Comparison of Two Optimal Guidance Methods for the Long-Distance Orbital Pursuit-Evasion Game

The orbital pursuit-evasion game (OPE) is a topic of research that has been attracting increasing attention from scholars. However, most works based on the relative dynamics under a short-distance assumption is not applicable when the distance between two spacecrafts is too large. Accordingly, there should be two phases in the OPE, a long-distance OPE (LDOPE) as well as a short-distance one. This article concerns on the optimal guidance problem for the LDOPE. Two different models are introduced in this article to formulate the LDOPE, namely, the Cartesian model, and the spherical model. Then, to overcome the unacceptable solution computation time of traditional algorithms, such as the differential evolution (DE), a well-designed algorithm called "mixed global-local optimization strategy" (MGLOS), which consists of the global optimization phase, and the local optimization phase, is introduced in this article. The MGLOS is nearly two orders of magnitude more efficient than the DE. Moreover, simulations under different initial conditions demonstrate the robustness of the algorithm, and the accuracy, and efficiency of the Cartesian, and spherical models, respectively. Finally, the robustness of two models is analyzed by Monte Carlo simulation, which provides a quantified way to make a choice between two models depending on the measurement accuracy, and permitted maximum error.