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# Network Based Mobile Positioning

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### INTRODUCTION:

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“Network Based Mobile Positioning”

• Mobile Based Techniques
• Network Based Techniques
• Indirect Techniques

• “Urban areas: LdB = A + B log10 R – E
• Suburban areas: LdB = A + B log10 R – C
• Open areas: LdB = A + B log10 R – D
• A = 69.55 + 26.16 log10 fc – 13.82 log10 hb
• B = 44.9 – 6.55 log10 hb
• C = 2 (log10 (fc / 28))2 + 5.4
• D = 4.78 ( log10 fc )2 + 18.33 log10 fc + 40.94
• E = 3.2 ( log10 ( 11.7554 hm ))2 – 4.97 for large cities, fc = 300MHz
• E = 8.29 ( log10 ( 1.54 hm ))2 – 1.1 for large cities, fc < 300MHz
• E = ( 1.1 log10 fc – 0.7 ) hm – ( 1.56 log10 fc – 0.8 ) for medium to small cities

Definition of parameters:

• hm mobile station antenna height above local terrain height [m]
• dm distance between the mobile and the building
• h0 typically height of a building above local terrain height [m]
• lhbbase station antenna height above local terrain height [m]
• rgreat circle distance between base station and mobile [m]
• R=r x 10-3 great circle distance between base station and mobile [km]
• f carrier frequency [Hz]
• fc=f x 10-6 carrier frequency [MHz]
• ? free space wavelength [m]”

