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- Title
基于CVD 和Radon 变换的旋翼无人机识别.
- Authors
蒋留兵; 姜风伟; 车 俐
- Abstract
Feature extraction is especially important in the detection and identification process of rotorcraft unmanned aerial vehicle(UAV). For the problem that the recognition effect is not ideal due to the difficulty in extracting features and poor robustness at low signal-to-noise ratio(SNR), a feature extraction method based on improved cadence-velocity diagram(CVD) and Radon transform is proposed. Through extracting frequency information, peak information, and edge information of the target as features, this method uses K nearest neighbor(KNN) classifier to classify and recognize the information. The simulation results show that the recognition accuracy of 96. 67% can be achieved when SNR is -15 dB. The recognition effect at low SNR is significantly better than that of support vector machine(SVM) and Naive Bayes.
- Subjects
FEATURE extraction; RADON transforms; SUPPORT vector machines; SIGNAL-to-noise ratio; CHARTS, diagrams, etc.; ROTORCRAFT
- Publication
Telecommunication Engineering, 2019, Vol 59, Issue 12, p1417
- ISSN
1001-893X
- Publication type
Article
- DOI
10.3969/j.issn.1001-893x.2019.12.008