For an autonomous underwater vehicle (AUV) underwater localization system based on acoustic two-way travel time measurements, the motion of the AUV during the interrogation-reception time interval, unknown underwater sound velocity, and beacon calibration error affect the performance of the positioning solution. Ignoring these factors significantly deteriorates the performance of the localization system. This paper focuses on the localization problem of AUV in motion with an unknown sound velocity and beacon calibration error. The Cramér–Rao lower bound of the concerned localization problem is first derived as the performance bound of an unbiased estimator. Next, through multi-stage processing, we design a closed-form solution that can attain the corresponding performance bound under small measurement noise conditions. Furthermore, considering that the AUV motion velocity is typically much lower than the sound velocity in practical applications, a simpler and more efficient simplified closed-form solution is proposed, which can guarantee localization performance under the condition that the AUV motion speed is low, as well as small measurement noise. Finally, the theoretical performance and superiority of the proposed solutions were verified through numerical simulation.

A deep understanding of the particularity of underwater emergency search and rescue missions is of great significance for the development of Internet of Underwater Things (IoUT). In various domains related to IoUT, utilizing autonomous underwater vehicle (AUV) for autonomous search and rescue (SAR) missions has become an inevitable trend. Considering the particularrity of underwater emergency SAR mission, we proposed a hybrid-algorithm-based emergency search and rescue (HASAR) method for target search in uncertain environment. First, the improved IBoustrophedon-BINN algorithm is used to calculate global path in uncertain environment to obtain the target position. In addition, to ensure timeliness of SAR missions, an IACO algorithm is designed to replan the optimal energy consumption path to obtain identity of target. During the AUV tracking replanning path process, complex obstacles which include static and dynamic may be encountered. Therefore, the ISSA-IDWA algorithm with adaptive coefficient is utilized to obtain real-time path to avoid obstacles. Extensive simulation results and practical experiments verify the performance of proposed HASAR method.

In the single-beacon underwater tracking system, vehicles rely on slant range measurements from an acoustic beacon to bound errors accumulated by dead reckoning. Ranges are usually obtained based on a presumed known effective sound velocity (ESV). Since the ESV is difficult to determine accurately, traditional methods suffer from large positioning error. By treating the unknown ESV as a state variable, a novel single-beacon tracking model (the so called “5-sv” model) and an extended Kalman filter (EKF)-based solution method have been discussed to solve the problem of ESV estimation. However, due to the uncertainty of underwater acoustic propagation, the probabilistic characteristics of the ESV uncertainty and acoustic measurement noise are unknown and varying both with time and location. EKF, which runs with presupposed noise parameters, cannot describe the practical noise specifications. To overcome the divergence issue of EKF-based single-beacon tracking methods, this paper proposes an adaptive Kalman filter-based single-beacon tracking algorithm which employs the “5-sv” model as the baseline model. Through numerical examples using simulated and field data, both the filter and smoother results show that while implementing the proposed algorithm, the tracking accuracy can be significantly improved, and the estimated noise parameter agrees well with its true value.

This paper addresses the challenge of robust navigation for unmanned underwater vehicles (UUVs) operating in harsh environments, where sensor data is often contaminated by outliers. To mitigate this issue, a dual-robust-kernel Kalman filter is proposed, which sequentially applies the Huber and Agarwal kernels during the measurement update phase. This method effectively combines the exceptional numerical stability of the Huber kernel with the superior robustness of the Agarwal kernel, leveraging their complementary strengths. The UUV experiments validate the proposed algorithm by benchmarking it against several representative methods.