Mobile Station Positioning Using Time Difference of ...

Mobile Station Positioning Using Time Difference of Arrival and Received Signal Strength. Eng. Sharief N. Abdel-Razeq, Full time lecturer of Electrical Engineering Department of Electrical and Electronics Engineering Al-Balqa’ Applied University / Al-Huson University College P.O. Box 2250 Irbid 21110, Jordan Phone: (962)-79-6751048 E-mail: [email protected] Dr. Yahya S. Khraisat, Associate Professor of Electrical Engineering Department of Electrical and Electronics Engineering Al-Balqa’ Applied University / Al-Huson University College P.O. Box 1375 Irbid 21110, Jordan Phone: (962)-77-7060985 E-mail: [email protected] Dr. Mohammad M. Al-Ibahim, Professor of Electrical Engineering Department of Electrical Engineering Jordan University of Science and Technology P.O. Box 3030 Irbid 22110, Jordan Phone: (962)-2-7201000 (ext.: 22551) E-mail: [email protected]

Abstract: In this paper, a new Mobile Station Positioning (MSP) technique is proposed and its performance is evaluated. The proposed technique combines the Received Signal Strength (RSS) and the Time Difference of Arrival (TDOA) techniques. Each RSS measurement yields a circle on which the Mobile Station (MS) may lie. Meanwhile, each TDOA measurement defines a hyperbola on which the MS may reside. The proposed 1

hybrid TDOA/ RSS technique uses Taylor series expansion to solve the hyperbolas and circles specified by the distance measurements obtained by the Base Stations (BSs). The MS position is determined based on Linear Least Squares (LLS) method in an iterative fashion. Without hardware modification, the proposed technique reduces location errors compared with either technique separately. Simulation results demonstrate superior performance over both TDOA and RSS techniques.

Keywords: Mobile communication, Global System for Mobile Communication (GSM), Mobile Station Positioning (MSP), Time Difference of Arrival (TDOA), Received Signal Strength (RSS), location error, cumulative probability of location error.

Reference to this paper should be made as follows: Abdel-Razeq, S.N., Khraisat, Y.S. and Al-Ibrahim, M.M. (2011) ‘Mobile Station Positioning Using Time Difference of Arrival and Received Signal Strength’, Int. J. Mobile Communications.

Biographical notes: Sharief Nasr S. Abdel-Razeq was born in Jordan on January 25, 1985. He received his B.Sc. degree in Communication and Software Engineering from Al-Balqa’ Applied University/Al-Huson University College, Irbid, Jordan in March 2008 and his M.Sc. degree in Electrical Engineering/Wireless Communications from Jordan University of Science and Technology, Irbid, Jordan in July 2010. Since September 2010, he has been at Al-Balqa’ Applied University/Al-Huson University College, where he is now a Full time lecturer of Electrical Engineering at Electrical and Electronics Engineering department. His current research interests include cell planning, mobile location, wireless communications, and mobile antenna design. 2

Yahya S. H. Khraisat received his PhD. in Radiolocation and Radionavigation in 1998 from the Kiev International University of Civil Aviation (renamed recently to National Aviation University/ Ukraine). From 2002 until now, he is working at Al-Balqa' Applied University/ Al-Huson University College. He is now a vice dean for academic affairs at Al – Balqa’ Applied University/Al-Huson University College. In 2008 he was promoted to the rank of associate professor. He has more than 30 publications in journals and IEEE conferences.

Mohammad Mefleh Al-Ibrahim received his B.Sc. degree from El-Mansoura University, Egypt in 1979, M.Sc. degree from Yarmouk University - Jordan in 1984 and his PhD degree from Syracuse University, USA in 1989 all in Electrical Engineering. He is currently a Professor of electrical engineering at Electrical Engineering department at Jordan University of science and technology. His research interests are in distributed detection systems, digital signal processing and digital communications.

