Systems Engineering and Electronics ›› 2022, Vol. 44 ›› Issue (12): 3747-3755.doi: 10.12305/j.issn.1001-506X.2022.12.19
• Systems Engineering • Previous Articles Next Articles
Yutang MA, Peng SUN*, Jieyong ZHANG, Peng WANG, Yunfei YAN, Liang ZHAO
Received:
2021-11-02
Online:
2022-11-14
Published:
2022-11-24
Contact:
Peng SUN
CLC Number:
Yutang MA, Peng SUN, Jieyong ZHANG, Peng WANG, Yunfei YAN, Liang ZHAO. Air group intention recognition method under imbalance samples[J]. Systems Engineering and Electronics, 2022, 44(12): 3747-3755.
Table 6
Evaluation index value of models in ablation experiment %"
评估指标 | 意图 | ||||||
攻击 | 佯攻 | 撤退 | 侦察 | 监视 | 电子干扰 | ||
精确率 | BiGRU-Attention | 86.67 | 99.55 | 100 | 98.85 | 72.09 | 99.75 |
GRU | 48.00 | 99.06 | 100 | 98.47 | 65.91 | 100 | |
BiGRU | 72.22 | 99.10 | 100 | 98.85 | 68.89 | 100 | |
GRU-Attention | 68.42 | 99.54 | 100 | 98.84 | 61.82 | 100 | |
召回率 | BiGRU-Attention | 86.67 | 99.10 | 100 | 98.47 | 77.50 | 100 |
GRU | 80.00 | 94.17 | 100 | 98.09 | 72.5 | 100 | |
BiGRU | 86.67 | 98.21 | 99.27 | 98.22 | 77.5 | 100 | |
GRU-Attention | 86.67 | 97.31 | 100 | 97.33 | 85 | 99.50 | |
F1-score | BiGRU-Attention | 86.67 | 99.33 | 100 | 98.66 | 74.70 | 99.87 |
GRU | 60 | 96.55 | 100 | 98.28 | 69.05 | 100 | |
BiGRU | 78.79 | 98.65 | 99.63 | 98.53 | 72.94 | 100 | |
GRU-Attention | 76.47 | 98.41 | 100 | 98.08 | 71.58 | 99.75 |
1 | AZAREWICZ J, FALA G, HEITHECKER C. Template-based multi-agent plan recognition for tactical situation assessment[C]// Proc. of the 5th Conference on Artificial Intelligence for Applications, 1989. |
2 | 夏曦. 基于模板匹配的目标意图识别方法研究[D]. 长沙: 国防科学技术大学, 2006. |
XIA X. The study of target intent assessment method based on the template-matching[D]. Changsha: School of National University of Defense Technology, 2006. | |
3 | CHANG L L , ZHOU Z J , YOU Y , et al. Belief rule based expert system for classification problems with new rule activation and weight calculation procedures[J]. Information Sciences, 2016, 336 (1): 75- 91. |
4 | YIN X , ZHANG M , CHEN M Q . Combat intention recognition of the target in the air based on discri-minant analysis[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2018, 38 (3): 46- 50. |
5 | ZHOU T L , CHEN M , CHEN S , et al. Intention prediction of aerial target under incomplete information[J]. ICIC Express Letters An International Journal of Research and Surveys, 2017, 8, 623- 631. |
6 | 戴革林, 陈伟, 刘志坚, 等. 基于区间灰关联度的飞机战术意图识别方法[J]. 数学的实践与认识, 2014, 44 (20): 198- 207. |
DAI G L , CHEN W , LIU Z J , et al. Method of target tactical intention recognition based on interval grey relational degree[J]. Mathematics in Practice and Theory, 2014, 44 (20): 198- 207. | |
7 |
CHEN Z G , WU X F . A novel multi-timescales layered intention recognition method[J]. Applied Mechanics and Materials, 2014, 644-650, 4607- 4611.
doi: 10.4028/www.scientific.net/AMM.644-650.4607 |
8 | XU Y H, CHENG S Y, ZHANG H B, et al. Air target combat intention identification based on IE-DSBN[C]//Proc. of the International Workshop on Electronic Communication and Artificial Intelligence, 2020: 36-40. |
9 | JIN Q, GOU X T, JIN W D, et al. Intention recognition of aerial targets based on Bayesian optimization algorithm[C]//Proc. of the IEEE International Conference on Intelligent Transportation Engineering, 2017: 356-359. |
10 |
欧微, 柳少军, 贺筱媛, 等. 基于时序特征编码的目标战术意图识别算法[J]. 指挥控制与仿真, 2016, 38 (6): 36- 41.
