Estimation of rapidly varying sea clutter using nearest Kronecker product approximation

In this paper, we propose a method to estimate the space-time covariance matrix of rapidly varying sea clutter. The method first develops a dynamic state space representation for the covariance matrix and then approximates the covariance using the nearest Kronecker product to reduce computational complexity. Particle filtering is then applied to estimate the dynamic elements of the covariance matrix. We validate the nearest Kronecker product approximation using real sea clutter radar measurements. We further demonstrate the use of the estimated space-time covariance matrix in the track-before-detect filter to track a low observable target in sea clutter.