This model is fairly simple so it is used for a large number of situations. The distance calculation is easy from this model using known path loss in pre-defined environment. The mobile will be located anywhere on the circle of estimated distance with centre at base station. A minimum of three base stations are used for such measurements. Ideally the three circles will intersect at a single point. This point will be the position of mobile station. The triangulation technique is used to find the intersection coordinates of circles. Time of Arrival: Although CGI provides the position of mobile station yet the accuracy is not sufficient for many purposes. To improve accuracy the time of arrival method is used. It gives good results than CGI in most of the situations. A number of algorithms describing time of arrival method are in literature. Each of them has some advantages and some short comings. Also each algorithm works best under some specific conditions e.g. in line of sight (LOS) or non-line of sight (NLOS) conditions. A good algorithm which gives acceptable results in many situations is “A Constrained Least Squares Approach to Mobile Positioning: Algorithms and Optimality”. This algorithm uses a least square approach to estimate position of mobile station. The distance between mobile station and base station is estimated by using the fact that signals travel in free space at a speed equal to speed of light. Thus mathematically, Di =Ti / c i = 1, 2… N Where ‘D’ is the estimated distance, ‘T’ is the TOA measurement, ‘i’ denote the number of base station and ‘c’ is the speed of light. The mobile station will be located anywhere on the circle with radius ‘D’ centered at base station ‘i’. Same TOA measurements are performed by at least three base stations. The position of mobile will be the intersection of three circles. Ideally this will be a single point. But in practice, due to multipath propagation and fading, it will give a small area. The mobile station will be located in this area. To reduce positioning error the algorithm uses a least square error approach. Thus the distance between every point in that area and each mobile station is calculated. The point where the sum of squares of distances is minimized will be the estimated position of mobile station. To get TOA measurements, base station and mobile station must be synchronized properly or there must be a reference point. Thus a strict timing requirement is necessary. Angle of Arrival: In LOS conditions this method is the best to use. A number of algorithms describing this method are in literature. All of these algorithms require a dominant LOS path to correctly perform angle of arrival measurements at base station. Thus this method is best in open areas and suburban areas. In dense urban environment this technique produces severe errors due to NLOS and multipath propagation. A number of algorithms are studied in detail. A good algorithm is “A Constrained Least Squares Approach to Mobile Positioning: Algorithms and Optimality”. It produces results with acceptable accuracy. According to this algorithm, to perform angle of arrival measurements, base station must be provided with multiple antenna arrays. A minimum of two base stations will be required to perform such measurements. The signal from mobile station will reach base station at a certain angle with horizontal axis. This angle can be measured by base station using antenna arrays. Mathematically, it is given by tan (fi) =(y – yi / x – xi) , i = 1, 2, . . . , M. The angle of arrival measurement from one base station will result in a straight line. This line is also called Line of Bearing (LOB). It will be at a certain angle between horizontal axis and base station. The mobile will be located any where on the LOB. A similar measurement will be done using another involved base station. It will result in producing another angle of arrival or LOB. The point where the two line of bearing intersects will be the position of mobile station. Ideally two lines will intersect at a unique point. However, practically they may not intersect at a point. In this situation the angle of arrival method need further measurements from other involved base station. This method produces very accurate results in LOS situation. However, the results depend critically on the measured angle. Thus a very small error in angle measurement may lead to positioning error of hundreds of meters. Another disadvantage is the cost of this method. It requires antenna arrays at each base station to measure AOA. Hence cost of implementation increases. Time Difference of Arrival: The time difference of arrival uses the difference in arrival times of signals at a pair of base stations. The time difference of arrival measurements are done with reference to primary base station. A good algorithm in literature is the “Performance Comparison of TOA and TDOA Based Location Estimation Algorithms in LOS Environment”. It explains the working of different types of TDOA approach. It also compares the performance of each of the type. However it uses a LOS approach. In open areas LOS assumption is valid but in heavily populated urban areas this assumption is invalid. Another good algorithm which explains the TDOA measurements is is “A Constrained Least Squares Approach to Mobile Positioning: Algorithms and Optimality” . In this algorithm, the primary (Serving) base station is the reference base station. The time of arrival measurements are performed using the reference base station. Thus the estimated distance between mobile station and reference base station is ‘d1’ and that of mobile station and a neighbouring base station is ‘di’. Thus the TDOA measurements are given by, d1 = t1 / c di = ti / c, i = 2,3 ….. N Where ‘d1’ is the distance between mobile station and reference (primary) base station and ‘di’ are the distances between mobile station and other three neighbouring base stations. The TDOA measurement between reference and second base station is given by TDOA= d1-di i = 2,3, ….N This will be the error free TDOA measurement at a pair of base stations. The measurement including error is given by; TDOA= (d1 – di) + error The error is modelled as a Gaussian distributed random variable with zero-mean. Such measurements are taken from at least three pairs of base stations. The triangulation technique is then employed to get the position of mobile station. The TDOA method is superior to time of arrival (TOA) in sense that it eliminates the need for timing reference. Thus it is easy to implement. Due to no timing requirement TDOA method is more frequently used than TOA method. Database correlation method: Despite of a number of algorithm which perform fairly well in urban areas there is still a need to further improve it. Due to severe multipath and fading effects LOS assumption is not valid in urban areas. The Database Correlation Method is a good method to counter effect multi path and fading. It can be implemented by utilizing the measurements performed in existing GSM systems. It can be implemented by making Signal Strength as a parameter. A ggod algorithm to implement Database Correlation Method is “Database Correlation Method for GSM Location by ‘Heikki Laitinen, Jaakko Lahteenmaki, Tero Nordstrom'”. In this algorithm the DCM is implemented by using signal strength measurements performed by handset. The algorithm explains the way database correlation method can be implemented in GSM. The measurements performed in the coverage area are performed by mobile station and are stored in database. Thus the database will consists pre-measured samples of signal measurements in the coverage location. When the need to locate mobile station arises, the primary base station asks mobile station to perform signal strength measurements and feedback to it. The BS sends these measurements to location server. The location server then calculates the difference between stored fingerprint and actual measurement. The point where the difference between fingerprint and actual measurement is minimum will be the estimated position of mobile station. The estimated position will also contain some error in it due to fading, NLOS path and multipath propagation. However this error will be drastically less than the error in other techniques applied under same conditions. The database correlation method has the advantage that it can be implemented in any type of system like GSM, CDMA,UMTS,etc. References:

• Network-Based Wireless Location IEEE SIGNAL PROCESSING MAGAZINE JULY 2005
• A New Time-Based Algorithm for Positioning Mobile Terminals in Wireless NetworksIsrael Martin-Escalona and Francisco Barcelo-Arroyo, EURASIP Journal on Advances in Signal Processing
• Mobile Positioning Using Wireless NetworksIEEE SIGNAL PROCESSING MAGAZINE JULY 2005
• Path loss models S-72.333 Physical layer methods in wireless communication systems Sylvain Ranvier / Radio Laboratory / TKK 23 November 2004
• Performance Comparison of TOA and TDOA Based Location Estimation Algorithms in LOS Environment Guowei Shen, Rudolf Zetik, and Reiner S. Thoma
• A Constrained Least Squares Approach to Mobile Positioning: Algorithms and Optimality K.W. Cheung,1 H. C. So,1 W.-K.Ma,2 and Y. T. Chan3
• Database Correlation Method for GSM Location Heikki Laitinen, Jaakko Lahteenmaki, Tero Nordstrom

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Network Based Mobile Positioning. (2017, Jun 26). Retrieved November 26, 2022 , from
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