基于贝叶斯的高斯杂波背景下MIMO雷达自适应检测算法

韩金旺; 张子敬; 刘军; 赵永波

doi:10.12000/JR18090

基于贝叶斯的高斯杂波背景下MIMO雷达自适应检测算法

doi: 10.12000/JR18090

韩金旺^1,,
张子敬^1,,
刘军^2, ,,
赵永波^1,

①.
西安电子科技大学雷达信号处理国家重点实验室西安 710071
②.
中国科学技术大学信息科学技术学院合肥 230027

基金项目: 国家自然科学基金(61871469, 61571349)，陕西省自然科学基金(2018JM6051)

详细信息

作者简介:
韩金旺(1993–)，男，河北石家庄人，西安电子科技大学硕士生，研究方向为MIMO雷达信号处理。E-mail: jwhan0828@163.com

张子敬(1967–)，男，西安电子科技大学教授，博士生导师，IEEE会员。研究方向为雷达目标检测、雷达极化信息处理、多速率滤波器组设计等。E-mail: zjzhang@xidian.edu.cn

刘　军(1983–)，男，现为中国科学技术大学信息技术学院副教授，中国电子学会高级会员。研究方向为雷达信号处理和机器学习。E-mail: junliu@ustc.edu.cn

赵永波(1972–)，男，现为西安电子科技大学教授，中国电子学会高级会员。研究方向为新体制雷达系统和MIMO雷达。E-mail: ybzhao@xidian.edu.cn

通讯作者:
刘军 junliu@ustc.edu.cn

中图分类号: TN957.51
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出版历程
- 收稿日期: 2018-10-25
- 修回日期: 2019-01-03
- 网络出版日期: 2019-08-28

Adaptive Bayesian Detection for MIMO Radar in Gaussian Clutter

HAN Jinwang^1
,,
ZHANG Zijing^1
,,
LIU Jun^{2
, ,},
ZHAO Yongbo^1
,

①.
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
②.
Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China

Funds: The National Natural Science Foundation of China (61871469, 61571349), The Natural Science Foundation of Shaanxi Province (2018JM6051)

More Information

Corresponding author: LIU Jun, junliu@ustc.edu.cn

摘要

摘要: 对于集中式多输入多输出(Multiple-Input Multiple-Output, MIMO)雷达，该文研究了高斯杂波背景下的目标检测问题。该文假设杂波的协方差矩阵是未知随机的，且服从逆复Wishart分布，基于贝叶斯方法和广义似然比检验准则设计了两种新型自适应检测器。该文提出的贝叶斯检测器具有两个显著的优点：(1)不需要训练数据；(2)杂波的先验知识体现在设计方案中，从而提高了检测性能。仿真结果显示该文提出的贝叶斯检测器的检测性能优于目前常用的非贝叶斯检测器，特别是在发射波形采样数较少时。另外，该贝叶斯检测器在参数失配条件下的性能会有一定程度下降。
- 多输入多输出雷达 /
- 自适应检测 /
- 贝叶斯 /
- 逆复Wishart分布 /
- 广义似然比检验
Abstract: For collocated Multiple-Input Multiple-Output (MIMO) radar, we investigate the target detection problem in Gaussian clutter with an unknown but random covariance matrix. An inverse complex Wishart distribution is chosen as prior knowledge for the random covariance matrix. We propose two detectors in the Bayesian framework based on the criteria of the Generalized Likelihood Ratio Test. The two main advantages of the proposed Bayesian detectors are as follows: (1) no training data are required; and (2) a prior knowledge about the clutter is incorporated in the decision rules to achieve detection performance gains. Numerical simulations show that the proposed Bayesian detectors outperform the current commonly used non-Bayesian counterparts, particularly when the sample number of the transmitted waveform is small. In addition, the performance of the proposed detector will decline in parameter mismatched situation.
- Multiple-Input Multiple-Output (MIMO) radar /
- Adaptive detection /
- Bayesian /
- Inverse complex Wishart distribution /
- Generalized Likelihood Ratio Test (GLRT)

HTML全文

图 1 K 取不同值时的ROC曲线

Figure 1. ROC curves for different K

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图 2 K 取不同值时${{{P}}_{\rm{d}}}$关于SCR的曲线

Figure 2. ${{{P}}_{\rm{d}}}$ versus SCR for different K

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图 3 L 取不同值时检测器的ROC曲线

Figure 3. ROC curves for different L

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图 4 1阶迟滞相关系数失配时检测器的ROC曲线

Figure 4. ROC curves when one-lag correlation coefficient is mismatched

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图 5 自由度L失配时检测器的ROC曲线

Figure 5. ROC curves when L is mismatched

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