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26-07-2021 | Special Issue Paper

Radar target recognition based on few-shot learning

Authors: Yue Yang, Zhuo Zhang, Wei Mao, Yang Li, Chengang Lv

Published in: Multimedia Systems | Issue 5/2023

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Abstract

With the continuous development of target recognition technology, people pay more and more attention to the cost of sample generation, tag addition and network training. Active learning can choose as few samples as possible to achieve a better recognition effect. In this paper, a small number of the simulation generated radar cross-section time series are selected as the training data, combined with the least confidence and edge sampling, a sample selection method based on few-shot learning is proposed. The effectiveness of the method is verified by the target type recognition test in multi time radar cross-section time series. Using the algorithm in this paper, 10 kinds of trajectory data are selected from all 19 kinds of trajectory data, and the training model is tested, which can achieve similar results with 19 kinds of trajectory data training model. Compared with the random selection method, the accuracy is improved by 4–10% in different time lengths.

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Title: Radar target recognition based on few-shot learning
Authors: Yue Yang
Zhuo Zhang
Wei Mao
Yang Li
Chengang Lv
Publication date: 26-07-2021
Publisher: Springer Berlin Heidelberg
Published in: Multimedia Systems / Issue 5/2023
Print ISSN: 0942-4962
Electronic ISSN: 1432-1882
DOI: https://doi.org/10.1007/s00530-021-00832-3

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