Cross-Spectral Metric Smoothing-Based GIP for Space-Time Adaptive Processing

Abstract

As training samples are not always target-free in heterogeneous environments, the generalized inner product (GIP) method is usually used to censor the training samples contaminated by targetlike signals (outliers). However, the GIP method incurs significant performance degradation when there are multiple outliers in the original training sample set. To deal with this problem, this letter proposes a novel GIP method. First, the principal component, which results in performance degradation of the GIP method, is obtained via extracting the maximum of cross-spectral metric (CSM) between the target steering vector and the eigenspace of the GIP’s test covariance matrix (TCM). Second, taking a sample covariance matrix (SCM) as the initial TCM, a new TCM is reconstructed by setting the eigenvalue of SCM that corresponds to the largest CSM between the target steering vector and the SCM’s eigenspace to be noise variance. Finally, the new TCM is combined with the conventional GIP method to form a novel GIP statistic to eliminate the contaminated training samples. Numerical results with both simulated and mountain-top data confirm the improvement of the proposed method.