Flexible indoor localization and tracking system based on mobile phone

Journal of Network and Computer Applications ∎ (∎∎∎∎) ∎∎∎–∎∎∎

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Flexible indoor localization and tracking system based on mobile phone Du Yuanfeng n, Yang Dongkai, Yang Huilin, Xiu Chundi School of Electronic and Information Engineering, Beihang University, PR China

art ic l e i nf o

a b s t r a c t

Article history: Received 9 August 2015 Received in revised form 11 February 2016 Accepted 11 February 2016

As the WIFI access points are widely deployed, the received WIFI signal strength is commonly adopted as a positioning characteristic for mobile phone based indoor localization systems. Although WIFI based localization has achieved great development, there are still several key challenges in tracking applications, such as how to modify irregular trajectory obtained from the sequential positioning results. To tackle those challenges, this paper integrates the typical WIFI indoor positioning system with a Pedestrian Dead Reckoning (PDR) system based on the sensors in the mobile phone as many newly emerged systems proposed. The Maximum Likelihood (ML) algorithm is proposed to retrieve the user's initial location and moving direction without any intervention from the user. During the tracking process, a filtering algorithm can revise the moving direction indicated by the sensors only if a straight walking is detected. To obtain more accuracy and efficiency, a combination of Kalman Filter (KF) and auto-adaptive dynamic grid filter (GF) named KAGF is proposed for the fusion of the results from WIFI and PDR system. Experiments in the real scenarios show that our fusion system achieves better results than the widely adopted one, in which the particle filter is used, both in accuracy and computational complexity. Furthermore, the system's effectiveness is improved largely with longer WIFI updating period and larger reference points’ interval to achieve the same encouraging results. & 2016 Elsevier Ltd. All rights reserved.

Keywords: WIFI positioning PDR KF GF

1. Introduction In recent years, location-based services have been widely used for vehicle navigation, tourist guidance, destination searching, and so on. Better indoor localization and tracking technologies are becoming more and more important. As the WIFI access points (APs) are widely deployed and mobile phones equipped with WIFI modules become popular, the WIFI indoor positioning system based on mobile phones has become one of the most attractive solutions for indoor localization and tracking. Because the time of arrival (TOA) or angle of arrival (AOA) is hard to be obtained from the WIFI module of mobile phones, most existing WIFI positioning systems adopt the received signal strength (RSS) as a positioning characteristic (Li and Pahlavan, 2004; Kaishun et al., 2013; Samuel Van de and Heidi, 2012). The fingerprint positioning technology is commonly used nowadays, in which the user's location is estimated by matching online RSS with the values collected offline. As the technology considers the RSS dissimilarity in different regions instead of calculating the absolute n Correspondence to: Beihang University, F607, New Main Building, 37# Xueyuan Road, Haidian District, Beijing 100191, PR China. E-mail address: [email protected] (D. Yuanfeng).

distance for positioning, the problem of establishing the signal attenuation model to get distance-RSS relationship is avoided (Youssef et al., 2003). The main characteristic of the fingerprint positioning technology is to calculate the similarities between the observed RSS and the known RSS fingerprints. Researchers have developed a great number of matching algorithms for signal processing and pattern recognition to perform positioning, such as the widely used K-nearest-neighbors (KNN) method (Youssef et al., 2003), maximum a posterior (MAP) approach (Chen et al., 2010) and the Kernel function based algorithm (Kushki et al., 2007). Although subsequent developed algorithms improve positioning accuracy compared to the basic KNN method, most of them require larger training sets and greater computational resources. Thus, many researchers have proposed novel methods to improve the accuracy and feasibility of WIFI fingerprint positioning system, such as the signal filtering algorithms, clustering algorithms, AP selection algorithms and so on (Hao and Van, 2014; Chen et al., 2012). Moreover, several key challenges about indoor positioning have been well studied, including the time-consuming and laborwasting problem of establishing and updating the fingerprint database (Wang et al., 2011; Shih et al., 2012; Lesser et al., 2012; Yungeun et al., 2012; Bolliger et al., 2009; Sorour et al., 2012), heterogeneity problem of WIFI devices (Arvin et al., 2009; Lyn-

http://dx.doi.org/10.1016/j.jnca.2016.02.023 1084-8045/& 2016 Elsevier Ltd. All rights reserved.

Please cite this article as: Yuanfeng, D., et al., Flexible indoor localization and tracking system based on mobile phone. Journal of Network and Computer Applications (2016), http://dx.doi.org/10.1016/j.jnca.2016.02.023i

