Self-adaption mixed filtering method of GPS/SINS (Global Positioning System/Strapdown Inertial Navigation System) super-compact integrated navigation system

Abstract

The invention relates to a self-adaption mixed filtering method of a GPS / SINS (Global Positioning System / Strapdown Inertial Navigation System) super-compact integrated navigation system. The self-adaption mixed filtering method is characterized by comprising the following steps: step 1. establishing a GPS / SINS super-compact integrated navigation system error model; step 2. carrying out data fusion by adopting a Kalman filtering method to obtain an optimal estimation value of a system state and an estimation error equation array P; step 3. carrying out output correction on navigation positioning information output to an SINS by utilizing the obtained optimal estimation value of the system state; step 4. carrying out an observability analyzing method by adopting a characteristic value and a characteristic vector of an error variance array; solving observability information of all the states by using the estimation error equation array P obtained in the step 2; and step 5. carrying out feedback correction on parameters of a GPS and the SINS by taking the observability information of the system state in the step 4 as a feedback factor and taking a product of the feedback factor and the optimal estimation value of the system state in the step 2 as a feedback amount.

Description

technical field [0001] The invention relates to an adaptive mixed filtering method for a GPS / SINS super-tight integrated navigation system. Background technique [0002] The integrated navigation system composed of Global Positioning System (GPS, Global Positioning System) and Strapdown Inertial Navigation System (SINS, Strapdown Inertial Navigation System), has become an important branch of integrated navigation system due to its good complementary advantages. GPS / SINS integrated navigation system can be divided into: loose combination, tight combination and super tight combination according to the combination method. Compared with loose combination and tight combination, ultra-tight combination is a deeper fusion of GPS and SINS information. The result of fusion can not only correct inertial devices, suppress error accumulation, but also correct receiver parameters and improve its ability to track satellite signals. Therefore, the GPS / SINS ultra-compact integrated navigat...

AI Technical Summary

Problems solved by technology

In engineering practice, the output correction is simple to implement, and the filter failure does not affect the SINS work, but it can only improve the accuracy of the output, and cannot correct the internal errors of the SINS. When working for a long time, the SINS errors accumulate, and finally the system model is consistent with the actual The model does not match, which leads to the decrease or divergence of the filtering accuracy; the feedback correction estimates the corrected navigation parameter error, which is kept small and has no model error, which is closer to the real system, but the feedback correction is complicated to implement, and the filter failure or estimation accuracy If it is not high, it will pollute the SINS output and reduce the reliability of the system

Combining the advantages and disadvantages of these two structures, a hybrid correction filter structure appears. This structure can only obtain better filtering results when all state estimation accuracy is high, and the state estimation accuracy is determined by its observability. Therefore, this structure can only improve the estimation accuracy of a fully observable system, and is not applicable to an incompletely observable system such as the GPS / SINS ultra-compact integrated navigation system

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