Paper Info
Title
Multi-AUV Collaborative Target Recognition Based on Transfer-Reinforcement Learning.
Abstract
Due to the existence of unfavorable factors such as turbid water quality and target occlusion, it is difficult to obtain valid data of target features. Due to the repeated calculation of similar data, the real-time performance of the algorithm is poor. In view of the above problems, this paper proposes a multi-AUV collaborative target recognition method based on transfer-reinforcement learning. The features of the target information which is collected by multi-AUV are fused based on wavelet transformation and affine invariance. The similarity of features is calculated by Mahalanobis distance and the learning model is selected autonomously based on the similarity threshold. Based on the Q-learning reinforcement learning model, the target information under the interference environment is trained intensively, and the effective features are extracted and stored in the source domain, which can reduce the impact of the environmental interference on the target recognition. The feature transfer learning model based on deep confidence network transfers the feature data of the source domain to the target domain, reducing the repeated calculation of similar data, and then ensuring the real-time performance of the algorithm. Simulation experiments are conducted in the SUN dataset under five underwater environments (turbid water, target occlusion, insufficient light, complex background, and overlapping targets), and the results demonstrate that the proposed model achieves better performance.
Year
DOI
Venue
2020
10.1109/ACCESS.2020.2976121
IEEE ACCESS
Keywords
DocType
Volume
Small sample,target recognition,multi-AUV collaboration,reinforcement learning,transfer learning
Journal
8
ISSN
Citations 
PageRank 
2169-3536
3
0.41
References 
Authors
0
5
Name
Order
Citations
PageRank
Lei Cai15319.97
Qiankun Sun230.41
Tao Xu331.09
Yukun Ma430.75
Zhenxue Chen57411.60