Stochastic-constraints method in nonstationary hot-clutter cancellation. II. Unsupervised training applications

Abstract

For pt. I see ibid., vol. 34, pp. 1271-1292 (1998). This paper considers the use of "stochastically constrained" spatial and spatio-temporal adaptive processing in multimode nonstationary interference ("hot clutter") mitigation for scenarios that do not allow access to a group of range cells that are free from the backscattered sea/terrain signal ("cold clutter"). Since supervised training methods for interference covariance matrix estimation using the cold-clutter-free ranges are inappropriate in this case, we introduce and analyze adaptive routines which can operate on range cells containing a mixture of hot and cold clutter and possible targets (unsupervised training samples). Theoretical and simulation results are complemented by surface-wave over-the-horizon data processing, recently collected during experimental trials in northern Australia.