A deconvolution method for ship detection in sea clutter environment

Ship detection in sea clutter environment using scanning radar is of vital importance, but with challenges due to low angular resolution. To solve the problem, an angular superresolution algorithm for radar imaging based on Bayesian deconvolution theory is proposed. Firstly, the statistic characteristics of the sea clutter are modeled using compound K-distribution. Then the signal model of radar echo in sea environment is formulated as the convolution of the antenna pattern and the reflectivity of the original scene plus the reflectivity of sea clutter. The ship detection task in sea clutter environment using the deconvolution method is converted into an equivalent maximum a posteriori estimation problem, which is solved using the optimization method in this paper. Simulation results demonstrate the validity of the proposed method in terms of ship detection in sea clutter environment.