes the tracking system convergent easily. 3.4. Data association for multi-target tracking If multiple targets are sufficiently separated in space or time which means they occupy distinct space–time cells, a different track is initiated and maintained for each target. But the collision often occurs in complex environments which may increase the difficulty. Data association is an important problem when multiple targets are present in a small region. Each node must associate its measurements with individual targets. Data association, where the measurement-to-track associations are decided, is used to determine that which of the received measurements should be used to update each track. There are many data association method such as probabilistic [11], Monte Carlo filtering [21] and so on. Here we adopt the algorithm present in [2], which uses the target class information to improve discrimination by yielding purer tracks and preserving their continuity. In this algorithm, the sensor’s classification capability is modeled by a “confusion matrix” which amounts to target class likelihood functions, and then these likelihoods together with priori probabilities are used to produce a posteriori probability using Bayesian updating. These probability vectors are the track’s classification information based on the sensor classification outputs. In assignment, the data association is formulated as a constrained optimization problem, where the cost function to be minimized is a combined negative log-likelihood ratio evaluated using the results from the state estimator. In this paper, we use a method which is similar to the nearest neighbor approach [13] to associate the measurements to tracks by using measurement that is closest to the predicted target-originated measurement. As illustrated in Fig. 6, for example, two image sensor nodes are used to track two targets, four potential target positions are formed. The predicted positions of targets are near to two of them, we can easily distinguish which ones are real positions. For simplifying the computation, we also use

Mahalanobis distance to determine the optimal associated results for each target:   arg min ass Ppredicted , Xˆ i , ˆ i 1
 T −1   = Ppredicted − Xˆ i ˆ i Ppredicted − Xˆ i , (33) where Ppredicted is the target position which is predicted by ˆ i are the mean and covariance of PF-RBF algorithm, Xˆ i and  probability distribution of ith potential position, N is the number of potential position. Because each node just fuses its results with former partially fused results, the maximum number of potential position is Nnode ×Npartially , where Nnode is the number of targets which are detected by individual node, and Npartially is the number of targets which are in the former partially fused results. The assignment between tracks and measurements is formulated as a discrete optimization problem to maximize a dimensionless global likelihood ratio of the measurements-totracks associations. The likelihoods are obtained from the state estimator, such as, PF-RBF algorithm, for the target kinematic state and the classifier outputs for the target classes. Compared with other data association methods, the computational complexity of this algorithm can be decreased because the state estimated result and classification result can be obtained in the former process. The remained computation just refers to likelihood ratios and confusion matrix, which can be afforded by the wireless sensor nodes. The detailed analysis about the computational cost can be found in [2]. 4. Experimental results Multi-target tracking in crowded environment is an important topic of research in target tracking. Especially, indoor multitarget tracking is a complex problem, because the collision often occurs. In this section, we describe an indoor multi-target tracking experiment for analyzing the activities of all targets, and illustrate the tracking performance of distributed multiview tracking system using CSP method. Because of the space limitation, just a small number of nodes are deployed and tested. But this scenario can also be considered as a part of the DWSN.

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Fig. 7. The setup scenario of the distributed wireless sensor network.

In this experiment, the tracking performance, energy consumption and communication latency of client/server based centralized data fusion and progressive distributed data fusion will be evaluated and compared with each other. 4.1. Deployment of experiment Eighteen wireless sensor nodes and one sink node are deployed in a room (see Fig. 7 as a reference of the node coverage). The low deployed position (1.5–2.5 m) and complex situations increase the difficulties of target detection, classification and tracking. Each sensor node contains one image/pyroelectric-infrared sensor pair which has 60 visual angle and 3.6 mm camera lens. For visual tracking, the image sensing module of each node cannot work all the time because of energy constraints. Some dynamic power management strategies have to be used to manage the state of each node. That is, when initialization, all sensor nodes are set into sleep with pyroelectric infrared sensor working; if special events are detected, the node will active the image sensing module and start target detection; if special events disappear, the sensor node will shut down the image sensing module for conserving energy. The information is processed in each node at a frame rate of 10 Hz with video down-sampled to 160 × 120 pixels, and each information package is 1 kbytes which carries predicted target information, partially integrated tracking result, and a list of passed itinerary. Moreover, each node can locally estimate the rough energy consumption and share the reserved energy information per minute. 4.2. Multi-target tracking results In this experiment, one person moved and put down a box at center, then walked to the other side of the room and hid himself, while the other person sit in the room all the time. As shown in Fig. 8, with extensive tracking experiments in individual sensor nodes, we can make a conclusion that the proposed single-node target detection and classification algorithm performs well, even if targets are partially blocked, drastically changed or separated. Furthermore, the classification accuracy for human type and non-human type are 89.8% and 93.9%,

