Maximum Eigenvalue Based Radar Signal Detection Method for K Distribution Sea Clutter Environment
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摘要: 针对K分布海杂波背景下的恒虚警检测问题，基于信息几何的矩阵CFAR检测器具有较好的检测性能，但其计算复杂度较高，从而影响其实际应用。该文根据奈曼-皮尔逊准则，推导了似然比检测统计量与最大特征值之间的关系，进而提出了基于最大特征值的矩阵CFAR检测方法(M-MED)。最后通过对所提方法的计算复杂度及仿真实验结果的分析表明了所提方法不仅计算复杂度低且具有较好的检测性能。Abstract: Information geometry based matrix Constant False Alarm Rate (CFAR) detector is an efficient solution to the intractable issue of target detection for K-distributed sea clutter environment. However, most existing matrix CFAR detectors cost heavy computation complexity, which leads to a limitation in practical application. Based on the Neyman-Pearson criterion, the Likelihood Ratio Test (LRT) is analyzed, the relationship between LRT statistic and the Maximum Eigenvalue is derived, and Matrix CFAR Detection method based on the Maximum Eigenvalue (M-MED) is designed. Simulation results verify that the proposed method can achieve better detection performance with relatively lower computational complexity.
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Key words:
- CFAR detection /
- K-distribution /
- Maximum eigenvalue
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表 1 不同方法的计算复杂度比较
算法 协方差矩阵 指数运算 对数运算 幂运算 特征值分解O(M3) 基本运算量 本文M-MED √ × × × √ $N$ KLD √ × × × × $O(M! + (N + 3){M^3} + 2{M^2} + M)$ REM √ √ √ √ √ $O({n_t}((N + 2){M^3} + 3N{M^2}) + {M^3} + 2{M^2} + 2M - 1)$ LD √ × × × × $M! + (N + 4){M^3} + 2{M^2} + M$ LE √ √ √ × √ $(N + 2){M^2} + 2M - 1$ Bha √ × × × × $O({n_t}((N + 1){M^3} + (2N + 1){M^2}) + 3M! + 2{M^2} + 3)$ HEL √ × × × × $O({n_t}(3NM! + (N + 3){M^3} + 4N{M^2}) + 3M! + 2{M^2} + 4)$ 
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