Global tracking performance analysis in near-real time for the space-based missile warning system is an architecture level demand for the system designers. The traditional method which based on Monte Carlo simulations cannot meet the demand for its time-consuming nature. In this paper, non-simulation rapid performance prediction methods in stereopsis and monocular observation conditions are studied. The latter, though inevitable, has not been taken into account in other literatures. Furthermore, we also find the geometrical symmetry for monocular observation and propose a rapid analysis method called compass of performance based on this symmetry.
This article presents a new DIRECT-type SCABALL (scattering balls) algorithm with a new partition method for derivation-free optimization problems. It does not focus on dividing the region of interest into specific geometric shapes, but rather scatters several balls to cover it. In SCABALL, several potential optimal regions are selected at each iteration, and they are covered by smaller balls sequentially. In this way, the SCABALL ensures the everywhere dense convergence. The center points and radii of the scattered balls significantly influence the efficiency of SCABALL; therefore, the minimax designs are used in the initial and sequential stages to obtain better coverage. The SCABALL parameters, including the number of balls and their radii, were analyzed by numerical investigation. We provided the empirical choices for those parameters and found that the balls’ radii can be contracted to balance efficiency and global convergence. Numerical experiments show that the SCABALL algorithm is locally biased and robust.
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