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H∞ Filtering and Its Application in INS CNS Integrated Navigation System

2013-09-22
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In the integrated navigation of inertial navigation system/starlight (INS/CNS), the H∞ filtering method is theoretically applied to the centralized Kalman filter due to its shortcomings such as dependence on the system model and the statistical characteristics of noise. The filtering equation of H∞ filtering is given, and the relationship between Kalman filtering and H∞ filtering is qualitatively discussed. Through the actual application in the INS/CNS integrated navigation system, the two filtering algorithms are further compared in terms of accuracy and robustness. The simulation results show that when the noise is colored noise or the statistical characteristics are uncertain, H∞ filtering is better than Kalman filtering, which shows the effectiveness and feasibility of the algorithm. Abstract: As the special dependence on accuracy of the system model and statistic character of noises of kalman filter when it is used in INS/CNS integrated navigation system， H∞ filter is applied. Equations of H∞ filtering algorithm is presented and the relationship between H∞ filtering algorithm and traditional kalman filter is discussed. Then in a INS/CNS integrated navigation system， the accuracy and robustness of the two algorithms are compared. The results of simulation demonstrate H∞ filtering is better than kalman filtering when the colored noises or the uncertainty of the model is existed， it proves the effectiveness and feasibility of the presented method.

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