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基于协作融合的无源多基地雷达检测。

Cooperative Fusion Based Passive Multistatic Radar Detection.

机构信息

School of Engineering, RMIT University, Melbourne 3000, Australia.

出版信息

Sensors (Basel). 2021 May 5;21(9):3209. doi: 10.3390/s21093209.

DOI:10.3390/s21093209
PMID:34063129
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8125025/
Abstract

Passive multistatic radars have gained a lot of interest in recent years as they offer many benefits contrary to conventional radars. Here in this research, our aim is detection of target in a passive multistatic radar system. The system contains a single transmitter and multiple spatially distributed receivers comprised of both the surveillance and reference antennas. The system consists of two main parts: 1. Local receiver, and 2. Fusion center. Each local receiver detects the signal, processes it, and passes the information to the fusion center for final detection. To take the advantage of spatial diversity, we apply major fusion techniques consisting of hard fusion and soft fusion for the case of multistatic passive radars. Hard fusion techniques are analyzed for the case of different local radar detectors. In terms of soft fusion, a blind technique called equal gain soft fusion technique with random matrix theory-based local detector is analytically and theoretically analyzed under null hypothesis along with the calculation of detection threshold. Furthermore, six novel random matrix theory-based soft fusion techniques are proposed. All the techniques are blind in nature and hence do not require any knowledge of transmitted signal or channel information. Simulation results illustrate that proposed fusion techniques increase detection performance to a reasonable extent compared to other blind fusion techniques.

摘要

近年来,被动多基地雷达因其与传统雷达相比具有许多优势而引起了广泛关注。在本研究中,我们的目标是在被动多基地雷达系统中进行目标检测。该系统包含一个单一的发射器和多个空间分布式接收器,包括监视和参考天线。该系统由两部分组成:1.本地接收器,2.融合中心。每个本地接收器检测信号、处理信号,并将信息传递到融合中心进行最终检测。为了利用空间多样性,我们应用了主要的融合技术,包括硬融合和软融合,用于多基地被动雷达。对于不同的本地雷达探测器,分析了硬融合技术。在软融合方面,针对空假设,基于随机矩阵理论的本地检测器的盲技术(称为等增益软融合技术)进行了分析和理论分析,并计算了检测阈值。此外,还提出了六种新的基于随机矩阵理论的软融合技术。所有技术本质上都是盲的,因此不需要任何关于发射信号或信道信息的知识。仿真结果表明,与其他盲融合技术相比,所提出的融合技术在很大程度上提高了检测性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/3544b7becd52/sensors-21-03209-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/e1a5099d8991/sensors-21-03209-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/4a820ee3d806/sensors-21-03209-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/95007cd774f3/sensors-21-03209-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/e8318aebbdef/sensors-21-03209-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/6db30630cf71/sensors-21-03209-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/b054c4028f82/sensors-21-03209-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/0a5a846bf727/sensors-21-03209-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/458441468fb2/sensors-21-03209-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/b284081bd57c/sensors-21-03209-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/3544b7becd52/sensors-21-03209-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/e1a5099d8991/sensors-21-03209-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/4a820ee3d806/sensors-21-03209-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/95007cd774f3/sensors-21-03209-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/e8318aebbdef/sensors-21-03209-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/6db30630cf71/sensors-21-03209-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/b054c4028f82/sensors-21-03209-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/0a5a846bf727/sensors-21-03209-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/458441468fb2/sensors-21-03209-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/b284081bd57c/sensors-21-03209-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29a6/8125025/3544b7becd52/sensors-21-03209-g010.jpg

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