An efficient geometry-constrained NLOS mitigation algorithm based on ML-detection

Abstract

Mobile location estimation has attracted much attention in recent years. However, the vital problem that affects location estimation accuracy is mainly due to the unavoidable non-line-of-sight (NLOS) propagation in mobile environments. In this paper, an effective technique is proposed to mitigate the NLOS errors when the range measurements corrupted by NLOS errors are not identifiable. In order to enhance the precision of the location estimate, the proposed scheme incorporates the geometric constraints within the Maximum Likelihood (ML) detection algorithm, which not only preserves the computational efficiency of the optimal ML detection algorithm, but also obtains precise location estimation under NLOS environments. Analysis and simulation results indicate that the proposed algorithm can significantly restrain the NLOS errors and achieve better location accuracy, compared with the existing mobile location estimation schemes.