CRLB analysis of wireless cognitive location with different short-range measurements

THE JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOMMUNICATIONS Volume 15, Issue 2, June 2008

CUI Qi-mei, LIU Jun, TAO Xiao-feng, ZHANG Ping

CRLB analysis of wireless cognitive location with different short-range measurements CLC number

TN915.01

Document A

Article ID 1005-8885 (2008) 02-0001-06

Abstract Because of the wide application and great market potential of location-aware services, the research of wireless location techniques for the fourth generation (4G) mobile communications is being paid more attention. Wireless cognitive location (WCL) techniques for next generation wireless networks have been proposed in recent years. This article investigates the changes of the positioning accuracy of WCL algorithm when different methods are adopted to measure the short-range (SR) information. By first completing Cramér-Rao lower bound (CRLB) analysis of the WCL algorithm with SR measurements based on time of arrival (TOA) and received signal strength (RSS), it is discovered that TOA-based or time difference of arrival (TDOA) -based SR measurement can make WCL algorithms achieve higher accuracy than RSS mode, which is also verified by numerical simulation in the article. The conclusions can instruct the design of novel WCL-based location algorithms.

and so on. For instance, multiple-input-multiple-output (MIMO) techniques and Ad-hoc short-range communications among the terminals will be adopted in 4G system [4]. These features will create the opportunity of the birth of new technologies in some application fields such as wireless location. Some innovative technologies for 4G mobile communication have been generated in location field [57]. The author proposed wireless cognitive location techniques for next generation wireless networks by adequately utilizing the self-organizing and self-connecting ability of mobile terminals (MT) [6]. WCL combines LR communication information between base station (BS) and MT and SR communication information among MTs. It dynamically applies higher-accuracy short-range location information among MTs into enhancing the accuracy of the existing positioning techniques using cellular systems’ signals. In-depth research based on WCL has been continuously made by this team. Some novel location schemes based on WCL for 4G mobile communications have been proposed in Refs. [811]. In Ref. [10] the traditional Taylor-series-based TOA cellular positioning algorithm was improved on by introducing the idea of WCL. The improved algorithm can achieve higher location accuracy. Here, TDOA method is adopted to estimate the distance among MTs, and the Cramér-Rao lower bound (CRLB) analysis of the modified algorithm based on TDOA is completed in Ref. [11]. During the research, it was discovered that the measurement method of SR positioning information among MTs also impacted the accuracy of location algorithms. Thus, in this article the research is focused on the issue that when the different methods are to measure the SR location information what effects will occur to the positioning accuracy of WCL algorithms. In this article, LR location information is estimated by TOA-based cellular positioning algorithm. TOA and RSS are applied to capture the SR location information. This paper completes CRLB analysis of the WCL algorithm with SR location information based on TOA and RSS, respectively.

Keywords wireless cognitive location, short range (SR), long range (LR), Cramér-Rao lower bound (CRLB)

1

Introduction

There is increasing interest in the development of wireless geolocation techniques, driven not only by their commercial and military potential but also by regulatory pressures [1]. The proliferation of mobile computing devices and the development of high-speed, low-cost wireless networks have created ample opportunity for geolocation systems, and a large number of techniques have been developed [2, 3]. Moreover, unlike the existing second generation (2G) and third generation (3G) mobile communication system, 4G mobile communication system has its distinct characteristics in network architecture, frequency spectrum efficiency, and transmission bandwidth Received date: 2007-06-13 CUI Qi-mei ( ), LIU Jun,TAO Xiao-feng, ZHANG Ping Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China E-mail: [email protected]

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Wireless cognitive location model

In 4G mobile communication network, MTs support Ad-hoc mode (Fig. 1). One MT can directly communicate with some other MTs nearby without BS’s relaying. Direct communications among MTs may be based on these wireless technologies such as wireless local area network (WLAN), ultra wideband (UWB), and Zigbee or others [1214]. Compared to BS’ coverage area, the range of the wireless communications based on the above technologies is much shorter. When the network measures the position of one MT, one MT to be located and some other MTs nearby are capable of converging to constitute a group terminal (GT) [14]. The typical scenes include offices, meeting place, shopping mall, and airfield so on, where MTs are densely distributed. The MT to be located is named as the destination terminal (DT) in Fig. 1. The other mobile terminals near DT in GT assisting DT is named as the reference mobile terminals (RT) in Fig. 1. DT dynamically selects RT under the practical condition. The number of RTs is not fixed

