The complex acoustic intensity method is one of the common methods used for the azimuth estimation of single-vector sensors. However, this method establishes a relationship between frequency and azimuth, which limits its practical applicability for multiple linear frequency modulation (LFM) signals with overlapping frequency domains. In this paper, the time–frequency distribution information of the LFM signal is combined with the complex acoustic intensity method, and more signal parameter information is used to expand the application scenario of the single-vector sensor. The proposed method first processes the time–frequency graph of the signal to obtain a stable and clear time–frequency distribution, and then obtains the acoustic intensity distribution of the signal using the time integration of the energy on the ridge of the signal to estimate the target orientation more stably. The simulation results show that the root mean square error of azimuth estimation is less than 1° when the SNR is greater than 0 dB. Furthermore, a pool experiment was carried out to verify the effectiveness of the proposed method.

To address the performance degradation of traditional underwater target localization algorithms in high-speed motion scenarios, this paper proposes a vertically moving localization method based on interference feature matching using a single hydrophone. Due to the overlapping of interference fringes in the time–frequency domain, the method first employs a two-dimensional homomorphic filter to separate different types of interference fringes while removing line-spectrum interference. Then, a theoretical interference period database is constructed, and feature matching is performed on the interference fringes to achieve localization of the vertically moving target. Simulation results show that, under conditions of strong broadband noise and line-spectrum interference, the proposed method effectively separates and restores the interference fringes of high-speed vertically moving targets and successfully locates the moving target under large Doppler conditions. Further validation using field data from Qiandao Lake confirms that the actual measurements align with theoretical analysis, validating the effectiveness of the proposed method.

The self-interference of an unmanned underwater vehicle (UUV) weakens its ability to detect targets of interest. Due to limitations in the size of the sonar array and the complexity of the interference, the performance of existing self-interference suppression methods in practical applications is unsatisfactory. Our research focuses on analyzing the influence of near-field interferences on the sample covariance matrix (SCM) and proposes an interference suppression algorithm based on an improved autoencoder. The proposed algorithm effectively learns the feature distribution of near-field interferences within the covariance domain and reconstructs the pure signal covariance matrix through the cancellation of the near-field interference features. Moreover, the proposed algorithm can meet the requirements of real-time processing and does not require prior knowledge about the positions or propagation of interference. Simulations demonstrate that the proposed algorithm outperforms comparison methods, particularly in scenarios with low signal-to-interference ratios and a limited number of sensors. Furthermore, lake experiments provide additional evidence of the proposed algorithm’s good performance in practical applications.