Quadratically Constrained Simplified Robust Adaptive Detection

Summary

This chapter presents an alternative approach to robust adaptive detection. This is based on the recursive fast steepest descent (RSD) adaptive algorithm, with an accurate technique for precisely computing the diagonal loading level without approximation or eigendecomposition. The chapter combines the quadratic inequality (QI) constraint with the RSD algorithm to produce a robust recursive implementation with O(N²) complexity. It develops and integrates a new optimal variable loading (VL) technique into the RSD adaptive algorithm. In addition, the diagonal loading term is optimally computed, with O(N) complexity, using a simple quadratic equation. The chapter illustrates and clarifies geometrical interpretations of the scaled projection (SP) and VL techniques along with recursive least‐squares (RLS) and RSD algorithms. It investigates and compares the performance of the simplified robust detector with the traditional blind minimum output energy (MOE) detector updated using the RLS algorithm and a robust MOE‐RLS w. quadratic constraint (QC) detector.