Stochastically constrained spatial and spatio-temporal adaptive processing for non-stationary hot clutter cancellation

Abstract

In this chapter, the use of spatio-temporal adaptive array processing in over-the-horizon radar application is considered in order to remove non-stationary multipath interference ('hot clutter'). Since the spatio-temporal properties of hot clutter cannot be assumed constant over the coherent processing interval, conventional adaptive techniques fail to provide effective hot clutter mitigation without simultaneously degrading the properties of the backscattered sea/terrain radar signals ('cold clutter'). The approach presented incorporates multiple stochastic constraints to achieve effective hot clutter suppression, while maintaining distortionless output cold clutter post-processing stationarity. The use of stochastically constrained spatial and spatio-temporal adaptive processing for hot clutter mitigation is discussed in scenarios that both do and do not allow access to a group of range cells that are free of cold clutter (supervised and unsupervised training, respectively). Theoretical and simulation results are complemented by surface-wave over-the-horizon data processing, collected during experimental trials in northern Australia. The final section discusses convergence rate issues for stochastically constrained adaptive algorithms based on loaded sample matrix inversion routines.