High-precision rapid filtering and smoothing algorithm of GNSS/SINS tight combination

Abstract

The invention relates to a high-precision rapid filtering and smoothing algorithm of GNSS / SINS tight combination. An approximation method including one-step prediction of a state equation is used to optimize the calculating efficiency of Kalman filtering; a GNSS state parameter is rejected and an SINS state parameter is decomposed to improve the calculating efficiency of an RTSS and FBC smoothing algorithm; RTSS is embedded into FBC, a new smoothing algorithm is obtained via fusion, and the resolving precision of the smoothing algorithm is improved; and a filtering result is stored in a low-frequency observation update rate, smoothing is carried out, memory consumption is reduced, and an SINS mechanical layout and high-frequency interpolation method is used to recover a tight combination solution of a random time from a low-frequency smoothing result. Thus, the GNSS / SINS tight combination can generate a high-precision resolution result rapidly.

Description

technical field [0001] The invention belongs to the field of GNSS / SINS combined navigation, and is a high-precision fast algorithm for GNSS / SINS tight combined filtering and smoothing. Background technique [0002] The mode of strapdown inertial navigation (SINS, Strapdown Inertial Navigation System) assisted by GNSS (Global Navigation Satellite System) has become the standard of integrated navigation, which combines the long-term high-precision positioning of GNSS and the movement of SINS In order to provide continuous, high-bandwidth, high long-term and short-term accuracy and complete navigation parameters. For military and civilian purposes, it can provide highly reliable navigation services for global aviation and navigation activities. In basic surveying, mapping and surveying operations, the integrated navigation system is the key to large-scale, efficient and fast acquisition of geographic information data. It maintains the coordinate system in the entire data colle...

AI Technical Summary

Problems solved by technology

[0005] 1) In integrated navigation, the sampling rate of SINS data is much higher than the sampling rate of GNSS data, and the status forecast update of the standard Kalman filter at the SINS sampling rate takes a lot of time;

[0006] 2) The standard tight combination RTSS smoothing algorithm needs to smooth all the states, and the GNSS ambiguity parameters are too many and change frequently with the satellite up and down, which greatly increases the complexity of the algorithm operation;

[0007] 3) In the tight combination, the single RTSS smoothing can significantly improve the accuracy of velocity and attitude, while the single FBC smoothing can significantly improve the position precision. Therefore, a single smoothing algorithm cannot effectively improve the precision of all navigation parameters;

[0008] 4) The standard smoothing algorithm stores the filtering results at the SINS high-frequency sampling rate, and then performs smoothing calculation according to this frequency. For typical 200HZ SINS data, the storage space of the smoothing algorithm can reach GB level, and the calculation time is about 30 minutes. It consumes a lot of hardware resources;

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