A branch-and-bound inspired technique to improve the computational efficiency of DOA estimation

Abstract

The former SEAD method was recently proposed by the authors and showed maximum likelihood performance in resolving closely located DOA (direction of arrival) angles at low signal-to-noise ratios (SNR). However, it required a prohibitively high computational effort. We propose a new estimate selection strategy inspired on branch-and-bound in order to reduce the computational effort of SEAD. Equations of computational cost were derived and validated for both SEAD versions. Also, we introduced an adaptive strategy that detects the signal peaks and estimates the amount of signal sources grouped in each peak. Experimental results demonstrated that the proposed strategy dramatically reduced the computational effort and yielded an improved SEAD. Compared to MODEX, improved SEAD showed competitive runtime and its root mean square error (RMSE) performance attained the Cramér–Rao lower bound (CRLB) up to SNR=−12dB, 8dB lower than that of MODEX. Also, computational effort grew slower for improved SEAD than for MODEX, as the number of sources was increased.