Using the conjugate gradient algorithm for reduced-rank adaptive detection

Abstract

In this paper, we introduce a group of reduced-rank (RR) space-time adaptive processing (STAP) detectors based on the conjugate gradient (CG) algorithm. The CG algorithm can be used for efficient calculation of the weight vector of several well-known STAP detectors. As an iterative algorithm, it produces a series of approximations to the fully adaptive solution, each of which can be used to filter the test signal and form a test statistic. This effectively leads to a family of RR adaptive detectors, referred to as the CG-RR detectors, which are indexed by k the number of iterations incurred. Performance of the proposed CG-RR detectors are examined in terms of the output signal-to-interference-plus-noise ratio (SINR). The conventional RR methods for STAP such as the data-independent DFT or DCT based rank reduction, the adaptive eigencanceler and cross-spectral metric (CSM) algorithm are also considered here. Simulation results show that the computationally efficient CG-RR detector often reaches the peak output SINR with a lower rank compared with the eigencanceler and CSM based detectors.