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On distributed signal detection with multiple local free parameters

机译:具有多个本地自由参数的分布式信号检测

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In this work, we propose an efficient approach to the optimization of distributed multiradar systems with parallel topology, employing decision fusion from local detectors with discrete and continuous free design parameters. This approach, termed hierarchical optimization approach, can be applied to a variety of optimization criteria including the Neyman-Pearson (NP) and the locally optimum detection (LOD) criteria. It avoids the exhaustive search for the optimal discrete parameters and greatly reduces the computational load required for global system optimization. The effectiveness of the proposed approach is demonstrated by means of a numerical example, where ordered statistic (OS) constant false-alarm rate (CFAR) decentralized radar detection of Swerling I targets in Gaussian noise is considered
机译:在这项工作中,我们提出了一种具有并行拓扑的分布式多雷达系统优化的有效方法,该方法采用了具有离散和连续自由设计参数的本地检测器的决策融合。这种称为分层优化方法的方法可以应用于各种优化标准,包括Neyman-Pearson(NP)和局部最优检测(LOD)标准。它避免了对最佳离散参数的详尽搜索,并大大减少了全局系统优化所需的计算量。通过数值示例证明了该方法的有效性,其中考虑了在高斯噪声中对有序I目标进行有序统计(OS)恒定误报率(CFAR)分散雷达检测

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