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Aspect-Aware Target Detection and Localization by Wireless Sensor Networks

机译:无线传感器网络的方面感知目标检测和定位

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摘要

This paper considers the active detection of a stealth target with aspect dependent reflection (e.g., submarine, aircraft, etc.) using wireless sensor networks (WSNs). When the target is detected, its localization is also of interest. Due to stringent bandwidth and energy constraints, sensor observations are quantized into few-bit data individually and then transmitted to a fusion center (FC), where a generalized likelihood ratio test (GLRT) detector is employed to achieve target detection and maximum likelihood estimation of the target location simultaneously. In this context, we first develop a GLRT detector using one-bit quantized data which is shown to outperform the typical counting rule and the detection scheme based on the scan statistic. We further propose a GLRT detector based on adaptive multi-bit quantization, where the sensor observations are more precisely quantized, and the quantized data can be efficiently transmitted to the FC. The Cramer-Rao lower bound (CRLB) of the estimate of target location is also derived for the GLRT detector. The simulation results show that the proposed GLRT detector with adaptive 2-bit quantization achieves much better performance than the GLRT based on one-bit quantization, at the cost of only a minor increase in communication overhead.
机译:本文考虑了使用无线传感器网络(WSN)主动检测具有依赖于方面的反射(例如,潜艇,飞机等)的隐身目标。当检测到目标时,其定位也很重要。由于带宽和能量的严格限制,传感器的观测值将分别量化为几位数据,然后传输到融合中心(FC),在该中心使用广义似然比测试(GLRT)检测器来实现目标检测和最大似然估计。同时定位目标位置。在这种情况下,我们首先使用一位量化数据开发了GLRT检测器,该数据显示出优于典型的计数规则和基于扫描统计量的检测方案。我们进一步提出了一种基于自适应多位量化的GLRT检测器,其中传感器的观测值被更精确地量化,并且量化后的数据可以有效地传输到FC。目标位置的估计值的Cramer-Rao下界(CRLB)也可用于GLRT检测器。仿真结果表明,所提出的具有自适应2位量化的GLRT检测器比基于一位量化的GLRT取得了更好的性能,其代价是通信开销仅稍有增加。

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