2008

that any linear estimator can achieve [15]. Considering a vector of device parameters J >J 1 , J 2 ,..., J M +N @ , each device has one parameter. Devices 1, 2,..., M are blindfolded devices, and devices M+1, M+2,…, M+N are reference devices. The unknown parameter vector is T [T1 , T 2 ,...,

T M ] , where Ti

J i for i 1, 2,..., M . Note that {J i , i

M  1, M  2,..., M + N } are known. Device i and j make pair-wise observations Vi , j with density pV |J (Vi , j | J i , J j ) . The case when devices make incomplete observations is allowed for as two devices may be out of range or have limited link capacity. Let H (i ) ={ j: device j makes pair-wise

observations with device i}. By convention, a device cannot make pair-wise observation with itself, so that i  H (i ) . By symmetry, if j  H (i ) , then i  H ( j ) . It is assumed by reciprocity that Vi , j

V j , i , thus, it is

sufficient to consider only the lower triangle of the observation matrix V = (Vi , j )i , j when formulating the joint likelihood function. In practice, if it is possible to make independent observations on the links from i to j and from j to i, when it is assumed that a scalar sufficient statistic can be found. Finally, it is assumed that {Vi , j } are statistically independent for

j  i . This assumption can be somewhat oversimplified but necessary for analysis. The log of the joint conditional probability distribution function (pdf) is

l (V | J )

M N

¦ ¦

li , j , li , j

ln pV |J (Vi , j | J i , J j )

(1)

i 1 j H ( i ) j i

The CRLB on the covariance matrix of any unbiased estimator Tˆ is cov(Tˆ)ıF 1 , where the Fisher information T

matrix (FIM) FT is defined as

Fig. 1 Wireless cognitive location

The network estimates the position of DT not only by utilizing the low-accuracy LR information between BS and DT, but also by utilizing the high-accuracy SR information between DT and RT in GT. SR measurement information may be the relative distance between DT and RT, or TOA /TDOA, or AOA etc. SR location information can be cognitively obtained by the different method, and the network can cognitively utilize SR location information to estimate the location of DT .

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Outline of the CRLB analysis

The CRLB is commonly used as a performance benchmark of an estimator because it gives the lowest possible variance

f1,2 ! f1, M º ª f1,1 «f f 2,2 " f 2, M »» 2,1 (2) FT  E (’T (’T l (V | J ))T ) « « # # % # » « » «¬ f M ,1 f M ,2 ! f M , M »¼ As derived in Ref. [15], the diagonal elements f k , k for

k 1, 2,..., M of FT reduce to a single sum over H(k) as there are card {H(k)} term in Eq. (1) that depend on T k

Jk .

The off-diagonal elements can be further reduced: when k z l for k 1, 2,..., M ; l 1, 2,..., M , there is at most one summand in Eq. (1) that is a function of both k and l, thus ­ ª w2 º l ; k l °  ¦ E« 2 k, j » T w ° j H ( k ) ¬ k ¼ f k ,l ® 2 ª w º ° ° I H ( k ) (l ) E « wT T lk ,l » ; k z l ¬ k l ¼ ¯

(3)

where I H ( k ) (l ) is an indicator function: 1 if l  H ( k ) or 0 otherwise.

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CUI Qi-mei, et al.: CRLB analysis of wireless cognitive location with…

CRLB analysis of WCL

In this section, device location estimation is specialized for using pair-wise TOA measurements in the cellular network part. And in SR communication part, the TOA and RSS measurements are used respectively for comparison. Specifically, consider a cooperative structure of M blindfolded mobile stations (DS and RTs are deemed to equality) and N base stations whose coordinates are known in advance. The device parameters J w > z1 , z2 ,..., z M  N @ , where, for a twoT

dimensional (2-D) system, zi = > xi , yi @ (although extension of these results to 3-D is also possible). The relative location problem corresponds to the estimation of blindfolded device coordinates T [T x ,T y ] , T x = > x1 , x2 ,..., xM @ , T y = > y1 , y2 ,..., yM @ . Given the known base stations coordinates [ xM 1 ,..., xM  N ,

yM 1 ,..., yM  N ] . In the cellular network, Ti , j (i 1, 2,..., M ; j

M  1, M  2,..., M  N ) is the measured TOA between

the base stations and the mobile stations. Assuming that Ti , j is independent Gaussian distributed with mean di , j / c

