Beidou deformation monitoring real-time processing method based on novel Kalman filtering

Abstract

The invention provides a Beidou deformation monitoring real-time processing method based on novel Kalman filtering. The Beidou deformation monitoring real-time processing method comprises the steps ofobtaining an optimal estimation value of an initial state vector and the like; obtaining a one-step prediction value and a one-step prediction variance of the state vector based on Kalman filtering;acquiring real-time monitoring data; obtaining a standard deviation and a Kalman gain of the time period data; calculating an optimal estimation value of the state vector and a variance matrix of theoptimal estimation value; constructing a sliding window residual vector, dynamically adjusting the size of a sliding window, and updating an observation noise variance matrix in real time; and calculating and outputting displacement. According to the invention, gross error detection is realized through the ratio of the residual error to the standard deviation and is corrected by scaling and measuring the value of the noise variance matrix in real time, and the pollution of the gross error to an observation result is weakened; on the basis of gross error detection, the convergence of a monitoring result is accelerated by updating an observation noise variance matrix in real time and dynamically adjusting the size of a sliding window, the real displacement of a monitoring point is quickly reflected, and the monitoring requirement of deformation can be met.

Description

technical field [0001] The invention relates to the technical field of Beidou deformation monitoring, in particular to a real-time processing method for Beidou deformation monitoring based on a novel Kalman filter. Background technique [0002] BeiDou Navigation Satellite System (BDS) is a global satellite navigation system independently developed by my country. With the gradual establishment of the third-generation system of BeiDou, BeiDou is more widely used in fields such as deformation monitoring and positioning. [0003] When Beidou is used for deformation monitoring, due to the influence of cycle slip, multipath effect and receiver signal noise, the observation data contains gross errors. In addition, the Beidou calculation process is affected by noise and calculation algorithms, making The calculation results contain high-frequency random errors, which reduce the accuracy and stability of the observation results, thus making it difficult for subsequent data analysis. ...

AI Technical Summary

Problems solved by technology

[0006] 1. The moving average algorithm model is simple, the calculation is convenient, and the requirements for data integrity are relatively low, but the moving average algorithm can only weaken gross errors, and the processing effect on gross errors and random errors is related to the sliding window. In addition, for real deformation the response is not accurate

[0007] 2. The polynomial fitting algorithm can handle gross errors and random errors well, but it does not respond well to real deformation, and it can only be used in post-processing algorithms, not suitable for real-time monitoring

[0008] 3. The arma model involves data stationarity testing and the determination of p and q values. The algorithm is relatively complicated, and it is also not applicable to real-time monitoring

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