Situation and Threat Assessment in BVR Combat

Rao, P Narayana; Kashyap, Sudesh K.; Gopalaratnam, Girija; Mandal, Debanjan

doi:10.2514/6.2011-6241

Cited by 10 publications

(3 citation statements)

References 2 publications

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“…The convolutional layer and the pooling layer are used to compress the data to reduce the calculation amount of cluster center optimization, and the LSHADE-Means algorithm is used for clustering, where K=4 [13], and the specific process is shown in Fig. 12.…”

Section: Ucav Air Combat Situation Knowledge Extractionmentioning

confidence: 99%

“…The uncertain knowledge of grey fuzzy theory is introduced into the uncertain reasoning of Bayesian network. Sun et al infered the purpose of enemy fighter pilots by constructing Bayesian networks [13]. Narayana et al used Bayesian formula, which is combined with maximum posterior probability and relevant situation judgment rules to infer the current situation [14].…”

Section: Introductionmentioning

confidence: 99%

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UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ

Xie

Tang

Wei

et al. 2023

J. of Syst. Eng. Electron.

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The unmanned combat aerial vehicle (UCAV) is a research hot issue in the world, and the situation assessment is an important part of it. To overcome shortcomings of the existing situation assessment methods, such as low accuracy and strong dependence on prior knowledge, a data-driven situation assessment method is proposed. The clustering and classification are combined, the former is used to mine situational knowledge, and the latter is used to realize rapid assessment. Angle evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into twodimensional features. A convolution success-history based adaptive differential evolution with linear population size reduction-means (C-LSHADE-Means) algorithm is proposed. The convolutional pooling layer is used to compress the size of data and preserve the distribution characteristics. The LSHADE algorithm is used to initialize the center of the mean clustering, which overcomes the defect of initialization sensitivity. Comparing experiment with the seven clustering algorithms is done on the UCI data set, through four clustering indexes, and it proves that the method proposed in this paper has better clustering performance. A situation assessment model based on stacked autoencoder and learning vector quantization (SAE-LVQ) network is constructed, and it uses SAE to reconstruct air combat data features, and uses the self-competition layer of the LVQ to achieve efficient classification. Compared with the five kinds of assessments models, the SAE-LVQ model has the highest accuracy. Finally, three kinds of confrontation processes from air combat maneuvering instrumentation (ACMI) are selected, and the model in this paper is used for situation assessment. The assessment results are in line with the actual situation.

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Section: Ucav Air Combat Situation Knowledge Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ

Xie

Tang

Wei

et al. 2023

J. of Syst. Eng. Electron.

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“…Many of them use game theory to model air combat [7], [8], [9], [10]. Other approaches found in the literature are as follows: Bayesian Networks [11], [12], [13], [14], fuzzy logic [15], [16], agent-based modeling [17], influence diagrams [18], reinforcement learning [19], [20], [21], [22], artificial neural networks [23], [24], evolutionary algorithms [25], [26], minimax method [27], and behavior trees [28].…”

Section: Introductionmentioning

confidence: 99%

Engagement Decision Support for Beyond Visual Range Air Combat

Dantas¹,

Costa²,

Geraldo³

et al. 2021

Preprint

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This work aims to provide an engagement decision support tool for Beyond Visual Range (BVR) air combat in the context of Defensive Counter Air (DCA) missions. In BVR air combat, engagement decision refers to the choice of the moment the pilot engages a target by assuming an offensive stance and executing corresponding maneuvers. To model this decision, we use the Brazilian Air Force's Aerospace Simulation Environment (Ambiente de Simulac ¸ão Aeroespacial -ASA in Portuguese), which generated 3,729 constructive simulations lasting 12 minutes each and a total of 10,316 engagements. We analyzed all samples by an operational metric called the DCA index, which represents, based on the experience of subject matter experts, the degree of success in this type of mission. This metric considers the distances of the aircraft of the same team and the opposite team, the point of Combat Air Patrol, and the number of missiles used. By defining the engagement status right before it starts and the average of the DCA index throughout the engagement, we create a supervised learning model to determine the quality of a new engagement. An algorithm based on decision trees, working with the XGBoost library, provides a regression model to predict the DCA index with a coefficient of determination close to 0.8 and a Root Mean Square Error of 0.05 that can furnish parameters to the BVR pilot to decide whether or not to engage. Thus, using data obtained through simulations, this work contributes by building a decision support system based on machine learning for BVR air combat.

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