1. Introduction The last few years have witnessed a dramatic boom in the number of MS users. As a result, the number of applications for location information is growing rapidly and MSP service in cellular networks becomes an important and hot topic. MSP service can support many useful applications, such as emergency services, roadside assistance, location-sensitive billing, fraud detection, and enhanced network performance (Caffery and Stuber, 1998). Everyday a large number of citizens are faced with unusual situations, in which callers are unable to make a phone call, disabled or do not know their location. Hence, many services are available to react quickly to protect the citizens during these situations. One of these critical services is emergency services which is available via one or several 3

emergency call centers. The emergency call center receives emergency calls and it could be reached by calling a simple number, such as 911 in Enhanced 911 (E-911) service. Accordingly, emergency cases can be resolved quickly as required. Each emergency call center covers only a specific area and is usually staffed at all times (Willassen and Andresen, 1998). There are minimum requirements for the emergency call center that performs MSP service. For example, the location must be calculated accurately to within a few hundred meters and swiftly within a few seconds after the identification of the call. From the management point of view and according to (Varshney, 2003), MSP is known as Location Management (LM), which is an important part of current and emerging wireless and mobile networks. LM involves maintaining location information as mobiles power-on, move or power-off. This can be broadly divided into two steps, namely, location tracking and location information storage. LM schemes were used in first and second generation wireless networks, such as analog cellular, Personal Computer Services (PCS) and GSM networks. It is known that these LM schemes have done a good job of providing basic LM service for mobile users with limited mobility, applications and battery power under low to moderate traffic conditions in a macro-cellular environment. One major issue is the LM support that is needed by many location-intensive mobile commerce applications, such as user and location-specific mobile advertising, mobile inventory management, wireless-business reengineering, and mobile financial services (Varshney, 2003).

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In (Vijayalakshmi and Kannan, 2009), they introduced an agent-based approach to provide location-dependent and context aware services proactively, which are always relevant to the user's context to provide a complete satisfaction of Location-Based Services. The services are based on the location of the user, the activity that he is involved in at a particular instance (based on user's daily planner), his profile (interest) and the time at which the data is retrieved. On the other hand; in (Lee et al, 2009), the authors studied the factors keeping the potential users away from using MSP service. They concluded that the adoption rates of MSP service are far from satisfactory. Hence, in their research, they integrated a qualitative method, Zaltman Metaphor Elicitation Techniques, with the quantitative questionnaire survey to elicit and validate underlying factors which influence potential users' attitude and intentions toward MSP service. Many MSP techniques were reported in the literature, such as; Global Positioning System (GPS), Angle and Time of Arrival (AOA, TOA), TDOA, RSS, and Signal Attenuation Difference of Arrival (SADOA). The rest of this paper is organized as follows. In Section 2, a literature review is made and an overview of previous work regarding MSP is presented. In section 3, the RSS technique is reviewed and its performance is evaluated. In Section 4, an overview of the TDOA technique is presented and its performance is evaluated as well. In Section 5, the proposed hybrid TDOA/RSS technique is presented, analyzed, and its performance is compared with that of the hybrid SADOA/TDOA technique. In section 6, the paper is concluded with some remarks and suggestions for future work.

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2. Literature Review GPS and TOA techniques were studied in details in (Kapplan, 1996) and (Caffery, 2000), respectively. The RSS technique was studied and reported in many papers (Hata and Nagatsu, 1980). MS position estimation using the Maximum Likelihood (ML) technique in sector cell systems was presented in (Aso, Saikawa and Hattori, 2002). TDOA technique was studied in (Abdulla, El-Hennawy and Mahrous, 2001). Specifically, the authors studied the effect of BSs configurations on the accuracy of hyperbolic position location in macro-cellular and microcellular GSM systems. In (Kbar and Mansoor, 2005), the authors presented a new hybrid positioning technique to locate the MS in urban area. Their technique uses signal strength to estimate the time delay and compare it with the measured signal time of arrival. They showed that their hybrid technique improves the accuracy and reduces the multi-path signaling effect which affect both TOA and RSS methods. In addition, a new calculation method involving three distance equations was presented to determine the location of MS based on the BSs coordinates. In (Stefanski, 2010), the multipath propagation effect was utilized to improve the accuracy of the estimated distance between the MS and the BS. A novel path loss formula for calculating the distance between the MS and a BS in the cell was presented. In (Guangqian et al, 2010), the authors studied the TDOA/AOA hybrid positioning system based on Kalman filtering under Line Of Sight (LOS) and None Line Of Sight propagation (NLOS) schemes. In (Juang, Lin and Lin, 2007), the authors proposed a SADOA/TDOA location scheme. According to their model a SADOA measurement, derived from the differences of signal attenuations, is the ratio of distances between the 6