Fig. 8. The results of single node detecting and classifying, where the solid rectangle represents human target and the dashed rectangle represents non-human target. The system can detect and classify multi-targets successfully, even if target is (a) severely blocked, (b) drastically changed, (c) separated, or (d) disappeared.

respectively, in a large set of test images, because human may make some poses, such as stoop, squat and grovel, which will lead to misclassification. Fig. 9 shows some detection and classification results from four wireless sensor nodes. The limitation of view angles and disturbances of obstacles cause the mistakes in classification results of some nodes, i.e., classification of the sitting person in Fig. 9(b) and (d). The multi-target tracking result of distributed multi-view tracking system using progressive distributed data fusion is illustrated in Fig. 10. The trajectories of all targets are labeled for classification where we can get some understanding about the activities of all targets. The analyzed trajectories accord with the real activities well. The results verify that the distributed multi-view tracking system using progressive distributed data fusion have good performance on multi-target detecting, classifying and tracking. 4.3. Performance comparison In this section, we track the targets by distributed multiview tracking systems which are based on client/server based centralized data fusion and progressive distributed data fusion, respectively. Two mechanisms adopt the same sensor nodes selection metric which is defined by (24). The number of fused nodes changes from 3 to 9 for analyzing the effect of the number of nodes in data fusion. The tracking trajectories of person 1 acquired by these fusion methods are illustrated in Fig. 11. It is obvious that the tracking performance of progressive distributed data fusion is much better than client/server based centralized data fusion. The reason is that, in client/server based centralized data fusion, the sensor node selection is determined before target tracking, while the progressive distributed data fusion dynamically determines the selection and the number of sensor nodes during tracking procedure. Fig. 11 also illustrates that, in client/server based centralized data fusion, while the fixed number of fused nodes is less than 7, the tracking performance becomes better as the number of fused nodes increases. Intuitively, a more number of fused nodes lead to a more accuracy result because the amount

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Fig. 9. Tracking results from multiple sensor nodes: (a) node N1, (b) node N8, (c) node N10, (d) node N18 at the same time instant.

wireless sensor nodes person 1 box person 2 sink node

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N3

N4

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N5

N7

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N17

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0 0

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N15

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200 300 400 X coordinate (cm)

N12 500

600

Fig. 10. The results of multi-target tracking. The shown labeled object trajectories records the movement of two human and one non-human target. From these labeled trajectories, we can get some understanding about the activities of all targets as mention above.

of effective information increases. However, while the number of fused nodes is more than 7, the tracking performance becomes worse as the number of fused nodes increases, because the confused transmission aggravates the congestion of wireless link which will cause the out-of-sequence of measurement and packet loss, this will also increase latency and energy consumption. In this experiment, when the fixed number of fused nodes is 9, the packet loss ratio of client/server based centralized data fusion is 12.9%. Thus, simply increasing the number of fused nodes may actually harm the performance of the network.

