For multisensor systems,when the model parameters and the noise variances are unknown,the consistent fused estimators of the model parameters and noise variances are obtained,based on the system identification algorit...For multisensor systems,when the model parameters and the noise variances are unknown,the consistent fused estimators of the model parameters and noise variances are obtained,based on the system identification algorithm,correlation method and least squares fusion criterion.Substituting these consistent estimators into the optimal weighted measurement fusion Kalman filter,a self-tuning weighted measurement fusion Kalman filter is presented.Using the dynamic error system analysis (DESA) method,the convergence of the self-tuning weighted measurement fusion Kalman filter is proved,i.e.,the self-tuning Kalman filter converges to the corresponding optimal Kalman filter in a realization.Therefore,the self-tuning weighted measurement fusion Kalman filter has asymptotic global optimality.One simulation example for a 4-sensor target tracking system verifies its effectiveness.展开更多
An effective autonomous navigation system for the integration of star sensor,infrared horizon sensor,magnetometer,radar altimeter and ultraviolet sensor is developed.The requirements of the integrated navigation syste...An effective autonomous navigation system for the integration of star sensor,infrared horizon sensor,magnetometer,radar altimeter and ultraviolet sensor is developed.The requirements of the integrated navigation system manager make optimum use of the various navigation sensors and allow rapid fault detection,isolation and recovery.The normal full fusion feedback method of federated unscented Kalman filter(UKF) cannot meet the needs of it.So a no-reset feedback federated Kalman filter architecture is developed and used in the autonomous navigation system.The minimal skew sigma points are chosen to improve the calculation speed.Simulation results are presented to demonstrate the advantages of the algorithm.These advantages include improved failure detection and correction,improved computational efficiency,and reliability.Additionally,its' accuracy is higher than that of the full fusion feedback method.展开更多
Remaining useful life(RUL) prognostics is a fundamental premise to perform conditionbased maintenance(CBM) for a system subject to performance degradation. Over the past decades,research has been conducted in RUL ...Remaining useful life(RUL) prognostics is a fundamental premise to perform conditionbased maintenance(CBM) for a system subject to performance degradation. Over the past decades,research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve,prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL.展开更多
Air quality in many poultry buildings is less than desirable.However,the measurement of concentrations of airborne pollutants in livestock buildings is generally quite difficult.To counter this,the development of an a...Air quality in many poultry buildings is less than desirable.However,the measurement of concentrations of airborne pollutants in livestock buildings is generally quite difficult.To counter this,the development of an autonomous robot that could collect key environmental data continuously in livestock buildings was initiated.This research presents a specific part of the larger study that focused on the preliminary laboratory test for evaluating the navigation precision of the robot being developed under the different ground surface conditions and different localization algorithm according internal sensors.The construction of the robot was such that each wheel of the robot was driven by an independent DC motor with four odometers fixed on each motor.The inertial measurement unit(IMU)was rigidly fixed on the robot vehicle platform.The research focused on using the internal sensors to calculate the robot position(x,y,θ)through three different methods.The first method relied only on odometer dead reckoning(ODR),the second method was the combination of odometer and gyroscope data dead reckoning(OGDR)and the last method was based on Kalman filter data fusion algorithm(KFDF).A series of tests were completed to generate the robot’s trajectory and analyse the localisation accuracy.These tests were conducted on different types of surfaces and path profiles.The results proved that the ODR calculation of the position of the robot is inaccurate due to the cumulative errors and the large deviation of the heading angle estimate.However,improved use of the gyroscope data of the IMU sensor improved the accuracy of the robot heading angle estimate.The KFDF calculation resulted in a better heading angle estimate than the ODR or OGDR calculations.The ground type was also found to be an influencing factor of localisation errors.展开更多
基金supported by the National Natural Science Foundation of China(No.60874063)the Innovation Scientific Research Foundation for Graduate Students of Heilongjiang Province(No.YJSCX2008-018HLJ),and the Automatic Control Key Laboratory of Heilongjiang University
文摘For multisensor systems,when the model parameters and the noise variances are unknown,the consistent fused estimators of the model parameters and noise variances are obtained,based on the system identification algorithm,correlation method and least squares fusion criterion.Substituting these consistent estimators into the optimal weighted measurement fusion Kalman filter,a self-tuning weighted measurement fusion Kalman filter is presented.Using the dynamic error system analysis (DESA) method,the convergence of the self-tuning weighted measurement fusion Kalman filter is proved,i.e.,the self-tuning Kalman filter converges to the corresponding optimal Kalman filter in a realization.Therefore,the self-tuning weighted measurement fusion Kalman filter has asymptotic global optimality.One simulation example for a 4-sensor target tracking system verifies its effectiveness.