§d · Ti , j  N ¨ i , j , G T2 ¸ c © ¹ di, j

1 2SG

2 T

where Fxx is given by Eq. (2) using only the x parameter vector T

e



(Ti , j  di , j c)

derived. The elements of the submatrix of Eq. (10) are derived in Ref. [15]. According to this cooperative structure, § X · M M  N °­ § T · °½ M M °­ § T c · °½ (11) l ¨ ¸ ¦ ¦ ln ® f T ¨ i , j ¸ ¾  ¦¦ ln ® fST ¨ i ,i ¸ ¾ © J w ¹ i 1 j M 1 ¯° © J w ¹ ¿° i 1 i c 1 ¯° © J w ¹ ¿° i cz i

And the FIM is ­ 1 ( xk  x j ) 2 1 ( xk  xi ) 2  2 2 ¦ ; ° 2 2 ¦ 2 2 ° c G T jH ( k ) || zk  z j || c G ST iH ( k ) || zk  zi || ° [ Fxx ]k ,l ® k l ° 2 °  1 I (l ) ( xk  xl ) ; k z l H (k ) 2 °¯ c2G ST || zk  zl ||2

[ Fyy ]k ,l

(5) 2

2G T2

(6) [ Fxy ]k ,l

where c is the speed of light propagation, and įT2 is not a function of

T x , and Fyy is given by Eq. (2) using only

T y . The off-diagonal blocks Fxy and Fyx are similarly

T

(4)

d ( zi , z j ) || zi  z j ||1/ 2

§T · fT ¨ i, j ¸ © Jw ¹

For simplification, the FIM will have a similar form to Eq. (2) if partitioned into blocks ª Fxx Fxy º (10) F « » ¬ Fyx Fyy ¼

and

variance įT2 , which is denoted as

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di , j . In SR communication part, different

(12)

­ 1 ( yk  y j ) 2 1 ( yk  yi ) 2  2 2 ¦ ; ° 2 2 ¦ 2 2 ° c G T jH ( k ) || zk  z j || c G ST iH ( k ) || zk  zi || ° (13) k l ® ° 2 °  1 I (l ) ( yk  yl ) ; k z l H (k ) 2 °¯ c2G ST || zk  zl ||2 ( xk  x j )( yk  y j ) ­ 1 1  2 2 ˜ ° 2 2 ¦ 2 || zk  z j || c G ST ° c G T j H ( k ) °° ( xk  xi )( yk  yi ) (14) ; k l ® ¦ || zk  zi ||2 i H k  ( ) ° ° 1 ( x  xl )( yk  yl ) ; k zl ° 2 2 I H ( k ) (l ) k || zk  zl ||2 °¯ c G ST

measuring methods are employed for comparison. No matter what method is used, the measuring process only implements among the blind mobile stations themselves with short range signals.

4.2

4.1

within SR communication part. It is assumed that Pi ,ic is

TOA

As same as the form in cellular networks, the TOA measurement in SR communication part is also a Gaussian distribution, which is denoted as §d 2 · Ti ,ic  N ¨ i ,ic , G ST ¸ ; i 1, 2,..., M ; ic 1, 2,..., M ; i z ic c © ¹ (7) di ,i c d ( zi , zic ) || zi  zi c ||1/ 2 (8)

§T c · fST ¨ i ,i ¸ © Jw ¹

1 2 2SG ST

e



(Ti ,ic  di , ic c)2 2 2G ST

(9)

2 where G ST is the variance of TOA measurements in SR

communication.

