Improved AR-Model-Based Rao Test in Complex Gaussian Clutter

Our study considers the adaptive detection of point targets in compound Gaussian clutter which is in possession of unknown covariance matrix. To overcome the performance degradation problem which is aroused by the limited number of training data, the autoregressive (AR) process is used for the modeling of the speckle component. We first derive the Rao detector under the assumption of known covariance matrix of the clutter, and then reconstruct it by AR parameters resorting to the matrix factorization. Meanwhile, the newly derived detector is proved asymptotically constant false alarm rate with respect to the clutter covariance matrix. Finally, simulation results have confirmed the effectiveness in small data record case of the newly derived detector.