High-precision positioning method and system for carrier phase-based integrated navigation of satellite and inertial device

Abstract

This invention discloses a high-precision positioning system based on carrier phase-based satellite-inertial integrated navigation. The system includes two independently operating first Kalman filters and a second Kalman filter. The first Kalman filter is used for estimating the inertial navigation error state variables, with a total dimension of 15. The second Kalman filter is used for estimating satellite navigation parameters, with a total dimension of 3+n. Compared to traditional carrier phase-based satellite-inertial integrated navigation systems that use a single filter with a dimension of 15+n, this invention controls the filter dimension to within 20, thereby reducing the computational load significantly and improving the real-time performance of the satellite-inertial integrated navigation. Furthermore, the covariance matrices of the inertial navigation system and the satellite navigation system are independent of each other, ensuring no impact on the success rate of fixing the satellite navigation ambiguity parameters. By using carrier phase observations with known ambiguity and performing satellite-inertial integrated navigation with the inertial navigation system, the zero bias of the inertial navigation system can be estimated more accurately, resulting in a high-precision and high-reliability navigation solution.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a high-precision positioning method and system based on carrier phase-based satellite-inertial navigation combined with inertial navigation. Background Technology

[0002] With the rapid development of industries such as autonomous driving, drones, and patrol robots, high-precision navigation in complex environments such as urban canyons and high-rise industrial parks has become a key challenge for integrated navigation systems, requiring improvements in stability and reliability. However, carrier phase-based satellite-inertial integrated navigation is one of the feasible methods currently available to address this issue.

[0003] Please refer to this as well. Figure 1 Traditional carrier-phase satellite-inertial integrated navigation methods simultaneously incorporate the attitude error, velocity error, zero bias, and ambiguity parameters of the inertial navigation system into a single Kalman filter. The state variables of the Kalman filter are:

[0004] in, For attitude error, δV = [δV x δV y δV z [δP] represents the velocity error, δP = [δP] x δP y δP z ] represents the position error, ε = [ε x ε y ε z [This is] zero bias for the gyroscope. For accelerometer zero bias, N is the ambiguity parameter of the satellite navigation system, with one ambiguity parameter corresponding to each satellite, 1×3 represents a 1-row, 3-column vector, and T represents matrix transpose.

[0005] The state equation is:

[0006] in, White noise from a gyroscope. This is white noise for the accelerometer; the subscript m indicates the number of satellites.

[0007]

[0008]

[0009]

[0010]

[0011]

[0012]

[0013]

[0014] Where L represents latitude, ω ie This is the Earth's rotational angular velocity. v is the angular rate of rotation of the navigation coordinate system. n =[v E v N v U ] T R represents the velocity in the navigation coordinate system (northeast-sky velocity). Mh R Nh These represent the radius of the Earth's major and minor axes, respectively.

[0015] The measurement equation is:

[0016]

[0017] Where, ρ I N S This represents the geometric pseudorange derived from the inertial navigation system, where λ is the satellite wavelength. For carrier observations, N is the ambiguity parameter. The transformation matrix is ​​given by e, which represents the transformation from the Earth-centered Earth-fixed (ECEF) coordinate system to the geographic coordinate system (navigation coordinate system). I This is the projection vector of the satellite in the line-of-sight direction.

[0018] The drawback of this scheme is that as the number of satellites involved in the combination increases, the resulting increase in the state dimension of the Kalman filter leads to an exponential increase in computational load. This is highly detrimental to the operation of the satellite-inertial compact integration algorithm on embedded processors with limited computing power. In complex environments, the position accuracy of the inertial navigation system (INS) decreases, while its covariance accuracy remains relatively high. It takes some time for the covariance accuracy of the INS to reflect its position accuracy, resulting in a mismatch between the position accuracy and its covariance accuracy. Furthermore, since only one Kalman filter is used, the covariance matrix of the INS and the covariance matrix of the satellite navigation ambiguity parameters will influence each other, thus degrading the accuracy of the covariance matrix of the satellite navigation ambiguity parameters. This affects the success rate of fixing the satellite navigation ambiguity parameters, leading to a decrease in the reliability of the entire satellite-inertial compact integrated navigation system.

