The estimation process of nonlinear system is a process of multi-sensor information fusion.During the process of data processing,Kalman filter has high computational complexity.Especially when there are random deviations in the system model,the amount of calculation increases greatly after dimension expansion,which is easy to cause system overflow and operation failure.By embedded the two-stage Cubature Kalman filter into the extended information filtering framework,Two-stage High degree Cubature Information Filter(TSHCIF)is proposed.The algorithm is easy to initialization and small in computation.It takes advantage of the equivalence relation between the inverse of covariance matrix and information matrix to participate in the process of filter recurrence,and reduces the computation of filter gain matrix.In the solution of the covariance matrix,there is a coupling relationship in the covariance matrices of the two-stage algorithm.Therefore,there is a coupling relationship between the two stage information matrix.In the algorithm,the nonlinear T transformation and the inverse of the matrix should be applied to the information matrix.The coupling relationship between the two-stage information matrix and the covariance matrix is obtained.Through the simulation experiment of bearings only tracking system,it is verified that TSHCIF is superior to CKF in accuracy,and the running time is also shorter than CKF,which proves the availability of the algorithm.
Key words
extended information filter framework /
cubature information filter /
five-order spherical-radial cubature rule /
two-stage high degree cubature information filter (TSHCIF)
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Footnotes
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Funding
National Natural Science Foundation of China (No.61503213)
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