Fast-Converging Adaptive Cascaded Cancellers Using A Novel Soft-Weighting and Reiteration(SWR) Technique

We introduce a methodology that creates a new class of reduced-rank adaptive cascaded canceller algorithms. Two example algorithms illustrate the benefits of the proposed technique. It consists of combining a novel soft-weighting technique with an existing reiteration technique. Denoted as soft-weighting and reiteration (SWR), its use significantly improves the convergence performance of cascaded cancellers while preserving other desired algorithm characteristics, such as robustness. Example results are shown for the benchmark Gram Schmidt cascaded canceller and for the robust reiterative median cascaded canceller, but the technique is applicable to generic forms of cascaded canceller algorithms. Moreover, the resulting algorithms exhibit near-optimal sidelobe levels in their adaptive beam patterns, which can significantly reduces false alarms. We illustrate the improvements using simulated data and measured data from the MCARM space-time adaptive processing (STAP) airborne radar database.