Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144627
Title: EMD-based entropy features for micro-doppler mini-UAV classification
Authors: Ma, Xinyue
Oh, Beom-Seok
Sun, Lei
Toh, Kar-Ann
Lin, Zhiping
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Ma, X., Oh, B.-S., Sun, L., Toh, K.-A., & Lin, Z. (2018). EMD-Based Entropy Features for micro-Doppler Mini-UAV Classification. 2018 24th International Conference on Pattern Recognition (ICPR), 1295-1300. doi:10.1109/icpr.2018.8546180
Abstract: In this paper, we first investigate into six popular entropies extracted from a set of intrinsic mode functions (IMFs) as a feature pattern for radar-based mini-size unmanned aerial vehicles (mini-UAV) classification. The six entropies include Shannon entropy, spectral entropy, log energy entropy, approximate entropy, fuzzy entropy and permutation entropy. Via an empirical comparison among the six entropies on real measurement radar data, the first three are selected as the representative due to their high efficiency and accuracy. To enhance the classification accuracy, the three selected entropies are then extracted from eight different sets of IMFs obtained by signal downsampling, and then fused at feature level. The nonlinear support vector machine classifier is adopted to predict the class label of unseen test radar signals. Our empirical results on a set of real-world continuous wave radar data show that the proposed method outperforms the state-of-the-art method in terms of the mini-UAV classification accuracy.
URI: https://hdl.handle.net/10356/144627
ISBN: 978-1-5386-3788-3
DOI: 10.1109/ICPR.2018.8546180
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICPR.2018.8546180.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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