The underwater direction-of-arrival (DOA) tracking using hydrophone array is an important research subject in passive sonar signal processing. To deal with the unknown measurement noise results from underwater environmental noise, the existing variational Bayesian adaptive extended Kalman filter (VB-AEKF) technique for robust underwater DOA tracking jointly estimates the measurement noise covariance matrix and the bearing angle of a target. The iteration process and the nonlinear measurement model result in significant computational complexity of the VB-AEKF. By deriving computational process which is equivalent to VB-AEKF but more computationally efficient, a fast variational Bayesian adaptive extended Kalman filter (FVB-AEKF) for robust DOA tracking is proposed. The proposed method is tested via Monte Carlo simulations of an underwater DOA tracking scenario and an experiment in the South China Sea in July 2021. The results of the simulations and the experiment verified the accuracy and the computational efficiency of the FVB-AEKF.

The robust parameter estimation of unknown space objects is essential to the on-orbit servicing missions. Based on the adaptive filtering techniques along with the dual quaternions modeling methods for pose estimation, this article proposes a dual vector quaternions–based extended Kalman filter and a dual vector quaternions–based adaptive fading factors extended Kalman filter to estimate the parameters of a free-floating tumbling space target. Using the dual vector quaternions to model the kinematics and dynamics of the system, the representation of the model is concise and compact. Also, the translational and rotational coupled effects are considered. In addition, the estimation algorithm is designed by the innovation-based multiple adaptive fading factors. As a result, the dual vector quaternions–based adaptive fading factors extended Kalman filter is robust against the faulty measurements which may lead to divergence of the traditional extended Kalman filter. As far as the authors know, both the designed filters are the first pose and inertial parameters estimation algorithms based on dual vector quaternions, and the dual vector quaternions–based adaptive fading factors extended Kalman filter is the first robust dual vector quaternions–based parameters estimating method. Finally, the proposed dual vector quaternions–based extended Kalman filter and dual vector quaternions–based adaptive fading factors extended Kalman filter are validated by mathematical simulations, and the dual vector quaternions–based adaptive fading factors extended Kalman filter is compared with the dual vector quaternions–based extended Kalman filter to show its robust performances.

The variational Bayesian extended Kalman filter (VB-EKF) based robust direction-of-arrival (DOA) tracking technique is proposed to make reliable estimations of the bearing angle of an underwater target with uncertain environment noise. By utilizing the VB-EKF scheme, the uncertain measurement noise caused by an unknown underwater environment along with the bearing angle of the target can be estimated simultaneously to provide reliable results at every tracking step. The proposed technique is demonstrated and verified by the sea trial data from the South China Sea in July 2021 and both the robustness and accuracy are proved superior to the traditional DOA estimating methods.

The bearing-only tracking of an underwater uncooperative target can protect maritime territories and allows for the utilization of sea resources. Considering the influences of an unknown underwater environment, this work aimed to estimate 2-D locations and velocities of an underwater target with uncertain underwater disturbances. In this paper, an adaptive two-step bearing-only underwater uncooperative target tracking filter (ATSF) for uncertain underwater disturbances is proposed. Considering the nonlinearities of the target’s kinematics and the bearing-only measurements, in addition to the uncertain noise caused by an unknown underwater environment, the proposed ATSF consists of two major components, namely, an online noise estimator and a robust extended two-step filter. First, using a modified Sage-Husa online noise estimator, the uncertain process and measurement noise are estimated at each tracking step. Then, by adopting an extended state and by using a robust negative matrix-correcting method in conjunction with a regularized Newton-Gauss iteration scheme, the current state of the underwater uncooperative target is estimated. Finally, the proposed ATSF was tested via simulations of a 2-D underwater uncooperative target tracking scenario. The Monte Carlo simulation results demonstrated the reliability and accuracy of the proposed ATSF in bearing-only underwater uncooperative tracking missions.

Utilization of ocean resources and defense of national security heavily rely on underwater target tracking technology, which consequently holds significant strategic importance. The passive tracking technology for underwater target bearings, known for its extensive detection range, capability for long-term observation, and robust real-time capabilities, has emerged as a new focal point of research. This paper reviews the essential concepts, research developments, applications, and limitations of key technologies for passive underwater target bearing tracking, concentrating on three main areas: underwater target bearing estimation technology, target tracking technology, and comprehensive underwater target bearing tracking technology. Specifically, it discusses highly robust methods for tracking single or multiple underwater targets. Ultimately, this paper highlights the primary challenges currently facing research in this field and provides a perspective on future developments.

Accurate 3D passive tracking of an underwater uncooperative target is of great significance to make use of the sea resources as well as to ensure the safety of our maritime areas. In this paper, a 3D passive underwater uncooperative target tracking problem for a time-varying non-Gaussian environment is studied. Aiming to overcome the low observability drawback inherent in the passive target tracking problem, a distributed passive underwater buoys observing system is considered and the optimal topology of the distributed measurement system is designed based on the nonlinear system observability analysis theory and the Cramer–Rao lower bound (CRLB) analysis method. Then, considering the unknown underwater environment will lead to time-varying non-Gaussian disturbances for both the target’s dynamics and the measurements, the robust optimal nonlinear estimator, namely the adaptive particle filter (APF), is proposed. Based on the Bayesian posterior probability and Monte Carlo techniques, the proposed algorithm utilizes the real-time optimal estimation technique to calculate the complex noise online and tackle the underwater uncooperative target tracking problem. Finally, the proposed algorithm is tested by simulated data and comprehensive comparisons along with detailed discussions that are made to demonstrate the effectiveness of the proposed APF.