Futai Liang, Yan Zhou, Zheng Zhang, Xin Chen, Xiaojie Tang, Qiang Sun
{"title":"基于lstm的飞机编队识别与预测方法","authors":"Futai Liang, Yan Zhou, Zheng Zhang, Xin Chen, Xiaojie Tang, Qiang Sun","doi":"10.1145/3549179.3549193","DOIUrl":null,"url":null,"abstract":"Aircraft formation recognition and prediction are of great significance in modern air combat. Aiming at the problems of many manual interventions and complex implementation through traditional aircraft formation recognition methods, an intelligent recognition and prediction method of aircraft formation is proposed. First, a formation coding method is designed, which is combined with Support Vector Machine (SVM) to construct a formation recognition model. Then, a formation prediction model is constructed based on the Long-Short-Term Memory network (LSTM) and the recognition model. Finally, a dataset is generated to train the two models, and the trained model can be used for formation recognition and prediction. After experimental verification, the method proposed in the paper has better recognition and prediction effects on formations, the recognition accuracy can reach 95.5%, and the accuracy of formation prediction can reach 95%.","PeriodicalId":105724,"journal":{"name":"Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems","volume":"606 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An LSTM-based Method for Recognition and Prediction of Aircraft Formation\",\"authors\":\"Futai Liang, Yan Zhou, Zheng Zhang, Xin Chen, Xiaojie Tang, Qiang Sun\",\"doi\":\"10.1145/3549179.3549193\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aircraft formation recognition and prediction are of great significance in modern air combat. Aiming at the problems of many manual interventions and complex implementation through traditional aircraft formation recognition methods, an intelligent recognition and prediction method of aircraft formation is proposed. First, a formation coding method is designed, which is combined with Support Vector Machine (SVM) to construct a formation recognition model. Then, a formation prediction model is constructed based on the Long-Short-Term Memory network (LSTM) and the recognition model. Finally, a dataset is generated to train the two models, and the trained model can be used for formation recognition and prediction. After experimental verification, the method proposed in the paper has better recognition and prediction effects on formations, the recognition accuracy can reach 95.5%, and the accuracy of formation prediction can reach 95%.\",\"PeriodicalId\":105724,\"journal\":{\"name\":\"Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems\",\"volume\":\"606 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3549179.3549193\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 International Conference on Pattern Recognition and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3549179.3549193","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An LSTM-based Method for Recognition and Prediction of Aircraft Formation
Aircraft formation recognition and prediction are of great significance in modern air combat. Aiming at the problems of many manual interventions and complex implementation through traditional aircraft formation recognition methods, an intelligent recognition and prediction method of aircraft formation is proposed. First, a formation coding method is designed, which is combined with Support Vector Machine (SVM) to construct a formation recognition model. Then, a formation prediction model is constructed based on the Long-Short-Term Memory network (LSTM) and the recognition model. Finally, a dataset is generated to train the two models, and the trained model can be used for formation recognition and prediction. After experimental verification, the method proposed in the paper has better recognition and prediction effects on formations, the recognition accuracy can reach 95.5%, and the accuracy of formation prediction can reach 95%.