Improved mixed Gaussian particle filtering method used in inertial integrated navigation system

Abstract

The invention discloses an improved mixed Gaussian particle filtering method used in an inertial integrated navigation system. The method comprises the following steps of: according to the characteristics of inertial integrated navigation, establishing a state equation, an observation equation and a noise model of a strapdown inertia navigation system / global position system (SINS / GPS) integrated navigation system; and according to the characteristics of the model, establishing a mixed Gaussian particle filtering algorithm structure. According to the algorithm structure, the particle filteringis divided into two steps; and Gaussian distribution parameters are acquired in the updating process of the mixed Gaussian particle filtering state by adopting an unscented kalman filtering (UKF) algorithm. The state updating and other part of algorithms are implemented by adopting the particle filtering algorithm; and the flow path for implementing the mixed Gaussian particle filtering is provided. A navigational computer performs data processing and resolving operation of the integrated navigation according to a filtering model and the algorithm flow path of the inertial integrated navigation. The method has the advantages of improving the filtering precision of the integrated navigation system, reducing the filtering time to a certain extent and solving the non-linear filtering problemof the integrated navigation system better.

Description

technical field [0001] The invention is a new improved mixed Gaussian particle filter method used in an inertial integrated navigation system, belongs to the multi-sensor information fusion technology, and is a multi-sensor data fusion method. The patent content is also applicable to inertial integrated navigation, target recognition and tracking, image processing, pattern recognition and other multi-sensor information fusion and multi-source data processing application fields. Background technique [0002] The inertial navigation system has strong autonomous navigation capability, and at the same time, the system dynamic performance is good, the data output update frequency is high, and the navigation accuracy is high in a short period of time. These advantages of inertial navigation make it widely used in military, civilian and other fields. However, due to the shortcomings of inertial navigation parameter errors that accumulate over time, the effect of long-term independ...

AI Technical Summary

Problems solved by technology

The effect of using a single filtering method for the above mixed model will be affected. If linear Kalman filtering or nonlinear EKF or UKF is used, the filtering accuracy will be affected by the strong nonlinear model in the observation equation

If the general particle filter method is used, due to the high dimensionality of the filter, the number of selected particles will be relatively large, and the amount of calculation will be large, which is difficult to meet the real-time requirements of the integrated navigation system.

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