用于检验散斑协方差矩阵估计性能的白化度评价方法

于涵; 水鹏朗; 杨春娇; 施赛楠

doi:10.12000/JR16146

用于检验散斑协方差矩阵估计性能的白化度评价方法

DOI: 10.12000/JR16146 CSTR: 32380.14.JR16146

西安电子科技大学雷达信号处理国家重点实验室西安 710071

基金项目: 国家自然科学基金(61671357)

详细信息

作者简介:
于涵(1993–)，女，籍贯山东，博士生，主要研究方向为海杂波特性分析等。E-mail: hyu_5@stu.xidian.edu.cn

水鹏朗(1967–)，男，籍贯陕西，博士，教授，研究方向为多速率滤波器理论及应用、图像处理和雷达目标检测。E-mail: plshui@xidian.edu.cn

杨春娇(1993–)，女，籍贯陕西，硕士生，主要研究方向为雷达目标检测等。E-mail: chunjiao_yang@163.com

施赛楠(1990–)，女，籍贯江苏，博士生，研究方向为雷达信号处理和微弱目标检测。E-mail: snshi@stu.xidian.edu.cn

通讯作者:
于涵 hyu_5@stu.xidian.edu.cn

中图分类号: TN957.51
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出版历程
- 收稿日期: 2016-12-16
- 修回日期: 2017-04-24
- 网络出版日期: 2017-06-28

Whitening Degree Evaluation Method to Test Estimate Accuracy of Speckle Covariance Matrix

National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (61671357)

摘要

摘要: 在海杂波背景下，散斑协方差矩阵估计性能严重影响着雷达自适应目标检测的准确性。针对不同散斑协方差矩阵估计方法，通常采用归一化F范数方法检验估计性能。但该检验方法需要已知真实协方差矩阵，在实际雷达系统中并不容易实现。鉴于该问题，该文提出了一种用于检验散斑协方差矩阵估计性能的白化度评价方法，充分利用了散斑协方差矩阵在白化滤波过程中的去相关作用。该方法将白化滤波后的杂波向量中脉冲间的相关程度作为评价指标，衡量散斑协方差矩阵估计方法的估计误差大小。与归一化F范数检验方法相比，该文提出的评价方法具有检验结果的一致性并且有效的避免了其在实测数据处理中的局限性。
- 协方差矩阵估计 /
- 白化度评价 /
- 归一化F范数 /
- 真实协方差矩阵
Abstract: In the background of sea clutter, the accuracy of adaptive target detection is heavily influenced by the estimated performance of speckle covariance matrix. Generally, Normalized Frobenius Norm (NFN) is used to test the estimated accuracy of different speckle covariance matrix estimators, in which the requirement of a known real covariance matrix is hardly realized in the radar system. Therefore, in this study, a whitening degree evaluation method is proposed wherein the decorrelation of speckle covariance matrix in whitening filter processing of the radar system is fully exploited. It considers the correlation degree among pulses in the whitening clutter vector as the criterion to evaluate the estimate error of the speckle covariance matrix. The proposed method shows consistent conclusions with NFN on simulated data and also avoids limitations of the latter method in real data processing.
- Covariance matrix estimation /
- Whitening degree evaluation /
- Normalized Frobenius Norm (NFN) /
- Real covariance matrix

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图 1 白化度评价结构示意图

Figure 1. Structural representation of WD evaluation

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图 2 3种估计方法的检测概率曲线

Figure 2. Detection probability curves of different estimators

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图 3 归一化F范数及白化度评价方法的性能对比

Figure 3. Performance comparison between NFN and WD evaluation

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图 4 3组实测数据的白化度及检测概率对比曲线

Figure 4. WD and detection probability of different datasets

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图 5 19组数据白化度及检测概率对比图

Figure 5. WD and detection probability of all datasets

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