Tao Chen

Bearing-only target tracking poses significant challenges due to the highly nonlinear nature of the measurements. To address this issue, this paper proposes a novel integrated-correction high-order extended Kalman filter (IC-HEKF). Unlike the conventional extended Kalman filter, the IC‑HEKF compensates the linearized measurement matrix through higher‑order approximations and integrates a correction step within the traditional prediction‑update framework. By relinearizing the bearing-only measurement model based on the corrected state, it overcomes the limitation of conventional methods that rely heavily on the quality of prior target information when using the one-step predicted state for linearization. As a result, the proposed method theoretically enhances the handling of nonlinearity in bearing-only measurement models and ultimately improves tracking performance. Numerical simulations demonstrate that, in a bearing‑only target tracking system, the proposed IC-HEKF achieves superior tracking performance compared to traditional methods. This improvement becomes even more pronounced under conditions where target prior information and sensor accuracy are more degraded. Finally, real‑world experiments further validate the superior performance of the IC‑HEKF.

This study investigates the dynamic formation reconfiguration problem for multi-UUV (multi-Unmanned Underwater Vehicle) systems, with a particular focus on the challenges posed by underwater acoustic communication. A two-dimensional grid model is established in the horizontal plane, taking the leader vehicle as a reference point. Based on this model, fundamental motion strategies for formation reconfiguration are proposed. To facilitate reconfiguration, the Particle Swarm Optimization (PSO) algorithm is utilized to assign desired position points to the follower UUVs within the new formation, enabling dynamic target point planning during reconfiguration. Furthermore, the process of generating motion guidance commands and the impact of acoustic communication delays during command transmission are analyzed. To address these delays, a fuzzy logic-based delay compensation method is proposed. Simulation experiments were conducted to validate the proposed approach. The results demonstrate that the formation reconfiguration planning method and the centralized command communication compensation strategy are both effective and practical for multi-UUV systems.

Formation rendezvous is a critical phase during the deployment or recovery of multiple unmanned underwater vehicles (UUVs) in cooperative missions, and represents one of the core problems in multi-UUV cooperative planning. In practical marine environments with obstacles and currents, multiple constraints must be simultaneously satisfied, including obstacle avoidance, inter-UUV collision prevention, kinematic limitations, and specified initial and terminal states. These requirements make energy-optimal trajectory planning for multi-UUV formation rendezvous highly challenging. Traditional integrated cooperative planning methods often struggle to obtain optimal or even feasible solutions due to the complexity of constraints and the vastness of the solution space. To address these issues, a dual-layer planning framework for multi-UUV formation rendezvous trajectory planning in environments with obstacles and currents is proposed in this paper. The framework consists of an initial individual trajectory planning layer and a secondary cooperative planning layer. In the initial individual trajectory planning stage, the Grey Wolf Optimization (GWO) algorithm is employed to optimize high-order terms of polynomial curves, generating initial trajectories for individual UUVs that satisfy obstacle avoidance, kinematic constraints, and state requirements. These trajectories are then used as inputs to the secondary cooperative planning stage. In the cooperative stage, a Self-Adaptive Particle Swarm Optimization (SAPSO) is introduced to explicitly address inter-UUV collision avoidance while incorporating all individual constraints, ultimately producing a cooperative rendezvous trajectory that minimizes overall energy consumption. To validate the effectiveness of the proposed method, a simulation environment incorporating vortex flow fields and real-world island topography was constructed. Simulation results demonstrate that the proposed hierarchical trajectory planning method is capable of generating energy-optimal formation rendezvous trajectories that satisfy multiple constraints for multi-UUV systems in environments with obstacles and ocean currents, highlighting its strong potential for practical engineering applications.

autonomous underwater vehicles (AUVs) have been extensively utilized in subsea exploration and surveying. However, accurately perceiving the surrounding environment remains a significant challenge for AUVs due to the complexities of subsea terrains. To address this issue, we propose a novel generative scene reconstruction method to enhance AUVs’ perception capabilities. Our method is primarily designed for reconstructing dense subsea terrain from 3-D multibeam echosounder data. We leverage local diffusion and denoising strategies to reconstruct complete subsea terrain at the scene scale directly, without requiring normalization from point clouds. Considering the motion dynamics of AUVs and the overlap between consecutive sonar frames, we introduce a spatial–temporal attention mechanism to aggregate features from consecutive point clouds and guide the reconstruction process as a condition. Then, the reconstructed point cloud is utilized for probabilistic terrain modeling through Bayesian updating, enabling path planning. Experiments conducted on simulation and real-world datasets demonstrate that our method can generate more accurate and complete terrain maps. Furthermore, path planning based on our reconstruction method achieves the shortest and smoothest motion path, further validating that our reconstruction method can provide more complete perception information for AUV navigation.

