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

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.

A Study of Performance Characteristics for Active Sonar in Korean Shallow Seawater Temperature Structures

It is obvious that understanding the effects of shallow water environment of Korea is very important to guarantee the optimal performance of active sonar such as monostatic and bistatic sonar. For this reason, in this paper, we analyzed the detection performance characteristics for various depth deployments of sonar in summer, winter and water temperature inversion environments, which environments are frequently observed in shallow water of Korea such as the Yellow sea. To analyze only effects of water temperature structures on target detection performance, we applied range independent conditions for bottom, sea surface and water temperature characteristics. To understand the characteristics of detection performance, we conducted transmission loss and signal excess modeling. From the results, we were able to confirm the characteristics of detection performance of active sonar. In addition, we verified that operation depth of transmitter and receiver affects the detection performance. Especially in the water temperature inversion environment, it was confirmed that the shadow zone could be minimized and the detection range could be increased through bistatic operation.