Underwater acoustic localization (UWAL) is widely applied in ocean exploration, and a key issue for the high-rate time-of-arrival (TOA)-based UWAL method is to select the direct signal in the multipath acoustic channel. However, the existing direct signal selection methods suffer from a deterioration of the selection accuracy since the received signals from adjacent periods are mixed with each other. Addressing the problem, we propose a high-rate direct signal selection algorithm in this article. First, we choose the amplitude, TOA, bandwidth, and Doppler frequency of the detected pulses as the input features. Second, a direct signal classifier is established utilizing the decision tree. Finally, the localization model is built based on the TDOA information of the direct signal, and the localization precision analysis evaluates the performance of the UWAL method. Sufficient experiments, including simulation, tank experiment, and sea trial, are implemented to verify the effectiveness of the proposed method.

The black box plays an important role in transportation security, and the underwater acoustic localization (UWAL) methods are the most effective way to find the black box sunken undersea. The traditional UWAL methods suffer from a significant decrease in the localization precision due to the signal period drift of the black box in the cold and high-pressure undersea environment. In our former work, we carried out the second-order time-difference-of-arrival (STDOA) algorithm to address the problem. However, for a real maneuvering ship, the localization precision has the potential to be further improved with the help of the uniformly distributed localization nodes. In this article, a generalized STDOA (GSTDOA) algorithm is proposed. First, we establish the general form of the STDOA information, where the localization nodes can be selected arbitrarily. Second, we analyze the localization precision of the GSTDOA algorithm, and it shows that the localization precision is closely related to the selected nodes. Third, we optimize the localization nodes for maximum localization precision. Finally, experiments in different scenes validate the effectiveness of the proposed method.

Inverse synthetic aperture sonar (ISAS) can obtain the image of a moving underwater vehicle (UV) with both high range resolution and high cross-range resolution. Existing ISAS imaging methods are based on the translational motion of the UV. Since a real UV also does 3-D rotations, these methods suffer from defocusing especially in severe sea condition. Hence, a novel ISAS imaging method utilizing the 3-D rotations is proposed in this paper. Under the 3-D rotational model, the echo in slow time is simplified as an amplitude modulated-cubic phase signal (AM-CPS), and a high precision signal parameter estimation algorithm based on the Radon transform cubic phase function is presented to process the AM-CPS. Moreover, the image plane is analyzed. The result shows that the ISAS imaging method based on the 3-D rotations can obtain the target's images on more image planes compared with the translational motion based methods, and the image quality is improved compared with other signal parameter estimation algorithms. The simulation verifies the effectiveness of the proposed method.

Depth discrimination is a key procedure in acoustic detection or target classification for low-frequency underwater sources. Conventional depth-discrimination methods use a vertical line array, which has disadvantage of poor mobility due to the size of the sensor array. In this paper, we propose a depth-discrimination method for low-frequency sources using a horizontal line array (HLA) of acoustic vector sensors based on mode extraction. First, we establish linear equations related to the modal amplitudes based on modal beamforming in the vector mode space. Second, we solve the linear equations by introducing the total least square algorithm and estimate modal amplitudes. Third, we select the power percentage of the low-order modes as the decision metric and construct testing hypotheses based on the modal amplitude estimation. Compared with a scalar sensor, a vector sensor improves the depth discrimination, because the mode weights are more appropriate for doing so. The presented linear equations and the solution algorithm allow the method to maintain good performance even using a relatively short HLA. The constructed testing hypotheses are highly robust against mismatched environments. Note that the method is not appropriate for the winter typical sound speed waveguide, because the characteristics of the modes differ from those in downward-refracting sound speed waveguide. Robustness analysis and simulation results validate the effectiveness of the proposed method.

In this paper, we study the problem of the joint detection and direction-of-arrival (DOA) tracking of a single moving source which can randomly appear or disappear from the surveillance volume. Firstly, the Bernoulli random finite set (RFS) is employed to characterize the randomness of the state process, i.e., the dynamics of the source motion and the source appearance. To increase the performance of the detection and DOA tracking in low signal-to-noise ratio (SNR) scenarios, the measurements are obtained directly from an array of sensors and allow multiple snapshots. A track-before-detect (TBD) Bernoulli filter is proposed for tracking a randomly on/off switching single dynamic system. Secondly, since the variances of the stochastic signal and measurement noise are unknown in practical applications, these nuisance parameters are marginalized by defining an uninformative prior, and the likelihood function is compensated by using the information theoretic criteria. The simulation results demonstrate the performance of the filter.

This study addresses the critical limitation of existing Ultra-Short Baseline (USBL) calibration algorithms in handling transducer positional errors and inter-element phase errors. We propose a novel positioning-calibration model based on vector projection theorem. The model achieves two key innovations: it eliminates the influence of inter-element positional errors through its structural design, and, for the first time, incorporates inter-element phase errors from acoustic array measurements as observational parameters to establish joint estimation equations for system installation angle errors and inter-element phase errors. The estimation process is implemented using an unscented Kalman filter (UKF). Simulation results demonstrate that the UKF outperforms the Gauss–Newton method (GNM), achieving estimation errors for installation angles and phase errors within 0.05°. Comparative evaluations confirm the model’s superiority over conventional calibration methods in accurately estimating installation angles under transducer positional errors. Field experiments further validate the algorithm’s effectiveness in real-world marine environments, successfully estimating system installation angle errors and inter-element phase errors to enhance final target positioning accuracy. This approach provides a practical solution to persistent calibration challenges in USBL systems.