An Enhanced Toa-Based Wireless Location Estimation Algorithm for Dense NLOS Environments

Abstract

Non-Line-Of-Sight (NLOS) signal propagation is the major source of error in conventional Time-Of-Arrival (TOA) based wireless location estimation algorithms. Previous research has mainly sought to address this problem in two ways: NLOS identification and NLOS mitigation. This paper focuses on the latter issue. It deals with the problem that even when NLOS measurements can be identified, among all the measurements obtained, there may still be not enough Line-Of-Sight (LOS) measurements for accurate location estimation using traditional TOA-based algorithms. With the assumptions that the total number of the measurements is greater than the minimum required and the NLOS measurements are identifiable, this paper proposes an enhanced TOA-based localization algorithm. It contains two parts: a combination stage and a Maximum Likelihood (ML) estimator. The proposed algorithm has an advantage that it does not require the information of the distribution of the NLOS bias. Simulation results show that the proposed algorithm outperforms all the other algorithms compared in dense NLOS environment.