LAUSR

Detection of floating small targets in sea clutter is a challenging problem for maritime surveillance radar. For sea-surface floating targets without regular movement, it is hard for traditional model-based methods to achieve satisfying performance. Considering that the radar echoes will exhibit stronger statistical correlations when the target is present, this work proposes a detector using mean spectral radius (MSR), which is a data-driven method based on the eigenvalue analysis of a random matrix. It is first illustrated that the inner radius of the first-order autoregressive model will decrease with the increase of data correlations when the outer radius is normalized. The spectral radius of the target echo distributes on a smaller value compared with sea clutter due to stronger statistical correlations. The MSR, which is defined to be a specific linear spectral statistics indicating data correlations, is demonstrated to be an effective test statistic to distinguish the target echo from sea clutter. It is shown that by the central limit theorem (CLT), the MSR follows a Gaussian distribution. Experiments on Intelligent PIXel Processing Radar (IPIX) datasets show that the proposed method can effectively improve the detection performance. In addition, the MSR can be applied to construct multiple-feature-based detector to further enhance robustness.