Double-rate Kalman filtering method based on GNSS/INS deep integrated navigation

Abstract

The invention discloses a double-rate Kalman filtering method based on GNSS / INS deep integrated navigation. The method comprises the following steps of 1, building a state equation according to the initial position, the rate and the attitude information of a carrier, and initializing the parameters of the Kalman filtering; 2, performing state prediction updating on M step lengths and obtaining a predicted value of a prior state quantity which is described in the specification; 3 correcting the prior state quantity which is described in the specification to obtain a predicted value of a posterior state quantity which is described in the specification; 4, adaptively updating the errors of the state quantities and a systematic error covariance matrix, and compensating an inertial navigation result by using the predicted value of the posterior state quantity which is described in the specification to obtain the position, the rate and attitude information of the carrier; and 5, updating thepredicted value of a posterior state quantity which is described in the specification after the compensation. The method can reduce a truncation error caused by the low update frequency of GNSS satellite data or the losing lock of the satellite data during a data fusion algorithm of the GNSS / INS deep integrated navigation, and simultaneously solve a navigation positioning error caused by the non-synchronization of INS data and GNSS data.

Description

technical field [0001] The invention belongs to the field of integrated navigation, and in particular relates to a double-rate Kalman filter method suitable for GNSS / INS deep integrated navigation. Background technique [0002] Due to the complementary advantages and disadvantages of GNSS and INS, GNSS / INS integrated navigation has become a common integrated navigation method with continuous, high bandwidth, and high long-term and short-term accuracy. GNSS suppresses the long-term divergence problem of INS, while INS smoothes the GNSS results and compensates for discontinuities when satellite signals are interrupted. There are many ways to combine GNSS / INS integrated navigation systems, the differences mainly lie in the correction of navigation parameters, the type of GNSS measurement and the way the algorithm assists individual subsystems. Deep combination integrates GNSS / INS and GNSS signal tracking together. Since the auxiliary acquisition provides approximate speed and ...

AI Technical Summary

Problems solved by technology

[0003] The existing integrated navigation algorithm generally assumes that the state variables such as misalignment angle, velocity error, and position error are 0 after each closed-loop Kalman filter compensation, and the system chooses first-order truncation in the discretization process, which will cause truncation errors. will inevitably cause errors

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