Memory-enhanced temporal feature learning framework for improved underwater maneuvering target tracking

Rapid trajectory shifts, complex kinetics, and strong measurement noise are major difficulties that arise in the tracking of underwater passive maneuvering targets. This study suggests an innovative framework of memory-augmented temporal features learning, based on a Long Short-Term Memory (LSTM) model, to overcome these challenges. The LSTM aims to effectively develop and preserve long-term temporal relationships in the formation of the target's state, leading to reliable prediction of location, velocity, and trajectory even during high maneuverability and extensive observed noise. The designed model is configured through state-space physics and investigated under distinct levels of Gaussian measurement distortion. The evaluation of performance is carried out in the mean squared error (MSE) sense to analyze the degree of accuracy. In comparison, the LSTM-based estimation model offers better results than generalized pseudo-Bayesian estimators, including the Interacting Multiple model Extended Kalman Filter (and the Interacting Multiple model Unscented Kalman Filter. The outcomes reveal that the proposed design significantly lowers state estimation deficiencies and shows significant flexibility for various maneuvering behaviors, proving a feasible option for real-time passive tracking in acoustically challenging underwater situations.