GLONASS phase inter-frequency bias real-time tracking and precise estimation method based on particle filtering

Abstract

The invention discloses a GLONASS phase inter-frequency bias real-time tracking and precise estimation method based on particle filtering. The method comprises the following steps that step 1: data preprocessing: the satellite ephemeris and the pseudo range observation value and the phase observation value of the current ephemeris cloud are imported; step 2: a double difference pseudo range and carrier phase observation equation is established, and a single epoch normal equation or a previous epoch accumulation normal equation is obtained after linearization; step 3: the phase bias in the normal equation is corrected according to the particle value to solve the normal equation; and the particle filtering weight is updated, and the value of the phase inter-frequency bias rate and the particle mean square root are calculated; and step 4: the steps 1-3 are repeated to perform real-time tracking or precise estimation of the value of the phase inter-frequency bias rate; and phase inter-frequency bias correction is performed through the value of the phase inter-frequency bias rate after filtering convergence, and the integer ambiguity is fixed so as to realize precise estimation. Real-time tracking of the inter-frequency bias and GLONASS precise positioning without receiver type difference can be efficiently realized.

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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