RSS

In the RSS case, Pi ,ic is the measured received power log-normal; thus, the random variable Pi ,ic ( d ) = 10log10 Pi , i c is Gaussian 2 Pi ,i c ( d )  N ( Pi ,i c ( d ), G SdB ); i 1, 2,..., M ; ic 1, 2,..., M ; i z ic (15)

§d c· (16) P0 (d )  10n p log10 ¨ i ,i ¸ © d0 ¹ 2 where Pi ,i c ( d ) is the mean power in decibel mill watts, įSdB Pi ,i c ( d )

is the variance of the shadowing, and P0 ( d ) is the received power in decibel mill watts at a reference distance d 0 . Typically, d 0 =1 m, and P0 is calculated from the free space path loss environment. The path loss exponent n p is a

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function of the environment. For particular environments, n p may be known from prior measurements. Although the CRLB is derived assuming n p is known, it could have been handled as an unknown “nuisance” parameter. Given Eq. (16), the density of Pi ,ic is

§P · fSP ¨ i ,i c ¸ © Jw ¹ where b

10 1 e 2 Pi ,ic ln10 2ʌG SdB

10n

p

2 b § d i ,ic  ¨ ln 2 8 ¨ di ,ic ©

2 (G SdB ln10) , di ,ic

· ¸ ¸ ¹

given received power

5.1

(17)

d 0  P0 Pi ,ic .

In this case, four BSs located in the corners of a 100 by 100 m square. It is assumed that only one MT exists in the square, but it is randomly distributed. įT2 =10. Figure 2 gives CRLB of the TOA cellular location method.

(18)

And the FIM is ­ 1 ( xk  x j ) 2 ( xk  xi ) 2 b ; k  ° 2 2 ¦ ¦ 2 4 iH ( k ) || z k  zi || ° c G T jH ( k ) || zk  z j || [ Fxx ]k ,l ® ( xk  xl ) 2 °  bI ( l ) ; k zl ( ) H k ° || zk  zl ||4 ¯ ­ 1 ( yk  y j ) 2 ( yk  yi ) 2 ; k b ¦ ° 2 2 ¦ 2 4 iH ( k ) || z k  zi || ° c G T jH ( k ) || zk  z j || ® ( yk  yl ) 2 ° °bI H ( k ) (l ) || z  z ||4 ; k z l k l ¯ ( xk  x j )( yk  y j ) ­ 1 b˜ ° 2 2 ¦ || zk  z j ||2 ° c G T j H ( k ) °° ( xk  xi )( yk  yi ) ; k l ® ¦ || zk  zi ||4 i H k ( )  ° ° ( x  xl )( yk  yl ) ; k zl °bI H ( k ) (l ) k || zk  zl ||4 °¯

[ Fxy ]k ,l

Single MT

Pi ,ic . According to this

cooperative structure, § X · M M  N °­ § T · °½ M M °­ § P · °½ l ¨ ¸ ¦ ¦ ln ® f T ¨ i , j ¸ ¾  ¦¦ ln ® fSP ¨ i ,ic ¸ ¾ © J w ¹ i 1 j M 1 ¯° © J w ¹ °¿ i 1 ii ccz1i ¯° © J w ¹ ¿°

[ Fyy ]k ,l

parameter set is changeable in accordance with specifically applied form (e.g. E-OTD to GSM, A-FLT to IS-95 and OTDOA to WCDMA) [1620]. The LR location information is estimated by TOA-based cellular measurement.

2

Here, di ,i c is the maxiLihood estimation (MLE) of range

di ,i c

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l (19) Fig. 2 Low bound of position estimation variance for the location model versus the coordinates of MT

l (20)

(21)

5.2

SR measurement based on TOA

In this case, four BSs located in the corners of a 100 by 100 m square. It is assumed that five MTs form a GT in Fig. 3 or 8MTs form a GT in Fig. 4. One of them is DT, which is used to evaluate the performance, the other RTs are randomly distributed around DT. The distance between mobile stations 2 is limited in 20 m. G T2 =10, G ST =1. Figures 3 and 4 give CRLB of the model with SR measurement based on TOA.

The formula derivation of FIM has been completed in Eqs. (10)(21). Then, the trace TˆCRLB of the covariance of the location estimate of the ith blind mobile station satisfies 1 1  ª¬ Fyy  Fxy Fxx1FxyT º¼ TˆCRLBı ª¬ Fxx  Fxy Fyy1FxyT º¼ (22) i ,i i ,i



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Numerical simulations

In the front section, CRLB of the model has been educed with SR Location Information based on TOA and RSS measurements. In this section, numerical simulation is performed to verify the theoretical analysis. Given a specific scenario, there are four BSs located in the corners of a 100 by 100 m square. The positions of BSs are (0,0), (0,100), (100, 0), (100,100). MT is randomly distributed in the square. The

Fig. 3 Low bound of position estimation variance for the location model versus the coordinates of DS for TOA, 4RTs distributed around DS

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CUI Qi-mei, et al.: CRLB analysis of wireless cognitive location with…

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found that the WCL with TDOA-based SR measurement can reach a little lower CRLB than TOA mode. In general, theoretical analysis and numerical simulation indicate that TOA-based or TDOA-based SR location measurement is better than RSS mode.

