Target Tracking in Underwater Multi-Sensor Systems Using Delayed Bearing-Only Measurements

In underwater multi-sensor systems for bearing-only tracking, propagation delays cause uncertainty in the state transition intervals. These intervals vary even for the same target observed by different sensors. Additionally, time offsets between sensors also exist, even when they receive signals simultaneously. As a result, directly applying the common Gaussian Markov model for state transitions introduces significant systematic errors. To address this problem, we propose a new tracking algorithm. It establishes motion and time delay constraints between multiple sensors. For state transitions, the Gauss-Helmert model is adopted instead of the conventional one. In underwater target tracking, environmental noise interference often leads to outliers in observational data, significantly affecting tracking accuracy. To address this issue, we propose a backtracking fusion method that merges backtracking reconstructed estimates with current estimates. Simulation results demonstrate that the proposed algorithm outperforms existing methods in estimation accuracy, significantly enhancing system performance.