Robust active tracking of multiple underwater targets in environments with strong clutter and nonstationary measurement noise is a key research topic in underwater acoustic signal and information processing. Under these underwater conditions, existing random finite set (RFS) multi-target tracking algorithms suffer from serious contamination of the observation likelihood by clutter, low discrimination between targets and clutter, and poor tracking accuracy. To address these issues, this paper proposes a two-stage modified variational Bayesian delta-generalized labeled multi-Bernoulli multi-target tracking algorithm. First, in the delta-generalized labeled multi-Bernoulli filtering update stage, this method introduces the Sage–Husa (SH) estimation technique based on the minimum residual criterion to roughly correct the measurement noise covariance matrix. It effectively alleviates the contamination of the likelihood function by clutter in adaptive RFS and improves the discrimination between targets and clutter under complex noise conditions. Second, in the stage of multi-target state estimation, the measurement noise covariance estimate is further optimized through variational Bayesian framework, thereby achieving real-time correction of measurement noise caused by unknown underwater environments and significantly enhancing the robustness of underwater multi-target active tracking. Both simulation and experimental results show that the proposed algorithm significantly outperforms traditional and existing adaptive generalized labeled multi-Bernoulli methods in scenarios with strong clutter and nonstationary measurement noise.
Hou et al. (Sun,) studied this question.