A Kalman filter numerical optimization method based on sins/gps integrated navigation

Abstract

The invention discloses an SINS / GPS integrated navigation based Kalman filter numerical optimization method. According to the method, on the basis of establishing of an SINS / GPS integrated navigation conventional Kalman filter model, a block matrix technology is used, high-order matrix operations involved in filter calculation is deduced in accordance with sub-blocks in the Matlab environment, and a great number of meaningless duplicate operations are prevented; numerical value decoupling with other updating processes is obtained in the sub-process level through a time-consuming parameter segmentation calculation process, and accordingly, a novel lossless parallel processing mechanism is given, and the filter calculation timeliness is improved greatly. By the aid of the method, the defects of complex deduction and the difficulty in project implementation of common filter decomposition optimization algorithms are overcome, traditional lossy accuracy decoupling in parallel filters is prevented, and efficient lossless data filters of the SINS / GPS integrated navigation system is achieved.

Description

technical field [0001] The invention relates to the technical field of data filtering of integrated navigation systems, in particular to a numerical optimization method of Kalman filtering based on SINS / GPS integrated navigation. Background technique [0002] In the data filtering link of the SINS / GPS integrated navigation system, the processing of "colored noise" is often included. At this time, it does not meet the applicable conditions of the classic Kalman filter. In order to still be able to use the optimal estimation technology, state expansion is required. However, with the increase of the state dimension, the amount of calculation will expand rapidly, the numerical stability will decrease, and even the "curse of dimensionality" will appear. For this problem, the current general method is to use square root information filtering SRIF, U-D decomposition filtering, SVD filtering and other decomposition filtering optimization algorithms to reduce the amount of calculatio...

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Problems solved by technology

However, the derivation process of the decomposition algorithm requires the use of complex matrix theory knowledge, which is difficult in engineering applications; and the complexity of the decomposition and filtering algorithm is still O(n 3 ), with the further increase of the state dimension, the real-time performance is also challenged

Although the parallel Kalman filter greatly improves the real-time performance, its decoupling process is implemented based on a forced lag of one filter cycle for measurement updates, which will bring a certain degree of accuracy damage, and may cause numerical instability after multiple iterations.

In addition, as a general optimization algorithm, none of the above filters are specially designed based on the characteristics of the SINS / GPS integrated navigation system, and there are few studies at home and abroad that use the numerical characteristics of the system to optimize the Kalman filtering process in depth

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