| 引用本文: | 高彦钊,占荣辉,万建伟.KK分布杂波下的距离扩展目标检测算法.[J].国防科技大学学报,2015,37(1):118-124.[点击复制] |
| GAO Yanzhao,ZHAN Ronghui,WAN Jianwei.Range-spread target detection in KK-distributed clutter[J].Journal of National University of Defense Technology,2015,37(1):118-124[点击复制] |
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| KK分布杂波下的距离扩展目标检测算法 |
| 高彦钊1,2, 占荣辉3, 万建伟3 |
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(1.信息工程大学 信息技术研究所，河南 郑州 450000;2.
2.国防科技大学 电子科学与工程学院， 湖南 长沙 410073;3.2.国防科技大学 电子科学与工程学院， 湖南 长沙 410073)
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| 摘要: |
| 针对服从KK分布的大拖尾雷达杂波背景下的扩展目标检测问题，利用球不变随机变量表示了KK分布雷达杂波模型。在假设目标回波幅度已知的情况下，研究了基于Neyman-Pearson准则的距离扩展目标最优积累检测器，并通过对目标幅度的最大似然估计，推导了广义最大似然比检验检测器模型。为了降低这两种检测器中因计算第二类修正的贝塞尔函数而引入的运算复杂度，提出了一种基于顺序统计量的广义似然比检测器。该检测器利用检测窗内幅度较大的距离单元回波作为目标回波进行判决。利用蒙特卡罗仿真对这三种算法的性能进行了验证与比较，虽然最优积累检测器与广义似然比检测器具有更好的检测性能，但实现困难，计算量大，而基于顺序统计量的广义似然比检测器则具有更高的实用性。 |
| 关键词: 距离扩展目标检测 广义似然比检验 球不变随机变量 KK分布 顺序统计量 |
| DOI：10.11887/j.cn.201501020 |
| 投稿日期：2013-12-11 |
| 基金项目:国家自然科学基金资助项目(61471370,61002022) |
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| Range-spread target detection in KK-distributed clutter |
| GAO Yanzhao1,2, ZHAN Ronghui3, WAN Jianwei3 |
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(1.Information Engineering Research Center, Information Engineering University, Zhengzhou 450000, China;2.
2.College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China;3.2.College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China)
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| Abstract: |
| Aiming at the range-spread target detection problem in KK-distributed heavy-tailed radar clutter, the KK-distributed radar clutter was taken as a spherically invariant random vector. The Neyman-Pearson optimal integrator for the range-spread target detection with known target amplitude was derived firstly. Then by replacing the ideal target amplitude with the maximum likelihood estimates, the detector model in generalized likelihood ratio test (GLRT) was obtained. Both of the detectors are dependent on the modified Bessel function of the second kind, which makes the detectors computationally complicated, so a suboptimal generalized likelihood ratio detector based on order statistics (OS-GLRT) was proposed. The OS-GLRT takes some range unit echoes with largest amplitude in detection window as target echoes. The performance assessment conducted by Monte Carlo simulation validates that: the optimal integrator and GLRT have better performance, however, they are hard to applied, and the OS-GLRT is a more practical detector. |
| Keywords: range-spread target detection generalized likelihood ratio test spherically invariant random variables KK-distribution order statistics |
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