Analysis of a robust Kalman filter in loosely coupled GPS/INS navigation system. (February 2016) - Zhao, Lin; Qiu, Haiyang; Feng, Yanming
Highlights: A detailed filter design procedure for possible application in similar case. A loosely coupled integration simulation model is easily recast. Convergence condition is given through H-inf theory. Bounded Riccati equation is derived for guaranteed estimation covariance. Abstract: GPS/INS integrated system is very subject to uncertainties due to exogenous disturbances, device damage, and inaccurate sensor noise statistics. Conventional Kalman filer has no robustness to address system uncertainties which may corrupt filter performance and even cause filter divergence. Based on the INS error dynamic equation, a robust Kalman filter is analyzed and applied in loosely coupled GPS/INS integration system. The norm bounded robust Kalman filter, with recursive form by solving two Riccati equations, guarantees a estimation variance bound for all the admissible uncertainties, and can evolve into the conventional Kalman filter if no uncertainties are considered. This paper will analyze the suitable case for the robust Kalman filter in GPS/INS system, the filter characteristics including parameter setting, parameter meaning, and filter convergence condition are discussed simutaneously. The robust filter performance will be compared with conventional Kalman filter through simulation results.