Convergence analysis of stochastically-constrained spatial and spatio-temporal adaptive processing for hot-clutter mitigation

Further considers the use of spatio-temporal adaptive array processing in radar applications in order to remove nonstationary multipath interference (known as “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 radar signals (known as “cold clutter”). The stochastically-constrained spatio-temporal adaptive processing (SC STAP) approach incorporates multiple data-dependent (“stochastic”) constraints to achieve effective hot-clutter suppression, whilst maintaining distortionless output cold-clutter post-processing stationarity. Here we discuss the efficiency of the algorithm with respect to the number of training snapshots