Adaptive Coherent Detection for Maritime Radar Range-Spread Targets in Correlated Heavy-Tailed Sea Clutter With Lognormal Texture

This letter addresses the problem of adaptive coherent detection of maritime high-resolution radar range-spread targets in correlated heavy-tailed sea clutter. We first model radar sea clutter by the compound Gaussian model with lognormal texture and unknown speckle covariance matrix. The lognormal-distributed texture can capture the tail level of sea clutter, and the speckle covariance matrix contains the pulse-to-pulse correlation of sea clutter. Then, based on the two-step generalized likelihood ratio test and the maximum a posteriori (MAP) estimation of unknown parameters, an adaptive coherent generalized likelihood ratio test with a lognormal texture detector is proposed to detect radar range-spread targets. The proposed detector can be adaptive to clutter power mean, non-Gaussianity, and pulse-to-pulse correlation. The performance evaluation experiments on simulated and measured data show that the proposed detector outperforms conventional adaptive detectors. More specifically, the detection results on measured data indicate that when the number of target range cells is 3 and the probability of detection reaches 0.8, the proposed detector has a signal-to-clutter ratio gain of about 1 dB over its competitors.