  Adaptive algorithms belong to an important class of algorithms used in radar
target detection to overcome prior uncertainty of interference covariance. 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 for the
adaptive algorithms based on sample covariance matrix inversion. 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. Similarly, least mean square algorithm with linear constraint or without
constraint, is also sensitive to the contamination of the learning sample with
the target signal. We synthesize two approaches to improve the convergence of
adaptive algorithms and protect them from the contamination of the learning
sample with the signal from the target. The proposed approach is based on the
maximization of empirical signal to interference plus noise ratio (SINR). Its
effectiveness is demonstrated using simulated data.
