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Range-Spread Target Detection using Consecutive HRRPs

机译：使用连续HTTP进行范围扩展目标检测

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摘要

In this paper, a heuristic detector is proposed to detect range-spread targets in white Gaussian noise using multiple consecutive high- resolution range profiles (HRRPs) received from a high-resolution radar (HRR). The detector consists of refiners of HRRPs and a cross-correlation integrator of refined HRRPs. Based on the fact that strong scattering cells are sparse in target HRRPs, nonlinear shrinkage maps are designed to refine received HRRPs before integration, by which most of the noise-only cells in received HRRPs are suppressed while strong scattering cells most probably relevant to target signature are preserved. Since the target's scattering geometry is almost unchanged except for range walking during integration, the refined target HRRPs from consecutive pulses are highly similar while refined noise-only HRRPs are dissimilar due to randomicity. The modified correlation matrix of multiple refined HRRPs is used to measure their similarity. The test statistic, a weighted integration of the entries of the modified correlation matrix, is constructed for target detection. The proposed detector does not depend on a strict target return model and can work in mild conditions. The real target data and simulated noise are used to evaluate the detector, and the experimental results show that it achieves better detection performance than some existing methods.
机译：在本文中，提出了一种启发式检测器，该方法使用从高分辨率雷达（HRR）接收到的多个连续高分辨率范围轮廓（HRRP）来检测高斯白噪声中的距离扩展目标。该检测器由HRRP的优化器和HRRP的互相关积分器组成。基于目标HRRP中稀疏的散射细胞这一事实，设计了非线性收缩图，以便在积分之前对接收到的HRRP进行精修，从而抑制了HRRP中的大多数纯噪声细胞，而最有可能与目标特征相关的强散射细胞被保留。由于目标的散射几何形状几乎没有变化，除了积分过程中的距离移动外，连续脉冲的精确目标HRRP非常相似，而纯噪声HRRP由于随机性而不同。使用多个改进的HRRP的改进的相关矩阵来衡量它们的相似性。构建测试统计量（修改后的相关矩阵的条目的加权积分）以进行目标检测。提出的探测器不依赖严格的目标返回模型，并且可以在温和的条件下工作。利用真实的目标数据和模拟的噪声对检测器进行评估，实验结果表明，与现有方法相比，该方法具有更好的检测性能。

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