Outlier suppression in adaptive filtering through de-emphasis weighting

A de-emphasis weighting approach is used to suppress the effect of outliers in background samples during the formation of a sample covariance matrix. The approach is relevant to a broad range of adaptive filtering techniques. Results from processing simulated and real coherent radar data using de-emphasis weighting are compared with results using no outlier suppression and censored sample matrix inversion pruning methods. De-emphasis techniques are shown to produce the most robust detection performance when outliers are present and are also shown to have minimal performance impact when clutter is homogeneous, that is no outliers present