Underwater acoustic navigation is extensively utilized across many industries due to its exceptional dependability and continuous real-time functionality. Due to the swift advancement of technology in recent years, the velocity of autonomous underwater vehicles (AUVs) has progressively increased. A high frame rate is required for the underwater acoustic navigation system in order to precisely obtain the trajectory of the AUV. Nevertheless, the discrepancy lies in the fact that the high frame rate and the unambiguous measurement could be seen as contradictory. Higher frame rates greatly raise the probability of range ambiguity, leading to various potential pseudo solutions that exacerbate the difficulty of accurate positioning and noticeably impact navigation performance. To address the aforementioned issue, a high frame rate acoustic navigation technique that relies on the suppression of range ambiguity has been studied in this paper. Initially, the relationship between measurements were investigated, specifically the time delay and Doppler shift, and the confusing periodic leap. Subsequently, the coupling model was employed to develop a high-precision acoustic navigation model, taking into account the statistical characteristics of measurements. We also examined a high-precision solving approach. Ultimately, the proposed method was validated through both simulation analysis and field testing to confirm its practicality and efficiency. The results demonstrate that it can attain a high level of accuracy in resolving the ambiguous problem and achieve high-precise acoustic navigation.

An underwater acoustical navigation (UWAN) system is widely used on an autonomous underwater vehicle (AUV). The navigation error prediction is of great significance to the design, use, and capability upgrade for the UWAN system. Owing to the trend of faster AUV and the need for higher navigation accuracy, traditional navigation models are mismatched. Besides, they adopt fixed input errors (including measurement errors of sound velocity, time delay, and array position), which are inconsistent with the real complex acoustic channel. As a consequence, the prediction accuracy of the navigation error is greatly reduced. The authors proposed a novel navigation error prediction method for UWAN based on AUV. Firstly, they established the acoustical navigation model for moving AUV and deduced the error prediction formula. Secondly, considering the complex underwater acoustic channel, they applied the variable error models as the inputs of the prediction formula. Finally, simulations and experiments in different situations verified the effectiveness of the proposed method. The method will provide effective theoretical support for the performance evaluation of the UWAN system based on moving AUV.

The underwater channel is bilateral, heterogeneous, uncertain, and exhibits multipath transmission, sound line curvature, etc. These properties complicate the structure of the received pulse, causing great challenges in direct signal identification for ranging purposes and impacts on back-end data processing, even accurate acoustic positioning. Machine learning (ML) combined with underwater acoustics has emerged as a prominent area of research in recent years. From a statistical perspective, ML can be viewed as an optimization strategy. Nevertheless, the existing ML-based direct-signal discrimination approaches rely on independent assessment, utilizing a single sensor (beacon or buoy), which is still insufficient for adapting to the complex underwater environment. Thus, discrimination accuracy decreases. To address the above issues, an accurate CW direct signal detection approach is performed using the decision tree algorithm, which belongs to ML. Initially, the pulse parameter characteristics in the underwater multipath channel are investigated and the parameter models are built. Then, based on multi-sensor localization performance feedback, fusion characteristics for diverse pulse are created. Next, the pulse parameter characteristics are preprocessed to mitigate the impact of varying magnitudes and units of magnitude on data processing. Then, the decision tree is built to obtain the desired output results and realize accurate recognition of the ranging direct signals. Finally, the feasibility and reliability of this paper’s method are verified by computer simulation and field testing.

Accurate estimating the velocity of a mobile object like AUV has received much attention in ocean exploration. The positioning-velocity estimation method is commonly used, which relies on observation information from multiple sensors (as nodes) over a time interval in underwater acoustics. However, its estimation accuracy heavily depends on the measurement accuracy of the positions. Also, there is a contradiction between estimation accuracy and the update rate. Thus, we propose a high-precision velocity estimation method in this paper. Firstly, we establish a two-node velocity estimation model based on the propagation delay and the Doppler shift and analyze the solution under different conditions. Then, we deduce the error transfer formula of velocity estimation theoretically and analyze the error spatial distribution characteristics. Besides, we introduce the estimation method in an underwater distributed sensor network with two steps to further improve the resolution of the velocity estimation using the redundant information generated by the pairwise combinations of multiple nodes. Meanwhile, we also give the idea of the post-processing method using the Kalman filter combined with the proposed method. Both simulation analysis and the lake test corroborate the feasibility and effectiveness of this method and higher precision than the pve method.