EMD-Based Entropy Features for micro-Doppler Mini-UAV Classification

Xinyue Ma, Beom Seok Oh, Lei Sun, Kar Ann Toh, Zhiping Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1295-1300
Number of pages6
ISBN (Electronic)9781538637883
DOIs
Publication statusPublished - 2018 Nov 26
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: 2018 Aug 202018 Aug 24

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2018-August
ISSN (Print)1051-4651

Other

Other24th International Conference on Pattern Recognition, ICPR 2018
CountryChina
CityBeijing
Period18/8/2018/8/24

Fingerprint

Unmanned aerial vehicles (UAV)
Entropy
Radar
Continuous wave radar
Radar measurement
Support vector machines
Labels
Classifiers

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

Cite this

Ma, X., Oh, B. S., Sun, L., Toh, K. A., & Lin, Z. (2018). EMD-Based Entropy Features for micro-Doppler Mini-UAV Classification. In 2018 24th International Conference on Pattern Recognition, ICPR 2018 (pp. 1295-1300). [8546180] (Proceedings - International Conference on Pattern Recognition; Vol. 2018-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2018.8546180
Ma, Xinyue ; Oh, Beom Seok ; Sun, Lei ; Toh, Kar Ann ; Lin, Zhiping. / EMD-Based Entropy Features for micro-Doppler Mini-UAV Classification. 2018 24th International Conference on Pattern Recognition, ICPR 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1295-1300 (Proceedings - International Conference on Pattern Recognition).
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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.",
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Ma, X, Oh, BS, Sun, L, Toh, KA & Lin, Z 2018, EMD-Based Entropy Features for micro-Doppler Mini-UAV Classification. in 2018 24th International Conference on Pattern Recognition, ICPR 2018., 8546180, Proceedings - International Conference on Pattern Recognition, vol. 2018-August, Institute of Electrical and Electronics Engineers Inc., pp. 1295-1300, 24th International Conference on Pattern Recognition, ICPR 2018, Beijing, China, 18/8/20. https://doi.org/10.1109/ICPR.2018.8546180

EMD-Based Entropy Features for micro-Doppler Mini-UAV Classification. / Ma, Xinyue; Oh, Beom Seok; Sun, Lei; Toh, Kar Ann; Lin, Zhiping.

2018 24th International Conference on Pattern Recognition, ICPR 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1295-1300 8546180 (Proceedings - International Conference on Pattern Recognition; Vol. 2018-August).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Ma X, Oh BS, Sun L, Toh KA, Lin Z. EMD-Based Entropy Features for micro-Doppler Mini-UAV Classification. In 2018 24th International Conference on Pattern Recognition, ICPR 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1295-1300. 8546180. (Proceedings - International Conference on Pattern Recognition). https://doi.org/10.1109/ICPR.2018.8546180