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2016•Advances in computer science researchOpen access

Research and Application of Moving Tracking of Stewart Based on Multi-innovation EKF Algorithm

Sujian Sheng, Bo Yang, Pinle Qin, Xiaoqing Chen

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Abstract

Because of the low estimation accuracy of normal extended Kalman Filter in strong nonlinear system, an improved extended Kalman Filter (MI-EKF) is presented to solve the problem, and the filtering accuracy is greatly improved.In this paper, multi-innovation theory is applied to EKF, and the multi-innovation EKF (MI-EKF) is proposed.MI-EKF has better precision and stability, because MI-EKF considers not only the current measured value, but also give full consideration to the time before state of motion.Finally, the improvement algorithm is used the moving tracking of six degree freedom stewart motion platform, the simulation results show that the improved MI-EKF algorithm is superior to the standard EKFalgorithm.

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What this paper is about

Because of the low estimation accuracy of normal extended Kalman Filter in strong nonlinear system, an improved extended Kalman Filter (MI-EKF) is presented to solve the problem, and the filtering accuracy is greatly improved.In this paper, multi-innovation theory is applied to EKF, and the multi-innovation EKF (MI-EKF) is proposed.MI-EKF has better precision and stability, because MI-EKF considers not only the current measured value, but also give full consideration to the time before state of motion.Finally, the improvement algorithm is used the moving tracking of six degree freedom stewart motion platform, the simulation results show that the improved MI-EKF algorithm is superior to the standard EKFalgorithm.

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Available abstract

Because of the low estimation accuracy of normal extended Kalman Filter in strong nonlinear system, an improved extended Kalman Filter (MI-EKF) is presented to solve the problem, and the filtering accuracy is greatly improved.In this paper, multi-innovation theory is applied to EKF, and the multi-innovation EKF (MI-EKF) is proposed.MI-EKF has better precision and stability, because MI-EKF considers not only the current measured value, but also give full consideration to the time before state of motion.Finally, the improvement algorithm is used the moving tracking of six degree freedom stewart motion platform, the simulation results show that the improved MI-EKF algorithm is superior to the standard EKFalgorithm.

Key concepts: Extended Kalman filter, Kalman filter, Computer science, Nonlinear system, Stability (learning theory), Invariant extended Kalman filter, Algorithm, Tracking (education)

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Research and Application of Moving Tracking of Stewart Based on Multi-innovation EKF Algorithm — Research Paper | ScholarLens