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.
|Title of host publication||2018 24th International Conference on Pattern Recognition, ICPR 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2018 Nov 26|
|Event||24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China|
Duration: 2018 Aug 20 → 2018 Aug 24
|Name||Proceedings - International Conference on Pattern Recognition|
|Other||24th International Conference on Pattern Recognition, ICPR 2018|
|Period||18/8/20 → 18/8/24|
Bibliographical noteFunding Information:
We wish to acknowledge the funding support for this project from Nanyang Technological University under the Undergraduate Research Experience on Campus (URECA) program. The authors thank the Thales Nederland for providing the CW radar data.
© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition