自适应结构化检测器与训练受限情况下的性能评估

2016 CIE International Conference on Radar (RADAR) Pub Date : 2016-10-01 DOI:10.1109/RADAR.2016.8059469

T. Jian, Xiaodong Huang, You He, Zhi Wang, Biao Ding, Jian Shen

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引用次数: 0

摘要

利用超对称协方差矩阵，研究了高斯杂波下雷达目标的自适应检测问题。利用协方差矩阵的超对称性，讨论了广义似然比检验的结构化检测器。进一步给出了一种更有效的实域版本。通过蒙特卡罗仿真进行的性能评估，证实了其虚警率恒定的特性以及在训练数据量不足的情况下检测性能的优势。

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Adaptive structured detector and performance assessment in training-limited cases

Adaptive detection of a radar target is addressed in Gaussian clutter with persymmetric covariance matrix. Resorting to the persymmetry of covariance matrix, a structured detector of generalized likelihood ratio test is discussed. A more efficient version is further given in real domain. The performance assessment conducted by Monte Carlo simulation confirms its constant false alarm rate property and its advantage of detection performance in the case of insufficient number of training data.

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来源期刊

2016 CIE International Conference on Radar (RADAR)

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