Tabassum Muhammad Naveed, Ollila Esa
Department of Signal Processing and Acoustics, Aalto University, P.O. Box 15400, FI-00076 Aalto, Finland.
J Acoust Soc Am. 2018 Jun;143(6):3873. doi: 10.1121/1.5042363.
This paper proposes efficient algorithms for accurate recovery of direction-of-arrivals (DoAs) of sources from single-snapshot measurements using compressed beamforming (CBF). In CBF, the conventional sensor array signal model is cast as an underdetermined complex-valued linear regression model and sparse signal recovery methods are used for solving the DoA finding problem. A complex-valued pathwise weighted elastic net (c-PW-WEN) algorithm is developed that finds solutions at the knots of penalty parameter values over a path (or grid) of elastic net (EN) tuning parameter values. c-PW-WEN also computes least absolute shrinkage and selection operator (LASSO) or weighted LASSO in its path. A sequential adaptive EN (SAEN) method is then proposed that is based on c-PW-WEN algorithm with adaptive weights that depend on previous solution. Extensive simulation studies illustrate that SAEN improves the probability of exact recovery of true support compared to conventional sparse signal recovery approaches such as LASSO, EN, or orthogonal matching pursuit in several challenging multiple target scenarios. The effectiveness of SAEN is more pronounced in the presence of high mutual coherence.
本文提出了一种高效算法，用于使用压缩波束形成（CBF）从单快照测量中精确恢复源的到达方向（DoA）。在CBF中，传统的传感器阵列信号模型被转化为一个欠定复值线性回归模型，并使用稀疏信号恢复方法来解决DoA查找问题。开发了一种复值逐路径加权弹性网（c-PW-WEN）算法，该算法在弹性网（EN）调谐参数值的路径（或网格）上的惩罚参数值的节点处找到解。c-PW-WEN还在其路径中计算最小绝对收缩和选择算子（LASSO）或加权LASSO。然后提出了一种顺序自适应EN（SAEN）方法，该方法基于c-PW-WEN算法，其自适应权重取决于先前的解。大量的仿真研究表明，在几种具有挑战性的多目标场景中，与传统的稀疏信号恢复方法（如LASSO、EN或正交匹配追踪）相比，SAEN提高了精确恢复真实支撑集的概率。在存在高互相关性的情况下，SAEN的有效性更为显著。
