Low-Rank STAP Algorithm for Airborne Radar Based on Basis-Function Approximation

TL;DR

This paper introduces a novel reduced-rank STAP algorithm for airborne radar that adaptively selects basis functions in real-time, improving clutter suppression and tracking performance with lower computational complexity.

Contribution

The paper presents an adaptive basis function approximation scheme for STAP, enabling real-time basis selection and efficient implementation via SG and RLS algorithms.

Findings

Outperforms existing reduced-rank schemes in convergence speed

Achieves better clutter and jamming suppression

Operates with significantly lower computational complexity

Abstract

In this paper, we develop a novel reduced-rank space-time adaptive processing (STAP) algorithm based on adaptive basis function approximation (ABFA) for airborne radar applications. The proposed algorithm employs the well-known framework of the side-lobe canceller (SLC) structure and consists of selected sets of basis functions that perform dimensionality reduction and an adaptive reduced-rank filter. Compared to traditional reduced-rank techniques, the proposed scheme works on an instantaneous basis, selecting the best suited set of basis functions at each instant to minimize the squared error. Furthermore, we derive stochastic gradient (SG) and recursive least squares (RLS) algorithm for efficiently implementing the proposed ABFA scheme. Simulations for a clutter-plus-jamming suppression application show that the proposed STAP algorithm outperforms the state-of-the-art reduced-rank schemes in convergence and tracking at significantly lower complexity.