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Sensors 2016, 16(12), 2127; doi:10.3390/s16122127

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

1
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
2
Collaborative Innovation Center for Resource Utilization and Ecological Restoration of Old Industrial Base, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Received: 13 October 2016 / Revised: 6 December 2016 / Accepted: 9 December 2016 / Published: 19 December 2016
(This article belongs to the Section Remote Sensors)
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Abstract

The 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
Keywords: adaptive Kalman filter; H-infinity filter; integrated navigation; robust estimation adaptive Kalman filter; H-infinity filter; integrated navigation; robust estimation
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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.

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