For a narrowband signal, an oscillating interference pattern is formed with a target's moving when receiving at the bottom of the sea. In this Letter, the interference pattern of a narrowband source is observed using a single vector sensor (SVS). A passive depth estimation method employing a SVS is proposed. This approach processes the signals after the adaptive line enhancing and extracts the vector intensity, which oscillates periodically with the vertical azimuth. The passive estimation is achieved based on the Fourier-transform relationship between the depth and interference period. The simulation and sea experiment verify this method.

In the deep ocean, a vector sensor is deployed near the seabed to receive direct waves from the reliable acoustic path (RAP). In the space domain, the vector sensor can estimate the vertical arrival azimuth of the signal, which is sensitive to distance. In the frequency domain, the periodic interference pattern formed by the direct and surface‐reflected wave is decided by both the source depth and the vertical arrival azimuth. In this work, a multi‐step method for passive broadband source localisation using a single‐vector sensor (SVS) is proposed. At first, the depth is estimated by the interference pattern in the frequency domain. Then the ranging problem is solved based on the depth estimated result. The impact of the surface‐generated noise is considered. Compared with the scalar vertical line array (VLA) in simulation, the localisation performance of SVS has higher requirements for the signal‐to‐noise ratio (SNR) but is much smaller in size. In the experiment, Gaussian white noise from 310 to 430 Hz was emitted to simulate the underwater target. Compared with traditional match‐field processing (MFP), the multi‐step method is much more stable, accurate, and efficient. The computing time is drastically shortened.

The low frequency line spectrum noise radiated by ships has strong stability and is difficult to eliminate, which is the key information required for passive signal detection. A vector coherent frequency‐domain batch adaptive line enhancement method is proposed to address the issue of insufficient detection capability of traditional scalar adaptive line enhancement (ALE) algorithms for ship characteristic line spectra in complex deep‐sea environments. This method not only introduces the idea of frequency‐domain batch processing, but also uses synchronously collected sound pressure and particle velocity as dual input, fully utilising the coherence characteristics between vector channels to output high gain line spectrum signals and improve computational efficiency. In simulation and sea trial data validation, compared with the time‐domain vector coherent adaptive line enhancement algorithm, this method has shorter time consumption, higher efficiency, and can improve the detection ability of line spectrum signals under low signal‐to‐noise ratio conditions. The bearing estimation results output by this algorithm is also more accurate.

The relationship between modal elevation angle and the relative arrival time between modes, derived from exploiting modal dispersion, provides source information that is less susceptible to environmental influences. However, the standard method based on modal dispersion has limitations for application. To overcome this, we propose a hybrid method for passive source ranging of low-frequency underwater acoustic-pulse signals in a range-independent shallow-water waveguide. Our method leverages deep learning, utilizing the intermediate results from the standard method as inputs, and short-time conventional beamforming to transform signals received by a vertical line array into a beam-time-domain sound-intensity map. The source range is estimated using an attention-based regression model with a ResNet backbone that has been trained on the beam-time-domain sound-intensity map. Our experimental results demonstrate the superiority of the proposed method, with a mean relative-error reduction of 71%, mean root-squared error reduction of 2.25 km, and an accuracy of 85%, compared to matched-field processing.

Direction-of-arrival (DOA) measurement for underwater wideband signals using a linear array is a critical task in oceanic instrumentation, with accuracy directly impacting applications like target tracking and navigation. However, current measurement methods often exhibit significant performance degradation in low signal-to-noise ratio (SNR) scenes. To address these measurement challenges, this paper proposes a novel deep fusion measurement framework that enhances measurement accuracy and comprehensively quantifies its measurement uncertainty, which introduces a dedicated input feature construction strategy to better preserve measurement information of the wideband signals, along with a physics-informed loss term designed to ensure physical consistency of the measurement model. The framework employs a hybrid deep learning architecture that captures intrinsic angle-amplitude relationships and longrange dependencies across frequency bands through a Cross- Modal Channel Attention (CMCA) mechanism and a Frequency Mamba (Fmamba) module. Functioning as a regression-based measurement system, the proposed approach can estimate an arbitrary number of targets without pre-defining the source count, thereby overcoming the grid precision limitation of classification-based methods. Additionally, uncertainty quantification via Monte Carlo dropout is integrated to evaluate how parametric uncertainty affects the model’s predictions. Extensive simulation and lake trial results demonstrate that the proposed method achieves state-of-the-art measurement performance in terms of Average Precision (AP), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), while the uncertainty analysis also confirms its value as a robust and self-aware solution for underwater acoustic measurement.