Pulsar timing noise estimation and forecasting method

Abstract

The invention relates to a pulsar timing noise estimation and forecasting method which comprises the steps as follows: timing observation is performed on a pulsar to obtain pulsar timing observation data; pulsar timing observation data are analyzed, self-correlation matrixes of measurement noise and timing noise are obtained; a maximum likelihood estimation equation is solved based on the pulsar timing observation data and the self-correlation matrixes of measurement noise and timing noise, and a timing noise estimation and forecasting value is obtained; and the maximum likelihood estimation equation is as follows:, wherein s represents the timing noise estimation and forecasting value, Cs represents the self-correlation matrix of timing noise, Cs<-1> represents an inverse matrix of the self-correlation matrix of timing noise, n represents the measurement noise, Cn represents the self-correlation matrix of measurement noise, Cn<-1> represents an inverse matrix of the self-correlation matrix of measurement noise, and the equation that o is equal to the sum of s and n represents pulsar timing observation data.

Description

technical field [0001] The invention relates to the research fields of pulsar timing and pulsar navigation, in particular to a method for estimating and forecasting pulsar timing noise. Background technique [0002] In order to accurately measure the pulsar proper motion, the timing noise needs to be removed, but it cannot affect any signal whose period is close to 1yr. Timing noise removal meeting this requirement can be accomplished by the fitwaves algorithm, which fits timing noise by superimposing multiple sinusoidal signals with a maximum period greater than 1.5yr. [0003] Recent studies have shown that the measurement accuracy of pulsar parameters can be improved by obtaining a simple and analytical timing noise spectrum model and calculating the timing noise and white noise autocorrelation matrices based on this. A linear transformation based on the autocorrelation matrix can effectively "whiten" the observed data. If the linear transformation is applied to the obs...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to overcome the defects of timing noise estimation and forecasting methods in the prior art, thereby providing an optimized pulsar timing noise estimation and forecasting method

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