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Han et al., 2014), and signal fluctuation problem (Yu and Dutkiewicz, 2013; FANG et al., 2014). However, the accuracy of indoor tracking remains a serious problem for applications, such as how to modify irregular trajectory obtained from the sequential positioning results. Though WIFI positioning system can provide the absolute positions, the irregular position jumps caused by unsteady RSS will make user confused when the navigation service is used for pedestrian. For a better tracking performance, the pedestrian dead reckoning (PDR) techniques have been proposed in former works. In this paper, we will focus on the positioning and tracking by using the fusion of WIFI and PDR system. The ML algorithm is proposed to retrieve the user's initial location and moving direction without any intervention from the user. During the tracking process, the ML algorithm can also revise the moving direction from sensors if a straight walking is detected. To obtain a more accurate and efficient system, a combination of KF and autoadaptive dynamic GF is proposed for the fusion. The main contribution of this paper is: 1) The ML algorithm is proposed to retrieve the user's initial location and moving direction without any user's intervention. 2) The proposed KAGF approach can focus on the most relevant regions of interest (ROI), which improves the tracking accuracy and computational efficiency. 3) A non-uniform density of candidate grids are chosen in the ROI to make sure that the grid density is high near the expected distribution and low at the extremities. 4) Though the proposed approach still requires the WIFI fingerprint database collection in advance, it can obtain encouraging results with a longer WIFI updating period and fewer reference points (RP). The remainder of this paper is organized as follows. Related work is reviewed in Section 2. Afterwards, the system frameworks are described in detail in Section 3. Firstly, the steps and directions detection algorithms are presented. Then, the ML algorithm is proposed to retrieve the user's initial location and moving direction. At last, a combination of KF and auto-adaptive dynamic GF is discussed for the fusion of the results from WIFI and PDR system. The experimental results for three real scenarios are described in Section 4 and the conclusion is given in Section 5.

2. Related works PDR techniques rely on the data collected by inertial sensors such as accelerometers, gyroscopes, or even magnetometers to estimate relative displacements, starting with the known user's initial location (Ruiz et al., 2012). At each detected step of a user, the location update is accomplished by adding the current estimated displacement to the previous location. Since most of the hand-held devices are equipped with inertial sensors, PDR approaches are more and more practicable in our daily lives. However, as the estimation is based on noisy inertial sensors, the tracking error of a PDR system would accumulate over time (Lan and Shih, 2014). Additionally, the user's initial location is not always available when the tracking service is requested, which makes the tracking process difficult and inaccurate. Therefore, a more effective way to enhance the accuracy of indoor positioning and tracking is proposed by fusing the WIFI positioning system with a PDR system (Liu et al., 2014). The WIFI positioning algorithm can provide robust location results to revise the cumulative PDR tracking error, while the PDR approach can provide continuous tracking and overcome the problem of fluctuation of RSS. Combining RSS measurements with knowledge of motion

dynamics can improve tracking performance since positions of human users carrying the mobile devices are continuous over time. Bayesian inference is the basic method for information fusion. It is a probabilistic framework which sequentially estimates the unknown state from noisy observations using a dynamic predictive model and observation likelihood. Studies in (Dhital et al., 2010) show that the performance of indoor positioning methods for a mobile user could be improved by incorporating the user's past position with a motion model. A number of mutations of probabilistic Bayesian inference approaches have also appeared in the literatures. In some studies, the tracking noise is assumed to be agreed with Gaussian distribution and the motion dynamics is assumed to be linear. Thus, KF is suitable to refine the position estimates (Zhenghua et al., 2015). Although the assumptions may not be often satisfied, the application of the KF as the post processing procedure improves the positioning accuracy in experiments. Furthermore, the mutations of KF, such as sigma-point kalman smoother in (Anindya et al., 2009), are proposed and have been proved to have superior accuracy. More recently, particle filters (PF) have been used and demonstrate encouraging performances, although at a high computational cost for real time tracking. For each iteration, the particles first move according to the transition model, their weights are then updated according to the observation model, and particles are then re-sampled according to their weights. The particle filter based system described in (Kannan et al., 2013) integrates WIFI positioning, inertial sensors and two different representations of the map information, resulting in a satisfactory accuracy. However, the traditional PF technology is based on the sampling importance resampling, which has the inherent blindness and particles degeneration problem. Therefore, several improved PFs, such as extended Kalman based PF (Yuanchao et al., 2014) and auxiliary PF (Han et al., 2011), are adopted to solve the problems and to improve the performance. Unfortunately, the computational complexity increases further and cannot be applied on mobile devices in real time. What's more, hidden Markov modeling (HMM) approaches based on Bayesian inference are adopted. The HMM algorithm combines motion dynamics information with RSS positioning, and it allows the use of current RSS measurements in the position estimate as well as historical information. Two HMM algorithms, grid-based filter and Viterbi algorithm, have been adopted to solve positioning problems. The former solution gives the state estimate which has the maximum posterior probability, while the latter obtains the most likely state sequence produced from the observable sequence (Bengio). Since the states of the HMM represent the user's potential locations, the number of the states determines the quantization error introduced by discretizing. To reduce the quantization error, the pseudo states are introduced in (Hoang et al., 2013), whose probabilities are obtained from the RSS measurements at the location. To make full use of the position information of mobile user, an indoor map is needed. The idea of map matching is to utilize the map information to aid the positioning. The system in (Kannan et al., 2013) used map data as constraints for their PF in a WIFI positioning system. What's more, corrections can be made for positioning and heading direction according to the map information, for instance, it is impossible that pedestrian walks across the walls. The latest research achievement in (Zhuoling et al., 2014) presents a practical and reliable indoor map matching solution based on the conditional random field algorithm, which only uses the information from accelerometer and magnetometer measurements.

