Satellite navigation tight integration positioning method based on mixed frequency observation fusion

Abstract

The invention discloses a satellite navigation tight integration positioning method based on mixed frequency observation fusion. The method comprises the following steps: constructing a pseudo-range and carrier inter-station single-difference observation equation of a moving station and a base station; the rank deficiency of the model is eliminated by selecting a reference frequency and re-parameterizing, while the integer estimability of ambiguity is maintained. In the multi-epoch filtering process, the time-invariant characteristics of the inter-system deviation and the inter-frequency deviation are fully utilized to apply constraints, so that the model strength is improved. Compared with a traditional RTK loose combination model and an existing RTK tight combination model based on the same frequency or the same frequency number, observation information of all systems, all satellites and all frequency points can be fully fused, and the positioning effect is improved under the good observation environment or the extreme observation environment.

Description

Technical Field

[0001] The present invention belongs to the field of GNSS (Global Navigation Satellite System) positioning and navigation technology, and in particular relates to a satellite navigation tightly combined positioning method based on mixed frequency observation fusion. Background Art

[0002] Due to the modernization of the Global Navigation Satellite System (GNSS), users now have access to a wider range of satellite and frequency resources. High-precision positioning requires the effective fusion of multi-frequency and multi-constellation signals. The number of satellites, the frequencies used, and the selected functional model all affect the model strength. Fully integrating observation data from different satellite systems in different frequency bands is essential to improve positioning accuracy and reliability, especially when satellite observation data is limited.

[0003] The traditional RTK positioning algorithm adopts the intra-system differential model, that is, each system and each frequency band selects its own reference satellite and performs double difference within the system. This model can eliminate common errors such as inter-frequency deviation and inter-system deviation when performing intra-system differential and is widely used. However, in extreme environments, when there is only one available observation value in a certain frequency band in the system, this observation value cannot be used because there is no other observation value to double-difference with it, resulting in a waste of observation values, especially in scenes with limited number of satellites such as urban canyons and road sections blocked by tree shades. This method will cause some observation values ​​to be lost to a certain extent, reduce the strength of the model, and sometimes even cause positioning interruption. At the same time, the current research on RTK tight combination mainly focuses on overlapping frequencies between different systems or using the same frequency number combination, and the scalability of mixed processing of heterogeneous frequency observations is insufficient. Therefore, it is very important to construct a function model that can fully integrate the observation information of each system, each satellite and each frequency point. Summary of the invention

[0004] In order to solve the above problems, the present invention discloses a satellite navigation tight combination positioning method based on mixed frequency observation fusion, and proposes a function model that is compatible with mixed frequency observation fusion processing and can give play to time-varying characteristics such as inter-system deviation and inter-frequency deviation. The model eliminates the rank deficiency of the model by selecting the reference frequency and re-parameterization operation. The method can fully integrate the observation information of each system, each satellite and each frequency point, and the positioning effect is improved in good or extreme observation environments.

[0005] To achieve the above object, the technical solution of the present invention is as follows:

[0006] The satellite navigation tightly combined positioning method based on mixed frequency observation fusion includes the following steps:

[0007] Step 1: Based on the mixed-frequency GNSS observation data, construct the pseudorange and carrier-to-carrier single-difference observation equations of the rover and reference stations;

[0008] Step 2: Select the reference frequency band, re-parameterize the pseudo-range and carrier station single-difference observation equations respectively, and achieve the full rank of the equations;

[0009] Step 3: Transform the parameters to be estimated in the filtering process.

[0010] The specific steps are:

[0011] Step 1. Based on the mixed-frequency GNSS observation data, construct the pseudorange and carrier-to-carrier single-difference observation equations of the rover and base stations, as follows:

[0012] The following single-difference observation equation between stations is established:

[0013]

[0014] where Δ(·) is the single difference operator between stations; P and φ represent pseudorange observations and carrier phase observations, respectively; subscripts 1 and 2 represent mobile stations and base stations, respectively; j and k represent the corresponding satellite frequency bands; superscripts G and C represent GPS and BDS satellites, respectively. Represents the GPS satellite index, represents the number of GPS satellites in the j-band; ρ is the geometric distance between the satellite and the receiver; dt represents the receiver clock error; d and l represent the pseudorange hardware delay and carrier hardware delay respectively; λ is the wavelength of the corresponding frequency band; Z is the integer ambiguity; ξ and ε represent the pseudorange and carrier observation noise respectively.

