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 language | English |
|---|---|
| Title of host publication | 2018 24th International Conference on Pattern Recognition, ICPR 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1295-1300 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538637883 |
| DOIs | |
| Publication status | Published - 2018 Nov 26 |
| Event | 24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China Duration: 2018 Aug 20 → 2018 Aug 24 |
Publication series
| Name | Proceedings - International Conference on Pattern Recognition |
|---|---|
| Volume | 2018-August |
| ISSN (Print) | 1051-4651 |
Other
| Other | 24th International Conference on Pattern Recognition, ICPR 2018 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 18/8/20 → 18/8/24 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
Cite this
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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 proceeding › Conference contribution
TY - GEN
T1 - EMD-Based Entropy Features for micro-Doppler Mini-UAV Classification
AU - Ma, Xinyue
AU - Oh, Beom Seok
AU - Sun, Lei
AU - Toh, Kar Ann
AU - Lin, Zhiping
PY - 2018/11/26
Y1 - 2018/11/26
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85059754533&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059754533&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2018.8546180
DO - 10.1109/ICPR.2018.8546180
M3 - Conference contribution
T3 - Proceedings - International Conference on Pattern Recognition
SP - 1295
EP - 1300
BT - 2018 24th International Conference on Pattern Recognition, ICPR 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -