Iterated cubature Kalman filter and its application | IEEE Conference Publication | IEEE Xplore

Iterated cubature Kalman filter and its application


Abstract:

We present the novel iterated cubature Kalman filter (ICKF) in which the measurement update of square root of cubature Kalman filter (SR-CKF) is refined to iterate proces...Show More

Abstract:

We present the novel iterated cubature Kalman filter (ICKF) in which the measurement update of square root of cubature Kalman filter (SR-CKF) is refined to iterate process for fully exploiting the latest measurement so as to achieve the high accuracy of state estimation. The ICKF is implemented easily and inherits the virtues of SR-CKF. We apply ICKF to state estimation for reentry ballistic target with unknown ballistic coefficient. Simulation results indicate ICKF outperforms over the unscented Kalman filter (UKF) and SR-CKF in state estimation accuracy.
Date of Conference: 20-23 March 2011
Date Added to IEEE Xplore: 05 September 2011
ISBN Information:
Conference Location: Kunming, China
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I. Introduction

In the domain of state estimation for nonlinear system, the extended Kalman filter (EKF) is the commonly used state estimator. The EKF is based on linearizing state transition and observation equations about the estimated state trajectory under the Gaussian assumption [1]. So the EKF may introduce large error and even diverge when the nonlinearities become severe. To improve the performance of the EKF, the iterated Extended Kalman filter (IEKF) proposed involves the use of an iterative measurement update [1]. The IEKF is more accurate on the condition the state estimate is close enough to the true value. The sequence of iterates generated by the IEKF has the property of globally convergence; however, the sequence generated does not go up the likelihood surface [2]. Whether EKF or IEKF, calculation of Jacobians required often introduces numerical instability and limits their applications.

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