Abstract:

High-resolution sea clutter can be modeled by the compound-Gaussian model and its texture component determines the non-Gaussian characteristics of the clutter and the following detection algorithm. Traditional adaptive-detectors assume that the texture is independent and identically distributed (IID) along the range, which loses sight of the texture correlation in real sea clutter. An adaptive detector based on texture spatial correlation is proposed, where the texture is modeled by the inverse Gaussian distribution. It selects part of range cells and embeds them into the likelihood ratio test, providing the information of speckle covariance matrix and texture for the cell under test. Experimental results using real datasets indicate that the proposed detector has better performance in comparison with the optimal detector in IID texture and adaptive normalized matched filter.