Multi-Maneuvering Sources DOA Tracking With Improved Interactive Multi-Model Multi-Bernoulli Filter for Acoustic Vector Sensor (AVS) Array

To solve the problem of the direction of arrival (DOA) maneuvering in acoustic vector sensor (AVS) array, we propose an interactive multi-model Multi-target Multi-Bernoulli (IMM-MeMBer) two-dimensional (2-D) DOA tracking algorithm. The idea of the IMM algorithm is employed to interactively estimate the predicted sampled particles at the expense of calculating the likelihood function of the predicted particles by pseudo-spectrum of multi-signal classification (MUSIC). The proposed method fully considers various possibilities of target motion, which makes the target state estimation more effective. Moreover, the de-noising and exponential weighting of MUSIC pseudo-spectrum improve the likelihood function of particles, thus enhancing the weight of particles in high likelihood region and making these particles more effective in the subsequent resampling process. Simulation results show that the IMM-MeMBer algorithm has better performance than projection approximation subspace tracking of deflation MUSIC (PASTD-MUSIC), MeMBer, particle filter (PF), and random finite set PF (RFS-PF) in the DOA tracking of maneuvering sources.

I. Introduction

Multi-Sources two-dimensional (2-D) (azimuth and elevation) direction of arrival (DOA) detection, location, and tracking play a significant role in signal processing and are widely used in many fields, such as communication, sonar, radar, underwater target surveillance, and radio astronomy. Traditionally, these tasks are accomplished by multiple pressure sensor arrays and estimation methods based on acoustic pressure measurements [1], but these technologies usually require large aperture arrays. Recently, as an important part of hydroacoustic array signal processing, a new source detection and localization technology called acoustic vector sensor (AVS) has achieved considerable attention [2]–[22]. Compared with the conventional array, AVS achieves higher accuracy. However, the aforementioned methods assume that the sources are stationary. In most cases, sources are dynamic and move smoothly, and DOAs are closely correlated with the adjacent time steps. Therefore, it is necessary to adopt the dynamic DOA tracking methods instead of DOA estimation methods [23].