Furthermore, we compare the execution time and energy consumption of two mechanisms. During tracking, the operations, such as waking up modules, image acquirement target detection and target classification, are automatically managed by sensor nodes. Moreover, computing process of progressive distributed data fusion is as same as computing process of client/server based centralized data fusion. The sensing cost and fusion cost are equal for both two methods. So we just compare communication latency and energy consumption. As illustrated in Fig. 12, time delay and energy consumption of these methods both increase with the number of fused nodes. Furthermore, progressive distributed data fusion performs much better than client/server based centralized data fusion, because progressive distributed data fusion transmits the data in order, while client/server based centralized data fusion transmits the data confusedly which will increase the time delay and energy consumption. For detailing the tracking procedure, we compare the number of fused nodes, execution time and energy consumption of progressive distributed data fusion with client/server based centralized data fusion at all 105 tracking instants in 10.5 s. The number of sensor nodes adopted in client/server based centralized data fusion is 7. As shown in Fig. 13(a), during tracking, the progressive distributed data fusion can dynamically determine the number of fused nodes. The number of fused nodes in progressive distributed data fusion is always less than client/server based centralized fusion. The time delay and energy consumption measurement are shown in Fig. 13(b) and (c), respectively. It is obvious that the trends of time delay and energy consumption are strongly relative to the number of fused nodes. Moreover, the time delay and energy consumption of progressive distributed data fusion are much less than client/server based centralized data fusion at each time instant,

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Tracking trajectory Actual trajectory

0.5 Progressive distributed Client/server centralized

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0.4 0.3 0.2 0.1 0 3

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9

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Fig. 12. Compare the (a) time delay and (b) energy consumption of two mechanisms for different numbers of fused nodes, where two factors of progressive distributed data fusion are both less than client/server based centralized data fusion all the time.

because, in client/server based centralized data fusion, once the event is sensed by a number of sensor nodes, a significant amount of traffic is triggered, this may easily lead to congestion in the forward path, which will cause serious time delay and energy consumption, while progressive distributed data fusion can avoid it and save a lot of time and energy.

5. Discussions 5.1. The initialization of multi-target tracking and data association

Fig. 11. (a) Tracking trajectory of person 1 which is acquired by progressive distributed data fusion and (b)–(h) tracking trajectories of person 1 which are acquired by client/server based centralized data fusion where the fixed number of fused nodes is from 3 to 9. The solid curves are the actual tracks, and the solid curves with the mark “×” are estimated tracks.

As mentioned above, we use the method which is similar to the nearest neighbor approach to associate the bearing results acquired from nodes to tracks by efficiently conducting a maximum likelihood search among the potential target positions. But during the initialization of whole tracking system, the predicted target information is not acquired, so how to track multi-target and associate the information is a key problem for precise tracking in this period. If several targets enter the tracking area at different time instant, the multi-target tracking and data association algorithm can distinguish them and start

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Number of fused nodes

12 Progressive distributed Client/server centralized

10 8 6 4 2 0 0

2

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8

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initialize each target, such as prior human shape model based tracking method [25].

0.5 Progressive distributed Client/server centralized Time delay (s)

0.4

5.2. Multi-target tracking in crowded and complex environment

0.3 0.2 0.1 0 0

0.15 Energe consumption (J)

Fig. 14. Multi-view fusion can track multiple targets well even if the occlusions occur in some nodes.

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0.05

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Fig. 13. Comparison between progressive distributed data fusion and client/server based centralized data fusion: (a) number of fused sensor node; (b) time delay; (c) energy consumption.

target information prediction separately at correlative time instant. This situation is normal in real world, so we did not consider other complex situation, such as that several targets appear at the same time instant in this article. However, this problem can be solved if we consider some other features and attribute data for target classification. Because the methods are computationally expensive, we can only use them when ambiguities occur during initialization. Furthermore, if several targets are too close to be separated by any sensor node when they enter the tracking area, more complex algorithm must be used to

In crowded environment, it is difficult to track multi-target interleaving involving many severe occlusions because the MBR do not always correspond to objects; single object may split into multiple MBR and multiple objects may merge into a single MBR. Some methods have been used to solve this problem by performing initialization based on segmentation by some heuristics (e.g., vertical projection of the foreground [10], head candidates by boundary analysis [26]) which are all based on single-node (single-view) tracking. In multi-view tracking, the problem can be easily solved by multi-view fusion if multiple targets can be separated correctly in at least one of the wireless sensor nodes. As illustrated in Fig. 14, the box is blocked by the walking person in the field of vision of node N8 and two targets merge into a single MBR, while the box is not blocked in the field of vision of node N1. The number of targets is 2 and 3 in node N8 and node N1 separately. After information fusion, the final fused result can present all three targets correctly, because the information of node N1 can distinguish and locate the targets and the information of node N8 can provide the locations of targets in despite of occlusions occurring. Moreover, it does not mean that there must be a node which can acquire all targets well. It is confirmed that if one target can be clearly separated with other targets in more than one node, it can be correctly separated and its 3-D location can be exactly calculated. If some targets, such as several close people, cannot be distinguished clearly in any sensor node, they will merge into a single MBR in the final result. For separating them, we can use PF-RBF to estimate the state of each target, such as, the number of objects, their information (e.g., positions) and the correlative uncertainty of the information by requiring knowledge of the entrances and exits (typically doors, crossings, entrances and so on). If the merged targets were separate when they entered the region of surveillance, they can be distinguished well because of the estimation. But if they were close all the time, it is difficult to distinguish them by only using multi-view fusion and estimation. Then we should use the prior human shape

