Parametric Clutter Rejection for Space-Time Adaptive Processing
A. Lee Swindlehurst and Peter Parker
Brigham Young University
Dept. of Computer & Electrical Engineering
Provo, UT 84602
Abstract Practical STAP implementations rely on reduced-dimension processing, using techniques such as principle components or "partially adaptive" filters. The dimension reduction not only decreases the computational load, it also reduces the sample support required for estimating the interference statistics. This is particularly advantageous in situations where the interference statistics are spatially non-stationary. The reduction in sample support results essentially because the clutter covariance is implicitly assumed to possess a certain (non-parametric) structure. In a recent ASAP presentation, we demonstrated how imposing a particular type of parametric structure on the clutter and jamming can lead to a further reduction in both computation and secondary sample support. Examples were presented in which the clutter and jamming were assumed to obey a vector autoregressive model.
In this presentation, we present a generalized version of the Space-Time AutoRegressive (STAR) filtering approach, and show that it can be interpreted as finding a structured subspace orthogonal to that in which the interference resides. Using a realistic simulated data set generated by MIT Lincoln Laboratory for the circular array STAP problem, we demonstrate that this approach achieves significantly lower SINR loss with a computational load that is less than that required by the reduced-dimension PRI-staggered STAP method. The STAR algorithm also yields good performance with very small secondary sample support, a feature that is particularly attractive for the circular array STAP problem where the clutter statistics are range dependent. In addition to these results, we will present methods for extending the STAR filtering idea to accommodate range-varying weights and to handle terrain scattered interference.
Presentation in PDF format