Training and signal cancellation in adaptive radar

It is well known that the performance of adaptive arrays is affected by calibration errors. In particular, target cancellation occurs when there are calibration errors and/or the target signal is present during the computation of the array covariance matrix. Due to the mismatch between the presumed steering vector and the true signal vector, the target is not properly protected by the steering vector, it is interpreted as an interference, and the array proceeds to cancel it. Signal cancellation effects are particularly evident when the SMI method is employed. Researchers have suggested one of two main approaches to mitigate the signal cancellation problem: (1) modification of the beamformer constraints to widen the desired signal protection region and lessen the effects of calibration errors, and (2) modifications of the steering vector. In this work we analyze the robustness of eigenanalysis-based adaptive beamforming and compare its performance to the SMI method. It is shown that the eigenanalysis-based method is more robust to array pointing errors and to the presence of the target signal during training. Analytical expressions are developed and the results are illustrated using realistic scenarios generated by the Rome Laboratory RLSTAP algorithm development tool