基金supported by the Aviation Science Foundation(20070852009)
文摘An effective autonomous navigation system for the integration of star sensor,infrared horizon sensor,magnetometer,radar altimeter and ultraviolet sensor is developed.The requirements of the integrated navigation system manager make optimum use of the various navigation sensors and allow rapid fault detection,isolation and recovery.The normal full fusion feedback method of federated unscented Kalman filter(UKF) cannot meet the needs of it.So a no-reset feedback federated Kalman filter architecture is developed and used in the autonomous navigation system.The minimal skew sigma points are chosen to improve the calculation speed.Simulation results are presented to demonstrate the advantages of the algorithm.These advantages include improved failure detection and correction,improved computational efficiency,and reliability.Additionally,its' accuracy is higher than that of the full fusion feedback method.
基金co-supported by the State Key Program of National Natural Science of China (No. 61232002)the Joint Funds of the National Natural Science Foundation of China (No. 60939003)+3 种基金China Postdoctoral Science Foundation (Nos. 2012M521081, 2013T60537)the Fundamental Research Funds for the Central Universities of China (No. NS2014066)Postdoctoral Science Foundation of Jiangsu Province of China (No. 1301107C)Philosophy and Social Science Research Projects in Colleges and Universities in Jiangsu of China (No. 2014SJD041)
文摘Remaining useful life(RUL) prognostics is a fundamental premise to perform conditionbased maintenance(CBM) for a system subject to performance degradation. Over the past decades,research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve,prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL.
基金the assistance of staff at the University of Southern Queensland and the National Centre of Engineering in Agriculture(NCEA),the funding support of science and technology project of Guangdong Province(2014A020208107)international agriculture aviation pesticide spraying technology joint laboratory project(2015B05050100).
文摘Air quality in many poultry buildings is less than desirable.However,the measurement of concentrations of airborne pollutants in livestock buildings is generally quite difficult.To counter this,the development of an autonomous robot that could collect key environmental data continuously in livestock buildings was initiated.This research presents a specific part of the larger study that focused on the preliminary laboratory test for evaluating the navigation precision of the robot being developed under the different ground surface conditions and different localization algorithm according internal sensors.The construction of the robot was such that each wheel of the robot was driven by an independent DC motor with four odometers fixed on each motor.The inertial measurement unit(IMU)was rigidly fixed on the robot vehicle platform.The research focused on using the internal sensors to calculate the robot position(x,y,θ)through three different methods.The first method relied only on odometer dead reckoning(ODR),the second method was the combination of odometer and gyroscope data dead reckoning(OGDR)and the last method was based on Kalman filter data fusion algorithm(KFDF).A series of tests were completed to generate the robot’s trajectory and analyse the localisation accuracy.These tests were conducted on different types of surfaces and path profiles.The results proved that the ODR calculation of the position of the robot is inaccurate due to the cumulative errors and the large deviation of the heading angle estimate.However,improved use of the gyroscope data of the IMU sensor improved the accuracy of the robot heading angle estimate.The KFDF calculation resulted in a better heading angle estimate than the ODR or OGDR calculations.The ground type was also found to be an influencing factor of localisation errors.