A Dynamic Air Combat Situation Assessment Model Based on Situation Knowledge Extraction and Weight Optimization
Air combat situation assessment is the basis of target assignment and maneuver decisions. The current air combat situation assessment models, whether nonparametric or parametric, ignore the continuity and timing of situation changes, making the situation assessment results lose tactical significance...
| Autori principali: | , , , , |
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| Natura: | Articolo |
| Lingua: | English |
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MDPI AG
2023-11-01
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| Serie: | Aerospace |
| Soggetti: | |
| Accesso online: | https://www.mdpi.com/2226-4310/10/12/994 |
| _version_ | 1827576068029022208 |
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| author | Zhifei Xi Yingxin Kou You Li Zhanwu Li Yue Lv |
| author_facet | Zhifei Xi Yingxin Kou You Li Zhanwu Li Yue Lv |
| author_sort | Zhifei Xi |
| collection | DOAJ |
| description | Air combat situation assessment is the basis of target assignment and maneuver decisions. The current air combat situation assessment models, whether nonparametric or parametric, ignore the continuity and timing of situation changes, making the situation assessment results lose tactical significance. Aimed at the shortcomings of current air combat situation assessment, a dynamic air combat situation assessment model based on situation knowledge extraction and weight optimization was proposed by combining a multiple regression model of hidden logic process, a weight optimization model based on grey prospect theory, a weight mapping model based on autoencoder and extreme learning machine (AE-ELM) and an air combat situation characteristic parameter prediction model based on dynamic weight online extreme learning machine (DWOSELM). Firstly, considering the timing and continuity of air combat situation change, a hidden logic process multiple regression model was introduced to realize the segmentation of air combat situation time series data and the extraction of air combat situation primitives. Secondly, the weight optimization method based on grey prospect theory was used to obtain the weight of the evaluation index under different air combat situations. On this basis, the dynamic mapping model between air combat situation characteristic parameters and the weight of index was constructed by using AE-ELM. Then, the dynamic weighted extreme learning machine was used to build the target maneuver trajectory prediction model, and the future position information of the target was predicted. On this basis, the future situation information between the enemy and us was obtained. Finally, the time weight calculation model based on normal cumulative distribution was used to determine the importance of the situation at each time. The situation information at multiple times in the air combat process was fused to obtain the comprehensive air combat situation assessment results at the current time. The simulation results show that the model can fully exploit the influence of historical information, effectively integrate the air combat situation information at multiple moments, and generate the air combat situation assessment results with practical tactical significance according to the individual differences of different pilots. |
| first_indexed | 2024-03-08T21:05:50Z |
| format | Article |
| id | doaj.art-c1368b159f2349bab6950872c26eb8e8 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-08T21:05:50Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-c1368b159f2349bab6950872c26eb8e82023-12-22T13:45:09ZengMDPI AGAerospace2226-43102023-11-01101299410.3390/aerospace10120994A Dynamic Air Combat Situation Assessment Model Based on Situation Knowledge Extraction and Weight OptimizationZhifei Xi0Yingxin Kou1You Li2Zhanwu Li3Yue Lv4Air Force Engineering University, Baqiao District, Xi’an 710043, ChinaAir Force Engineering University, Baqiao District, Xi’an 710043, ChinaAir Force Engineering University, Baqiao District, Xi’an 710043, ChinaAir Force Engineering University, Baqiao District, Xi’an 710043, ChinaAir Force Engineering University, Baqiao District, Xi’an 710043, ChinaAir combat situation assessment is the basis of target assignment and maneuver decisions. The current air combat situation assessment models, whether nonparametric or parametric, ignore the continuity and timing of situation changes, making the situation assessment results lose tactical significance. Aimed at the shortcomings of current air combat situation assessment, a dynamic air combat situation assessment model based on situation knowledge extraction and weight optimization was proposed by combining a multiple regression model of hidden logic process, a weight optimization model based on grey prospect theory, a weight mapping model based on autoencoder and extreme learning machine (AE-ELM) and an air combat situation characteristic parameter prediction model based on dynamic weight online extreme learning machine (DWOSELM). Firstly, considering the timing and continuity of air combat situation change, a hidden logic process multiple regression model was introduced to realize the segmentation of air combat situation time series data and the extraction of air combat situation primitives. Secondly, the weight optimization method based on grey prospect theory was used to obtain the weight of the evaluation index under different air combat situations. On this basis, the dynamic mapping model between air combat situation characteristic parameters and the weight of index was constructed by using AE-ELM. Then, the dynamic weighted extreme learning machine was used to build the target maneuver trajectory prediction model, and the future position information of the target was predicted. On this basis, the future situation information between the enemy and us was obtained. Finally, the time weight calculation model based on normal cumulative distribution was used to determine the importance of the situation at each time. The situation information at multiple times in the air combat process was fused to obtain the comprehensive air combat situation assessment results at the current time. The simulation results show that the model can fully exploit the influence of historical information, effectively integrate the air combat situation information at multiple moments, and generate the air combat situation assessment results with practical tactical significance according to the individual differences of different pilots.https://www.mdpi.com/2226-4310/10/12/994situation segmentationsituation extractionweight optimizationsituation predictiondynamic situation assessment |
| spellingShingle | Zhifei Xi Yingxin Kou You Li Zhanwu Li Yue Lv A Dynamic Air Combat Situation Assessment Model Based on Situation Knowledge Extraction and Weight Optimization Aerospace situation segmentation situation extraction weight optimization situation prediction dynamic situation assessment |
| title | A Dynamic Air Combat Situation Assessment Model Based on Situation Knowledge Extraction and Weight Optimization |
| title_full | A Dynamic Air Combat Situation Assessment Model Based on Situation Knowledge Extraction and Weight Optimization |
| title_fullStr | A Dynamic Air Combat Situation Assessment Model Based on Situation Knowledge Extraction and Weight Optimization |
| title_full_unstemmed | A Dynamic Air Combat Situation Assessment Model Based on Situation Knowledge Extraction and Weight Optimization |
| title_short | A Dynamic Air Combat Situation Assessment Model Based on Situation Knowledge Extraction and Weight Optimization |
| title_sort | dynamic air combat situation assessment model based on situation knowledge extraction and weight optimization |
| topic | situation segmentation situation extraction weight optimization situation prediction dynamic situation assessment |
| url | https://www.mdpi.com/2226-4310/10/12/994 |
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