[0019] Therefore, the traditional carrier phase satellite-inertial integrated navigation method mainly has the following problems:

[0020] (1) The traditional carrier phase satellite-inertial compact integration navigation method integrates the position error, attitude error, velocity error, zero bias, and satellite ambiguity parameters of the inertial navigation system into a single Kalman filter. When the number of satellites increases, the dimension of the filter increases, which leads to an exponential increase in the amount of computation, which is very unfavorable for the satellite-inertial compact integration algorithm to run in an embedded processor.

[0021] (2) In complex environments, the mismatch between the position accuracy of the inertial navigation system and its covariance affects the covariance matrix of the satellite navigation ambiguity, which in turn reduces the success rate of fixing the ambiguity parameters of the satellite navigation system, thus failing to obtain a high-precision navigation solution and causing the reliability of the entire satellite-inertial integrated navigation system to deteriorate. Summary of the Invention

[0022] The main objective of this invention is to provide a new high-precision positioning method and system based on carrier phase-based satellite-inertial integrated navigation to solve the aforementioned technical problems.

[0023] To achieve the above objectives, the present invention provides a high-precision positioning system for satellite-inertial integrated navigation based on carrier phase, comprising two independently operating first Kalman filters and second Kalman filters; wherein, the first Kalman filter is used for estimating the position error, velocity error, attitude error, and zero bias of inertial navigation; and the second Kalman filter is used for estimating the position and ambiguity parameters of satellite navigation.

[0024] Furthermore, it includes two independently operating first Kalman filters and second Kalman filters; wherein, the first Kalman filter is used for estimating the position error, velocity error, attitude error, and zero bias of inertial navigation; and the second Kalman filter is used for estimating the position and ambiguity parameters of satellite navigation.

[0025] Furthermore, the state equation of the second Kalman filter based on satellite navigation is: I 3×3 It is a 3D identity matrix, where m represents the number of satellites and m×3 represents a vector with m rows and 3 columns.

[0026] Furthermore, the measurement equation for the second Kalman filter based on satellite navigation is:

[0027]

[0028] Where, ρ INS This represents the geometric pseudorange derived from the inertial navigation system, where λ is the satellite wavelength. For satellite carrier observations, N is the ambiguity parameter, e I This is the projection vector of the satellite in the line-of-sight direction, with superscripts 1, 2, and 3 representing the satellite number, respectively.

[0029] Furthermore, the state variables of the first Kalman filter based on inertial navigation are:

[0030] in, For attitude error, δV = [δV x δV y δV z [δP] represents the velocity error, δP = [δP] x δP y δP z ] represents the position error, ε = [ε x ε y ε z [This is] zero bias for the gyroscope. This is for zero bias of the accelerometer.

[0031] Furthermore, the state equation of the first Kalman filter based on inertial navigation is:

[0032]

[0033] in, This is the transformation matrix from the vehicle coordinate system to the navigation coordinate system. White noise from a gyroscope. For accelerometer white noise,

[0034]

[0035]

[0036] Where L represents latitude, ω ie This is the Earth's rotational angular velocity. v is the angular rate of rotation of the navigation coordinate system. n =[v E v N v U ] T R is the velocity in the navigation coordinate system. Mh R Nh These represent the radius of the Earth's major and minor axes, respectively.

[0037] Furthermore, the measurement equation for the first Kalman filter based on inertial navigation is:

[0038]

[0039] Where ρ INS This represents the geometric pseudorange derived from the inertial navigation system, where λ is the satellite wavelength. For satellite carrier observations, N is the ambiguity parameter (known), H e n is the transformation matrix between the geocentric Earth-Fixed (ECEF) coordinate system and the geographic coordinate system (navigation coordinate system), e I This is the projection vector of the satellite in the line-of-sight direction.

