Moving Horizon Estimation for Underwater Target Tracking Based on Time-Difference-of-Arrival Measurements

Abstract

There has been a flurry of activity in the development of robotic systems to localize and track underwater man-made or natural targets based on sparse acoustic data. Compelling examples include the development of surface tracking systems to aid in the navigation of groups of underwater vehicles performing environmental monitoring missions or to study the motion patterns of large underwater fauna. With current technology, the latter case can only be tackled using Time-Difference-of-Arrival (TDoA) techniques. Recent progress in nonlinear state estimation indicates that optimization-based methods may overcome the limitations of classical recursive filtering. However, achieving reliable estimator performance in the case of nonlinear target dynamics and sparse measurements remains a key challenge. In this paper, we study a Moving Horizon Estimation (MHE) approach to TDoA-based underwater target tracking. Through a 2D simulation environment capturing typical marine conditions, we show that the MHE-based estimator maintains reliable tracking in the considered scenarios even when the classical EKF becomes unreliable. The results highlight that multi-step trajectory coupling and physically consistent constraints, which are key advantages of the MHE approach, significantly enhance estimator robustness. It is shown that the MHE approach offers promise as a practical and scalable building block for future multi-agent tracking systems based on TDoA measurements operating in real underwater missions.