Dual-Station Tracking Algorithm for Small Underwater Targets Based on AIMM-UKF with Probability Lower Bound Constraint

The complex underwater environment (boundary scattering, multipath effects, and strong noise) makes it difficult to achieve real-time, precise tracking of the maneuvering trajectories of small underwater targets (e.g., unmanned underwater vehicles and frogmen). Targeting the dual-station active sonar cooperative tracking system, this paper proposes an Adaptive Interacting Multiple Model-Unscented Kalman Filter (AIMM-UKF) algorithm incorporating probability lower bound constraints. In traditional AIMM algorithms, when a target remains in a long-term steady state, the prior probability of transitioning to a maneuvering model decays toward zero, leading to switching hysteresis and increased tracking errors during sudden maneuvers. To mitigate this issue, this study innovatively introduces a probability lower bound constraint and a decision-window secondary correction mechanism based on traditional likelihood ratio modification. During long-endurance steady-state periods, the decision-window mechanism guarantees high steady-state precision; at the instant of sudden maneuvers, the probability lower bound mechanism cooperates with likelihood ratio scaling to achieve rapid and accurate model switching. Monte Carlo simulations demonstrate that the proposed algorithm effectively overcomes the degradation problem caused by excessive probability absorption. It reduces the peak error during the initial maneuvering stage, achieves the lowest global position and velocity errors, and attains an optimal balance between transient switching and steady-state accuracy. This provides robust technical support for the continuous tracking and security early warning of small underwater targets.