Please cite this article as: Yuanfeng, D., et al., Flexible indoor localization and tracking system based on mobile phone. Journal of Network and Computer Applications (2016), http://dx.doi.org/10.1016/j.jnca.2016.02.023i

In our approach, a combination of KF and auto-adaptive dynamic GF is used for information fusion. In KAGF, a two-stage method is applied to replace the use of all grids in HMM, forming a more relevant regions of interest (ROI) around the prediction made by KF, so that only grid points with high enough weights would be considered in further calculations. In the first stage, the steps obtained from PDR are considered as the input of motion dynamics for the KF, and KF filters the results of PDR with the WIFI fingerprint results. Afterwards, the coarse location and the covariance matrix could be used to obtain the ROI. Then, the grids of non-uniform density are chosen to make sure that the grid density is high near the expected distribution and low at the extremities. Furthermore, the direction of grid axes should be properly aligned with state variants of ROI to reduce wasted grids. For each iteration process of GF, the interesting regions are updated and the grids are selected and restricted again. The grids’ weights are updated by the dynamic model together with the WIFI measurements. As the latest WIFI measurement is adopted in the process of selecting grids, the posterior distribution can be represented by fewer grids. Therefore, the sample impoverishment problems together with the high calculation load can be avoided effectively. The autoadaptive dynamic GF requires less memory space and computing power than the traditional PFs and GFs. What's more, our proposed filter KAGF has better positioning results. 3.1. Steps and directions An off-the-shelf smartphone is usually equipped with an inertial measurement unit (IMU), which has several sensors, including accelerometers, gyroscopes and magnetometers. Theoretically, the moving distance of the user can be obtained by using the acceleration data. However, the acceleration for an indoor pedestrian is so small that is can be easily affected by sensor noises. An alternative approach is proposed to detect the walking steps. Hence, the moving distance, denoted by D , can be estimated as following.

D = dnum × dlength

(1)

dnum represents the number of walking steps and dlength represents the length of each walking step. Typical stance detection algorithms are threshold-based. Since the resultant acceleration fluctuates periodically due to human motion, the widely used algorithm of peak detection is adopted to estimate the number of steps (Robert, 2013). Notice that the peaks can be affected by the user's walking attitude, the minimum time interval between two continuous peaks is restricted as T = 0.3s (Lee et al., 2011). If more than two peaks are detected during the interval, only the first peak is counted as a step to prevent a redundant counting. The value of step length is ranged between 0.5 m and 2 m and is different for each person, which can be easily estimated by counting the number of steps in a specified distance. Although the gyroscope can provide direction information at high sampling rates, it is not adopted in our proposed system due to rapid accumulation of errors and high power consumption (Zhuoling et al., 2014). Therefore, the moving direction or heading information can be obtained by the aid of the magnetometer. By using the information of the moving distance and moving direction, the relative displacement can be estimated accordingly.

( xk + 1, yk + 1 ) = ( xk , yk ) + dlengthk × ⎡⎣ sin (θ k ) cos (θ k ) ⎤⎦ where (xk , yk ) and (xk + 1, yk + 1) stand for the kth and (k + 1) th location respectively. dlengthk denotes the length of the kth step and θk is the kth moving direction.

Moving direction(o)

3. Methodology

Resultant acceleration(m/s2 )

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14 12 10 8 6

0

100

200

300

400

500 600 Samples

700

800

900

1000

0

100

200

300

400

500 600 Samples

700

800

900

1000

200 150 100 50 0

Fig. 1. Step detections and moving directions of path along the corridor.

The results in Fig. 1 show the step detections and moving directions of a trajectory walking along the corridor and back to the starting point, with a sampling rate of 20 Hz. The star symbols are the peaks of resultant acceleration. From the samples between 450 and 500, a period of static can be detected correctly. Moreover, the difference between the forward direction and backward direction is about 180∘ . 3.2. Proposed indoor positioning and tracking system In this section, a flexible indoor positioning and tracking system based on mobile devices that integrates a PDR system with a RSS-based WIFI positioning system is proposed. In order to enhance positioning accuracy, the user's initial location and moving direction must be determined before executing a fusion algorithm. When the positioning or navigation service is requested by a user, the initial location and moving direction of the user are determined in a few seconds. Then, a combination of KF and adaptive moving GF is performed for information fusion. 3.2.1. Determination of user's initial location and moving direction Since the fluctuation of RSSs is severe in indoor environments, the location estimated by the RSS-based WIFI positioning system may be inaccurate. Thus, the method using the location estimated by the WIFI positioning system as the initial location of a user may cause large error. Instead of using the WIFI positioning results directly, the location of the user should be considered as locating at the area with center of WIFI positioning result and radius of the expected WIFI positioning error. As the assumption that the initial moving path of user is approximately straight can be satisfied in most cases, a sequence of steps can be used in the initialization. Consequently, a Bayesian ML algorithm is proposed to retrieve the user's initial location and moving direction information. The initial sequential WIFI RSSs are set as RSSj (j = 1, 2, ... , Tinit ) and Tinit is the initialization time. The moving steps and directions are set as li and θi (i = 1, 2, ... , N ) separately and N is the number of steps during Tinit . Step 1: the coarse initial positioning result ⌢ p0 is obtained by WIFI positioning system with the average WIFI RSS RSSave . The coarse initial moving direction θave is obtained with the average step directions. Tinit

RSSave =

∑ RSSj j=1

(2)

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et al., 2010).

