Sparse Bayesian learning (SBL) is widely applied in direction‐of‐arrival (DOA) estimation. However, it is limited by complexity, grid mismatch and inappropriate initial values of noise. To address these problems, a DOA estimation algorithm using grid pruning and adaptation based on noise integral‐based sparse Bayesian learning (AGNISBL) method is proposed in this letter. To reduce complexity, grid pruning is introduced for expectation maximization (EM) framework. Furthermore, a novel adaptive‐grid method is proposed for solving grid mismatch. A noise integral‐based inference framework is used to improve the robustness of the sparse Bayesian method. Simulation results show that the performance of the proposed AGNISBL approaches CRB at high signal‐to‐noise ratios (SNR) and with lower time complexity compared to other methods.

In the ocean environment, passive acoustic monitoring (PAM) is an important technique for the surveillance of cetacean species. Manual detection for a large amount of PAM data is inefficient and time-consuming. To extract useful features from a large amount of PAM data for classifying different cetacean species, we propose an automatic detection and unsupervised clustering-based classification method for cetacean vocal signals. This paper overcomes the limitations of the traditional threshold-based method, and the threshold is set adaptively according to the mean value of the signal energy in each frame. Furthermore, we also address the problem of the high cost of data training and labeling in deep-learning-based methods by using the unsupervised clustering-based classification method. Firstly, the automatic detection method extracts vocal signals from PAM data and, at the same time, removes clutter information. Then, the vocal signals are analyzed for classification using a clustering algorithm. This method grabs the acoustic characteristics of vocal signals and distinguishes them from environmental noise. We process 194 audio files in a total of 25.3 h of vocal signal from two marine mammal public databases. Five kinds of vocal signals from different cetaceans are extracted and assembled to form 8 datasets for classification. The verification experiments were conducted on four clustering algorithms based on two performance metrics. The experimental results confirm the effectiveness of the proposed method. The proposed method automatically removes about 75% of clutter data from 1581.3MB of data in audio files and extracts 75.75 MB of the features detected by our algorithm. Four classical unsupervised clustering algorithms are performed on the datasets we made for verification and obtain an average accuracy rate of 84.83%.