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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