Steady filtering method based on robust estimation

Abstract

The invention discloses a steady filtering method based on robust estimation, belonging to the technical field of data processing. The method comprises the following steps of: acquiring measurement data of a motion carrier by using an inertia sensor; carrying out robust estimation on measurement data to acquire a robust estimation value of the measurement data; respectively carrying out INS (Information Network System) solution and DR (Data Recorder) solution on the robust estimation value of the measurement data to acquire INS navigation information and DR navigation information of the motion carrier; carrying out steady Kalman filtering on the INS navigation information and the DR navigation information of the motion carrier to acquire INS / DR navigation fault information of the motion carrier. By introducing the robust estimation into a combined navigation method, the combined navigation system has better robust performance; and the steady Kalman filtering weakens the influence on a filtering result by uncertainty of a system model, thereby the method improves the robustness of the system, and has the wide application prospect in the fields of the information fusion and combined navigation system.

Description

technical field [0001] The invention relates to a method in the technical field of data processing, in particular to a robust filtering method based on robust estimation. Background technique [0002] The inertial navigation system (Inertial Navigation System, INS) is an autonomous navigation system that does not depend on external information and does not radiate energy to the outside. It uses inertial sensors (gyroscopes and accelerometers) to measure the motion For angular motion and linear motion, the position, velocity and attitude angle of the moving carrier can be calculated in real time and accurately according to the differential equations of the carrier motion. Since the inertial navigation system is a time-integrating system, and in practical applications, there are errors in the measurement of inertial sensors, which will lead to errors in the navigation parameters of the inertial navigation system, especially position errors, which accumulate rapidly over time. ...

AI Technical Summary

Problems solved by technology

[0004] However, in the actual application of the INS / DR (inertial navigation system / dead reckoning) integrated navigation system, due to the interference in the working environment of the inertial sensor, there will be outliers with obviously large errors

In addition, in the process of building the INS / DR integrated navigation system model, errors always exist

Linearization of nonlinear systems, variable disturbances, parameter changes in system models, and statistical properties of noise are difficult to obtain accurately, which will bring errors to the system

[0005] After searching the existing literature, it was found that the literature "A new approach to linear filtering and prediction problems (a new linear filtering and prediction method)" (Transactions of the ASME-Journal of Basic Engineering, 82 (Series D): 35-45. ) proposed a standard Kalman filter, which can get good results when there is no interference in the system and the model is accurate, but this technology is not robust to system interference and model uncertainty, and these errors will be It leads to an increase in the estimation error of the Kalman filter and even causes the filter to diverge

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