Dynamic Air Target Threat Assessment Based on Interval-Valued Intuitionistic Fuzzy Sets, Game Theory, and Evidential Reasoning Methodology

Abstract

In order to reduce the uncertainty of target threat assessment results and improve exact target assessment in the complicated and changeable air combat environment, a novel method based on the combination of interval-valued intuitionistic fuzzy sets (IVIFSs), game theory, and evidential reasoning methodology is proposed in this paper. First, the imprecise and fuzzy information of battlefield air target is expressed by IVIFS. Second, the optimal index weight is determined by the interval intuitionistic fuzzy entropy and game theory. And the time series weight is calculated by the inverse Poisson distribution method. Then, the target evaluation information at different times is dynamically fused through an evidential reasoning algorithm. Finally, the accuracy function is applied to obtain the threat ranking of all the targets. A case of the threat assessment of air targets is provided to demonstrate the implementation process of the method proposed in this paper. Simulation experiments show that in a rapidly evolving combat environment, this algorithm can effectively reduce the uncertainty of target threat assessment results. It provides us with a useful way for target threat assessment based on interval-valued intuitionistic fuzzy sets, game theory, and evidential reasoning methodology.

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

“…The most common traditional theories are operations research and statistical methods, such as multiattribute decision-making theory [4], technique for order preference by similarity to an ideal solution (TOPSIS) theory [5], Bayesian network [6], and fuzzy theory [7]. Zhen et al [6] established the threat indicator system and constructed a threat level model based on expert experience and dynamic Bayesian theory, which can reliably and dynamically evaluate the threat of group targets in complex environments. Gao et al [8] and Xu et al [9] combined the intuitionistic fuzzy theory with the multiattribute decision-making method to handle the target threat assessment.…”

Air Target Threat Assessment: A Kernel Extreme Learning Machine Based on a Multistrategy Improved Sparrow Search Algorithm

“…Bayesian network-based threat assessment models have been proposed for decision-making related to battlefeld uncertainty [2]; however, the prior probability is highly dependent on expert experience. As such, combinations of interval-valued intuitionistic fuzzy sets, game theories, and evidential reasoning methods have been used for dynamic threats [3]; however, their generalization capability may be insufcient. Treat assessment based on statistical methods has high accuracy in specifc battlefeld environments; however, the targets of information warfare are complicated, and thus it is difcult for statistical methods to analyze the data in real time as they lack selfadaptation and self-learning capabilities.…”

Target Threat Assessment in Air Combat with BP Neural Network for UAV

“…Analysis of the threat degree of the target is difficult task which contains many different complicated factor. Till now many target threat assessment methods in Air Combat have been proposed, such as Bayesian networks method [2][3], gray relational analysis theory method [4], rough set theory [5], Agent-based method [6], genetic algorithms [7], information entropy-based method [8], a combination of multiple methods [9] and so on. These methods are suitable for different battlefield condition in simulation scene, but cannot adapt to the complicated real battlefield environment.…”