The Kalman filter (KF) is widely used in the field of target tracking. In practical target tracking systems th...
Yongchen Li, Jianxun Li Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240
A novel algorithm, termed a Boosted Adaptive Particle Filter (AAPF), for integrated face detection and face tr...
Jianfang Dou 1 , Jianxun Li 1,2 , Zhi Zhang 2 , Shan Han 2 1. Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 2002402. Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240
For wireless sensor networks, target tracking is an important application areas, but the communication consump...
Zhi Zhang 1 , Jianxun Li 2 , Shan Han 3 , Qiang Zhu 4 Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240
Because of the limitation of linear controllers at the present stage, model reduction of complicated system st...
Shan Han 1 , Jianxun Li 2 ,Zhi Zhang 3 Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240
The stability of quantized innovations Kalman filtering (QIKF) is analyzed. In the analysis, the correlation between quantization errors and measurement noises is considered. By taking the quantization errors as a random perturbation in the observation system, the QIKF for the original system is equivalent to a Kalman-like filtering for the equivalent state-observation system. Thus, the estimate error covariance matrix of QIKF can be more exactly analyzed. The boundedness of the estimate error covariance matrix of QIKF is obtained under some weak conditions. The design of the number of quantized levels is discussed to guarantee the stability of QIKF. To overcome the instability and divergence of QIKF when the number of quantization levels is small, we propose a Kalman filter using scaling quantized innovations. Numerical simulations show the validity of the theorems and algorithms.
Interval-valued data and incomplete data are two key problems for failure analysis of thruster experimental data and have been basically solved by the proposed methods in this paper. Firstly, information data acquired from the simulation and evaluation system formed as intervalvalued information system （IIS） is classified by the interval similarity relation. Then, as an improvement of the classical rough set, a new kind of generalized information entropy called ＂H＇-information entropy＂ is suggested for the measurement of uncertainty and the classification ability of IIS. There is an innovative information filling technique using the properties of H＇-information entropy to replace missing data by some smaller estimation intervals. Finally, an improved method of failure analysis synthesized by the above achievements is presented to classify the thruster experimental data, complete the information, and extract the failure rules. The feasibility and advantage of this method is testified by an actual application of failure analysis, whose performance is evaluated by the quantification of E-condition entropy.