⎛ Ψ ⎜ ri m, rpw , ⎜ w= 1 ⎝ W

p (RSSi |ri m) =

1 N

N

∑ θi

(3)

i=1

Step 2: obtaining the interesting region R (⌢ p0 as the p0 , λ ) with ⌢ center and the estimated positioning variance λ as the radius. λ signifies the average estimated error of WIFI positioning. Then, the ROI is equally divided into M candidate locations as pm , m = 1, 2... , M . As the coarse initial moving direction θave is obtained from the magnetometers, the measurement could be disturbed by metal materials or other interferences. The candidate are obtained by divided directions θs, s = 1, 2... , S [θave − σθ , θave + σθ ] with the interval of 10°. σθ is the estimated angle variance. Thus, the correct moving direction and initial location could be selected from M*S possible traces (shown in Fig. 2). Setp 3: for each candidate trace, the posterior Bayesian probability p (RSS1: Tinit |pm , l1: N , θs ) can be calculated. Then, the user's initial location x0 and moving direction θ0 can be obtained from the trace with the largest probability as follows. ri m is the ith location with the initial location of pm .

I: ( x 0 , θ 0 ) = arg maxp ( RSS1: Tinit |pm , l1: N , θs )

(4)

m, s

i

ri m = pm +

∑ l j *[ sin (θs )

cos (θs )] (5)

j=1 Tinit

Tinit

p (RSS1: Tinit |pm , l1: N , θs ) =

∏ i =1

p (RSSi |pm , l1: N , θs ) =

∏ i =1

p (RSSi |r i m)

⎞

∑ w ⎟⎟

Ψ (RSSi, Rw , ηw ) W

⎠ ∑ j = 1 Ψ (RSSi, Rj , ηw )

(7)

As the coarse WIFI positioning result ⌢ p0 is only used for determining the R (⌢ p0 , λ ), it does not need a high accuracy and the WIFI database can be simplified with fewer samples or reference points. Thus, many auto-constructing algorithms for WIFI database can be adopted for practical applications (Wang et al., 2011; Shih et al., 2012; Lesser et al., 2012; Yungeun et al., 2012; Bolliger et al., 2009; Sorour et al., 2012). To enhance the quality of positioning service, the initial determination time should be as small as possible. However, longer time can provide more WIFI RSSs and step information and the proposed Bayesian ML algorithm will get more accurate results. Consequently, it is important to select a compromise initialization time Tinit to obtain the best system performance. Due to the complex environments, the cases that the coarse initial WIFI positioning result ⌢ p0 with very large error are rare but unavoidable. Then, if R (⌢ p0 , λ ) is far away from the true location, the above algorithms would result in a wrong initial location and direction. In that case, all of the differences between the initial location and the following WIFI positioning results will be larger than a threshold proposed and the above mentioned initialization approach will be restarted.

Fig. 2. Candidate of initial locations and moving directions.

θave =

∑

(6)

After the neighboring WIFI reference points RPw (rpw , Rw , ηw ) w = 1, 2, ... , W are selected, p (RSSi |ri m) can be obtained based on the kernel function. rpw is the location of the wth reference point. Rw and ηw are the average value and variance of RSS at location rpw . Ψ is the probability density function of normal distribution. The parameters of location variance ∑w and RSS variance ηw are selected as (Azadeh

3.2.2. A combination of KF and auto-adaptive dynamic GF algorithm After the initial location and the starting direction are obtained, the tracking can be done. PDR systems can provide continuous tracking, and thus, they can be used to overcome the fluctuation of RSS-based WIFI positioning system. However, in the PDR system, the nature of the accumulation of tracking errors increases the difficulty of accurate positioning. How to fuse the WIFI positioning results and the PDR tracking is worth investigating. As presented in former sections, the KF, PF and HMM algorithms have been widely used to solve this problem, with the drawback of low accuracy, high calculating complexity and high memory load respectively. Therefore, in this section, a combination of KF and autoadaptive dynamic GF algorithm is proposed to fuse the PDR and the WIFI positioning systems effectively. 3.2.2.1. Obtaining approximate probability distribution and boundaries of the positioning results. In the traditional GF algorithm, the whole positioning area is considered as ROI and the state space is divided into N grid points. For each grid point xj , the probability is N mj and ∑ j = 1 mj = 1. Then, the probability density may be apN proximated by points as p (x ) ∝ ∑i = 1 mi δ (x − xi ). In our proposed algorithm, KF has been used before using GF to obtain an approximate distribution. Though the KF provides only an inaccurate estimate, it combines the information of the latest WIFI positioning results and the PDR results, which theoretically provides more reliable approximate estimations than the updated particles in PF obtained from only the PDR information. From the approximate mean and covariance, provided by the above approximation, we obtain not only the boundaries of the grid but also orientation of the grid expanding axes. Refer to Fig. 3, the line with solid points represents the trajectory produced by the PDR system, which deviates from the true moving trajectory over time. To solve this problem, firstly, the steps obtained from PDR are used as the moving model for the KF, and the WIFI fingerprint results are used as the measurements. The squares and triangles show the WIFI positioning results and KF results respectively. Then, the ROIs for each true location represented by the dotted ellipses can be determined based on the

