A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation
AbstractThe Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Jiang, C.; Zhang, S.-B.; Zhang, Q.-Z. A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation. Sensors 2016, 16, 2127.
Jiang C, Zhang S-B, Zhang Q-Z. A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation. Sensors. 2016; 16(12):2127.Chicago/Turabian Style
Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao. 2016. "A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation." Sensors 16, no. 12: 2127.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.