KNOWLEDGE-AIDED PARAMETRIC ADAPTIVE MATCHED FILTER WITH AUTOMATIC COMBINING FOR COVARIANCE ESTIMATION

In this paper, a knowledge-aided parametric adaptive matched filter (KA-PAMF) is proposed that utilizing both observations (including the test and training signals) and a priori knowledge of the spatial covariance matrix. Unlike existing KA-PAMF methods, the proposed KA-PAMF is able to automatically adjust the combining weight of a priori covariance matrix, thus gaining enhanced robustness against uncertainty in the prior knowledge. Meanwhile, the proposed KAPAMF is significantly more efficient than its KA non-parametric counterparts when the amount of training signals is limited. One distinct feature of the proposed KA-PAMF is the inclusion of both the test and training signals for automatic determination of the combining weights for the prior spatial covariance matrix and observations. Numerical results are presented to demonstrate the effectiveness of the proposed KA-PAMF, especially in the limited training scenarios.