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基于 RSS 和 TOA 测量的集成和分离测距的目标定位。

Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements.

机构信息

COPELABS, Universidade Lusófona de Humanidades e Tecnologias, Campo Grande 376, 1749-024 Lisboa, Portugal.

ISR/IST, LARSyS, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal.

出版信息

Sensors (Basel). 2019 Jan 9;19(2):230. doi: 10.3390/s19020230.

Abstract

This work addresses the problem of target localization in adverse non-line-of-sight (NLOS) environments by using received signal strength (RSS) and time of arrival (TOA) measurements. It is inspired by a recently published work in which authors discuss about a below and above which employing combined RSS-TOA measurements is inferior to employing RSS-only and TOA-only measurements, respectively. Here, we revise state-of-the-art estimators for the considered target localization problem and study their performance against their counterparts that employ each individual measurement exclusively. It is shown that the hybrid approach is not the best one by default. Thus, we propose a simple heuristic approach to choose measurement for each link, and we show that it can enhance the performance of an estimator. The new approach implicitly relies on the concept of the critical distance, but does not assume certain link parameters as given. Our simulations corroborate with findings available in the literature for line-of-sight (LOS) to a certain extent, but they indicate that more work is required for NLOS environments. Moreover, they show that the heuristic approach works well, matching or even improving the performance of the best fixed choice in all considered scenarios.

摘要

这项工作通过使用接收信号强度 (RSS) 和到达时间 (TOA) 测量来解决在不利的非视距 (NLOS) 环境中的目标定位问题。它的灵感来源于最近发表的一篇文章,该文章讨论了一个下界和一个上界,分别在这两个下界和上界以下和以上,采用组合 RSS-TOA 测量分别不如仅采用 RSS 测量和仅采用 TOA 测量。在这里,我们修改了针对所考虑的目标定位问题的最新估计器,并研究了它们与仅使用每个单独测量的对应估计器的性能。结果表明,混合方法并非默认的最佳方法。因此,我们提出了一种简单的启发式方法来为每个链路选择测量值,并表明它可以增强估计器的性能。该新方法隐式地依赖于临界距离的概念,但不假设某些链路参数是给定的。我们的模拟与文献中在一定程度上针对视距 (LOS) 的结果相吻合,但它们表明在 NLOS 环境中还需要做更多的工作。此外,它们表明启发式方法效果很好,在所有考虑的情况下都匹配甚至提高了最佳固定选择的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca0d/6359246/c1dec748e2bc/sensors-19-00230-g001.jpg

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