An Enhanced Toa-Based Wireless Location Estimation Algorithm for Dense NLOS Environments
An Enhanced Toa-Based Wireless Location Estimation Algorithm for Dense NLOS Environments
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DOI：
10.1109/wcnc.2009.4917582
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发表时间：
2009-04
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在传统的基于到达时间(TOA)的无线位置估计算法中，非视距(NLOS)信号传播是主要的误差来源。以往的研究主要通过两种方式来解决这一问题：识别非视距和缓解非视距。本文主要研究的是后一个问题。它解决的问题是，即使可以识别非视距测量，在所有获得的测量中，可能仍然没有足够的视距(LOS)测量来使用传统的基于TOA的算法进行准确的位置估计。在假设测量总数大于最小要求且非视距测量可识别的前提下，提出了一种基于TOA的改进定位算法。它包括两部分：组合阶段和最大似然(ML)估计器。该算法的优点是不需要NLOS偏差的分布信息。仿真结果表明，在密集非视距环境下，该算法的性能优于其他算法。
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.