[0015] Step 2. Select the reference frequency band and reparameterize the pseudorange and carrier station single difference observation equations to achieve full rank of the equations, as follows:

[0016] In order to construct a full-rank model, it is necessary to select a solution benchmark to eliminate these two types of correlations. In this process, assuming that the reference frequency is 1, the parameter of the pseudo-range receiver clock error and hardware delay merged at the reference frequency is the "new clock error", which actually constitutes an inter-system deviation model, that is, each system shares the same receiver clock error parameter:

[0017]

[0018] In the formula is the "new clock error", and the subscript L1 is the frequency band corresponding to the satellite. Then the pseudorange observation equation of the reference frequency band and the pseudorange observation equation of the non-reference frequency band are transformed as follows:

[0019]

[0020] The difference in hardware delay separated by the non-reference frequency band at this time That is, the differential inter-system or differential inter-frequency deviation on the pseudo-range observation value, which has time domain stability;

[0021] The selected reference will be further transferred to the carrier observation values ​​of each system and each frequency band, and the carrier observation equation at the reference frequency changes as follows:

[0022]

[0023] The non-reference frequency band carrier observation equation is expressed as follows:

[0024]

[0025] In the formula In order to introduce the pseudo-range clock error reference and carrier solution reference, is the carrier hardware delay between differential systems or differential frequencies, which has time domain stability. For the carrier observation equation, there is still a linear correlation between the integer ambiguity and the carrier hardware delay. Therefore, it is necessary to further select a reference in the carrier observation value to eliminate the rank deficiency problem caused by this correlation. Assuming that the ambiguity of the satellite with index 1 is selected as the reference in each system and each frequency, the carrier observation equation of the non-reference frequency band is further transformed into:

[0026]

[0027] in It constitutes the final solution benchmark parameters on each frequency band. is an estimable carrier differential inter-system bias or differential inter-frequency bias parameter that contains the reference satellite ambiguity; so far, the full rank of the equation is achieved, and the ambiguity has restored its integer characteristics, which is consistent with the double difference ambiguity in form.

[0028] Step 3: Transform the estimated parameters in the filtering process, as follows:

[0029] From the above process of eliminating rank deficiency, we can know that the setting of some benchmark parameters is related to specific frequencies or specific satellites. In complex observation environments, when the observation value of a certain frequency band of the satellite is missing, the form of the estimated parameters also changes accordingly. The basic idea is to make corresponding transformations based on the parameter estimation at the previous moment according to the actual needs at the current moment, so that the parameter form predicted at the previous moment is consistent with the parameter state at the current moment; parameter transformation mainly involves two situations:

[0030] The first is when the reference frequency is the GPS L1 band. When all observations on the reference band disappear, and the GPS system L2 band is currently used as the reference, the predicted parameters need to implement the following epoch transformations:

[0031]

[0032] In the second case, when the reference frequency does not change, and the reference star corresponding to the reference frequency changes from 1 to s, the predicted parameters change as follows:

[0033]

[0034] Where t and t-1 represent two consecutive epochs, and their corresponding variances and covariances are transformed in the same way. Similar transformations are also performed when the double-difference ambiguity corresponds to the observed value changes.

[0035] The beneficial effects of the present invention are:

[0036] The satellite navigation tightly combined positioning method based on mixed frequency observation fusion proposed in the present invention avoids the conventional RTK loose combination being limited by the number of observation values ​​in the system and the current tightly combined RTK only considering overlapping frequencies or the same frequency number combination. The advantages are reflected in two aspects. On the one hand, compared with the conventional method, the present invention utilizes more observation values ​​and improves the distribution of satellites; on the other hand, even if the observation values ​​used by the two are consistent, in the scene with severe observation conditions, the model has the constraint of time-invariant parameters. During multi-epoch filtering, the parameter can be regarded as a known value. At this time, even if it is consistent with the observation values ​​involved in the solution of the conventional method, the present model has higher redundancy and higher model strength. In the scene with limited satellites, it can improve positioning accuracy and positioning continuity. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is an implementation flow chart of the present invention;

[0038] FIG2 is a comparison diagram of the positioning effects in the E, N, and U directions of the conventional RTK method (a) and the present invention (b) when the cut-off elevation angle is 40° in a static scene.

