Air Target Threat Assessment Based on Improved Moth Flame Optimization‐Gray Neural Network Model

Yue, Longfei; Yang, Rennong; Zuo, Jialiang; Luo, Hao; Li, Qiuliang

doi:10.1155/2019/4203538

Cited by 14 publications

(6 citation statements)

References 23 publications

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“…The threat assessment model achieved good adaptive resolution, fine approximation ability, and fault tolerance. Zhai et al [14] introduced the residual structure into the fully connected neural network to improve the accuracy of the evaluation of individual targets ' threats. Yue et al [15] optimized the grey neural network evaluation model with an improved moth extinguishing algorithm and verified the effectiveness of the model through simulation experiments.…”

Section: Introductionmentioning

confidence: 99%

Aerial Target Threat Assessment Based on Gated Recurrent Unit and Self-Attention Mechanism

Chen,

Quan,

Shao

2024

J. of Syst. Eng. Electron.

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Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit (SA-GRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform (FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features. Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.

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Section: Introductionmentioning

confidence: 99%

Aerial Target Threat Assessment Based on Gated Recurrent Unit and Self-Attention Mechanism

Chen,

Quan,

Shao

2024

J. of Syst. Eng. Electron.

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“…Therefore, the situation is embodied as the entities' mission in this article. The general methods of situation assessment include the analytic hierarchy process (AHP) [2,3], the technique for order preference by similarity to ideal solution (TOPSIS) [4,5], neural networks [6,7], fuzzy logic [8,9], Bayesian networks [10,11], etc.…”

Section: Introductionmentioning

confidence: 99%

The Situation Assessment of UAVs Based on an Improved Whale Optimization Bayesian Network Parameter-Learning Algorithm

Li,

Zhang,

Liu

et al. 2023

Drones

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To realize unmanned aerial vehicle (UAV) situation assessment, a Bayesian network (BN) for situation assessment is established. Aimed at the problem that the parameters of the BN are difficult to obtain, an improved whale optimization algorithm based on prior parameter intervals (IWOA-PPI) for parameter learning is proposed. Firstly, according to the dependencies between the situation and its related factors, the structure of the BN is established. Secondly, in order to fully mine the prior knowledge of parameters, the parameter constraints are transformed into parameter prior intervals using Monte Carlo sampling and interval transformation formulas. Thirdly, a variable encircling factor and a nonlinear convergence factor are proposed. The former and the latter enhance the local and global search capabilities of the whale optimization algorithm (WOA), respectively. Finally, a simulated annealing strategy incorporating Levy flight is introduced to enable the WOA to jump out of the local optimum. In the experiment for the standard BNs, five parameter-learning algorithms are applied, and the results prove that the IWOA-PPI is not only effective but also the most accurate. In the experiment for the situation BN, the situations of the assumed mission scenario are evaluated, and the results show that the situation assessment method proposed in this article is correct and feasible.

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“…Target threat assessment aims to analyze the potential combat capabilities of enemy targets through the target's attribute information and target combat intentions to obtain a quantitative description of the target's threat level [2]. Due to the complexity of the combat environment and the limitation of battlefield commanders' cognition, it is difficult for commanders to give accurate target threat assessment information in actual battlefield decision-making.…”

Section: Introductionmentioning

confidence: 99%

An Extended Fuzzy CPT-TODIM Model Based on Possibility Theory and its Application to Air Target Dynamic Threat Assessment

Zhao

Yang

2022

IEEE Access

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Dynamic target threat assessment can be regarded as a classical multiple attribute group decision-making (MAGDM) issue. Triangular fuzzy numbers (TIFNs) can fully describe imprecise and fuzzy information for target threat assessment. Thus, to consider the changing trend of target detection information in a certain period and the influence of commanders' psychological factors on target threat assessment results, this study proposes an extended CPT-TODIM method based on possibility theory (PT-CPT-TODIM) to solve target threat assessment. Of note, the CRITIC method is used to determine the attribute weights of the probability mean matrix and the probability standard deviation matrix, which can make the target threat assessment result more rational. A numerical example and comparison analysis demonstrate the efficiency and feasibility of the newly proposed CPT-TODIM method.INDEX TERMS CPT-TODIM, Possibility theory, Triangular fuzzy number, CRITIC method, dynamic air target threat assessment.

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Air Target Threat Assessment Based on Improved Moth Flame Optimization‐Gray Neural Network Model

Cited by 14 publications

References 23 publications

Aerial Target Threat Assessment Based on Gated Recurrent Unit and Self-Attention Mechanism

Aerial Target Threat Assessment Based on Gated Recurrent Unit and Self-Attention Mechanism

The Situation Assessment of UAVs Based on an Improved Whale Optimization Bayesian Network Parameter-Learning Algorithm

An Extended Fuzzy CPT-TODIM Model Based on Possibility Theory and its Application to Air Target Dynamic Threat Assessment

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