Nannuru Santosh, Gerstoft Peter, Ping Guoli, Fernandez-Grande Efren
Signal Processing and Communications Research Center, IIIT Hyderabad, India.
Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, USA.
J Acoust Soc Am. 2021 Jan;149(1):167. doi: 10.1121/10.0002988.
Sparse arrays are special geometrical arrangements of sensors which overcome some of the drawbacks associated with dense uniform arrays and require fewer sensors. For direction finding applications, sparse arrays with the same number of sensors can resolve more sources while providing higher resolution than a dense uniform array. This has been verified numerically and with real data for one-dimensional microphone arrays. In this study the use of nested and co-prime arrays is examined with sparse Bayesian learning (SBL), which is a compressive sensing algorithm, for estimating sparse vectors and support. SBL is an iterative parameter estimation method and can process multiple snapshots as well as multiple frequency data within its Bayesian framework. A multi-frequency variant of SBL is proposed, which accounts for non-flat frequency spectra of the sources. Experimental validation of azimuth and elevation [two-dimensional (2D)] direction-of-arrival (DOA)estimation are provided using sparse arrays and real data acquired in an anechoic chamber with a rectangular array. Both co-prime and nested arrays are obtained by sampling this rectangular array. The SBL method is compared with conventional beamforming and multiple signal classification for 2D DOA estimation of experimental data.
稀疏阵列是传感器的特殊几何排列，它克服了与密集均匀阵列相关的一些缺点，并且所需传感器更少。对于测向应用，具有相同数量传感器的稀疏阵列能够比密集均匀阵列分辨出更多信号源，同时提供更高的分辨率。这已通过一维麦克风阵列的数值模拟和实际数据得到验证。在本研究中，使用稀疏贝叶斯学习（SBL，一种压缩感知算法）来研究嵌套阵列和互质阵列，以估计稀疏向量并确定其支撑。SBL是一种迭代参数估计方法，能够在其贝叶斯框架内处理多个快照以及多频率数据。本文提出了一种SBL的多频率变体，该变体考虑了信号源的非平坦频谱。利用在消声室中使用矩形阵列采集的稀疏阵列和实际数据，对方位角和仰角[二维（2D）]到达方向（DOA）估计进行了实验验证。互质阵列和嵌套阵列均通过对该矩形阵列进行采样获得。将SBL方法与传统波束形成和多重信号分类方法进行比较，用于对实验数据进行二维DOA估计。