[0039] FIG3 is a comparison diagram of the positioning effects in the E, N, and U directions of the conventional RTK method (a) and the present invention (b) in a dynamic scene. DETAILED DESCRIPTION

[0040] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0041] As shown in the figure, this embodiment discloses a satellite navigation tightly combined positioning method based on mixed frequency observation fusion, and the specific steps are as follows:

[0042] Step 1. Based on the mixed-frequency GNSS observation data, construct the pseudorange and carrier-to-carrier single-difference observation equations of the rover and base stations, as follows:

[0043] The following single-difference observation equation between stations is established:

[0044]

[0045] where Δ(·) is the single difference operator between stations; P and φ represent pseudorange observations and carrier phase observations, respectively; subscripts 1 and 2 represent mobile stations and base stations, respectively; j and k represent the corresponding satellite frequency bands; superscripts G and C represent GPS and BDS satellites, respectively. Represents the GPS satellite index, represents the number of GPS satellites in the j-band; ρ is the geometric distance between the satellite and the receiver; dt represents the receiver clock error; d and l represent the pseudorange hardware delay and carrier hardware delay respectively; λ is the wavelength of the corresponding frequency band; Z is the integer ambiguity; ξ and ε represent the pseudorange and carrier observation noise respectively.

[0046] Step 2. Select the reference frequency band and reparameterize the pseudorange and carrier station single difference observation equations to achieve full rank of the equations, as follows:

[0047] In order to construct a full-rank model, it is necessary to select a solution benchmark to eliminate these two types of correlations. In this process, assuming that the reference frequency is 1, the parameter of the pseudo-range receiver clock error and hardware delay merged at the reference frequency is the "new clock error", which actually constitutes an inter-system deviation model, that is, each system shares the same receiver clock error parameter:

[0048]

[0049] In the formula is the "new clock error", and the subscript L1 is the frequency band corresponding to the satellite. Then the pseudorange observation equation of the reference frequency band and the pseudorange observation equation of the non-reference frequency band are transformed as follows:

[0050]

[0051] The difference in hardware delay separated by the non-reference frequency band at this time That is, the differential inter-system or differential inter-frequency deviation on the pseudo-range observation value, which has time domain stability;

[0052] The selected reference will be further transferred to the carrier observation values ​​of each system and each frequency band, and the carrier observation equation at the reference frequency changes as follows:

[0053]

[0054] The non-reference frequency band carrier observation equation is expressed as follows:

[0055]

[0056] In the formula In order to introduce the pseudo-range clock error reference and carrier solution reference, is the carrier hardware delay between differential systems or differential frequencies, which has time domain stability. For the carrier observation equation, there is still a linear correlation between the integer ambiguity and the carrier hardware delay. Therefore, it is necessary to further select a reference in the carrier observation value to eliminate the rank deficiency problem caused by this correlation. Assuming that the ambiguity of the satellite with index 1 is selected as the reference in each system and each frequency, the carrier observation equation of the non-reference frequency band is further transformed into:

[0057]

[0058] in It constitutes the final solution benchmark parameters on each frequency band. is an estimable carrier differential inter-system bias or differential inter-frequency bias parameter that contains the reference satellite ambiguity; so far, the full rank of the equation is achieved, and the ambiguity has restored its integer characteristics, which is consistent with the double difference ambiguity in form.

[0059] Step 3: Transform the estimated parameters in the filtering process, as follows:

[0060] From the above process of eliminating rank deficiency, we can know that the setting of some benchmark parameters is related to specific frequencies or specific satellites. In complex observation environments, when the observation value of a certain frequency band of the satellite is missing, the form of the estimated parameters also changes accordingly. The basic idea is to make corresponding transformations based on the parameter estimation at the previous moment according to the actual needs at the current moment, so that the parameter form predicted at the previous moment is consistent with the parameter state at the current moment; parameter transformation mainly involves two situations:

[0061] The first is when the reference frequency is the GPS L1 band. When all observations on the reference band disappear, and the GPS system L2 band is currently used as the reference, the predicted parameters need to implement the following epoch transformations:

[0062]

[0063] In the second case, when the reference frequency does not change, and the reference star corresponding to the reference frequency changes from 1 to s, the predicted parameters change as follows:

[0064]

[0065] Where t and t-1 represent two consecutive epochs, and their corresponding variances and covariances are transformed in the same way. Similar transformations are also performed when the double-difference ambiguity corresponds to the observed value changes.