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x0i i = 1, 2, ... , Nk can be obtained based on the user's initial location x0 described in 3.2.1 with the method provided in Section 3.2.2.2 of 3.2.2. Nk is the number of grids selected in the kth loop. ˜ 0i of each grid can be calculated by the distance dg i The weight w between x0i and x0 . Afterwards, the weights are normalized and the normalized weights are denoted as w0i . σ is the standard variance of the expected WIFI positioning error. Fig. 3. The ROI obtained from KF.

location result and error covariance obtained from KF. As KF is proposed to find the ROI, for the following procedure of GF, the grid points could be constrained within it instead of the whole state space. The ROI should be made as small as possible. Then, the span should be divided into a number of intervals consistent with the required resolution. To further quantify the ROI, the m-sigma contour in (Bhaumik et al., 2008) is used. The m-sigma contour is defined where m is a real number for the 2-dimensional distribution with a covariance matrix P, like the locus of the points satisfying the 2-dimensional quadratic equation xT P −1x = m2 in the state space. The principal axes of the area would be along the eigenvectors of the symmetric matrix P −1. In our case, P is the covariance matrix obtained by KF. Then, the extremities of the ellipse would be at ±m 1/β11 , ± m 1/β22 as shown in Fig. 3.

⎡ β11 β12 ⎤ ⎥ P −1 = ⎢ ⎣ β21 β22 ⎦

(8)

3.2.2.2. Adaptive selection of grids. In conventional grid-based filters, fixed supporting points are used and computational efficiency is low. Many supporting points are redundant, only leading to the numerical load without improving the accuracy. Moreover, if too coarse the grids are chosen, estimation accuracy suffers. Thus, in our proposed GF method, the number of ineffective grid points is minimized by adaptive selection of supporting points. Consequently, a non-uniform density of grids should be chosen, so that the grid density is high near the expected distribution and low at the extremities. Furthermore, the direction of grid axes should be properly aligned to reduce ineffective points. This is exemplified in Fig. 3 where the grid axes are inclined with respect to the state variables’ axes to reduce wasted supporting points. Typically, the core area may be taken as the one-sigma boundary around the estimated state and the grid size is set as L*L , while the rest area is divided into grid of size 2L*2L (shown in Fig. 4). L is the parameter for the grid size. 3.2.2.3. KAGF algorithm. Based on the KF and adaptive grid selection processes described as above, the detailed KAGF algorithm is presented as follows. Firstly, the algorithm initialization is performed and the loop parameter k is set as 0. Then, the initial group of grids’ locations

˜ 0i = w

w0i =

− 1 e 2πσ

1 N

˜ 0i ∑i =k1 w

(dgi ) 2 2σ 2

(9)

˜ 0i w

i = 1, 2, ... , Nk (10)

Once a WIFI data is received, the positioning result Yw can be obtained. Due to the signal fluctuation characteristic, the WIFI positioning results are not stable. Therefore, the restrictions on the candidate reference points for matching will be very effective. The improved fingerprint matching algorithm in (Liu et al., 2014) is adopted in this paper, which uses the directions and moving distance together with the former location to obtain the restricted area. Afterwards, an approximate value of the posterior mean can be obtained at the (k + 1) th loop by KF. The basic KF equations are shown in formula (11) and the calculation processes are shown in formula (12). The moving information is the summary of the t steps between the two WIFI values, with the step length li and directions θi . t

∑i = 1

Xk + 1 | k = Xk +

li *[ sin (θi )

cos (θi )] + R

Yw = Xk + 1 | k + Q

(11)

Pk + 1 | k = Pk + R K = Pk + 1 | k (Pk + 1 | k + Q )−1 Xk + 1 | k + 1 = Xk + 1 | k + K *(Yk − Xk + 1 | k ) Pk + 1 | k + 1 = (1 − K )*Pk + 1 | k

(12)

Xk and Pk are the positioning result and variance separately in the kth loop. Xk + 1 | k and Pk + 1 | k are the estimated location and covariance separately based on the step information. R is the covariance of the estimated results and Q is the variance of the WIFI positioning results. K is the kalman gain. The KF results, including the mean Xk + 1 | k + 1 and covariance Pk + 1 | k + 1, are used to obtain the new ROI. Then, the updated supporting grids xki + 1 i = 1, 2, ... , Nk + 1 can be selected for the pro˜ ki + 1 is related posed adaptive GF. The weight of each updated grid w to the step moving probability p (xki + 1|xkj ) and the posterior probability of WIFI measurement p (RSSk + 1|xki + 1). To describe the moving probability more accurately, the probability is obtained by the product of distance probability and moving direction probability. Ψ is the probability density function of normal distribution. w xki + 1 − xkj and Dis xki + 1 − xkj are the angle and length between xki + 1 and xkj separately. l¯ is the summary of the t steps between the two WIFI data. θ¯ is the average moving direction during the (k ) th loop. σθ and σL are the expected measurement variances of directions and lengths separately.

(

(

)

)

(

)

( ( ( x − x ) ), θ¯, σ ) × Ψ ( Dis ( ( x − x ) ), l¯ , σ )

p xki + 1 xkj = Ψ w

j k

i k+1

i k+1

θ

j k

2

L

2

(13)

As the probability is the same for WIFI measurement, the posterior probability can be transferred into the prior probability p (Rssk + 1|xk + 1). Then, the kernel function proposed in (Azadeh et al., 2010) is adopted. The high correlated reference points Fig. 4. Grid selection and moving.

