Real-time tracking and precise estimation method of glonass phase-frequency deviation based on particle filter

Abstract

The invention discloses a method for real-time tracking and precise estimation of GLONASS phase-frequency deviation based on particle filtering, comprising the following steps: Step 1: data preprocessing, importing satellite ephemeris, pseudo-range observation value and phase observation value of current historical cloud ; Step 2: Establish double-difference pseudorange and carrier phase observation equations, and obtain the single epoch method equation or the previous epoch accumulation method equation after linearization; Step 3: Correct the phase deviation in the method equation according to the particle value, and solve the algorithm equation ; Update the weight of the particle filter, calculate the value of the phase-frequency deviation rate and the root mean square of the particle; Step 4: Repeat step 1-3, real-time tracking or precise estimation of the value of the phase-frequency deviation rate; after the filter converges, pass the phase frequency The value of the inter-frequency deviation rate is corrected for the phase-frequency deviation, and the ambiguity of the whole cycle is fixed to realize precise estimation; the invention can efficiently realize the real-time tracking of the inter-frequency deviation and GLONASS precise positioning with no difference in the receiver type.

Description

technical field [0001] The invention relates to the technical field of satellite positioning system and positioning measurement, in particular to a method for real-time tracking and precise estimation of GLONASS phase-frequency deviation based on particle filter. Background technique [0002] GLONASS is a global navigation satellite system established by Russia. At present, the two frequency bands adopt frequency division multiple access technology, and different satellites received by the receiver have different frequencies; because of the different frequencies, there are different phase delays at the receiver, namely The inter-frequency bias (IFB) at the receiver side; receivers of the same brand and type will have similar IFB, and the differential observations between the composed receivers can ignore the IFB; but when different types of receivers form the receiver When the difference between observations, the IFB may not be zero and remain in the observations; the residu...

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Problems solved by technology

[0002] GLONASS is a global navigation satellite system established by Russia. At present, frequency division multiple access technology is used in the two frequency bands. Different satellites received by the receiver have different frequencies; because of the different frequencies, there are different phase delays at the receiver, namely The inter-frequency bias (IFB) at the receiver side; receivers of the same brand and type will have similar IFB, and the differential observations between the composed receivers can ignore the IFB; but when different types of receivers form the receiver When the difference between observations, the IFB may not be zero and remain in the observations; the residual IFB error seriously affects the entire cycle characteristics of the GLONASS phase observations, so that the ambiguity of GLONASS cannot be fixed, and the ambiguity of GLONASS cannot be quickly obtained. Solution; there is a proportional relationship between the GLONASS phase inter-frequency deviation and the frequency number. In the solution of the double-difference observation equation, as long as the inter-frequency deviation rate (IFB rate) of this proportional relationship is known, the double-difference observation equation can be corrected

The traditional processing methods mainly include the post-processing method and the introduction of the hypothetical deviation value method; the former first accurately solves the integer ambiguity and then inverts the error value. ambiguity; the latter will have the opposite effect when the bias value assumed is far from the true value, affecting the ambiguity fixation in the bias estimation.

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