Mobile Location Estimation in NLoS Environment Base on Interior Point Method

Increasing applications of mobile communication services makes the challenges in location estimation as a fundamental problem in many applications. There are error sources such as multipath fading, Non Line of Sight (NLoS) propagation and multi-user interferences that deteriorate the accuracy of location estimation. To mitigate this errors two approach is feasible, statistical methods and geometric optimization algorithms. In this paper we focus on NLoS error and employ the time of arrival (ToA) for range measurement. A new location estimation is proposed based on geometric optimization approach that adopts the interior point method (IPM) for better location estimation, which is a non linear constrained optimization method. This method has a salient feature that can solve optimization problems including more inequality constraints than equality constraints. The estimation can be network or terminal based and doesn't discriminate between LoS and NLoS base stations. The proposed algorithm requires ToA measurements only from three BSs and doesn't require any priori probabilistic information. Simulations were conducted to evaluate the performance of the algorithm for different NLoS error distributions, and the results show significant improvement than previous works such as range scaling (RSA) and density clustering (DCA) algorithms and also satisfies the location accuracy demand of FCC E-911 specification.