Mobile location based on signal strength in cellular network

Pao Yue-kong Library Electronic Theses Database

Mobile location based on signal strength in cellular network


Author: Zhao, Jingbo
Title: Mobile location based on signal strength in cellular network
Degree: M.Sc.
Year: 2009
Subject: Hong Kong Polytechnic University -- Dissertations.
Global Positioning System.
Global system for mobile communications -- China -- Hong Kong.
Mobile communication systems -- China -- Hong Kong.
Department: Dept. of Land Surveying and Geo-Informatics
Pages: xv, 124 leaves : ill. (some col.) ; 30 cm.
Language: English
InnoPac Record:
Abstract: Recently, mobile location estimation has been a critical technology for mobile computing. So the purpose of this thesis is to provide the estimation of the location of mobile handset in GSM, which is one of the dominant cellular networks in Hong Kong. In addition, for real applicatbn in all cellular networks, these models and algorithms presented in this thesis are all based on signal strength which is a common attribute in different cellular networks. Then in theory, these models and algorithms can be applied in all cellular networks. Firstly, the free space propagation model (Zhou, 2007; Tonteri, 2002) is described which is the basic radio propagation model Then with the free space propagation model, three simple algorithms are presented, the Centre of Gravity (CG) algorithm (Ng et al, 2002; Chen et al, 2006), the Crude Estimation Method (CEM) (Leung et al, 2003) and the Circular Trilateration algorithm (CT) (Zhou & Ng, 2006) which are all based on the received signal strength. The free space model can only be used in free space environments, where there are no reflections, absorption, diffraction, and other distortions. In real environment, a Line of Sight (LOS) path seldom occurs especially in urban environment. Then if the LOS between mobile handset and base station is obstructed, the received signal power will be significantly lower than the free space propagation model suggests. Furthermore, even in a LOS environment, the free space propagation model doesn't necessarily give a good approximation of received signal power (Tonteri, 2002; Zhou, 2005). With the experimental data in Hong Kong, these three algorithms are tested and compared, and the results show they are all not promising in Hong Kong And in some big cities such as Hong Kong, some of the base station antennas are directional. The directional antenna transmits the largest power in one specific direction, and transmits small or none power in other directions. As a result, the relationship between RSS measurements and their corresponding distances from mobile station to base stations is not obvious. And strong RSS measurement does not mean that mobile station is near to base station, and on the other hand, weak RSS measurement can be observed from a nearby base station. Then in view of this point, it can be expected that the algorithms only based on the received signal strength such as CGM, CEM and CT are not suitable for mobile location which involves the directional antenna. While the signal path loss can be an indicator of distance between base station and mobile station regardless the antenna is omnidirectional or directional. Then the signal bss can be converted into distance between the base station and mobile station with the radio propagation model, three or more base stations as the centers, the corresponding distances as radius, and the trilateration can be used for location. Then several popular empirical models (Parsons, 2000; Saunders, 2007; Sizun, 2005; Tonteri, 2001; Wolfle et al, 2002; Xie, 2008; Abhayawardhana et at, 2005) are descried. These empirical models are improved based on the basic free space propagation model and many practical data in real environments. Empirical models can be implemented rapidly without any extremely accurate or expensive geographical databases. The best known empirical model is the Okumura-Hata model Under these empirical models, least square approach (Cheung el at, 2003) has been presented. And in order to evaluate which model is fit for the urban and suburban environments of Hong Kong, with the experiments, the popularly used classical empirical propagation models are tested and compared based on the practical measurements in Hong Kong and the results show that all these models are not applicable. In view of this point, we should improve the model to provide accurate location estimation. With the experimental data in Hong Kong, two customized models are estimated to fit the gathered practical measurements in urban and suburban environments respectively in Hong Kong. Through comparing the new models with the classical empirical models, the results show the customized models are more applicable for mobile positioning in Hong Kong. But since the experimental measurements from base stations far away (> 1km) are not involved when customizing the new model in urban environment, they may result in large errors when using them for mobile positioning. And even though they are involved when customizing the new model in suburban environment, small predicted signal loss error may lead to large estimated range error when the distance from mobile station to base station is long enough, for example several km. And based on analysis, we found the modeled distance error is almost proportional to the true distance between the base station and mobile handset. If all the measurements are used for location, the large modeled distance error will induce large location error. And we also found when the true distance between the base station and mobile handset is less than 1 km, most of the modeled distance errors are less than 400 m. Then if only the properly selected partial practical measurements are used for location, the accuracy may be higher than that based on all measurements. In view of that, a position algorithm based on partial measurements is proposed for location in the populated area like MongKok. The criteria to select partial measurements are given. To ensure the reliability of the solution, it is suggested to compare or match with previous solutions when mobile station is moving. The numerical results show that the new method is obviously better than those position algorithms based on all measurements. Also the test results demonstrate that the new method is fit for populated areas with dense base station distrfcution and not fit for suburban areas with sparse base stations. Then for the accurate location in all areas, another new mobile positioning method should be proposed. The test results of new customized models show that for those experimental measurements, if they are from same base station especially ones far away from mobile station and gathered around a small area, their modeled distance errors are generally similar. Based on that, a differential algorithm is proposed in order to correct these spatially related errors. And due to the complicated propagation environments, some of the measurements may not be fit for our customized model and their modeled distance errors are not spatially correlated either. To avoid the effects of these measurements, a robust least square method is also adopted instead of classical least square approach. The numerical tests show the proposed differential position method can obviously improve positioning accuracy.

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