We describe the design of a multiple maneuvering targets tracking algorithm under the framework of Gaussian mixture probability hypothesis density(PHD) filter.First,a variation of the generalized pseudo-Bayesian estimator of first order(VGPB1) is designed to adapt to the Gaussian mixture PHD filter for jump Markov system models(JMS-PHD).The probability of each kinematic model,which is used in the JMS-PHD filter,is updated with VGPB1.The weighted sum of state,associated covariance,and weights for Gaussian components are then calculated.Pruning and merging techniques are also adopted in this algorithm to increase efficiency.Performance of the proposed algorithm is compared with that of the JMS-PHD filter.Monte-Carlo simulation results demonstrate that the optimal subpattern assignment(OSPA) distances of the proposed algorithm are lower than those of the JMS-PHD filter for maneuvering targets tracking.
Shi-cang ZHANGJian-xun LILiang-bin WUChang-hai SHI
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 classifed by the interval similarity relation.Then,as an improvement of the classical rough set,a new kind of generalized information entropy called ‘‘H0-information entropy'' is suggested for the measurement of uncertainty and the classifcation ability of IIS.There is an innovative information flling technique using the properties of H0-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 testifed by an actual application of failure analysis,whose performance is evaluated by the quantifcation of E-condition entropy.
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.
