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

机译：一种提高DOA估计计算效率的启发式分支定界技术

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摘要

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 Cramer-Rao lower bound (CRLB) up to SNR=-12dB, 8 dB lower than that of MODEX. Also, computational effort grew slower for improved SEAD than for MODEX, as the number of sources was increased.
机译：作者最近提出了前一种SEAD方法，该方法在解析低信噪比（SNR）时靠近DOA（到达方向）角度时显示出最大的似然性能。但是，这需要非常高的计算量。为了减少SEAD的计算量，我们提出了一种受分支和边界启发的新估计选择策略。推导了两种SEAD版本的计算成本方程并进行了验证。另外，我们引入了一种自适应策略，该策略可检测信号峰值并估计每个峰值中分组的信号源的数量。实验结果表明，所提出的策略大大减少了计算量，并提高了SEAD。与MODEX相比，改进的SEAD显示出具有竞争力的运行时间，其均方根误差（RMSE）性能达到了Cramer-Rao下限（CRLB），最高SNR = -12dB，比MODEX低8 dB。而且，随着源数量的增加，改进SEAD的计算工作比MODEX慢。

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