Zheng Zheng, Yang T C, Gerstoft Peter, Pan Xiang
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China.
Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093-0238, USA.
J Acoust Soc Am. 2020 Mar;147(3):1738. doi: 10.1121/10.0000920.
Large aperture towed arrays are widely used underwater to detect weak targets. During maneuvering, the beamformer performance degrades significantly if a wrong array configuration is assumed. Currently, engineering sensors and/or (augmented) acoustic sources are used to estimate the array element positions. The results are often inadequate depending on the number of measurements available. In this paper, an adaptive bow (AB) sparse Bayesian learning (SBL) algorithm is proposed, called ABSBL. Assuming the towed array follows a parabola shape during slow turns and treating the array bow as a hyperparameter in SBL, the bow and directions of arrival (DOAs) of the signals can be jointly estimated from the received acoustic data. Simulations show that ABSBL yields accurate estimates of the bow and target DOAs if the turning direction is known. ABSBL is applied to the MAPEX2000 data. The estimated array bow and DOA agrees with that estimated from relative time delays measured from acoustic pings and SBL, better than that estimated from the GPS data using the water-pulley model. The method can potentially be applied without engineering sensors.
大孔径拖曳阵列在水下被广泛用于探测微弱目标。在机动过程中，如果假设了错误的阵列配置，波束形成器的性能会显著下降。目前，工程传感器和/或（增强型）声源被用于估计阵列元件的位置。根据可用测量数量的不同，结果往往并不理想。本文提出了一种自适应弓形（AB）稀疏贝叶斯学习（SBL）算法，称为AB-SBL。假设拖曳阵列在缓慢转弯时呈抛物线形状，并将阵列弓形视为SBL中的一个超参数，则可以从接收到的声学数据中联合估计弓形和信号的到达方向（DOA）。仿真表明，如果转弯方向已知，AB-SBL能够准确估计弓形和目标DOA。AB-SBL被应用于MAPEX2000数据。估计的阵列弓形和DOA与通过声学脉冲测量的相对时间延迟和SBL估计的结果一致，优于使用水滑轮模型从GPS数据估计的结果。该方法有可能在无需工程传感器的情况下应用。