MS and BSs. Each SADOA measurement yields a circle on which the MS may lie and the circles intersect at the estimated MS position. The common goal of all MS positioning techniques is meeting the Federal Communications Commission (FCC) accuracy requirements. Unfortunately, each MSP technique has its own strengths and weaknesses. Clearly, the addition of more BSs makes positioning errors smaller. Moreover, the use of a combination of techniques would be beneficial in term of accuracy without increasing the number of BSs. Consequently, many hybrid techniques have been proposed in the literature (Juang, Lin and Lin, 2007). In (Liu et al, 2007), a survey of known MPS techniques was presented. The survey results motivate us to combine the TDOA and RSS techniques as this combination was not reported in the literature. Moreover, the performance level of both the TDOA and RSS techniques reported in the literature suggests that by combining the two techniques, the overall performance can be improved significantly. In this paper, a new MSP technique is proposed to efficiently locate the MS in cellular networks. The technique is a hybrid of the TDOA and RSS techniques and is applied on the model that was reported in (Juang, Lin and Lin, 2007). The RSS technique estimates the distance between the MS and the BSs based on the relationship between the signal level and the travelled distance. Each RSS measurement yields a circle on which the MS may lie. The circles intersect at the estimated MS position. Although RSS is easy and low-cost technique, its accuracy is not good enough due to the complex propagation mechanisms. On the other hand, the TDOA estimates the MS position by accurately computing the difference between time of arrival of a signal emitted from that MS to three or more BSs. Each TDOA measurement defines a 7

hyperbola on which the MS may reside. The hyperbola has foci at one of the BSs and the MS position is at the intersection of the hyperbolas (Juang, Lin and Lin, 2007; Hata and Nagatsu, 1980). In contrast with RSS, This technique is far more accurate for positioning a MS. However, the lack of LOS propagation is a potential disadvantage of TDOA technique. As a result, the proposed hybrid TDOA/RSS location technique solves hyperbolas and circles defined by the TDOA and RSS measurements, respectively, and iteratively. The proposed hybrid TDOA/RSS technique is intended to have the accuracy of the TDOA technique and the simplicity of the RSS technique while avoiding the drawbacks of the two techniques.

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3. Received Signal Strength (RSS) Technique In the absence of measurement error, the RSS or equivalently received power at the denoted by

,

, can be modeled as (Song H, 1994)

(1) where

is the transmitted power,

is a parameter that accounts for all factors which

affect the received power, including the antenna height and antenna gain, and propagation constant. In free space;

is the

equals to 2, but in some urban and suburban areas,

can vary from 3 to 6. We assume that the measurements are taken in urban and suburban areas and take a = 4. From (1), the range measurements based on the RSS data with the use of the known {

}

and { }, denoted by { }, are determined as

(2) For a = 4, we obtain (3) Determining the MS position at the mean of the intersections of (3),

,

would be faster but with lower accuracy. Instead, a mathematical approach that was presented in (Caffery, 2000) called Linear Of Position (LOP) approach is adopted to solve the MS position using the Linear Least Squares (LLS) solution. 9

For simplicity, let N = 3 and consider BS1 and BS2. The line which passes through the intersection of the two circular LOPs for those two BSs can be found by taking the mean square and differencing the ranges in (3) for i = 1, 2, which results in (Caffery, 2000) (4) For the new LOP. Following the same procedure for

= 2, 3 yields the line

(5) Expressing the set of linear equations in matrix form, we have (6) where

The LLS solution is derived from (7) To evaluate the RSS technique performance, Figure 1 (Juang, Lin and Lin, 2007) demonstrates the hexagonal tested cell surrounded by six neighboring cells with radius of 500 m.

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Figure 1: Hexagonal cell geometry. According to the received signals, N (

) BSs with higher received signal

powers were used for MS position calculation. The position performance is assessed in terms of the value of the distance error defined as where

and

,

are the actual and estimated MS locations, respectively

(Juang, Lin and Lin, 2007). Figure 2 shows the cumulative probability of location errors based on fixed independent runs of RSS location estimation for a MS.

