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Predictive Iterated Kalman Filter for INS/GPS Integration and Its Application to SAR Motion Compensation

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2 Author(s)
Jiancheng Fang ; School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing, China ; Xiaolin Gong

This paper deals with the problem of state estimation for the integration of an inertial navigation system (INS) and Global Positioning System (GPS). For a nonlinear system that has the model error and white Gaussian noise, a predictive filter (PF) is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) is proposed and is called predictive iterated Kalman filter (PIKF). The basic idea of the PIKF is to compensate the state estimate by the estimated model error. An INS/GPS integration system is implemented using the PIKF and applied to synthetic aperture radar (SAR) motion compensation. Through flight tests, it is shown that the PIKF has an obvious accuracy advantage over the IEKF and unscented Kalman filter (UKF) in velocity.

Published in:

IEEE Transactions on Instrumentation and Measurement  (Volume:59 ,  Issue: 4 )