In the system with unknown random bias, it is common to treat the bias as part of the system state, that is augmented state Kalman filter (ASKF) which will lead to overflow and can not work in computer system. To avoid use the ASKF, two-stage Kalman filter was proposed. It is generally known that the computational complexity of Kalman Filter is highly when all sensor measurements are processed centrally. In order to overcome this disadvantage, High-degree Cubature Information Filter (HCIF) is introduced. Based on HCIF, two-stage High-degree Cubature Information Filter (TSHCIF) is proposed for the nonlinear system with the unknown random bias in order to estimate the high-dimensional state and centralized process the measurement values. The key idea for TSHCIF is using the matrix transformation technique which is called Two-stage transformation in the framework of HCIF and make the covariance matrices block diagonal. The estimate of TSHCIF can be expressed as the output by the bias free filter and bias filter. Simulation results that validate the predicted efficiency improvements and it is shown that the proposed TSHCIF is mathematically equivalent to the augment HCIF (AHCIF).
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