Clutter suppression methods based on reduced-dimension transformation for airborne passive radar with impure reference signals

Abstract

For an airborne passive radar with impure reference signals, the clutter caused by multipath (MP) signals involved in the reference channel (MP clutter) corrupts the space–time adaptive processing performances. To eliminate the influence of the MP clutter, two clutter suppression methods based on reduced-dimension (RD) transformation are proposed herein. RD transformation is exploited to reduce the size of the sparse recovery dictionary. Subsequently, the sparse recovery problem is revised, and the MP clutter is suppressed using the least mean square (LMS) algorithm and the exponentially forgetting window LMS algorithm. Compared with the existing L1-based recursive least square algorithm, the proposed algorithms significantly reduce computational complexity without degrading the MP clutter suppression performance. In addition, the proposed algorithms provide more robust characteristics to the errors in prior knowledge than the modified blind equalization method. A range of simulations is conducted to test the proposed algorithms.