Figure 2: Cumulative probability of location errors using RSS technique without measurement errors. 11

In the presence of measurement error, we add the measurement error term RSS or at

, denoted by

. Hence, the

, can be modeled as (8)

The measurement noise variance

is modeled as a Gaussian random variable with zero mean and

. It is also assumed that different noise errors are statistically independent and

identically distributed random variables (i.i.d). From (8), the range measurements based on the RSS data with the use of the known {

}

and { }, denoted by { }, are determined as

(9) For a = 4, we obtain (10) Again, Determining the MS position is done using LOP approach and the LLS criterion. Let N = 3 and consider BS1 and BS2. The line which passes through the intersection of the two circular LOPs for those two BSs can be found by taking the mean square and differencing the ranges in (10). For i = 1, 2, we obtain (11) Following the same procedure for i = 2, 3 yields the line (12) Expressing the set of linear equations in matrix form, we have (13) 12

where ,

The LLS solution is derived from (14) Figure 3 shows the cumulative probability of location errors for RSS technique in the presence of measurement errors.

Figure 3: Cumulative probability of location errors using RSS technique with measurement errors.

4. Time Difference of Arrival (TDOA) Technique Suppose that each observation is defined as

is capable of performing TOA observation, , then TDOA ,

. Expressing TDOA observation as a

function of station coordinates, a hyperbola has the form (Juang, Lin and Lin, 2007) 13

(15) is obtained. Where and

and

are the coordinates of

and

, respectively,

is the unknown MS position. Consequently, the MS position is determined by

solving the intersections of a set of N -1 hyperbola. Denote the initial guess of the MS position as

, the LLS solution is a common

technique to solve TDOA equations by using the first two terms of their Taylor series. Set (16) Recall that the Taylor series expansion of

around the point

is given by + Higher Terms

Hence;

This yield;

14

(17)

As a result, the linearization of (15) can be written as (18) where

Expressing the set of linear equations in matrix form, we have (19) where

,

,

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The LLS solution is derived from (20) It’s known that the LLS solution is performed iteratively by solving hyperbolas about the point

. The estimate

is fed back from previous estimate into the new estimate

until One of the dominant factors that significantly affect the location estimation accuracy is Non-Line-Of-Sight (NLOS) propagation. Under NLOS condition, the direct Line-OfSight (LOS) path is blocked, and the transmitted signal may experience reflection, diffraction and scattering, causing extra path length. Previous research deal with the NLOS propagation problem can be divided mainly into two categories which are NLOS identification and NLOS mitigation. The former tries to identify whether each range measurement is LOS or not. If there are at least three LOS measurements, the traditional techniques can be used and the NLOS measurements are ignored. The latter tries to mitigate the NLOS effect and utilizes the NLOS measurements. We deal with the latter problem that even when NLOS measurements can be identified, among all the measurements obtained, there may still be not enough LOS measurements for accurate location estimation using traditional technique. For the rest of the paper, we assume that the total number of the measurements is greater than the minimum required and the NLOS measurements are identifiable. As NLOS propagation is one of the primary factors that affects the accuracy of TDOA technique, it makes sense to evaluate the performance of TDOA technique under NLOS rather than LOS environments.

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The initial guess is set as the centre of the polygon formed by the nearest BSs to the MS. Figure 4 shows the cumulative probability of location errors in different NLOS propagation environments, where the errors of TDOA observations are assumed to be zero-mean Gaussian random variables with standard deviations of 100, 200, and 300 m. Moreover, we assume that only three BSs are used to determine the MS position.

Figure 4: Cumulative probability of location errors using TDOA technique in different NLOS propagation environments.

