杂乱
计算机科学
稳健性(进化)
恒虚警率
探测器
假警报
雷达
目标检测
高斯分布
似然比检验
事先信息
人工智能
算法
估计理论
正规化(语言学)
模式识别(心理学)
数学
统计
电信
物理
基因
化学
量子力学
生物化学
作者
Zhouchang Ren,Wei Yi,Wenjing Zhao,Lingjiang Kong
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:61: 1-14
标识
DOI:10.1109/tgrs.2023.3235062
摘要
Proper prior knowledge of target scattering centers (SCs) can help to obtain better detection performance of range-spread targets. However, target SCs are sensitive to the target’s attitude relative to the radar and vary significantly among different targets. The existing approaches that employ predetermined prior knowledge may suffer performance degradation when the prior information does not match the practical scenarios. A possible way to circumvent this drawback is to estimate the SCs of different targets adaptively and check the presence of a target utilizing the range cells occupied by the most likely target SCs. For this reason, this article develops a generalized likelihood ratio test based on adaptive SCs estimation (ASCE-GLRT) for range-spread target detection in compound-Gaussian clutter. Under the assumption that the target SCs are sparse, we model the problem of SCs estimation as a sparse signal representation. Moreover, since the sparse assumption may not always be satisfied in practice, a modified sparsity regularization method is proposed to enhance the robustness of the estimation performance of targets with different scattering characteristics. A theoretical analysis shows that the proposed detector can achieve the constant false alarm rate (CFAR) property. The performance assessments conducted by numerical simulation and field tests confirm the effectiveness and robustness of the proposed detector.
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