Optimal-Switched H_∞ Robust Tracking for Maneuvering Space Target

Abstract

For non-cooperative maneuvering target tracking, H _∞ robust filtering is a usual and effective way of gaining fast and accurate target trajectory in real time. But unfortunately, the H _∞ filter (H∞F) is a conservative solution with infinite-horizon robustness. That means, the filtering optimality is sacrificed excessively so that the estimation precision is decreased unnecessarily. In order to balance the filtering conservativeness, we discuss an optimal-switched filtering mechanism and provide an optimal-switched H _∞ filter (OSH∞F) in this chapter. The discussed switching mechanism is built based on the typical structure that switches alternatively between optimal and H _∞ robust robust filtering, using an optimality-robustness cost function (ORCF) to evaluate the filtering performance and optimize the switching condition in real time. For a given ORCF, the estimation result of the OSH∞F is optimal in the meaning of the user-defined ratio of filtering optimality to robustness. Actually, the presented OSH∞F is a generalized version of H _∞ filtering, introducing an auxiliary parameter dimension to achieve conservativeness optimization. The estimation performance of the OSH∞F has been illustrated superior to other typical H _∞ filters by applying to a simulation example of maneuvering space target tracking.