5. Proposed Technique - Hybrid TDOA/RSS The proposed hybrid TDOA/RSS positioning technique solves hyperbolas and circles defined by the TDOA and RSS measurements, respectively. It iteratively performs the LLS solution until the solution converges to a minimum. We assume that the measurements are taken in urban and suburban areas, and consequently a must be taken from 3 to 6. For simulations, we assume that a = 4. 17

Moreover, the model in Figure 1 is used and the initial guess is set as the centre of the polygon formed by the nearest BS to the MS. Without measurement errors, the LOP derived from the linearization of (3) using Taylorseries expansion about the point (x0, y0) has the form

(21)

Set

(22)

Recall that the Taylor series expansion of

around the point

is given by

+ Higher Terms

(23)

Hence;

This yield;

0= 0− 2+

0 − 2−

12+2 0−

2 0−

0+ 2 0−

−2 0− ( 0)=0

(24)

Rearranging (24) yields; (25) 18

where

Expressing the set of linear equations in matrix form, we have (26) where

,

,

The LLS solution is performed iteratively by feeding back until

into new estimate

Figure 5 shows the performance of the proposed technique

without measurement errors.

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Figure 5: Cumulative probability of location errors using TDOA/RSS technique without measurement errors.

With measurement errors, the LOP derived from the linearization of (10) using Taylorseries expansion about the point (x0, y0) has the form (27) (28) This yield; (29) Set

(30)

Recall that the Taylor series expansion of

around the point

is given by

+ Higher Terms 20

(31)

Hence;

This yield;

(32) Rearranging (32) yields; (33)

where

Expressing the set of linear equations in matrix form, we have (34) where 21

,

,

The LLS solution is performed iteratively by feeding back until

into new estimate

Figure 6 shows the performance of the proposed technique

performance with measurement errors.

Figure 6: Cumulative probability of location errors using TDOA/RSS technique with measurement errors.

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It can be easily observed that when there are no measurement errors, all measurements taken for determining MS position, will be within 10 meters from that MS. This indicates that the location of MS can be specified with accuracy up to 99%. However, this case is ideal and measurement errors are inherent in the system. In the presence of measurement errors, the technique still can give us the position of MS but with reasonable cumulative probability of location error. Figure 7 shows the performance of the proposed technique and its counterparts for TDOA and RSS techniques. The performance is obtained assuming that only three BSs are used for location estimation.

Figure 7: Cumulative probability of location errors with three BSs in range of the MS.

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Figure 7 shows that the performance of both the TDOA and RSS techniques is inadequate. For the TDOA and RSS techniques, the MS can be located within 279 and 355 meters, respectively, with a confidence of 67%. These results do not meet the FCC accuracy requirements. For the proposed hybrid technique, the 67% of the location errors are around 123 meters. This is considered a large improvement over both TDOA and RSS techniques and almost meets the FCC accuracy requirements. Table 1 shows the location estimation error at different cumulative probability points.

Table 1: Location estimation error at different cumulative probability points (meters). Percentage of Location error for 3BSs Expected cumulative Technique (meters) improvement probability TDOA

214

Proposed

110

TDOA

279

Proposed

124

TDOA

476

Proposed

170

50%

49%

67%

56%

95%

64%

One of the good techniques for MS determination is the hybrid SADOA/TDOA technique which was presented in (Juang, Lin and Lin, 2007). A comparison between the performance of the proposed technique and that of the hybrid SADOA/TDOA technique is important. 24

We regenerate the cumulative probability of location errors for both the hybrid SADOA/TDOA technique as presented in (Juang, Lin and Lin, 2007) and our hybrid TDOA/RSS technique and plot these results versus location errors for the case of three BSs as shown in Figure 8 below.

Figure 8: Cumulative probability of location errors with three BSs in range of the MS. From Figure 8, there is a noticeable performance improvement of the proposed technique over the hybrid SADOA/TDOA technique. This is o illustrated further in Table 2 which shows the location estimation error for Hybrid SADOA/TDOA and proposed technique at different cumulative probability points.

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Table 2: Location estimation error for Hybrid SADOA/TDOA and proposed technique at different cumulative probability points (meters). Percentage of Location error for 3BSs Expected cumulative Techniques (meters) improvement probability Hybrid 162 50%

SADOA/TDOA

32%

Proposed

110

Hybrid 200 67%

SADOA/TDOA

38%

Proposed

124

Hybrid 245 95%

SADOA/TDOA

31%

Proposed

170

6. Conclusions and Future Work In this paper we presented a new technique that could be used to efficiently find the position of a MS in urban and suburban areas. By combining the TDOA and RSS techniques, this paper shows that the proposed hybrid technique outperforms the TDOA technique without hardware modifications to currently available handsets. The extra computation loading at the network can be relieved by using modern powerful computation machines.