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rpw w = 1, 2, ... , W are selected in the KF estimated result Xk + 1 | k + 1 with a range of 20 m. The parameters of location variance ∑w and RSS variance ηw are selected as in (Azadeh et al., 2010).

p (xk + 1|Rssk + 1) ∝ p (Rssk + 1|xk + 1) ⎛ Ψ ⎜ xki + 1, rpw , ⎜ w= 1 ⎝ W

p (Rssk + 1|xki + 1) =

(14)

∑

⎞

∑ w ⎟⎟

Ψ (Rssk + 1, Rw , ηw ) W

⎠ ∑ j = 1 Ψ (Rssk + 1, Rj , ηw )

(15)

The weights of each new grid can be obtained as following. λ is the normalization parameter.

p (Rssk + 1|xki + 1) λ

wik + 1 =

Nk

∑ p (xki + 1|xkj ) w kj j=1

(16)

At last, the estimated result ⌢ xk + 1 | k + 1 of our proposed KAGF can be obtained from N ⌢ xk + 1 | k + 1 = (1/Nk + 1) ∑i =k1+ 1 wki + 1xki + 1. Furthermore, the posterior covariance matrix Pk + 1 | k + 1 for KF can also be updated by Nk + 1

Pk + 1 | k + 1 =

∑

T wik + 1 | k + 1⋅⎡⎣ xki + 1 − ⌢ xk + 1 | k + 1⎤⎦ ⎡⎣ xki + 1 − ⌢ xk + 1 | k + 1⎤⎦

i=1

(17)

The recursion can be done by repeating the above-stated processes for the subsequent steps. 3.2.2.4. Revision of the step length and moving direction. During the fusion of the PDR system and WIFI fingerprint system, the accumulative error of PDR can also be revised by WIFI positioning results regularly. In general, the step length is set as 1 m for initialization. Then, the step length d¯length can be updated with the moving distance S¯ in Nstep steps.

d¯length = S¯/Nstep

(18)

Compared to the error introduced by the step length, the deviation of moving direction would result in more serious problem. In this paper, the correction is performed after a straight walk is detected, with the variance σdirection of the directions sequence obtained from magnetometer which is smaller than 20∘ . Afterwards, the least squares estimating algorithm is adopted to revise the moving direction from the KAGF positioning results (shown in Fig. 5). We assume that the sequence of positioning results from the straight walking path is (xi , yi ) i = 1, 2, ... , R . The slope of the walking path k and the revised moving direction θupdate can be obtained as following. The parameter n is decided from 0, 1, 2 to find the nearest approximation of real measurement direction θdirection .

(∑ k=

) × (∑ y ) − R(∑ x y ) (∑ x ) − R × (∑ x )

R x i=1 i

R i=1 i

2 R i=1 i

R i=1 i i

2 R i=1 i

θupdate = ( arctan (k ) + nπ + θdirection )/2

(19)

3.2.2.5. Map information. Since the positioning results are always presented in the map, the map matching based indoor positioning technologies have been adopted and researched in many former papers (Chen et al., 2012; Lyn-Han et al., 2014; Yuanchao et al., 2014). As the level of details of the map information is with great difference in practical applications, the map matching technology is only performed for the optimization of our system. Besides the restriction on the moving directions and positioning results, the proposed adaptive GF can also use the map information to improve the selected grid points. If the coarse estimated location is in the corridor, the state variant of ROI will be along the direction of the corridor and no additional calculation described in Section B is needed. Moreover, the location of the corner will provide important information for candidate supporting points of grids.

4. Experiments 4.1. Experimental setup In this section, the performance of the proposed indoor positioning system is evaluated. Real RSS data are collected by a smartphone (MI 2A with an Android 4.3 operating system). Though multiple sensors are equipped by the mobile device, only the accelerometer module and digital compass in the smartphone are used in the PDR system. The sampling frequency of the PDR is 20 Hz. The traditional K nearest neighbors (KNN) (K¼ 3) algorithm is adopted in the WIFI fingerprint positioning. For the WIFI positioning system, during the online stage, the shortest sampling period is set to one second. Experiments were carried out in three different real scenarios. There are 103, 25 and 35 IEEE 802.11b/g APs (2.4 GHz) in the three scenarios respectively. The locations of the APs are unknown. At each reference point (RP), there are 60 training RSSs collected from each RP to build the radio map. Scenario 1: An office area in the 12th floor of Tian Chuang building stands for the simple office environment (Fig. 6, 90 m*10 m). The RPs’ interval is 3 m. Scenario 2: A 115 m*25 m area in the second floor of the Zhong Guan Cun shopping center in Beijing stands for the complex environment (Fig. 7). The RPs’ interval is 2 m. Scenario 3: A hall in the 2th floor of Tian Chuang building stands for the open environment (Fig. 8, 50 m*15 m). The RPs’ interval is 3 m. In our proposed system, the user's initial locations estimated by formula (4) are regarded as the user's locations. When the proposed fusion algorithm is executed, the locations estimated by formula (17) are regarded as the user's locations. We compare the performances of our proposed method with the traditional KF and PF scheme. In the following experiments, the user's initial locations and directions are unknown. The distance error is the distance between the estimated locations, denoted by (X¯x , X¯ y ), and the real location, denoted by (C¯ x, C¯ y ) of the mobile user. Then, the

(20)

Consequently, the revised moving direction is used in the following PDR model unless the difference between the real-time angle measurement and θupdate is larger than 20∘ .

