Eigencanceler: Space Time Adaptive Radar by Eigenanalysis Methods

摘要

In airborne surveillance radar applications, adaptive antennas provide for the detection of small targets in severe clutter environments. Adaptive antennas are currently being considered for the design of next generation surveillance platforms. The radar problem is two-dimensional with radar returns being a function of both angle and Doppler. Space-Time Adaptive Processing (STAP) is required for rejection of interferences in the space-time domains. To make STAP feasible for incorporation into future systems, low complexity algorithms are required. The space-time radar problem is well suited to the application of techniques that take advantage of the low-rank properties associated with the interference in such radars. The Eigencanceler is an interference cancellation method based on the spectral decomposition (eigenanalysis) of the space-time covariance matrix. The linear space spanned by the columns of the space-time covariance matrix is formed by the union of the algebraic spaces of the interference and the noise. The Eigencanceler's weight vector is designed to lie in the noise subspace and to provide a prescribed gain to the desired signal. Thus significant computational savings are realized since a Weiner filter requires a matrix inversion, while the eigencanceler is implemented by computing only a limited number of interference eigenvectors.