In this study, we propose a reverberation suppression algorithm for linear frequencymodulated (LFM) pulse sonar systems using a non-negative matrix factorization (NMF) method. Because conventional NMF-based reverberation suppression algorithms are only applicable to continuous wave reverberation, we propose two pre-processing methods, namely dechirping transformation and modulo operation, to facilitate application of the NMF method to LFM reverberation. Moreover, we impose additional sparse constraints on the NMF method to improve its performance. To evaluate the proposed algorithm, an experiment involving simulated LFM reverberation is performed. The results thereof show improved detection performance at several signal-to-reverberation ratios and false alarm conditions. Moreover, the proposed algorithm is also applied to sea experiment data. According to the sea experiment analysis, the algorithm is able to suppress the LFM reverberation effectively and improve detection performance in practical LFM pulse sonar systems.
In active sonar systems, detection always suffers from reverberation interference from multiple scatterers in oceanic environments; therefore, numerous studies have been conducted on reverberation suppression. Recently, a non-negative matrix factorization (NMF)-based method was proposed and successfully applied to reverberation suppression. However, the conventional NMF-based method makes convergence challenging because the frequency basis matrix is initialized without considering reverberation characteristic information from oceanic environments. To solve these problems, We propose an improved NMF-based reverberation suppression method adopting a pre-trained reverberation basis matrix and modified sparse update rule. The proposed method is evaluated by analyzing simulation and sea experiment data and the study confirmed that the detection performance was improved compared to the conventional method under various signal-to-reverberation ratio conditions. Several topics are also discussed to analyze the proposed method in detail.
Machine learning (ML)-based approaches are desirable for discriminating targets from clutter signals to enhance the performance of active sonar systems. However, a small dataset and imbalanced data samples between the target and clutter hinder ML applications in active sonar classification. Anomaly detection (AD), which effectively exploits the imbalance, is adopted to enhance the generalization of ML-based active sonar classifiers for small and imbalanced datasets. Generally, deep AD focuses on learning a representation of normal data samples (clutter) and finding a sphere embracing normal data samples in latent space. However, abnormal samples from artificial objects (underwater targets) have similar physical experiences as normal clutter samples from geological and biological scattering objects. Therefore, it is difficult to discriminate between the target and the clutter using conventional deep AD. To overcome the problem of active sonar classification, we propose semi-supervised learning-based bi-sphere anomaly detection (BiSAD) to find two spheres, embracing target and clutter samples each, by modifying conventional deep AD. Simultaneously, BiSAD searches for the latent space where two sphere centroids locate distantly to promote generalization. In the generalization test, the receiver operating characteristic (ROC) curve of BiSAD indicates a detection probability of 0.8 at a false alarm rate of 0.01, and the area under the ROC curve was 0.989, which was superior to the conventional deep AD and supervised learningbased approaches.INDEX TERMS Active sonar classification, machine learning, anomaly detection, sonar clutter suppression.
The propeller tip vortex cavitation (TVC) localization problem involves the separation of noise sources in proximity. This work describes a sparse localization method for off-grid cavitations to estimates their precise locations while keeping reasonable computational efficiency. It adopts two different grid (pairwise off-grid) sets with a moderate grid interval and provides redundant representations for adjacent noise sources. To estimate the position of the off-grid cavitations, a block-sparse Bayesian learning-based method is adopted for the pairwise off-grid scheme (pairwise off-grid BSBL), which iteratively updates the grid points using Bayesian inference. Subsequently, simulation and experimental results demonstrate that the proposed method achieves the separation of adjacent off-grid cavitations with reduced computational cost, while the other scheme suffers from a heavy computational burden; for the separation of adjacent off-grid cavitations, the pairwise off-grid BSBL took significantly less time (29 s) compared with the time taken by the conventional off-grid BSBL (2923 s).
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