Underwater target detection is a pivotal technology in marine observation. Traditional detection methods primarily rely on acoustic and optical techniques. However, their accuracy is constrained in turbid waters and complex environments. Inspired by weakly electric fish, we present a spatially sensitive detection system based on active electrosense, capable of autonomously generating electric fields to identify underwater targets. This system incorporates the concept of multi-measurement vectors (MMVs) from compressed sensing and the direction of arrival estimation problem, adapting it to our specific application by utilizing spatial geometric information for multi-electrode signal detection. This approach effectively enhances detection sensitivity in both short distance and turbid water environments. Experiments in a saltwater pool demonstrated the system’s excellent current sensitivity to metallic targets, such as submarine cables, particularly at short distances. This capability addresses inherent challenges in traditional optical and acoustic methods, highlighting the practicality and effectiveness of the proposed system.

For specific wideband sound source localization tasks performed in underwater environments, high precision, low computational complexity, and high robustness are required to meet the demand for accurate location in real-time processes. Traditional sound source localization methods that rely on models and signal processing techniques perform poorly in complex but common scenarios where noise, reverberation, and array deviations exist. Sound source localization systems based on deep neural networks are proposed recently and show superiority over conventional localization methods. However, lighter neural network structures for common wideband signals localization have been underexplored, which are crucial for improving the operational efficiency and practicability in underwater localization systems. In this paper, we propose a light neural network structure to locate the typical underwater wideband sound sources in communication and localization systems. The network mainly consists of convolutional and residual blocks. We train the network with simulated single tones and finetune it with multiple sets of single tones received in the experiment. As for the training feature, we choose the phase component of the covariance matrix and reshape the upper triangle into one column to reduce the feature dimension. The test signals include chirps, single-carrier modulation quadrature phase shift keying(SC-QPSK), multi-carrier modulations such as multi-tone and orthogonal frequency division multiplexing(OFDM). Both the simulation and experiment results have verified the high accuracy and robustness of our proposed method. Compared with the state-of-the-arts, our method obtains superior performance, especially in scenarios with low signal-to-noise ratio (SNR).

With the rapid advancement of underwater communication and unmanned aerial vehicle (UAV) technologies, the potential applications of cross-medium communication in environmental monitoring, maritime Internet of Things (IoTs), and rescue operations, in particular, have attracted great attention. This study explores the feasibility of achieving cross-medium direct acoustic communication through the air–water interface. Specifically, it investigates challenges such as acoustic impedance mismatches and signal attenuation caused by energy loss during interface transmission, aiming to understand their impact on communication performance. Experimental tests employed underwater acoustic transducers as signal transmitters to propagate sound waves directly into the air, attempting to establish communication links with aerial UAV nodes. Preliminary experimental results indicate that even conventional underwater acoustic transducers can achieve information exchange between underwater nodes and UAVs, laying a foundation for further research and application of cross-medium communication systems.