Design of digital signal processor based adaptive beamformer using hybrid residual least mean square algorithm for improved performance

Abstract

This paper proposes a novel hybrid residual least mean square (HRLMS) algorithm for adaptive filtering followed by an antenna beamformer using 16-element linear array. The hybridization process involves a switching between the residual-LMS (RLMS) and the conventional-LMS (CLMS) algorithms after the eighth iteration, if the square errors for four consecutive iterations are less than a threshold. The novelty of HRLMS lies in estimating best step size factor through residuals for speedy convergence followed by the CLMS switching for minimum steady state error (SSE). The novelty also includes in realizing a real-time antenna beamformer with significant sidelobe level (SLL) reduction and improved interference nulling by integration of HRLMS and space selective digital filter (SSDF). The adaptive filter and smart beamformer, based on HRLMS and HRLMS-SSDF have been implemented on TMS320VC5416 digital signal processor. The comparative performance evaluation of HRLMS has been done for convergence speed, SSE, interference nulling and SLL reduction with the existing variable step size LMS (VSSLMS) algorithms. The iteration count for convergence has been reduced by about 50% with paltry additional computational burden over the other VSSLMS algorithms. The HRLMS-SSDF provide attenuations of about 76, 33, and >50 dB, respectively for interfering signals, first SLL and higher order SLLs of beamformer.