A dolphin's biosonar may effectively discriminate subtle differences among targets. In order to investigate the possible physical mechanism of target discrimination, in this study, a finite element model excited by a biomimetic click pulse was proposed. The acoustic scattering field and stress distribution of a stainless steel shell were simulated. The biomimetic click experiments were then conducted to verify the theoretical predictions in an anechoic tank. The experimental results showed a good agreement with the model simulations. Furthermore, the elastic time-frequency features of three cylindrical shells with different wall thickness were obtained using a fractional Fourier transform filter to eliminate specular reflection and cross-term interference. To compare discrimination capacity of the time-frequency features with and without the specular reflection, a time-frequency correlator was applied to calculate the correlation coefficient between different shells. The results indicated that the time-frequency features can be represented in high resolution with less cross-term interference, and these features without specular reflection showed a good capacity to discriminate the shells with different wall thickness.

In this letter, we propose a mathematical method to evaluate the bit error rate (BER) of orthogonal frequency division multiplexing with cyclic prefix (CP-OFDM) in an underwater acoustic (UWA) channel with statistical characterization. We adopt general statistical modeling for the UWA channel and specifically study long-distance horizontal UWA transmission in shallow water. Moreover, we perform a rigorous derivation in which the large- and small-scale channel characterization and Doppler shifting are divided into two parts: unified fading and the residual Doppler effect. Furthermore, this approach can be extended to other typical UWA communication channels with different statistical characterizations. Finally, simulation results show good agreement with theoretical results, regardless of whether the motion is drift or intentional movement.

Orthogonal Time–Frequency Space (OTFS) is an innovative modulation method that ensures efficient and secure communication over a time-varying channel. This characteristic inspired us to integrate OTFS technology with underwater acoustic (UWA) communications to counteract the time-varying and overspread characteristics of UWA channels. However, implementing OTFS in UWA communications presents challenges related to overspread channels. To handle these challenges, we introduce a specialized OTFS system and offer frame design recommendations for UWA communications in this article. We propose a Doppler compensation method and a dual-domain joint channel estimation method to address the issues caused by severe Doppler effects in UWA communication. Additionally, we propose an OTFS system detection approach. This approach incorporates an iterative detection process which facilitates soft information exchange between a message passing (MP) detector and a low-density parity check (LDPC) decoder. By conducting simulations, we demonstrate that the proposed UWA OTFS system significantly outperforms Orthogonal Frequency-Division Multiplexing (OFDM), Initial Estimate Iterative Decoding Feedback (IE-IDF-MRC), and two-dimensional Passive Time Reversal Decision Feedback Equalization (2D-PTR-DFE) in UWA channels.

This study proposes a novel application of neural computing based on deep learning for the real-time prediction of motion parameters for underwater maneuvering object. The intelligent strategy utilizes the capabilities of Scaled Conjugate Gradient Neural Intelligence (SCGNI) to estimate the dynamics of underwater target that adhere to discrete-time Markov chain. Following a state-space methodology in which target dynamics are combined with noisy passive bearings, nonlinear probabilistic computational algorithms are frequently used for motion parameters prediction applications in underwater acoustics. The precision and robustness of SCGNI are examined here for effective motion parameter prediction of a highly dynamic Markov chain underwater passive vehicle. For investigating the effectiveness of the soft computing strategy, a steady supervised maneuvering route of undersea passive object is designed. In the framework of bearings-only tracking technology, system modeling for parameters prediction is built, and the effectiveness of the SCGNI is examined in ideal and cluttered marine atmospheres simultaneously. The real-time location, velocity, and turn rate of dynamic target are analyzed for five distinct scenarios by varying the standard deviation of white Gaussian observed noise in the context of mean square error (MSE) between real and estimated values. For the given motion parameters prediction problem, sufficient Monte Carlo simulation results support SCGNI’s superiority over typical generalized pseudo-Bayesian filtering strategies such as Interacting Multiple Model Extended Kalman Filter (IMMEKF) and Interacting Multiple Model Unscented Kalman Filter (IMMUKF).

Baleen whales produce a wide variety of frequency-modulated calls. Extraction of the time–frequency (TF) structures of these calls forms the basis for many applications, including abundance estimation and species recognition. Typical methods to extract the contours of whale calls from a spectrogram are based on the short-time Fourier transform and are, thus, restricted by a fixed TF resolution. Considering the low-frequency nature of baleen whale calls, this work represents the contours using a pseudo-Wigner–Ville distribution for a higher TF resolution at the cost of introducing cross terms. An adaptive threshold is proposed followed by a modified Gaussian mixture probability hypothesis density filter to extract the contours. Finally, the artificial contours, which are caused by the cross terms, can be removed in post-processing. Simulations were conducted to explore how the signal-to-noise ratio influences the performance of the proposed method. Then, in experiments based on real data, the contours of the calls of three kinds of baleen whales were extracted in a highly accurate manner (with mean deviations of 5.4 and 0.051 Hz from the ground-truth contours at sampling rates of 4000 and 100 Hz, respectively) with a recall of 75% and a precision of 78.5%.

The in-band full-duplex underwater acoustic communication (IBFD-UWAC) mode has twice the information throughput of the traditional half-duplex communication mode, significantly increasing the communication efficiency. Extracting the weak desired signal from the high-power self-interference signal without distortion remains a challenging problem in implementing IBFD-UWAC systems. This paper proposes a spatial-digital joint self-interference cancellation (SDSIC) method for IBFD-UWAC. We first perform spatial self-interference cancellation (SSIC) and propose an improved wideband constant-beamwidth beamformer to overcome the problem of direction- and array-dependent interference in IBFD-UWAC systems. Convex optimization is used to maintain a constant beam response in the main flap and cancel the self-interference signal from a fixed direction, thus increasing the signal-to-interference ratio of the desired signal. Subsequently, we perform digital self-interference cancellation (DSIC) on the residual self-interference signal, and propose a variable-step-size least-mean-squares algorithm based on the spatial noise threshold. This algorithm modifies the least-mean-squares step-size adjustment criterion according to the noise level after SSIC and the desired signal, resulting in better DSIC. A series of simulations are implemented in a hardware-in-the-loop platform to verify the practicality and real-time performance of the proposed SDSIC method. The results show that the self-interference signal power can be reduced by 41.5 dB using the proposed method, an improvement of 13.5 dB over the conventional SIC method.