Fig. 4 Low bound of position estimation variance for the location model versus the coordinates of DS for TOA, 8RTs distributed around DS

5.3

SR measurement based on RSS

In this case, four BSs located in the corners of a 100 by 100 m square. It is assumed that five MTs form a GT in Fig. 5 or 8MTs form a GT in Fig. 6. One of them is DT, which is used to evaluate the performance, the other RTs are randomly distributed around DT. The distance between mobile stations is limited in 20 m. įT2 =10, įSdB / n =1.7. Figures 5 and 6 give CRLB of the model with SR measurement based on RSS.

Fig. 6 Low bound of position estimation variance for the location model versus the coordinates of DS for RSS, 8RTs distributed around DS

The positioning accuracy is mainly restricted by two factors in WCL, the number of the participants as RT and the signal bandwidth for measuring SR information among MTs. On one side, when the number of RTs is increased the performance of the model will be improved. On the other side, when SR measurement adopts wider band wireless transmission technology such as UWB, the location model can get more accuracy. The reason is explained by analyzing the derived mathematic formula. In Eqs. (12)(22), the diagonal elements of Fxx are added with an extra value compared with the traditional TOA cellular location technology. For example, the extra value deriving from SR communication in Eq. (12) is 1 ( xk  xi ) 2 || zk  zi ||2 . The extra value deriving ¦ 2 c 2G ST iH ( k ) from SR communication in Eq. (19) is b

¦

( xk  xi ) 2

iH ( k )

Fig. 5 Low bound of position estimation variance for the location model versus the coordinates of DS for RSS, 4RTs distributed around DS

The above simulation results indicate that CRLB of the WCL model with SR measurement based on TOA and RSS (Figs. 3 and 5) is remarkably lower than that of the TOA cellular location method (Fig. 2). Besides, with the increasing of the number of RTs, CRLB of the model ulteriorly declines (Figs. 36). The above analysis proves that WCL can get better positioning accuracy than the corresponding cellular location algorithm. In WCL, comparing with RSS-based SR measurement, the location performance of the model is better when SR measurement is based on TOA (Fig. 3). Moreover, according to the simulation results in Ref. [10], it is ulteriorly

|| zk  zi ||4 . The same changes also happen to Fyy and Fxy . The lower CRLB value in WCL can be obtained because of the effect of these extra values. However, the effect in RSS mode is less effective than that in TOA mode, which is shown in Figs. 3 and 5. The reason is that the increasing of the signal bandwidth can improve the localization veracity more effectively in time-based location method than in power-based location method theoretically.

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Conclusions

On the basis of the achieved contribution in [10], CRLB analysis of the WCL algorithm with SR location information

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based on TOA and RSS, respectively, is ulteriorly completed. Theoretical analysis and numerical simulation indicate that TOA-based or TDOA-based SR location measurement is better than RSS mode. The conclusions can instruct the design of novel WCL-based location schemes. Acknowledgements

This work is supported by the National

Natural Science Foundation of China (60496312), The Hi-Tech Research and Development Program of China (2006AA01Z260 and 2006AA01Z283), NCET project (05-01160) and 111 Project (B07005).

