Maximum eigenvalue-based target detection for the K-distributed clutter environment

Maximum eigenvalue-based target detection for the K-distributed clutter environment

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Information geometry-based matrix constant false alarm rate (CFAR) detector is an efficient solution to target detection, especially for the K-distributed clutter environment with respect to a small bunch of pulses. The main reason for the matrix CFAR detector to achieve better detection performance is covariance matrix captures the correlations between the data. However, most existing matrix CFAR detectors suffer from heavy computational complexity, which leads to a limitation in practical detection scenarios. Motivated by this, the authors utilise the eigenvalues of the covariance matrix to capture the correlation and propose an eigenvalue-based detection method with lower computational complexity. Based on the Neyman–Pearson criterion, they first analyse the likelihood ratio test (LRT) in the eigenvalue domain and derive the relationship between the LRT test statistic and the maximum eigenvalue. To meet the practical requirement, they further design a totally blind scheme: the maximum eigenvalue-based matrix CFAR detector. By employing the group invariant theory, they show that the proposed detector presents the CFAR property. In addition, the theoretical performance analysis is also provided. Simulation results based on the numerical experiment and real sea clutter data verify that the proposed method with a low computational complexity can achieve a better detection performance.

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