Claims

1. A satellite navigation tightly integrated positioning method based on mixed frequency observation fusion, characterized in that: include: Step 1: Based on the mixed-frequency GNSS observation data, construct the pseudorange and carrier-to-carrier single-difference observation equations of the rover and reference stations; Step 2: Select the reference frequency band, re-parameterize the pseudo-range and carrier station single-difference observation equations respectively, and achieve the full rank of the equations; Step 3: Transform the parameters to be estimated in the filtering process.

2. The satellite navigation tightly integrated positioning method based on mixed frequency observation fusion as claimed in claim 1, characterized in that: According to the mixed frequency GNSS observation data, the pseudorange and carrier station single difference observation equations of the rover and reference stations are constructed in step 1; the details are as follows: The following single-difference observation equation between stations is established: where Δ(·) is the single difference operator between stations; P and φ represent pseudorange observations and carrier phase observations, respectively; subscripts 1 and 2 represent mobile stations and base stations, respectively; j and k represent the corresponding satellite frequency bands; superscripts G and C represent GPS and BDS satellites, respectively. Represents the GPS satellite index, represents the number of GPS satellites in the j-band; ρ is the geometric distance between the satellite and the receiver; dt represents the receiver clock error; d and l represent the pseudorange hardware delay and carrier hardware delay respectively; λ is the wavelength of the corresponding frequency band; Z is the integer ambiguity; ξ and ε represent the pseudorange and carrier observation noise respectively.

3. The satellite navigation tightly integrated positioning method based on mixed frequency observation fusion as claimed in claim 1, characterized in that: In step 2, the reference frequency band is selected, and the single-difference observation equations between pseudorange and carrier stations are re-parameterized to achieve the full rank of the equation; specifically, as follows: In order to construct a full-rank model, it is necessary to select a solution benchmark to eliminate these two types of correlations. In this process, assuming that the reference frequency is 1, the parameter of the pseudo-range receiver clock error and hardware delay merged at the reference frequency is the "new clock error", which actually constitutes an inter-system bias model, that is, each system shares the same receiver clock error parameter: In the formula is the "new clock error", the subscript L1 is the frequency band corresponding to the satellite; then the pseudorange observation equation of the reference frequency band and the pseudorange observation equation of the non-reference frequency band are transformed as follows: The difference in hardware delay separated by the non-reference frequency band at this time That is, the differential inter-system or differential inter-frequency deviation on the pseudo-range observation value, which has time domain stability; The selected reference will be further transferred to the carrier observation values ​​of each system and each frequency band, and the carrier observation equation at the reference frequency changes as follows: The non-reference frequency band carrier observation equation is expressed as follows: In the formula In order to introduce the pseudo-range clock error reference and carrier solution reference, is the carrier hardware delay between differential systems or differential frequencies, which has time domain stability. For the carrier observation equation, there is still a linear correlation between the integer ambiguity and the carrier hardware delay. Therefore, it is necessary to further select a reference in the carrier observation value to eliminate the rank deficiency problem caused by this correlation. Assuming that the ambiguity of the satellite with index 1 is selected as the reference in each system and each frequency, the carrier observation equation of the non-reference frequency band is further transformed into: in It constitutes the final solution benchmark parameters on each frequency band. is an estimable carrier differential inter-system bias or differential inter-frequency bias parameter that contains the reference satellite ambiguity; so far, the full rank of the equation is achieved, and the ambiguity has restored its integer characteristics, which is consistent with the double difference ambiguity in form.

4. The satellite navigation tightly integrated positioning method based on mixed frequency observation fusion as claimed in claim 1, characterized in that: The parameters to be estimated in the filtering process described in step 3 are transformed as follows: From the above process of eliminating rank deficiency, we can know that the setting of some benchmark parameters is related to specific frequencies or specific satellites. In complex observation environments, when the observation value of a certain frequency band of the satellite is missing, the form of the estimated parameters also changes accordingly. The basic idea is to make corresponding transformations based on the parameter estimation at the previous moment according to the actual needs at the current moment, so that the parameter form predicted at the previous moment is consistent with the parameter state at the current moment; parameter transformation mainly involves two situations: The first is when the reference frequency is the GPS L1 band. When all observations on the reference band disappear, and the GPS system L2 band is currently used as the reference, the predicted parameters need to implement the following epoch transformations: In the second case, when the reference frequency does not change, and the reference star corresponding to the reference frequency changes from 1 to s, the predicted parameters change as follows: Among them, t and t-1 represent two consecutive epochs, and their corresponding variances and covariances are also transformed in the same way; when the double-difference ambiguity corresponds to the change of the observation value, a similar transformation is also performed.

Images