References 1. FCC. Revision of the commission's rules to ensure compatibility with enhanced 911 emergency calling systems (2001-9-30). http://www.fcc.gov/911/enhanced/reports/ 2. Hiqhtower J, Borriello G. Location systems for ubiquitous computing. Computer, 2001, 34(8): 5766 3. Tekinay S. Wireless geolocation systems and services. IEEE Communications Magazine, 1998, 36(1): 2840 4. Sun Jun Zhao, Sauvola J, Howie D. Features in future: 4G visions from a technical perspective. Proceedings of IEEE Global Telecoms Conference: Vol 6, Nov 19Dec 3, 2004, Texas, USA: 2004: 35333537 5. Cui Qi-mei. A method of improving the accuracy of cellular location. Patent CN 1747605A. 2005 6. Cui Qi-mei, Liu Jun, Tao Xiao-feng et al. A novel location model for 4G mobile communication networks. Proceedings of IEEE Vehicular Technology Conference (IEEE VTC 2007-fall), Sept 30Oct 3, 2007, Baltimore, MD, USA. Piscataway, NJ, USA: IEEE, 2007: 274278 7. Frattasi S, Monti M, Prasad R. Cooperative mobile user location for next-generation wireless cellular networks. IEEE International Conference on Communications: Vol 12, June 1115, 2006, Istanbul, Turkey. 2006: 57605765 8. Cui Qi-mei. A method of improving the accuracy of cellular location for mobile terminals in mobile status. Patent 200610076395.9. 2006 9. Cui Qi-mei. A Method of improving the accuracy of cellular location for mobile terminals in highway. Patent 200610075983.0. 2006 10. Liu Jun, Cui Qi-mei, Tao Xiao-feng. A method to enhance the accuracy of location systems based on TOA-location algorithms. Proceedings of the 6th International Conference on ITS Telecommunications (ITST, 06), June 2123, 2006, Chengdu, China. 2006: 895900 11. Liu Jun, Cui Qi-mei, Tao Xiao-feng. Improved taylor-series location algorithm for cooperation networks. The Journal of China Universities of Posts and Telecommunications, 2007, 14(2): 3844 12. Lionel M N, Ni L M, Lau Yiu Cho, et al. LANDMARC: indoor location sensing using active RFID. Wireless Networks, 2004,

2008

10(6): 701710 13. Win M Z, Scholtz R Z. Impulse-radio: how it works. IEEE Communications Letters, 1998, 2(2): 3638 14. Cui Qi-mei, Tao Xiao-feng, Yu Wen, et al. A UWB-based virtual MIMO communication architecture for beyond 3G cellular networks. Proceedings of IEEE International Conference of Ultra-Wideband (ICU’05), Sep 58, 2005, Zurich, Switzerland. 2005: 747751 15. Patwari N, Hero A O, Perkins M. Relative location estimation in wireless sensor networks. IEEE Transactions on Signal Processing, 2003, 51(8): 21372148 16. Charitanetra S, Noppanakeepong S. Mobile positioning location using E-OTD method for GSM network. Proceedings of IEEE Student Conference on Research and Development, Aug 2526, 2003, Putrajaya, Malaysia. 2003: 319324 17. Laitinen H, Lahteenmaki J, Nordstrom T. Database correlation method for GSM location. Proceedings of 53rd Vehicular Technology Conference (IEEE VTC 2001-spring), Vol 4, Mar 69, 2001, Rhodes, Greece. Piscataway, NJ, USA: IEEE, 2001: 25042508 18. Nissani D N, Shperling I. Cellular CDMA (IS-95) location, A-FLT (assisted forward link triangulation) proof-of-concept interim results. Proceedings of 21st IEEE Convention of the Electrical and Electronic Engineers, Apr 1112, Tel-Aviv, Israel. 2000: 179182 19. Wang Lin-lin, Sun Yong. Discriminating and restraining of NLOS error in wireless orientation technology of cellular network. Journal of Chongqing University of Posts and Telecommunications: Natural Science Edition, 2006, 6(2): 231235 (in Chinese) 20. Zhang Yi-heng, Cui Qi-mei, Zhang Ping, et al. TDOA estimation in the distributed multi-antenna system. Journal of Beijing University of Posts and Telecommunications, 2007, 29 (6): 1822 (in Chinese) Biographies: CUI Qi-mei, Ph. D. degree in 2006. Lecturer

of

School

of

Telecommunication

Engineering in Beijing University of Posts and Telecommunications. Her research covers multiantenna technologies, wireless location algorithum and short-range wireless communication techniques.

ZHANG Ping, professor of Beijing University of Posts and Telecommunications, and also serves as a member of China FuTURE Program General Group, senior consultant of China Communication Standardization Association (CCSA). He is famous for his outstanding contribution for the world and his research interest mainly focus on wireless communication, mobile communications, especially in physical layer techniques.