doi: 10.3969/j.issn.1673-3819.2016.06.008 |
OU W , LIU S J , HE X Y , et al. Tactical intention recognition algorithm based on encoded temporal features[J]. Command Control & Simulation, 2016, 38 (6): 36- 41.
doi: 10.3969/j.issn.1673-3819.2016.06.008 |
|
11 | 周旺旺, 姚佩阳, 张杰勇, 等. 基于深度神经网络的空中目标作战意图识别[J]. 航空学报, 2018, 39 (11): 200- 208. |
ZHOU W W , YAO P Y , ZHANG J Y , et al. Combat intention recognition for aerial targets based on deep neural network[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39 (11): 200- 208. | |
12 | 陈浩, 任卿龙, 滑艺, 等. 基于模糊神经网络的海面目标战术意图识别[J]. 系统工程与电子技术, 2016, 38 (8): 1847- 1853. |
CHEN H , REN Q L , HUA Y , et al. Fuzzy neural network based tactical intention recognition for sea targets[J]. Systems Engineering and Electronics, 2016, 38 (8): 1847- 1853. | |
13 | XUE J J , ZHU J , XIAO J Y , et al. Panoramic convolutional long short-term memory networks for combat intension recognition of aerial targets[J]. IEEE Access, 2020, 8, 183312- 183323. |
14 | ZHANG T W , ZHANG X L , LIU C , et al. Balance learning for ship detection from synthetic aperture radar remote sensing imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 182, 190- 207. |
15 | CHAWLA N V , BOWYER K W , HALL L O , et al. SMOTE: synthetic minority over-sampling technique[J]. The Journal of Artificial Intelligence Research, 2002, 16, 321- 357. |
16 | YEN S J , LEE Y S . Cluster-based under-sampling approaches for imbalanced data distributions[J]. Expert Systems with Applications, 2009, 36 (3): 5718- 5727. |
17 | RAMENTOL E , CABALLERO Y , BELLO R , et al. Smote-RSB*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using smote and rough sets theory[J]. Knowledge and Information Systems, 2012, 33 (2): 245- 265. |
18 | LEE Z J , LEE C Y , CHOU S T , et al. A hybrid system for imbalanced data mining[J]. Microsystem Technologies, 2020, 26, 3043- 3047. |
19 | GYOTEN D , OHKUBO M , NAGATA Y . Imbalanced data classification procedure based on SMOTE[J]. Total Quality Science, 2020, 5 (2): 64- 71. |
20 | BATISTA G , PRATI R C , MONARD M C . A study of the behavior of several methods for balancing machine learning training data[J]. ACM Sigkdd Explorations Newsletter, 2004, 6 (1): 20- 29. |
21 | GARCIA S , LUENGO J , HERRERA F . Tutorial on practical tips of the most influential data preprocessing algorithms in data mining[J]. Knowledge-Based Systems, 2016, 98, 1- 29. |
22 | FERNANDEZ A , GARCIA S , CHAWLA N V , et al. SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary[J]. Journal of Artificial Intelligence Research, 2018, 61, 863- 905. |
23 | HUSSEIN A S , LI T , CHUBATO W Y , et al. A-SMOTE: a new preprocessing approach for highly imbalanced datasets by improving SMOTE[J]. International Journal of Computational Intelligence Systems, 2019, 12 (2): 1412- 1422. |
24 | CHEN B Y , XIA S Y , CHEN Z Z , et al. RSMOTE: a self-adaptive robust SMOTE for imbalanced problems with label noise[J]. Information Sciences, 2021, 553, 397- 428. |
25 | CHEN J X , JIANG D M , ZHANG Y N . A hierarchical bidirectional GRU model with attention for EEG-based emotion classification[J]. IEEE Access, 2019, 7, 118530- 118540. |
26 | HUI H, WANG W Y, MAO B H. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning[C]//Proc. of the in Intelligent Computing, 2005: 878-887. |
27 | MUKHERJEE M , KHUSHI M . SMOTE-ENC: a novel SMOTE-based method to generate synthetic data for nominal and continuous features[J]. Applied System Innovation, 2021, 4 (1): 18. |
28 | ZHOU T L , CHEN M , WANG Y H , et al. Information entropy-based intention prediction of aerial targets under uncertain and incomplete information[J]. Entropy, 2020, 22 (3): 279. |
