Air-ground cooperative operations intention recognition based on Dynamic Series Bayesian Network

Abstract

In modern military warfare, the pattern of air-ground coordination with multi-formation has become more and more important. However, the existing target intention recognition methods are effective for single formation, but lack of effective solutions for multi-formation scenarios with air and ground coordination. In this paper, Dynamic Series Bayesian Network is used to identify the intention of air-to-ground cooperative formation. This method firstly constructs an overall model of intention recognition of air-to-ground cooperative formation by using DSBN, which is used to describe the cooperative action process between air and ground formations. Then, events in different battlefield domains and their related probability relations are fused together with auxiliary battlefield information. The inference network is used to recognize the intention of enemy cooperative formation. This method fully considers the behavior rules of the air target, describes its behavior pattern and trend in detail, and is more suitable for the scenario of multi-cooperative target formation. Finally, the feasibility and effectiveness of this method is verified by simulation example.

Keywords

• intention recognition|air-ground coordination|dynamic series bayesian network|rule knowledge