  Adaptive algorithms based on sample matrix inversion belong to an important
class of algorithms used in radar target detection to overcome prior
uncertainty of interference covariance. Sample matrix inversion problem is
generally ill conditioned. Moreover, the contamination of the empirical
covariance matrix by the useful signal leads to significant degradation of
performance of this class of adaptive algorithms. Regularization, also known in
radar literature as sample covariance loading, can be used to combat both ill
conditioning of the original problem and contamination of the empirical
covariance by the desired signal. However, the optimum value of loading factor
cannot be derived unless strong assumptions are made regarding the structure of
covariance matrix and useful signal penetration model. In this paper an
iterative algorithm for loading factor optimization based on the maximization
of empirical signal to interference plus noise ratio (SINR) is proposed. The
proposed solution does not rely on any assumptions regarding the structure of
empirical covariance matrix and signal penetration model. The paper also
presents simulation examples showing the effectiveness of the proposed
solution.
