A Deep Learning-Based Satellite Target Recognition Method Using Radar Data

A novel satellite target recognition method based on radar data partition and deep learning techniques is proposed in this paper. For the radar satellite recognition task, orbital altitude is introduced as a distinct and accessible feature to divide radar data. On this basis, we design a new distanc...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-04, Vol.19 (9), p.2008
Main Authors: Lu, Wang, Zhang, Yasheng, Xu, Can, Lin, Caiyong, Huo, Yurong
Format: Article
Language:English
Subjects:
Artificial intelligence
Clustering
Correlation coefficients
Deep learning
gated recurrent unit (GRU)
high resolution range profile (HRRP)
International conferences
Machine learning
Machine translation
Methods
Multilayers
Neural networks
Partitions
Pattern recognition
Principal components analysis
radar automatic target recognition (RATR)
Radar data
radar data partition
Radar satellites
Researchers
Space surveillance
Target recognition
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Summary:A novel satellite target recognition method based on radar data partition and deep learning techniques is proposed in this paper. For the radar satellite recognition task, orbital altitude is introduced as a distinct and accessible feature to divide radar data. On this basis, we design a new distance metric for HRRPs called normalized angular distance divided by correlation coefficient (NADDCC), and a hierarchical clustering method based on this distance metric is applied to segment the radar observation angular domain. Using the above technology, the radar data partition is completed and multiple HRRP data clusters are obtained. To further mine the essential features in HRRPs, a GRU-SVM model is designed and firstly applied for radar HRRP target recognition. It consists of a multi-layer GRU neural network as a deep feature extractor and linear SVM as a classifier. By training, GRU neural network successfully extracts effective and highly distinguishable features of HRRPs, and feature visualization technology shows its advantages. Furthermore, the performance testing and comparison experiments also demonstrate that GRU neural network possesses better comprehensive performance for HRRP target recognition than LSTM neural network and conventional RNN, and the recognition performance of our method is almost better than that of other several common feature extraction methods or no data partition.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19092008