Abstract
A novel training samples selection method is proposed to improve the performance of space-time adaptive processing in non-homogeneous environments, where training samples are contaminated by interference target signals (outliers). First, the outlier component is removed from the sample covariance matrix based on clutter covariance matrix reconstruction, which utilises the clutter Capon spectrum integrated over a sector separated from the location of outlier. Secondly, the integral is calculated approximately by discrete sum method. Finally, the reconstructed clutter covariance matrix is combined with the generalised inner products algorithm to form the proposed statistics and the contaminated training samples are eliminated. Simulation results validate the effectiveness of the proposed method.
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