Remote sensing of water parameter and flow dynamic progress is of critical importance to water environment observation. Underwater acoustic tomography technology uses multiple sound stations to construct distributed sensing network for large scale measuring, which has big superiority in water parameter observation compared with fix-point measurement. Water parameter variation progress can be monitored with underwater acoustic tomography sensing system. This paper develops a Grid-averaged Trust-Region Method for water parameter detailed profiling with coastal acoustic tomography (CAT) systems. A remote sensing experiment was conducted with 5 Coastal Acoustic Tomography (CAT) systems from February 28th to March 7th, 2022 in the Huangcai Reservoir in Changsha, China. Four CAT systems were deployed in same position with different depth of sound transceivers and another CAT station was set 650m apart. Water temperature and flow current reconstruction results along sound propagate section was analyzed with trust-region method and compared with the temperature data acquired with temperature sensor. The accuracy and feasibility of this method are verified with observation results.

High-precision and spatiotemporal flow field measurement in small-scale waters has a decisive impact on navigation and water resource management.Multi transmission rays and position drift, as two main difficulties in improving the accuracy of small-scale flow field, need to be solved urgently. In this paper, four coastal acoustic tomography (CAT) systems are arranged in Qingyang Lake, Hunan, China on 1, March 2022 for a short-term observation. As the GPS error and shipping caused station drift contaminate the reconstructed flow field, a real-time station position correction method, based on travel time and Temperature, Depth sensors (TD), is applied in our inversion. The inversion range average current (RAC) is in agreement with forward calculation, which proves the reliability of the reconstructed horizontal flow field. Besides, comparisons of net flow and Acoustic Doppler Current Profiler (ADCP) velocity are performed to validate the improvement of the real-time station position correction algorithm. The errors of corrected net flow are less than 3.818 m ² /s, with a minimum of 0.0042 m ² /s. The root-mean-square error (RMSE) between the CAT inversion results and the ADCP velocity is 0.123 m/s after correction, relatively reduced 5.95% than before correction. The smaller the RMSE, the higher the inversion accuracy, so the CAT method with real-time station position correction is feasible for high-precision horizontal flow field reconstruction.

Water temperature is a critically important parameter for characterizing aquatic ecosystem, particularly in hydrodynamic processes produced by human activities or climate effects in shallow water regions (oceanic pasture, tidal currents, upwelling currents, etc). The variation of water temperature is a slight and slow process, as technology advances, high-precision observation for of the spatial and temporal variation of water temperature in three-dimensional scale is crucial to environment research and predictions. To address this issue, three-dimensional water temperature field is reconstructed by costal acoustic tomography technology, and inversion water temperature variations are obtained. A multi acoustic station experiment was conducted in a reservoir in Changsha, China, from March 4th to March 5th in 2022. Two grid-averaged three-dimensional inversion methods (Lagrangian-Least-Square method and Improved-Landweber iteration method) with multi-station underwater sensing network are processed for reveal water temperature fluctuations. Rotated empirical orthogonal function is used to further analyze and evaluate the sensitivity of temperature variations in the individual spatial grids. In this paper, water temperature distribution and dynamic variation process in the whole spatial structure is obtained and discussed using three-dimensional sound transmission information, and feasibility of the methods are verified by comparing with temperature depth sensor array observation results. Our study provides high-precision ideas and methods for three-dimensional water temperature activities of underwater acoustic tomography network in shallow environments.

Utilizing hydrophone arrays for detecting underwater acoustic communication (UWAC) signals leverages spatial information to enhance detection efficiency and expand the perceptual range. This study redefines the task of UWAC signal detection as an object detection problem within the frequency–azimuth (FRAZ) spectrum. Employing Faster R-CNN as a signal detector, the proposed method facilitates the joint prediction of UWAC signals, including estimates of the number of sources, modulation type, frequency band, and direction of arrival (DOA). The proposed method extracts precise frequency and DOA features of the signals without requiring prior knowledge of the number of signals or frequency bands. Instead, it extracts these features jointly during training and applies them to perform joint predictions during testing. Numerical studies demonstrate that the proposed method consistently outperforms existing techniques across all signal-to-noise ratios (SNRs), particularly excelling in low SNRs. It achieves a detection F1 score of 0.96 at an SNR of −15 dB. We further verified its performance under varying modulation types, numbers of sources, grating lobe interference, strong signal interference, and array structure parameters. Furthermore, the practicality and robustness of our approach were evaluated in lake-based UWAC experiments, and the model trained solely on simulated signals performed competitively in the trials.

Underwater acoustic tomography is an advanced technology in water environment observation. Sound propagation duration between transceivers is used for underwater parameter distribution profile reconstruction in the inverse problem. The key points of acoustic tomography are accurate station distance and time synchronization. Two methods are introduced in this study for sound station position correction. The direct signal transmission correction (DSC) method corrects the multi-peak (expect direct ray) travel time via the travel time difference between different sound rays and reference direct ray. The ray-model position correction (RMC) method calculates exact station position by the station drift distance obtained from transceiver depth variations to correct direct ray travel time; the other multi-peak travel time is revised by the corrected direct ray travel time. A water temperature observation experiment was carried out in a reservoir using coastal acoustic tomography (CAT) systems to verify the flexibility of these two methods. Multi-ray arrival peaks are corrected using DSC and RMC methods; water temperature inversion results in a 2D vertical profile are obtained. The reliability of the method is proved by comparison with temperature depth sensor (TD) data. The methods improve the quality of initial data and can be attempted for further water environment observation in acoustic tomography observation studies.

Flow noise is an important factor that limits the low-frequency detection performance of towed linear array sonar. Reducing flow noise is therefore one of the key problems in the design and application of towed sonar. In practice, it is often difficult to determine the contribution of flow noise directly from experimental data, which is a mixture of many noise sources. Based on the difference of spatial coherence scales among various noise sources, this paper gives a method that separates the flow noise component from received data. The theory of the method is given mathematically and the efficiency of the method is validated by real lake trial data.