Frequency-modulated pulse trains can be applied in active sonar systems to improve the performance of conventional transmitted waveforms. Recently, two pulse trains have been widely researched as the transmitted waveforms for active sonars. The LFM-Costas pulse train was formed by modulating the linear frequency-modulated (LFM) waveform via the Costas sequence to remove the Doppler ambiguity of LFM pulses. The generalized sinusoidal frequency-modulated (GSFM) waveform, another frequency-modulated pulse train, achieved an ideal ambiguity function shape with thumbtack mainlobe. In this paper, we focus on constructing an optimization model to optimize the LFM-Costas and GSFM pulse trains with the genetic algorithm. The pulse trains can be improved on properties of both ambiguity function and correlations between sub-pulses. The optimized pulse trains are proven to have better detection performance than those of the initial pulse trains, including the lower sidelobe levels of ambiguity function, as well as lower cross-correlation property. Moreover, it is affirmed that the reverberation suppression performance of pulse trains has also been improved through the optimization model.

Phase-coded sequences are widely studied as the transmitted signals of active sonars. Recently, several design methods have been developed to generate phased-coded sequences satisfying specific aperiodic or periodic autocorrelation sidelobe level metrics. In this paper, based on the majorization–minimization strategy and the squared iterative acceleration scheme, we propose a method to generate sequences with the periodic weighted integrated sidelobe level metric. Numerical simulations illustrate that the proposed method can effectively suppress the periodic autocorrelation sidelobe levels in specific time lags. Compared with other sequence design methods satisfying the periodic weighted integrated sidelobe level metric, our method improves the computational efficiency significantly. In addition, the proposed sequence demonstrates better matched filter performance in specific range intervals compared with its counterpart. The results suggest that the method could be applied as a valid and real-time design method for transmitted signals of active sonars.

Whale sound is a typical transient signal. The escalating demands of ecological research and marine conservation necessitate advanced technologies for the automatic detection and classification of underwater acoustic signals. Traditional energy detection methods, which focus primarily on amplitude, often perform poorly in the non-Gaussian noise conditions typical of oceanic environments. This study introduces a classified-before-detect approach that overcomes the limitations of amplitude-focused techniques. We also address the challenges posed by deep learning models, such as high data labeling costs and extensive computational requirements. By extracting shape statistical features from audio and using the XGBoost classifier, our method not only outperforms the traditional convolutional neural network (CNN) method in accuracy but also reduces the dependence on labeled data, thus improving the detection efficiency. The integration of these features significantly enhances model performance, promoting the broader application of marine acoustic remote sensing technologies. This research contributes to the advancement of marine bioacoustic monitoring, offering a reliable, rapid, and training-efficient method suitable for practical deployment.

The traditional recursive least squares (RLS) algorithm is limited in highly dynamic and noisy underwater channels. To overcome this, we introduce the time-varying Gaussian sliding window-based RLS (VGSRLS) algorithm, designed for enhanced channel tracking. The VGSRLS algorithm adaptively adjusts window length based on the signal’s instantaneous frequency variation. A rotation matrix reorients the Gaussian window toward the highest signal-to-noise ratio (SNR) direction, increasing channel tracking accuracy. Further, the algorithm adapts the Gaussian window shape along the highest SNR direction by combining dynamic window length and anisotropic adjustments, effectively suppressing noise from other directions and enhancing SNR. Simulation results confirm that VGSRLS achieves superior channel estimation accuracy, showing reduced mean squared deviation (MSD) under typical noise conditions in underwater environments compared to the SRLS-DCD algorithm.

Array gain (AG) is significant in evaluating the detection performance of the vertical line array, which is directly determined by the correlation of signal and noise, respectively. In this paper, we analyze the vertical correlation for a 16-element vertical line array experimented in the deep ocean in 2016. The ray interference theory is utilized to interpret the mechanism of the vertical correlation of the sound field in different zones. In the direct-arrival zone, the direct rays and once-surface-reflected rays are two dominated components, whose arrival time difference for each element are nearly the same, and the vertical correlation is high. In the shadow zone, the sound field is mainly dominated by bottom-reflected rays and the vertical correlation decreases due to different grazing angles and arrival times of each ray. Different from the previous assumption of noise independence, the effect of noise correlation on the AG is analyzed through the measured marine environmental noise. Results indicate that the noise correlation coefficients in two zones are low but not 0. In the direct-arrival zone, AG is about 10 dB, very close to the ideal value of $10\log M$ . AG even exceeds it when NG is negative. Moreover, AG in the direct-arrival zone is higher than the one in the shadow zone.

Passive localization of broadband impulsive sources in shallow water is particularly challenging when using a single hydrophone, especially over a sloping seabed. This study presents a novel single-hydrophone localization method for broadband impulsive sources in sloping shallow water. The approach utilizes warping-transformed eigenfrequencies derived from the energy density function of received signals. A key innovation is the method’s capability for simultaneous range and depth estimation in sloping seabed environments, achieved through eigenfrequency pattern matching for range estimation and normalized amplitude matching for depth estimation. The method depends primarily on waveguide depth and average sound speed, enabling robust estimation while exhibiting inherent insensitivity to variations in other environmental parameters. Both simulated and experimental data from a winter sea trial in the East China Sea validate the performance of this dual-parameter estimation approach. The results demonstrate practical applicability for underwater acoustic monitoring and remote sensing in challenging shallow water environments with complex seabed topography.