The traditional target tracking is a process of estimating the state of a moving target using measurement information obtained by sensors. However, underwater passive acoustic target tracking will confront further challenges, among which the system incomplete observability and time delay caused by the signal propagation create a great impact on tracking performance. Passive acoustic sensors cannot accurately obtain the target range information. The introduction of Doppler frequency measurement can improve the system observability performance; signal time delay cannot be ignored in underwater environments. It varies with time, which has a continuous negative impact on the tracking accuracy. In this paper, the Gauss–Helmert model is introduced to solve this problem by expanding the unknown signal emission time as an unknown variable to the state. This model allows the existence of the previous state and current state at the same time, while handling the implicit equations. To improve the algorithm accuracy, this paper further takes advantage of the estimated state and covariance for the second stage iteration and propose the Gauss–Helmert iterated Unscented Kalman filter under a three-dimensional environment. The simulation shows that the proposed method in this paper shows superior estimation accuracy and more stable performance compared with other filtering algorithms in underwater environments.

Compared with the traditional tracking problem, the underwater 3‐D passive manoeuvring target tracking faces the following challenging problems: (1) Since the signal propagation delay is varying and unknown, the relationship between the two adjacent target states is hard to describe; (2) limited by the underwater environment, it is more difficult to establish a target manoeuvring motion model; (3) the propagation speed of the underwater acoustic signal varies with the depth, and the propagation path will be curved instead of straight. Various parameters including signal propagation delay, target pitch angle and receiving frequency measured by the observer cannot be calculated as usual. In this paper, we improved a 3‐D constant turn (3D‐CT) model to match the underwater target motion. For the problems (1) and (3), this paper proposes a novel interacting multiple model underwater Gauss‐Helmert unscented Kalman filter (IMM‐UGHUKF) with isogradient sound velocity profile, which analytically describes the relationship between the state to be estimated and the passive measurement. This method augments the unknown signal emission time into the state with implicit equations, and transits the state by the Gauss–Helmert model. Simulation tests have been carried out, and the results show that IMM‐UGHUKF can significantly reduce the estimation error compared with other approaches.

To address the issue of persistent high-magnitude outlier interference affecting long-baseline (LBL) positioning systems in complex marine environments, this paper proposes a kinematic constraint-based Robust Interacting Multiple Model Kalman Filter algorithm. Combined with anchor point initialization and multi-step historical observations, the algorithm constructs a spatial Euclidean distance discriminant criterion. By further incorporating the maximum velocity constraint of the Autonomous Underwater Vehicle (AUV), dynamic decision thresholds are established, and final detection decisions are output to the positioning system. Within the Kalman Filter recursion process, a measurement mask matrix is introduced to instantly isolate measurement outliers, preventing abnormal data from participating in state updates and model probability evolution. Simulation results demonstrate that, compared with standard LBL positioning, conventional single outlier detection, and the conventional maximum correntropy criterion-based Kalman filter (MCC-KF) algorithm, the proposed approach enhances outlier identification and suppression—particularly under consecutive anomaly conditions—thereby improving the positioning accuracy of maneuvering targets in complex underwater scenarios.