Information Entropy-Based Intention Prediction of Aerial Targets under Uncertain and Incomplete Information

Abstract

To improve the effectiveness of air combat decision-making systems, target intention has been extensively studied. In general, aerial target intention is composed of attack, surveillance, penetration, feint, defense, reconnaissance, cover and electronic interference and it is related to the state of a target in air combat. Predicting the target intention is helpful to know the target actions in advance. Thus, intention prediction has contributed to lay a solid foundation for air combat decision-making. In this work, an intention prediction method is developed, which combines the advantages of the long short-term memory (LSTM) networks and decision tree. The future state information of a target is predicted based on LSTM networks from real-time series data, and the decision tree technology is utilized to extract rules from uncertain and incomplete priori knowledge. Then, the target intention is obtained from the predicted data by applying the built decision tree. With a simulation example, the results show that the proposed method is effective and feasible for state prediction and intention recognition of aerial targets under uncertain and incomplete information. Furthermore, the proposed method can make contributions in providing direction and aids for subsequent attack decision-making.

Attention-TCN-BiGRU: An Air Target Combat Intention Recognition Model

“…In order to meet the needs of decision support system, many researchers have conducted research on the problem of intention identification, and air target tactical intention recognition has gradually become one of the research hotspots in modern air combat. The existing studies on the identification of tactical intentions against enemy targets mainly include evidence theory [2][3][4], template matching [5], expert systems [6], Bayesian networks [7][8][9], and neural networks [10][11][12][13][14][15][16][17]. The establishment of a standard template base of template matching technology, the collection of information and the construction of probability distribution function in evidence theory, the determination of the structure of Bayesian network and probability distribution parameters, and the construction of knowledge base and inference engine of the expert system all need to organize, abstract, and explicitly describe the empirical knowledge and knowledge representation of experts in related fields and the great difficulty of engineering implementation.…”

“…The establishment of a standard template base of template matching technology, the collection of information and the construction of probability distribution function in evidence theory, the determination of the structure of Bayesian network and probability distribution parameters, and the construction of knowledge base and inference engine of the expert system all need to organize, abstract, and explicitly describe the empirical knowledge and knowledge representation of experts in related fields and the great difficulty of engineering implementation. References [10][11][12][13][14] use different deep learning methods, build and simulate the deep neural network of the human brain, and use data-driven methods to extract the semantic characteristics from the original data from low-level to high-level, from concrete to abstract, from general to specific, simulating the memory mechanism and reasoning mode of commander when judging the battlefield situation, overcoming the difficulties of traditional models in knowledge expression [15]. However, the target's tactical intention is realized through a series of tactical actions.…”

A GRU‐Based Method for Predicting Intention of Aerial Targets

“…As technological development and application have led to a dramatic increase in the amount of battlefield information, it has become difficult to recognize the enemy's intention from multiple sources of battlefield data in a timely and effective manner by relying solely on the experience of domain experts. There is a need for intelligent methods to eliminate the drawbacks of manual methods [ 1 , 2 ].…”

Panoramic Convolutional Long Short-Term Memory Networks for Combat Intension Recognition of Aerial Targets

“…That is to say, it is possible to extract the typical characteristics of aerial targets from the chaotic intelligence information, and then recognize their combat intentions. The existing research methods of targets intention recognition mainly include template matching method [4], expert system method [5]- [7], decision tree [8]- [9], Bayesian network [10]- [12] and neural network [13]- [15]. Floyd et al [4] applies the template matching method to combat intention recognition in beyond-visual-range air combat.…”

“…In decision tree, Niu et al [8] has applied it to the study of intention recognition of naval vessel. By integrating information entropy into the decision tree, T. Zhou et al [9] improved the effectiveness of combat intention recognition of aerial targets. Considering the advantage of Bayesian network which is easy to calculate, Jin Qing et al [10] tries to use it to solve the problem of intension recognition of aerial targets.…”