This paper has addressed the terrain-following problem of an autonomous underwater vehicle for widely used ocean survey missions. Considering the terrain feature description with limited sensing ability in underwater scenarios, a vertically installed multi-beam sonar and a downward single-beam echo sounder are equipped to obtain seafloor detecting data online, and a local polynomial fitting algorithm is carried out with a receding horizon strategy in order to generate a proper tracking path to keep the desired height above the sea bottom. With the construction of the autonomous underwater vehicle dynamic model in the North East Down frame regarding the vertical plane, an online sparse identification algorithm is implemented to obtain the model parameters during the diving process. Then, a fractional-order sliding mode controller is proposed to enable accurate tracking of the path planned and Lyapunov-based theory is utilized to prove the stability of the control algorithm. With the simulation results, the tracking effectiveness of the fractional-order sliding mode controller with in situ identification is verified.

In response to the problems encountered during underwater bearing-only target tracking, namely, the increase in the actual measurement error of the flank array as the target’s relative azimuth deviates from the UUV abeam direction and the poor tracking performance due to the end-fire blind spot of the flank array, a path optimization method for UUV tracking of maneuvering targets that is specifically oriented toward achieving the best observation by the flank array is designed. A system model is established, and the variation in measurement accuracy with respect to the relative azimuth of the target is analyzed and modeled. The maneuvering target is detected when the sonar accuracy changes in real time. According to the bearing-only information measured, the target state is estimated by matching the measurement noise variance with the sonar measurement error in real time. On the basis of the estimated state, the best observation method is used to generate the maneuvering trajectory of the UUV online to reduce measurement noise during target detection. Constraints related to the end-fire blind spot are also considered to avoid situations where the target becomes unobservable. The simulation results show that the proposed method can achieve persistent target tracking while considering the actual characteristics of the flank array.

This paper investigates the terrain-following problem for an autonomous underwater vehicle (AUV) from control perspectives with full consideration of terrain characteristics. By equipping the AUV with three simple single-beam echo sounders, a set of precise along-track bottom slopes are obtained in a real-time manner, and the occurrence of the lost bottom lock phenomenon caused by a single sonar altimeter is eliminated. A slope-based data processing method is developed, which enables an AUV to characterize seafloor features with complementary sensing modalities to generate proper adaptive height-modified values. In order to keep a fixed height when maneuvering over rugged terrains, a back-stepping depth control is implemented by adjusting horizontal rudder angle, and Lyapunov theory is adopted to analyze the asymptotic stability of the resulting terrain-following control system. At last, simulation results indicate the feasibility and effectiveness of the proposed methodology with a discussion of various sensor configurations.

This work studied two sub-problems of the cooperative state estimation and cooperative optimization of tracking paths in multiple unmanned underwater vehicle (multi-UUV) cooperative target tracking. The mathematical model of each component of the multi-UUV cooperative target tracking system was established. According to the target bearing-only information obtained by each unmanned underwater vehicle’s (UUV) detection, the extended Kalman filter algorithm based on interacting with multiple model bearing-only data was used to estimate the target state in a distributed way, and the federal fusion algorithm was used to fuse the estimated results of each UUV. The fused target state was predicted, and, based on the predicted target state, to achieve the persistent tracking of the target, the particle swarm optimization algorithm was used for the online collaborative optimization of the UUV tracking path. The simulation results showed that the multi-UUV distributed fusion filtering algorithm could obtain a better target state estimation effect, and the online path collaborative optimization method based on the prediction of the target state could achieve persistent target tracking.

Aiming at the control problem of autonomous underwater vehicle (AUV) pilot-following formation with communication delay and communication interruption, a controller based on feedback linearization and the PD control method is designed in this paper. Firstly, the nonlinear, strongly coupled vehicle model is transformed into a second-order model via the feedback linearization method, and then the formation coordination controller is designed based on consistency theory and the PD control method. The Markov random jump process is used to simulate the formation topology in the event of communication interruption. The condition of stable convergence of the AUV pilot-following formation is analyzed in the presence of time-varying delay and Markov transformation topology. A Lyapunov–Krasovskii equation is established, and linear matrix inequality (LMI) is used to solve the problem of communication interruption and communication delay. The boundary conditions of error convergence of the control system are obtained. Finally, the effectiveness of the formation coordination controller based on the second-order integral model under the unstable conditions of underwater acoustic communication is verified by simulation.

Due to the complexity of the marine environment, underwater target search and interception is one of the biggest problems faced by an autonomous underwater vehicle (AUV). At present, there is quite a lot of research in terms of the two-dimensional environment. This paper proposes an improved rapidly exploring random trees (RRT) algorithm to solve the problem of target search and interception in an unknown three-dimensional (3D) environment. The RRT algorithm is combined with rolling planning and node screening to realize path planning in an unknown environment, and then the improved RRT algorithm is applied to the search and interception process in a 3D environment. Combined with the search decision function and the three-point numerical differential prediction method, the RRT algorithm can search for and effectively intercept the target. Numerical simulations in various situations show the superior performance, in terms of time and accuracy, of the proposed approach.