Active Sonar Target Classification Method Based on Fisher’s Dictionary Learning

Applied Sciences

Tongjing Sun¹  Jiwei Jin¹  Tong Liu¹  Jun Zhang² 

[1] Automation Department, Hangzhou Dianzi University, Hangzhou 310018, China;Zhejiang Electronic Information Products Inspection and Research Institute, Hangzhou 310000, China;

关键词: active sonar target classification;    fisher criteria;    dictionary learning;    sparse representation classification;   

DOI  :  10.3390/app112210635

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown