Efficient and optimal penetration path planning for stealth unmanned aerial vehicle using minimal radar cross-section tactics and modified A-Star algorithm

Penetration path planning for stealth unmanned aerial vehicles (SUAVs) in the integrated air defense system (IADS) has been a hot research topic in recent years. The present study examines penetration path planning in different threat environments. Firstly, for the complex terrain and static radar threats, a modified A-Star algorithm containing the bidirectional sector expansion and variable step search strategy is proposed to elude static threats rapidly. Then, with regard to bandit threats, the minimal radar cross-section (RCS) tactics are presented to achieve path replanning. Furthermore, the combinatorial methodology of the minimum RCS tactics and the modified A-Star algorithm is applied to achieve the dynamic path planning for SUAV. The simulation results indicate that the modified A-Star algorithm and minimal RCS tactics can significantly reduce the probability of radar system, which has better superiority in calculation efficiency, path cost and safety. And the minimal RCS tactics have better real-time performance and are more convenient in dealing with dynamic threats, which enhances the survivability of SUAV and verifies the effectiveness of the proposed methodology. • This paper presents a novel framework to settle dynamic path planning problem for stealth unmanned aerial vehicles. • A modified A-Star algorithm that adopts the bidirectional sector variable step search strategy. • The minimal radar cross-section tactics are convenient to deal with the bandit threats and enhance real-time performance. • The combinatorial methodology has better superiority in calculation efficiency, path cost and safety.