A Base Station Identification Algorithm for SFN Positioning Systems in NLOS Environment

Abstract

Owing to the characteristic of single frequency network (SFN) and non-line of sight (NLOS) environment, base station (BS) identification becomes a prominent problem in SFN positioning systems. In this work, a universal BS identification algorithm is proposed for any DTV standard. The basic idea is to formulate the base station identification problem as a data classification which is then solved by the least distance classifier. The state prediction model is utilized to estimate kinematics parameters and thus decrease the system hardware requirement. In order to obtain accurate state prediction for BS identification, an interacting multiple model (IMM) method is adopted to mitigate the NLOS effect for performance improvement. Because of close relation between the position estimation and BS identification, the proposed method is more effective as compared to some existing methods. Simulation results show that the proposed algorithm can perform well in both unfixed BS set and NLOS environments.