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Sensor Selection for TDOA-Based Localization in Wireless Sensor Networks With Non-Line-of-Sight Condition

机译:非视距条件下无线传感器网络中基于TDOA定位的传感器选择

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This paper investigates the selection of a subset of sensors for time difference of arrival (TDOA) localization under the non-line-of-sight (NLOS) condition in wireless sensor networks (WSN). Specifically, we aim to optimize the sensor activation for the sake of minimizing the localization error when considering NLOS condition subject to energy constraints. In contrast to existing sensor selection strategies, two independent Boolean selection vectors are utilized to determine the reference sensor and other sensors simultaneously in TDOA localization. Upon presenting expressions of the Cramer-Rao lower bound (CRLB) under three different scenarios, including: 1) line-of-sight (LOS), 2) Prior statistics unknown NLOS (PSU-NLOS), 3) Prior statistics known NLOS (PSK-NLOS), the optimization problems for sensor selection are formulated to minimize the CRLB based on two independent Boolean selection vectors. Analytical scheme is developed by solving the tractable semidefinite program (SDP) problems which are converted from the original nonconvex problem. Furthermore, two low-complexity heuristic algorithms, namely best option filling (BOF) algorithm and iterative swapping greedy (ISG) algorithm, are proposed for the sake of practical implementation. Simulation results validate that the localization accuracy for sensors selected by the SDP with randomization algorithm and the ISG algorithm achieves the exhaustive search method. Additionally, these two algorithms are stable under several random sensor network geometries.
机译:本文研究了在无线传感器网络(WSN)的非视距(NLOS)条件下,针对到达时间差(TDOA)定位的传感器子集的选择。具体来说,我们的目的是在考虑受能量限制的NLOS条件时,为了使定位误差最小化而优化传感器的激活。与现有的传感器选择策略相比,在TDOA定位中,两个独立的布尔选择向量被用来同时确定参考传感器和其他传感器。在三种不同情况下呈现Cramer-Rao下界(CRLB)的表达式后,包括:1)视线(LOS),2)先前统计未知的NLOS(PSU-NLOS),3)先前统计已知的NLOS( (PSK-NLOS),基于两个独立的布尔选择向量制定了传感器选择的优化问题,以最小化CRLB。通过解决从原始非凸问题转换而来的易处理半定程序(SDP)问题,开发了解析方案。此外,为便于实际实现,提出了两种最简单的启发式算法,即最佳选择填充算法(BOF)和迭代交换贪婪算法(ISG)。仿真结果表明,采用随机算法和ISG算法的SDP选择的传感器定位精度达到了穷举搜索的目的。此外,这两种算法在几种随机传感器网络几何结构下均稳定。

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