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model [25] to initialize human targets and provide constraints during tracking by the methods which are used in single-node multi-target tracking. 6. Conclusions In this paper, we focused on target tracking in DWSNs and presented a CSP based distributed multi-view tracking system in DWSNs. This system contains background subtraction based target detection, SVM and PCA based target classification, multi-view fusion based target localization, PF-RBF based target tracking, and classification-aided data association. In multiview fusion localization method, progressive distributed data fusion is introduced for CSP, it completes the fusion procedure progressively with transmission of information. Finally, we expressed the indoor experiment results, and compared the tracking performance, energy consumption and time delay of progressive distributed data fusion with client/server based centralized data fusion. The experimental results verify that: (1) The proposed CSP based distributed multi-view tracking system in DWSNs can succeed in multi-target extraction, classification, localization, tracking, and data association. (2) The multi-view fusion localization method can improve robust and accuracy of target tracking which provide a simple and practically feasible solution for visual target tracking. It confirms that the targets can be well tracked if they can be extracted separately in more than one node. (3) Progressive distributed data fusion is a scalable, progressive, energy efficient and high performance fusion framework for DWSNs, which can satisfactorily solve the problems about limited bandwidths, limited energy and real-time processing. Compared with centralized data fusion, progressive distributed data fusion has better performance on tracking accuracy, congestion, resource consumption and communication latency in DWSNs. (4) PF-RBF based target tracking is a novel state estimator which can provide real-time and robust estimation for target tracking, especially when maneuver occurs. Acknowledgments This paper is supported by the National Basic Research Program of China (973 Program) under Grant No. 2006CB303000 and National Natural Science Foundation of China under Grant #60673176, #60373014 and #50175056. References [1] Y. Bar-Shalom, H. Chen, IMM estimator with out-of-sequence measurements, IEEE Trans. Aerospace Electron. Systems 41 (1) (2005) 90–98. [2] Y. Bar-Shalom, T. Kirubarajan, C. Gokberk, Tracking with classificationaided multiframe data association, IEEE Trans. Aerospace Electron. Systems 41 (3) (2005) 868–878. [3] C. Chong, S.P. Kumar, Sensor networks: evolution, opportunities, and challenges, Proc. IEEE 91 (8) (2003) 1247–1256.

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Xue Wang received his M.S. degree in measurement and instrument from Harbin Institute of Technology, Harbin, China, in 1991, and his Ph.D. degree in mechanical engineering from Huazhong University of Science and Technology, Wuhan, China, in 1994. He was a Postdoctoral Fellow at the Huazhong University of Science and Technology, Wuhan, China, in electrical power system from 1994 to 1996. He is now an Associate Professor in Department of Precision Instruments at Tsinghua University, Beijing, China. Dr. Wang is a member of IEEE. He is now the Director of Institute of Instrument Science and Technology at Tsinghua University. From May 2001 to July 2002, he was a visiting professor in the Mechanical Engineering Department, Wisconsin University. His research interests include topics in engineering measurement technology and signal processing, wireless sensor networks and intelligent maintenance.

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Sheng Wang received his B.S. degree in mechanical engineering from Tsinghua University, Beijing, China, in 2004. He is now pursuing a M.S. degree in the Department of Precision Instruments, Tsinghua University. He is a student member of IEEE. His research interests are in the area of signal and information processing and wireless sensor networks.