For improving the estimation accuracy and the convergence speed of the unscented Kalman filter(UKF),a novel adaptive filter method is proposed.The error between the covariance matrices of innovation measurements and t...For improving the estimation accuracy and the convergence speed of the unscented Kalman filter(UKF),a novel adaptive filter method is proposed.The error between the covariance matrices of innovation measurements and their corresponding estimations/predictions is utilized as the cost function.On the basis of the MIT rule,an adaptive algorithm is designed to update the covariance of the process uncertainties online by minimizing the cost function.The updated covariance is fed back into the normal UKF.Such an adaptive mechanism is intended to compensate the lack of a priori knowledge of the process uncertainty distribution and to improve the performance of UKF for the active state and parameter estimations.The asymptotic properties of this adaptive UKF are discussed.Simulations are conducted using an omni-directional mobile robot,and the results are compared with those obtained by normal UKF to demonstrate its effectiveness and advantage over the previous methods.展开更多
We perform the updated constraints on the Hubble constant H_0 by using the model-independent method, Gaussian processes.Utilizing the latest 30 cosmic chronometer measurements, we obtain H_0= 67.38 ± 4.72 km s^(-...We perform the updated constraints on the Hubble constant H_0 by using the model-independent method, Gaussian processes.Utilizing the latest 30 cosmic chronometer measurements, we obtain H_0= 67.38 ± 4.72 km s^(-1)Mpc^(-1), which is consistent with the Planck 2015 and Riess et al. analysis at 1σ confidence level. Different from the results of Busti et al. by only using 19 H(z) measurements, our reconstruction results of H(z) and the derived values of H_0 are insensitive to the concrete choice of covariance functions of Matern family.展开更多
基金Supported by National High Technology Research and Development Program of China(863 Program)Hi-Tech Research and Development Program of China(2003AA421020)
文摘For improving the estimation accuracy and the convergence speed of the unscented Kalman filter(UKF),a novel adaptive filter method is proposed.The error between the covariance matrices of innovation measurements and their corresponding estimations/predictions is utilized as the cost function.On the basis of the MIT rule,an adaptive algorithm is designed to update the covariance of the process uncertainties online by minimizing the cost function.The updated covariance is fed back into the normal UKF.Such an adaptive mechanism is intended to compensate the lack of a priori knowledge of the process uncertainty distribution and to improve the performance of UKF for the active state and parameter estimations.The asymptotic properties of this adaptive UKF are discussed.Simulations are conducted using an omni-directional mobile robot,and the results are compared with those obtained by normal UKF to demonstrate its effectiveness and advantage over the previous methods.
文摘We perform the updated constraints on the Hubble constant H_0 by using the model-independent method, Gaussian processes.Utilizing the latest 30 cosmic chronometer measurements, we obtain H_0= 67.38 ± 4.72 km s^(-1)Mpc^(-1), which is consistent with the Planck 2015 and Riess et al. analysis at 1σ confidence level. Different from the results of Busti et al. by only using 19 H(z) measurements, our reconstruction results of H(z) and the derived values of H_0 are insensitive to the concrete choice of covariance functions of Matern family.
