Abstract
The presence of None-line-of-sight (NLOS) is one of the major challenging issues in time of arrival (TOA) based source localization, especially for the sparse anchor scenarios. Sparse anchors can reduce the system deployment cost, so this has become increasingly popular in the source location. However, fewer anchors bring new challenges to ensure localization precision and reliability, especially in NLOS environments. The maximum likelihood (ML) estimation is the most popular location estimator for its simplicity and efficiency, while it becomes extremely difficult to reliably identify the NLOS measurements when the redundant observations are not enough. In this study, we proposed an NLOS detection algorithm called misclosure check (MC) to overcome this issue, which intends to provide a more reliable location in the sparse anchor environment. The MC algorithm checks the misclosure of different triangles and then obtains the possible NLOS from these misclosures. By forming multiple misclosure conditions, the MC algorithm can identify NLOS measurements reliably, even in a sparse anchor environment. The performance of the MC algorithm is evaluated in a typical sparse anchor environment and the results indicate that the MC algorithm achieves promising NLOS identification capacity without abundant redundant measurements. The real data test also confirmed that the MC algorithm achieves better position precision than other three robust location estimators in an NLOS environment since it can correctly identify more NLOS measurements.
Highlights
The none-line-of-sight (NLOS) problem is one of the most challenging issues in time of arrival (TOA) based source localization
We proposed a misclosure check (MC) algorithm which can improve NLOS detection in a sparse anchor environment
The misclosure test statistics are formed with every pair of TOA measurements and the test statistics are checked to address a deceived NLOS set
Summary
The none-line-of-sight (NLOS) problem is one of the most challenging issues in time of arrival (TOA) based source localization. When the redundant observations are insufficient, the ML-estimator will be significantly biased due to the presence of NLOS measurement, the posterior residuals based approach cannot work well Another case is that sometimes the effect of NLOS bias is swamped or merged into the normal measurement [13,14], which leads to misidentification or false identification. There are a few other NLOS mitigation algorithms, such as the temporal correlation based approaches, including the semi-static method [24], the autocorrelation method [25], the sliding window approach [26,27], inertial measurement unit (IMU) aided approaches [28], the machine learning approach [29,30] etc These methods are only applicable to particular NLOS patterns or require additional information, and are not discussed in this study.
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