Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5003089 | IFAC-PapersOnLine | 2016 | 5 Pages |
Abstract
For maneuvering target tracking problem, robust filtering is an effective way to gain fast and accurate target trajectory in real time. The Hâ filter (HâF) is a conservative solution with infinite-horizon robustness, leading to excessive cost of filtering optimality and reduction of estimation precision. In order to retrieve the filtering optimality sacrificed by conservativeness of the HâF design, in this paper, an optimal-switched filtering mechanism is developed and established on the standard HâF to propose an optimal-switched Hâ filter (OSHâF). The optimal-switched mechanism adopts a switched structure that switches filtering mode between optimal and Hâ robust by setting a switching threshold, and introduces an optimality-robustness cost function (ORCF) to on-line optimize the threshold such that the switching structure can be optimized. In the ORCF, a non-dimensional weight factor (WF) is used to quantify the ratio of the filtering robustness and optimality. As the only tunable parameter in the filter, when the WF is given, the proposed OSHâF can obtain the optimal state estimates with filtering optimality and robustness kept at the WF-determined ratio. With the conservativeness of the HâF optimized, the developed OSHâF can be used as a generalized HâF form. A simulation example of space target tracking has demonstrated the superior estimation performance of the OSHâF compared with that of Kalman filter and other typical Hâ filters.
Keywords
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
Yuankai Li, Shuang Zhang, Liang Ding, Zhiguo Shi,