Study On Location Algorithms Based On TDOA In Cellular Networks

With the rapid development of mobile communication, the research on location services has been a hot point. Therefore, in order to focus on the core problem of mobile location accuracy improvement, this thesis aims at TDOA location techniques, and investigates the following cellular network based locationtechniques: performance analysis of basic TDOA location algorithms, hybrid location algorithms, and mobile position estimation techniques based ondata fusion. The main works of the paper concentrate on the following:1) cellular network wireless location ways, channel models, and evaluating indicators is introduced. The mathematical model of TDOA hyperbolic equations is established, three major location algorithms based time-difference-of-arrival (TDOA) are analyzed in detail.2) In order to offset the limitation of these traditional algorithms as Chan and Taylor, the location results of Chan are used as original value of Taylor Series Expansion algorithm, and this algorithm was used in TDOA/AOA .The performance of location is improved effectively.3) A simple multiple-algorithm cooperative location is analyzed. It processes information fusion for the location estimates by Chan algorithm ,Taylor algorithm and SX algorithm. In the real situation, the performance of the cooperative location of Chan and Taylor is improved further .But the performance of the other cooperative location is not obvious. At last, Through weighting the location results of the two excellent algorithms , The performance of location is improved effectively to the certain extent.4) The enhanced data fusion model with higher degree fusion is improved. This method uses a simple multiple-algorithm cooperative location in the first data fusion of this model .Through improving the location performance of TDOA and TOA, the accuracy of the hybrid location method employing AOA measurement is improved further. The performance of location is improved more effectively to the certain extent by this method than the original enhanced data fusion model.