Fig. 5. Revision of the moving directions.

Fig. 6. Experiment scenario one.

Please cite this article as: Yuanfeng, D., et al., Flexible indoor localization and tracking system based on mobile phone. Journal of Network and Computer Applications (2016), http://dx.doi.org/10.1016/j.jnca.2016.02.023i

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7

Comparison of the average initial location error between the WIFI and proposed method 6 Scenario One Scenario Two Scenario Three

Fig. 7. Experiment Scenario two.

Average error[m]

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results from the proposed method are similar as the average measurement θave calculated by formula (3) in most cases. However, in case that the magnetometers are jammed by the environment interferences, the error of the measurement θave may exceed 50°, but our proposed method can get more accurate initial moving direction θ0 in formula (4) with the average error less than 20°. 4.3. Positioning and tracking accuracy

Fig. 8. Experiment Scenario three.

distance error is computed as

poserror =

(X¯ x − C¯ x )2 + (X¯ y − C¯ y )2

To verify the positioning accuracy of the proposed method, a path is set in each scenario, shown in Figs. 6, 7 and 8. The proposed method can effectively obtain the correct trajectory with the fusion of PDR results and the WIFI positioning results. In Fig. 10 and Fig. 11, the bold black lines are the true paths. Because there are many iron doors and other interferences in the environment, the moving directions derived from PDR are highly affected especially in area A and the results from PDR (cross in black line) are far away from the true path. With the introduction Tracking results of various positioning algorithms and filters 10

4.2. Performance of the initial location and direction estimation

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The initialization time Tinit in formula (4) should be adjusted to obtain a compromise between the positioning delay and accuracy. To evaluate the impact of initialization time Tinit on the accuracy of initial location and direction, experiments are performed in the three scenarios. The average errors of the initial location in scenario 1, 2 and 3 are shown in Fig. 9. Ten initial points are selected for all scenarios and five repeated traces are performed from each initial point. When Tinit = 1, the results can be considered as the average error of the single WIFI method, as the shortest sampling period is 1 s. The errors are 5.5 m, 4 m and 5 m in the three scenarios respectively. The results show better accuracy improvement of estimated user's initial locations for larger values of Tinit . When the value of Tinit is larger than 3 s, the accuracy increases slowly, because the inherent error from the WIFI RSSs and cumulative error from the PDR system increase. To select a compromise initialization time Tinit to ensure both the accuracy and quality of service, we choose Tinit = 3 in the subsequent experiments. For the accuracy of the initial moving direction, the obtained

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Please cite this article as: Yuanfeng, D., et al., Flexible indoor localization and tracking system based on mobile phone. Journal of Network and Computer Applications (2016), http://dx.doi.org/10.1016/j.jnca.2016.02.023i

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8

proposed KAGF. For our proposed KAGF in path one, there are less than 9 selected grids in each ROI, so that the number of the calculation elements from the former ROI to the latter one is smaller than 9*9. To further verify the proposed algorithm, another experiment in scenario two is performed. The results exemplified in Fig. 11 present the tracking performance of various algorithms and filters. As observed, the proposed WIFI þPDR þKAGF method obtains the best tracking results, with the mean positioning error and standard deviation 2.5 m and 3.1 separately. Due to the error of estimated direction affected by the environment, the results of PDR only algorithm shows a serious deviation from the true trajectory. On the other hand, only using WIFI fingerprint algorithm cannot obtain satisfactory results, with many retraces and large fluctuations. Since some latest locations estimated by the WIFI and the PDR system are used in the fusion algorithm, the positioning stability is enhanced substantially, and hence, the positioning accuracy increases. Comparing the traditional PF with the proposed KAGF, the performance of KAGF is improved with smaller calculating time. For scenario three, the results in Fig. 12 also demonstrate that our proposed KAGF algorithm has the best performance, with the mean positioning error and standard deviation 2.8 m and 2.6. As the WIFI positioning results in the right part of the area are already with large error due to the weak WIFI signals, our proposed method using WIFI information is also disturbed by the error. In summary, the proposed method has better positioning results than the others.

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of KF and PF to PDR, the tracing accuracy is highly improved. However, the KF and PF algorithms show lots of winding appearance. For PDR þKF, this appearance is introduced from step information. For PDR þPF, the PF moves particles by step information and then calculates the particle weights based on the WIFI measurement to estimate the approximate distribution. Although this approach can improve the performance, the improvement is subject to the number of effective particles and accuracy of step information. On the other hand, the proposed KAGF filter firstly estimates the initial distribution from the KF with the step information and the latest WIFI measurement. Afterwards, the adaptive selection approach of grid points is proposed. Non-uniform density grids are chosen, so that the grid density is high near the expected estimate and low at the extremities. The results obtained from WIFI þPDR þproposed KAGF algorithm show the best tracking performance. Table 1 summarizes the mean distance errors and standard deviations of distance errors of different methods in Path 1, including the WIFI fingerprint algorithm, PDR algorithm, WIFIþ PDRþKF, WIFI þPDR þPF and WIFIþ PDRþ proposed KAGF. The results are obtained based on Matlab 8.1, Windows 7, 4 GHz. As shown in Table 1, combining WIFI positioning algorithm and PDR with various filters can improve the positioning accuracy both in mean error and standard deviation. Furthermore, the WIFIþ PDRþproposed KAGF algorithm achieves the best performances both in mean error and standard deviation. The largest part of the computation time lies in the WIFI fingerprint algorithms, with 9.923 seconds. 300 particles are used in the particle filter and the additional calculating time is more than that of

