An LMS-based adaptive generalized sidelobe canceller with decision feedback

Conventional least-mean-square (LMS) based adaptive generalized sidelobe canceller (GSC) suffers from the slow convergence problem. When the environment is fast varying, the conventional GSC cannot perform satisfactorily. In this paper, we propose a new decision feedback (DF) technique to overcome this problem. The proposed algorithm introduces a feedback filter with one tap weight in the GSC structure. The Wiener solution for the proposed structure is derived and the convergence behavior of the adaptive DF-GSC is also analyzed. Simulation results show that the proposed structure can significantly accelerate the convergence and enhance the overall performance.