Abstract

This paper presents the flight penetration path planning algorithm in a complex environment with Bogie or Bandit (BB) threats for stealth unmanned aerial vehicle (UAV). The emergence of rigorous air defense radar net necessitates efficient flight path planning and replanning for stealth UAV concerning survivability and penetration ability. We propose the improved A-Star algorithm based on the multiple step search approach to deal with this uprising problem. The objective is to achieve rapid penetration path planning for stealth UAV in a complex environment. Firstly, the combination of single-base radar, dual-base radar, and BB threats is adopted to different threat scenarios which are closer to the real combat environment. Besides, the multistep search strategy, the prediction technique, and path planning algorithm are developed for stealth UAV to deal with BB threats and achieve the penetration path replanning in complex scenarios. Moreover, the attitude angle information is integrated into the flight path which can meet real flight requirements for stealth UAV. The theoretical analysis and numerical results prove the validity of our method.

Highlights

  • Numerical simulations of penetration path planning are performed by employing the improved A-Star algorithm, LRTAStar algorithm, and D-Star algorithm in different threat scenarios, which aims to verify the effectiveness of the improved A-Star algorithm

  • This paper presented a new solution for path replanning for stealth unmanned aerial vehicle in a complex radar net environment with Bogie or Bandit (BB) threats

  • We are focus on analyzing the kinematics model of stealth unmanned aerial vehicles (UAV), threat source in penetration environment, and path planning algorithm

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Summary

Introduction

We focus on analyzing the penetration path planning for stealth UAV based on an improved A-Star algorithm, which aims to achieve rapid penetration path planning in the dynamic combat environment with BB threats The novelty of this method is summarized as follows: firstly, the main idea of the model-based predictive control (MPC) and learning real-time A-Star algorithm (LRTA) is integrated into the path to devise the improved A-Star algorithm.

The System Modeling
Radar Detecting Probability
17 Calculate σ and PNet
Path Planning Algorithm
Numerical Results
Conclusions
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