Research on Situation Assessment of Short-range Air Combat with Adaptive Variable Weight

Jiang, Long-ting; Kou, Ya-nan; Wang, Dong; Ren, F.; Huang, Zhenyu; Sun, Yao; Liu, Ying

doi:10.12783/dtcse/iteee2019/28811

Cited by 1 publication

(2 citation statements)

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“…The parametric method uses dynamic Bayesian network [7][8][9], decision tree [10], neural network [11][12][13], and other machine learning methods to approximate the mapping relationships between air combat situation information indicators by building a situation reasoning network [8]. In [7], by analyzing the factors influencing a situation assessment, a situation assessment model based on the Gaussian cloud Bayesian network was proposed.…”

Section: Introductionmentioning

confidence: 99%

“…An air combat situation assessment model based on a discrete fuzzy dynamic Bayesian network was proposed in [8], but this method still relies on the experience and knowledge of domain experts. Reference [9] used the prior probability distribution of air combat situation factors to infer the situation and proposed a variable weight short-range air combat situation assessment method based on Bayesian theory. In [10], a situation assessment model based on decision tree was established by taking the situation information of both sides as input and the situation as output.…”

Section: Introductionmentioning

confidence: 99%

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A Dynamic Air Combat Situation Assessment Model Based on Situation Knowledge Extraction and Weight Optimization

Xi,

Kou,

et al. 2023

Aerospace

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%