29 | 刘钻东, 陈谋, 吴庆宪, 等. 非完备信息下无人机空战目标意图预测[J]. 中国科学: 信息科学, 2020, 50 (5): 704- 717. |
LIU Z D , CHEN M , WU Q X , et al. Prediction of unmanned aerial vehicle target intention under incomplete information[J]. Scientia Sinica Informationis, 2020, 50 (5): 704- 717. | |
30 | ZHAO X S, SHAO Y B, MAI J Y, et al. Respiratory sound classification based on BiGRU-Attention network with XGBoost[C]//Proc. of the IEEE International Conference on Bioinformatics and Biomedicine, 2020: 915-920. |
[1] | Xiao HAN, Shiwen CHEN, Meng CHEN, Jincheng YANG. Open-set recognition of LPI radar signal based on reciprocal point learning [J]. Systems Engineering and Electronics, 2022, 44(9): 2752-2759. |
[2] | Pingliang XU, Yaqi CUI, Wei XIONG, Zhenyu XIONG, Xiangqi GU. Generative track segment consecutive association method [J]. Systems Engineering and Electronics, 2022, 44(5): 1543-1552. |
[3] | Tao WU, Lunwen WANG, Jingcheng ZHU. Camouflage image segmentation based on transfer learning and attention mechanism [J]. Systems Engineering and Electronics, 2022, 44(2): 376-384. |
[4] | Tao JIN, Xiaofeng WANG, Runlan TIAN, Xindong ZHANG. Rapid recognition method of radar emitter based on improved 1DCNN+TCN [J]. Systems Engineering and Electronics, 2022, 44(2): 463-469. |
[5] | Yiqiang TANG, Xiaopeng YANG, Shengming ZHU. Low-orbit satellite channel prediction algorithm based on the hybrid CNN-BiLSTM using attention mechanism [J]. Systems Engineering and Electronics, 2022, 44(12): 3863-3870. |
[6] | Lingzhi QU, Junan YANG, Hui LIU, Keju HUANG. Method for individual identification of communication radiation source embedded in attention mechanism [J]. Systems Engineering and Electronics, 2022, 44(1): 20-27. |
[7] | Ziyan LIU, Shanshan MA, Jing LIANG, Mingcheng ZHU, Lei YUAN. Attention mechanism based CNN channel estimation algorithm in millimeter-wave massive MIMO system [J]. Systems Engineering and Electronics, 2022, 44(1): 307-312. |
[8] | Bangyan CUI, Runlan TIAN, Dongfeng WANG, Gang CUI, Jingyuan SHI. Radar emitter identification based on attention mechanism and improved CLDNN [J]. Systems Engineering and Electronics, 2021, 43(5): 1224-1231. |
[9] | Shiyang GAO, Huixu DONG, Runlan TIAN, Xindong ZHANG. Radar emitter signal recognition method based on SRNN+Attention+CNN [J]. Systems Engineering and Electronics, 2021, 43(12): 3502-3509. |
[10] | Ruochen ZHAO, Jingdong WANG, Siyu LIN, Dongze GU. Small building detection algorithm based on convolutional neural network [J]. Systems Engineering and Electronics, 2021, 43(11): 3098-3106. |
[11] | Guangshuai LI, Juan SU, Yihong LI, Xiang LI. Aircraft detection in SAR images based on convolutional neural network and attention mechanism [J]. Systems Engineering and Electronics, 2021, 43(11): 3202-3210. |
[12] | Yifan ZHANG, Shuanghui ZHANG, Yongxiang LIU, Feng JING. Radar HRRP sequence target recognition method of attention mechanism based stacked LSTM network [J]. Systems Engineering and Electronics, 2021, 43(10): 2775-2781. |
[13] | Chunrong HE, Jiang ZHU. Security situation prediction method of GRU neural network [J]. Systems Engineering and Electronics, 2021, 43(1): 258-266. |
[14] | CHEN Hao, REN Qing-long, HUA Yi, QIU Yu-ning. Fuzzy neural network based tactical intention recognition for sea targets [J]. Systems Engineering and Electronics, 2016, 38(8): 1847-1853. |
[15] | DENG Hai-jun,YIN Quan-jun,HU Ji-wen,ZHA Ya-bing. Tactical intention recognition based on multi-entity Bayesian network [J]. Journal of Systems Engineering and Electronics, 2010, 32(11): 2374-2379. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||