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
Inherent flaws in the extended Kalman filter(EKF) algorithm were pointed out and unscented Kalman filter(UKF) was put forward as an alternative.Furthermore,a novel adaptive unscented Kalman filter(AUKF) based on innov...Inherent flaws in the extended Kalman filter(EKF) algorithm were pointed out and unscented Kalman filter(UKF) was put forward as an alternative.Furthermore,a novel adaptive unscented Kalman filter(AUKF) based on innovation was developed.The three data-fusing approaches were analyzed and evaluated in a mathematically rigorous way.Field experiments conducted in lake further demonstrate that AUKF reduces the position error approximately by 65% compared with EKF and by 35% UKF and improves the robust performance.展开更多
In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accele...In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line.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.
基金Projects(2009AA093302,2002AA401003)supported by the National High-Tech Research and Development Program of ChinaProject(YYYJ-0917)supported by the Knowledge Innovation of Chinese Academy of Sciences+1 种基金Projects(61273334,61233013)supported by the National Natural Science Foundation of ChinaProject(2011010025-401)supported by the Natural Science Foundation of Liaoning Province,China
文摘Inherent flaws in the extended Kalman filter(EKF) algorithm were pointed out and unscented Kalman filter(UKF) was put forward as an alternative.Furthermore,a novel adaptive unscented Kalman filter(AUKF) based on innovation was developed.The three data-fusing approaches were analyzed and evaluated in a mathematically rigorous way.Field experiments conducted in lake further demonstrate that AUKF reduces the position error approximately by 65% compared with EKF and by 35% UKF and improves the robust performance.
基金Project(61301181) supported by the National Natural Science Foundation of China
文摘In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line.
