Abstract
Reduced-dimension space-time adaptive processing (STAP) techniques are often used in multichannel wide-area surveillance airborne systems to suppress ground clutter. However, it is well known that their performance degrades to some extent in non-homogeneous environments because of inaccurate estimation of the interference covariance matrix from secondary data. In this study, the authors propose an algorithm which applies Kalman filtering to suppress ground clutter based on the data model of radar echoes from multichannel wide-area surveillance systems. Since the proposed algorithm does not need to estimate the interference covariance matrix, it has a major advantage when processing the data from non-homogeneous environments. Simulated data from phased-array multifunctional imaging radar (PAMIR) system and real data from an X-band wide-area surveillance system are used to validate the proposed algorithm. The results show that the new algorithm works well in both radar systems.
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