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Robust predetection data fusion for enhanced target detection

机译:强大的预传递数据融合,用于增强目标检测

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A robust Constant False Alarm Rate (CFAR) distributed detection system that operates in heavy clutter with unknown distribution is presented. The system is designed to provide CFARness under clutter power fluctuations and robustness under unknown clutter and noise distributions. The system is also designed to operate successfully under different-power sensors and exhibit fault-tolerance in the presence of sensor power fluctuations. The test statistic at each sensor is a robust CFAR t-statistic. In addition to the primary binary decisions, confidence levels are generated with each decision and used in the fusion logic to robustify the fusion performance and eliminate weaknesses of the Boolean fusion logic. The test statistic and the fusion logic are analyzed theoretically for Weibull and lognormal clutter. The theoretical performance is compared against Monte-Carlo simulations that verify that the system exhibits the desired characteristics of CFARness, robustness, insensitivity to power fluctuations and fault-tolerance. The system is tested with experimental target-in-clear and target-in-clutter data and its experimental performance agrees with the theoretically predicted behavior.
机译:呈现了一种强大的常量误报率(CFAR)分布式检测系统,其具有未知分布的重杂波。该系统旨在在杂波功率波动和未知杂波和噪声分布下提供CFarness。该系统还设计用于在不同功率传感器下成功运行,并且在存在传感器功率波动的情况下表现出容错。每个传感器的测试统计是强大的CFAR T统计。除了主要的二进制决策外,每个决定都会生成置信水平,并用于融合逻辑,以强调融合性能并消除布尔融合逻辑的弱点。理论上,测试统计和融合逻辑用于Weibull和Lognormal Clutter。将理论性能与Monte-Carlo模拟进行比较,验证系统的表现出CFarness,鲁棒性,对功率波动的不敏感性的所需特征和容错。该系统用实验目标内清晰的杂波数据进行测试,其实验性能与理论上预测的行为一致。

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