Real-time route planning for low observable unmanned combat aerial vehicle

The next generation of low observable (LO) unmanned combat aerial vehicle (UCAV) with highly autonomy to implement a penetration mission requires advanced methods for flyable and safe route planning (i.e., respecting physical capability of vehicle and threat coverage by hostile air defense radars) at a real-time manner. Currently, the main challenge of real-time route planning for LO UCAV is to achieve computationally efficiency under dynamic (pop-up/moving) threats by air defense radars. In this paper, a real-time planning paradigm in compliance with complex penetration requirements is proposed, and a complete modeling of route planning for LO UCAV's penetration as an optimal control problem is designed. The paper at first devises a direct method to transform the optimal control problem into a nonlinear programming (NLP) problem and then solves the formulated NLP problem under a moving planning horizon. The proposed method can give computationally efficient route planning results for LO UCAV's penetration under multiple kinds of radar threats. Numerical test results based on F-16 uninhabited platform demonstrate the effectiveness of the proposed method.