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Regarding the FCC accuracy requirement; FCC requires that 67% of readings be within 100 meters of the MS and 95% of the readings be within 300 meters of the MS. Simulation results show that 67% of the readings are within 123 meters of the MS under using hybrid technique and 95% of the readings are within 169 meters. Hence, there will be up to 54% improvement when we use the proposed hybrid TDOA/RSS technique over the TDOA and RSS techniques. For further future study, we recommend the following:  Increasing the number of BSs used in solving MSP problem. This will increase the complexity and size of the computation algorithm. However, increasing the number of BSs will, definitely, lead to an improvement in the accuracy.  Our work can be extended to locate more than one MS at a time; this will save time and make the positioning problem easier and more efficient.  A combination of more than two techniques can be investigated.

7. References Caffery, J. and Stuber, G. (1998) ‘Overview of radiolocation in CDMA cellular systems’, IEEE Communication Magazine, Vol. 36, pp.38–45. Varshney, U. (2003) ‘Location management for wireless networks: issues and directions’, International Journal of Mobile Communications, Vol. 36, pp. 91-118. Varshney, U. (2003) ‘Issues, requirements and support for location-intensive mobile commerce applications’, International Journal of Mobile Communications, Vol. 1, pp. 247-263.

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Lee, T., Chen, S., Wang, S. and Chang, S. (2009) ‘Adoption of mobile Location-Based Services with Zaltman Metaphor Elicitation Techniques’, International Journal of Mobile Communications, Vol. 7, pp. 117-132. Vijayalakshmi, M. and Kannan A. (2009) ‘Proactive location-based context aware services using agents’ International Journal of Mobile Communications, Vol. 7, pp. 232252. Kapplan, E. (1996) Understanding GPS: principles and applications, Artech House. Caffery, J. (2000) ‘A new approach to the geometry of TOA location’. Paper presented in the Proceedings of IEEE Conference on Vehicular Technology. September 2000. Willassen, S. and Andresen, S. (1998) ‘A Method of implementing Mobile Station Location in GSM. Master Thesis. Norwegian University of Science and Technology, Trondhjem, Norway. Aso, M., Saikawa, T. and Hattori, T. (2002) ‘Mobile station location estimation using the maximum likelihood method in sector cell systems’. Paper presented in the proceedings of IEEE Conference on Vehicular Technology. September 2002. Abdulla, Y., El-Henawy, H., and Mahrous, S. (2001) ‘The effect of base stations configurations on the accuracy of hyperbolic position location in macrocellular and microcellular GSM systems’, Paper presented in the proceedings of 18th National Radio Science Conference. March 2001. Kbar, G. and Mansoor, W. (2005) ‘Mobile station location based on hybrid of signal strength and time of arrival’, Paper presented in the proceedings of International Conference on Mobile Business. July2005.

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Juang, R., Lin, D., and Lin, H. (2007) ‘Hybrid SADOA/TDOA mobile positioning for cellular networks’, IET Communication magazine, Vol. 1, No. 2, pp.282-287. Liu, H., Darabi, H., Banerjee, P., and Liu J. (2007) ‘Survey of Wireless Indoor Positioning Techniques and Systems’ IEEE Transactions on Systems, Man, and Cybernetics, Vol. 37, No. 6, pp. 1067–1080. Hata, M. and Nagatsu, T. (1980) ‘Mobile location using signal strength measurements in a cellular system’. IEEE Transactions on Vehicular Technology, Vol. 29, No. 2, pp.245251. Song H. (1994). ‘Automatic vehicle location in cellular communications systems’, IEEE Transactions on Vehicular Technology, Vol. 43, No. 4, pp.902–908. Stefanski, J. (2010) ‘Accuracy Analysis of Mobile Station Location in Cellular Networks’, Paper presented in the proceedings of the 2nd International Conference on Information Technology, June 2010. Guangqian, C., Zuowei, L., Xiaohong, W., Shiying, S., and Jianwen, H. (2010) ‘Research on TDOA/AOA Hybrid Positioning System Based on Kalman filtering’, Paper presented in the 2nd International Conference on Signal Processing Systems, July 2010.

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