| Citation: | XIANG Qian, WANG Xiaodan, SONG Yafei, et al. Ballistic target recognition based on cost-sensitively pruned convolutional neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(11): 2387-2398. doi: 10.13700/j.bh.1001-5965.2020.0437(in Chinese)
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Aimed at reducing the overall misrecognition cost of ballistic targets, A One-Dimensional Convolutional Neural Network (1D-CNN) based on Cost-Sensitively Pruning (CSP) is proposed for ballistic target high-resolution range profile recognition. Firstly, based on the lottery ticket hypothesis, a unified framework is proposed to reduce the model complexity and overall misidentification cost concurrently. On this basis, a gradient-free optimization method of network structure based on artificial bee colony algorithm is proposed, which can automatically find the cost-sensitive subnetwork of 1D-CNN, namely, cost-sensitively pruning. Finally, in order to make the cost-sensitive sub-network still be aimed at minimizing the cost of misrecognition during the fine-tuning process, a novel Cost-Sensitive Cross Entropy (CSCE) loss function is proposed to optimize the training, so that the cost-sensitive sub-network focuses more on correctly classifying the categories with higher misrecognition cost to further reduce the overall misrecognition cost. The experimental results show that the proposed 1D-CNN combined with the CSP and CSCE loss function has a lower overall misrecognition cost than traditional 1D-CNN under the premise of maintaining a higher recognition accuracy, and reduces the computational complexity by more than 50% as well.
| [1] |
PERSICO A R, ILIOUDIS C V, CLEMENTE C, et al. Novel classification algorithm for ballistic target based on HRRP frame[J]. IEEE Transactions on Aerospace and Electronic Systems, 2019, 55(6): 3168-3189. doi: 10.1109/TAES.2019.2905281
|
| [2] |
赵振冲, 王晓丹. 引入拒识的最小风险弹道目标识别[J]. 西安交通大学学报, 2018, 52(4): 132-138. https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT201804019.htm
ZHAO Z C, WANG X D. A minimum risk recognition method of ballistic targets with rejection options[J]. Journal of Xi'an Jiaotong University, 2018, 52(4): 132-138(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT201804019.htm
|
| [3] |
KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012, 1: 1097-1105.
|
| [4] |
CHEN W, WANG Y, SONG J, et al. Open set HRRP recognition based on convolutional neural network[J]. The Journal of Engineering, 2019, 19(21): 7701-7704. http://ieeexplore.ieee.org/document/8879042
|
| [5] |
GUO C, HE Y, WANG H P, et al. Radar HRRP target recognition based on deep one-dimensional residual-inception network[J]. IEEE Access, 2019, 7(2): 9191-9204. http://www.onacademic.com/detail/journal_1000041624730599_3db9.html
|
| [6] |
WAN J, CHEN B, XU B, et al. Convolutional neural networks for radar HRRP target recognition and rejection[J]. EURASIP Journal on Advances in Signal Processing, 2019, 5(19): 1-27. doi: 10.1186/s13634-019-0603-y
|
| [7] |
WEN Y, SHI L C, YU X, et al. HRRP target recognition with deep transfer learning[J]. IEEE Access, 2020, 8(22): 57859-57867. http://ieeexplore.ieee.org/document/9040527
|
| [8] |
XIANG Q, WANG X D, SONG Y F, et al. One-dimensional convolutional neural networks for high-resolution range profile recognition via adaptively feature recalibrating and automatically channel pruning[J]. International Journal of Intelligent Systems, 2021, 36(1): 332-361. doi: 10.1002/int.22302
|
| [9] |
SANTURKAR S, TSIPRAS D, ILYAS A, et al. How does batch normalization help optimization [EB/OL]. (2018-05-29)[2020-08-14]. https://arxiv.org/abs/1805.11604.
|
| [10] |
IOFFE S, SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[EB/OL]. (2015-02-11)[2020-08-14]. https://arxiv.org/abs/1502.03167.
|
| [11] |
MISRA D. Mish: A self regularized non-monotonic neural activation function[EB/OL]. (2019-08-23)[2020-08-14]. https://arxiv.org/abs/1908.08681.
|
| [12] |
GOHIL V, NARAYANAN S D, JAIN A. One ticket to win them all: Generalizing lottery ticket initializations across datasets and optimizers[EB/OL]. (2019-06-06)[2020-08-14]. https://arxiv.org/abs/1906.02773.
|
| [13] |
FRANKLE J, CARBIN M. The lottery ticket hypothesis: Finding sparse, trainable neural networks[EB/OL]. (2018-03-09)[2020-08-14]. https://arxiv.org/abs/1803.03635.
|
| [14] |
LIU Z, SUN M, ZHOU T, et al. Rethinking the value of network pruning[J/OL]. (2018-10-11)[2020-08-14]. https://arxiv.org/abs/1810.05270.
|
| [15] |
李秋洁, 赵亚琴, 顾洲. 代价敏感学习中的损失函数设计[J]. 控制理论与应用, 2015, 32(5): 689-694. https://www.cnki.com.cn/Article/CJFDTOTAL-KZLY201505015.htm
LI Q J, ZHAO Y Q, GU Z. Design of loss function for cost-sensitive learning[J]. Control Theory & Applications, 2015, 32(5): 689-694(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-KZLY201505015.htm
|
| [16] |
向前, 王晓丹, 李睿, 等. 基于DCNN的弹道中段目标HRRP图像识别[J]. 系统工程与电子技术, 2020, 42(11): 2426-2433. https://www.cnki.com.cn/Article/CJFDTOTAL-XTYD202011004.htm
XIANG Q, WANG X D, LI R, et al. HRRP image recognition of midcourse ballistic targets based on DCNN[J]. Systems Engineering and Electronics, 2020, 42(11): 2426-2433(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XTYD202011004.htm
|
| [17] |
DUBEY S R, CHAKRABORTY S, ROY S K, et al. diffGrad: An optimization method for convolutional neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 55(2): 1-12. http://ieeexplore.ieee.org/document/8939562
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