經(jīng)典kalman濾波ppt-資料下載頁(yè)
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【正文】 Measuring Devices Estimator Measurement Error Sources System State External Controls Observed Measurements Optimal Estimate of System State System Error Sources System Black Box 19 Quick Example – Constant Model Prediction ?k = ?k + K(zk H ?k ) Correction K = Pk(Pk + R)1 ?k = yk1 Pk = Pk1 Pk = (I K)Pk 20 Quick Example – Constant Model 0 10 20 30 40 50 60 70 80 90 100 0 . 7 0 . 6 0 . 5 0 . 4 0 . 3 0 . 2 0 . 1021 Quick Example – Constant Model 0 10 20 30 40 50 60 70 80 90 10000 . 10 . 20 . 30 . 40 . 50 . 60 . 70 . 80 . 91Convergence of Error Covariance Pk 22 0 10 20 30 40 50 60 70 80 90 100 0 . 7 0 . 6 0 . 5 0 . 4 0 . 3 0 . 2 0 . 10Quick Example – Constant Model Larger value of R – the measurement error covariance (indicates poorer quality of measurements) Filter slower to ?believe? measurements – slower convergence 23 References 1. Kalman, R. E. 1960. “A New Approach to Linear Filtering and Prediction Problems”, Transaction of the ASMEJournal of Basic Engineering, pp. 3545 (March 1960). 2. Maybeck, P. S. 1979. “Stochastic Models, Estimation, and Control, Volume 1”, Academic Press, Inc. 3. Welch, G and Bishop, G. 2022. “An introduction to the Kalman Filter”,
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