College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province, Zhoushan 316021, China.
Sensors (Basel). 2018 Nov 18;18(11):4022. doi: 10.3390/s18114022.
In this paper, improved Bernoulli filtering methods are developed to deal with the problem of joint passive detection and tracking of an underwater acoustic target with multiple arrays. Three different likelihood calculation methods based on local beamforming results are proposed for the Bernoulli filter updating. Firstly, multiple peaks, including both mainlobe and sidelobe peaks, are selected to form the direction-of-arrival (DOA) measurement set, and then the Bernoulli filter is used to extract the target track. Secondly, to make full use of the informations in the beamforming output, not only the DOAs but also their intensities, the beam powers are used as the input measurement sets of the filter, and an approach based on Pearson correlation coefficient (PCC) is developed for distinguishing between signal and noise. Lastly, a hybrid method of the former two is proposed in the case of fewer then three arrays. The tracking performances of the three methods are compared in simulations and experiment. The simulations with three distributed arrays show that, compared with the DOA-based method, the beam-based method and the hybrid method can both improve the target tracking accuracy. The processing results of the shallow water experimental data collected by two arrays show that the hybrid method can achieve a better tracking performance.
本文提出了改进的贝努利滤波方法，以解决多阵联合被动检测和跟踪水下声目标的问题。针对贝努利滤波器的更新，提出了三种基于局部波束形成结果的不同似然计算方法。首先，选择多个峰值（包括主瓣和旁瓣峰值）形成到达方向（DOA）测量集，然后使用贝努利滤波器提取目标轨迹。其次，为了充分利用波束形成输出中的信息，不仅使用 DOA 而且使用其强度，将波束功率用作滤波器的输入测量集，并开发了一种基于皮尔逊相关系数（PCC）的方法来区分信号和噪声。最后，在少于三个阵的情况下提出了前两种方法的混合方法。在仿真和实验中比较了这三种方法的跟踪性能。具有三个分布式阵的仿真结果表明，与基于 DOA 的方法相比，基于波束的方法和混合方法都可以提高目标跟踪精度。由两个阵采集的浅海水实验数据的处理结果表明，混合方法可以实现更好的跟踪性能。
