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.