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Underwater acoustic communications with multiple autonomous underwater vehicles (AUVs) is critically important because it supports the coordinated team mission in the ocean. AUVs, as compact mobile platforms, have limited computing resources and physical space, thus impeding the adoption of spectrally-efficient acoustic communication strategies including multi-antennas transmissions. For example, only a single transmitter is installed on an AUV. Allocation of limited communication resources is also a critical issue when multiple vehicles are involved in the mission. Here, we propose a virtual multiple-input/multiple-output (MIMO) strategy for communications with multiple AUVs. The virtual MIMO strategy selects the transmitters from a subset of AUVs to form a virtual transmit array. The objective is to increase spectral efficiency for the uplink, referring to the transmission connection from the AUVs to moored communication base stations. A user selection algorithm is used to determine the active AUV subset for data transmissions, to ensure the fairness in access to the time slots. Adaptive modulation is combined to improve the throughput further. The user selection and modulation choice are decided by predicted data rates derived from the communication range rather than the channel state information, thus avoiding the high overhead. The performance of the underwater virtual MIMO scheme has been examined in computer simulations, where the vehicle-specific mission tracks, simulated acoustic communication channels from BELLHOP, and time reversal communication receivers were used. The simulation results showed that the proposed algorithm achieved a high overall throughput in a scenario with five AUVs. Further, the assignment fairness of time slots was considered while maximizing the overall throughput.

This paper presents an omnidirectional surface vehicle (OSV) developed for evaluating underwater acoustic communication performance in confined water space. The OSV features centimeter-level positioning accuracy, onboard waveform probing and generation, omnidirectional maneuverability, and outstanding safety. The preliminary experimental results have successfully demonstrated the basic functionalities of this acoustic communication testbed.

In this paper, we present a Long Short-Term Memory (LSTM)-based Kalman Filter for data assimilation of a 2D spatio-temporally varying depth-averaged ocean flow field for underwater glider path planning. The data source to the filter combines both the Eulerian flow map with the Lagrangian mobile sensor data stream. The depth-averaged flow is modeled as two components, the tidal and the non-tidal flow component. The tidal flow is modeled with ADCIRC (Advanced Three-Dimensional Circulation Model), while the non-tidal flow field is modeled by a set of spatial basis functions and their time series coefficients. The spatial basis functions are the principal modes derived by performing EOF (Empirical Orthogonal Functions) analysis on the historical surface flow field measured by high frequency radar (HFR), and the temporal coefficients of the spatial basis function are modeled by an LSTM neural network. The Kalman Filter is performed to combine the dynamics derived from the LSTM network, and the observations from the glider flow estimation data. Simulation results demonstrate that the proposed data assimilation method can give flow field prediction of reasonable accuracy.

With the rapid development of underwater resource exploitation, underwater construction and underwater Internet of Things (IoT), micro autonomous underwater vehicle is drawing more and more attention due to its small size, low-cost and capability for dense deployment. To mitigate the impact of underwater acoustic channel multipath effect and Doppler frequency shift on acoustic communication for micro-AUV, this paper presents the research and development of a low-complexity mobile OFDM underwater acoustic communication modem. By adopting time-frequency differential coding, time-domain Doppler estimation and hardware resampling, no channel estimation, equalization and resampling processing is needed for the modem microprocessor, thus enabling low complexity. The sea test results show that the modem can work effectively under the condition of complex multi-path and certain Doppler effect in shallow water channel, and realize the mobile OFDM underwater acoustic communication of micro-AUV.

This paper presents a novel Kriged Compressive Sensing (KCS) approach for the reconstruction of underwater acoustic intensity fields sampled by multiple gliders following sawtooth sampling patterns. Blank areas in between the sampling trajectories may cause unsatisfying reconstruction results. The KCS method leverages spatial statistical correlation properties of the acoustic intensity field being sampled to improve the compressive reconstruction process. Virtual data samples generated from a kriging method are inserted into the blank areas. We show that by using the virtual samples along with real samples, the acoustic intensity field can be reconstructed with higher accuracy when coherent spatial patterns exist. Corresponding algorithms are developed for both unweighted and weighted KCS methods. By distinguishing the virtual samples from real samples through weighting, the reconstruction results can be further improved. Simulation results show that both algorithms can improve the reconstruction results according to the PSNR and SSIM metrics. The methods are applied to process the ocean ambient noise data collected by the Sea-Wing acoustic gliders in the South China Sea.

This paper presents a hardware Doppler suppression method for underwater acoustic (UWA) communication with compact-sized underwater platforms such as autonomous underwater vehicles (AUVs). The proposed method adopts an adjustable AD (analog-to-digital) sampling rate, leading to significant reductions in the computational complexity of the communication receiver. Both direct sequence spread spectrum (DSSS) and channel coding technology are incorporated into the proposed Doppler compensation method to design a mobile UWA communication system. Sea experiments have been conducted with moving vehicles, and the experimental results have demonstrated the effectiveness of the proposed method.

In this paper, inspired by speed-up and speed-down (SUSD) mechanism observed by the fish swarm avoiding light, an SUSD strategy is proposed to develop new swarm intelligence based optimization algorithms to enhance the accuracy and efficiency of swarm optimization algorithms. By comparing with the global best solution, each particle adaptively speeds up and speeds down towards the best solution. Specifically, a new directed speed term is added to the original particle swarm optimization (PSO) algorithm or other PSO variations. Due to the SUSD mechanism, the algorithm shows a great improvement of the accuracy and convergence rate compared with the original PSO and other PSO variations. The numerical evaluation is provided by solving recent benchmark functions in IEEE CEC 2013.