4.4. Impact of the grid parameter in GF In our proposed algorithm, the supporting grid points are selected from the ROI bounded by the coarse positioning results. Then, the grid size will affect the number of grid points and computational complexity together with the tracking performance. As the grid parameter L increases, fewer grids would be obtained during the GF process to reduce the computational complexity and the discretization error would increase. The average positioning errors of the grid parameter L from 1 m to 4 m are shown in Table 2 for all scenarios. The best grid parameter should be chosen as 2 m, 3 m and 3 m in the three scenarios respectively. 4.5. Impact of WIFI updating period and WIFI fingerprint database As shown in the above experiments, the longest part of the runtime lies in the WIFI fingerprint algorithm, with lots of reference points for similarity calculation. To improve the system efficiency and the flexibility, the methods of increasing the WIFI updating period together with decreasing the number of reference points are adopted in our system. As the short-term positioning accuracy of PDR is reliable, the modified PDR results are outputted during the updating period of the WIFI results. The experiment results are shown in Figs. 13 and 14 with good tracking performances, with the WIFI updating period of 1 s, 2 s and 3 s. When

Table 1 Comparison of different algorithms and filter. Scenario

WIFI PDR WIFI þPDR þ KF WIFI þ PDRþ PF WIFI þPDR þ proposed KAGF

Runtime (s)

Mean error (m)

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3

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6.8 7.6 6.3 4.6 2.5

5.7 8.9 5.0 4.6 2.8

2.9 3.2 2.2 2.1 0.9

3.1 3.5 2.7 2.5 3.1

3.5 3.2 2.9 2.4 2.6

Please cite this article as: Yuanfeng, D., et al., Flexible indoor localization and tracking system based on mobile phone. Journal of Network and Computer Applications (2016), http://dx.doi.org/10.1016/j.jnca.2016.02.023i

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Tracking results of various positioning algorithms and filters

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Tracking results of various WIFI update periods

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Table 2 Impact of the grid parameter L on average positioning errors.

L

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2m

3m

4m

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2.1 m 4.2 m 3.5 m

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and better the system's efficiency is improved. The system's efficiency can be further improved by simplifying the WIFI fingerprint database, which reduces great manpower by constructing a database with fewer RPs. Since the WIFI positioning results are used as the revised measurements in the fusion with PDR, larger interval of the RPs in the fingerprint database is acceptable. The experiment results are shown in Fig. 15 with the RP

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Please cite this article as: Yuanfeng, D., et al., Flexible indoor localization and tracking system based on mobile phone. Journal of Network and Computer Applications (2016), http://dx.doi.org/10.1016/j.jnca.2016.02.023i

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interval increases from 1.5 m to 12 m, and the WIFI positioning updating period of 1 s. Even if the RP interval is set as 12 m with a great reduction of manpower for collecting the WIFI database, the positioning result is still accurate enough. 4.6. Results discussion Overall, the experiments in Section 4 firstly prove the high accuracy of the determination of initial location and direction. Then, compared with the traditional KF and PF, the effectiveness of the proposed KAGF has been validated in three difference real environments. Afterwards, the best grid size for our proposed KAGF is analyzed. As the positioning errors with various grid sizes are very different, we have to select the best parameter L for each application. Though the proposed approach still requires the time-consuming collection of WIFI fingerprint database in advance, we try to cover the shortage by lengthening the WIFI updating period and decreasing the number of RPs. The results in Section 4.5 are very encouraging, showing a promising performance in real applications.

5. Conclusion In this paper, a flexible positioning and tracking system based on mobile phone is proposed. The ML algorithm is firstly proposed to retrieve the user's initial location and moving direction information without any user's intervention. To obtain higher accuracy and energy-efficiency, a combination of KF and adaptive moving grid filter is proposed for fusing the information obtained from PDR and WIFI positioning systems. Experiments in three real scenarios are performed and show that our proposed fusion algorithms with the name of KAGF achieves better results than the widely adopted PF both in accuracy and computational complexity. Furthermore, experiments show that the proposed system can obtain encouraging results with longer WIFI updating period and larger RPs’ interval. In the future, we will perform further research to enhance the performance of our proposed KAGF system for practical positioning applications. Finding a better solution to revise the measurement errors of PDR in the fusion system is also our key interest.

Acknowledgment This research work was supported by National High-Tech Research and Development Program of China (863 Program) under Grant no. 2013AA12A201 and Doctor innovative research fund of Beihang University.

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Please cite this article as: Yuanfeng, D., et al., Flexible indoor localization and tracking system based on mobile phone. Journal of Network and Computer Applications (2016), http://dx.doi.org/10.1016/j.jnca.2016.02.023i