Abstract
The marine environment is complex and changeable, and the interference of noise and reverberation seriously affects the classification performance of active sonar equipment. In particular, when the targets to be measured have similar characteristics, underwater classification becomes more complex. Therefore, a strong, recognizable algorithm needs to be developed that can handle similar feature targets in a reverberation environment. This paper combines Fisher’s discriminant criterion and a dictionary-learning-based sparse representation classification algorithm, and proposes an active sonar target classification method based on Fisher discriminant dictionary learning (FDDL). Based on the learning dictionaries, the proposed method introduces the Fisher restriction criterion to limit the sparse coefficients, thereby obtaining a more discriminating dictionary; finally, it distinguishes the category according to the reconstruction errors of the reconstructed signal and the signal to be measured. The classification performance is compared with the existing methods, such as SVM (Support Vector Machine), SRC (Sparse Representation Based Classification), D-KSVD (Discriminative K-Singular Value Decomposition), and LC-KSVD (label-consistent K-SVD), and the experimental results show that FDDL has a better classification performance than the existing classification methods.
Highlights
Active sonar target classification [1,2] uses impulse acoustic signals transmitted by sonar, and makes a decision on the target category and attributes according to the characteristics of the received echo signal
In order to achieve the classification of similar targets under the condition of a low-signalreverberation ratio, we propose an active sonar target classification method based on Fisher’s dictionary learning
In order to classify the lingual signals of the four types of active sonar targets, we use an active sonar target classification method based on Fisher discriminant dictionary learning (FDDL), and validate its performance by comparing it with a support vector machine (SVM), sparse representation classification (SRC), discriminative K-singular value decomposition (D-KSVD), and LC-KSVD
Summary
Active sonar target classification [1,2] uses impulse acoustic signals transmitted by sonar, and makes a decision on the target category and attributes according to the characteristics of the received echo signal. Active sonar completely utilizes the information carried in the echo, which is conducive to classification and recognition, and reflects the essential characteristics of the target. This is an important and effective method of target recognition. The main problem is that active sonar works in a complex and changeable marine environment, which incurs various interferences, such as noise and reverberation [3,4]. The echo signal of the active sonar target gradually weakens, and even completely submerges in the complex marine environment of noise and various other interferences.
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