[0040] The present invention also provides a carrier phase-based satellite-inertial integrated navigation high-precision positioning method for a satellite-inertial integrated navigation high-precision positioning system as described in any of the preceding claims, comprising the following steps:

[0041] The second Kalman filter, which estimates the ambiguity parameters using the position for satellite navigation, is used to obtain carrier observations with known ambiguity.

[0042] Error estimation and compensation results are obtained through the first Kalman filter used for estimating position error, velocity error, attitude error, and zero bias in inertial navigation;

[0043] Positioning calculations are performed based on the error estimation and compensation results.

[0044] Furthermore, the second Kalman filter based on satellite navigation sends the calculated carrier observations with known ambiguity to the first Kalman filter based on inertial navigation; the first Kalman filter estimates the position error, velocity error, attitude error, and zero bias based on the carrier observations with known ambiguity.

[0045] In the technical solution of this invention, two threads can be opened in the embedded processor to process the first Kalman filter and the second Kalman filter respectively. Based on practical experience, the dimension of the two Kalman filters can be controlled within 20 dimensions. This reduces a large amount of computation and is more conducive to the operation of the satellite-inertial compact combination algorithm in the embedded processor, improving real-time performance. Since this solution uses two Kalman filters, the covariance matrix of the inertial navigation system and the covariance matrix of the satellite navigation system are independent of each other, so it will not affect the success rate of fixing the satellite navigation ambiguity parameters. Furthermore, in complex environments, corresponding conditions can be set to block the mutual influence between the inertial navigation system and the satellite navigation system (the deterioration of the inertial navigation system's position accuracy affects the fixing of the satellite navigation ambiguity parameters, while avoiding the satellite navigation system's flying points from affecting the inertial navigation system's position accuracy). When the conditions are met, the inertial navigation system and the satellite navigation system can assist each other, eliminate gross errors, detect cycle slips, etc., and finally obtain a high-precision navigation solution. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of a satellite-inertial integrated navigation framework based on carrier phase in the prior art;

[0047] Figure 2This is a schematic diagram of a satellite-inertial integrated navigation framework based on carrier phase in one embodiment of the present invention;

[0048] Figure 3 This is a flowchart illustrating a high-precision positioning method based on carrier phase-inertial integrated navigation according to an embodiment of the present invention.

[0049] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0050] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0051] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0052] Please see Figure 1 To achieve the above objectives, the present invention provides a high-precision positioning system for satellite-inertial navigation based on carrier phase, comprising two independently operating first Kalman filters and second Kalman filters; wherein, the first Kalman filter is used for estimating the position error, velocity error, attitude error, and zero bias of inertial navigation; and the second Kalman filter is used for estimating the position and ambiguity parameters of satellite navigation.

[0053] Furthermore, the state variables of the second Kalman filter based on satellite navigation are: X = [δP δV N] i ] T Where δP is the position error (derived from the inertial navigation system position), δV is the velocity error (derived from the inertial navigation system position), and N... i Let be the ambiguity parameter of the i-th satellite, and each satellite corresponds to one such ambiguity parameter.

[0054] Furthermore, the state equation of the second Kalman filter based on satellite navigation is: I 3×3 It is a 3D identity array, and the subscript m indicates the number of satellites.

[0055] Furthermore, the measurement equation for the second Kalman filter based on satellite navigation is:

[0056]

[0057] Where, ρ INS This represents the geometric pseudorange derived from the inertial navigation system, where λ is the satellite wavelength. For satellite carrier observations, N is the ambiguity parameter, e I This is the projection vector of the satellite in the line-of-sight direction.

[0058] Furthermore, the state variables of the first Kalman filter based on inertial navigation are:

[0059]

[0060] in, For attitude error, δV = [δV x δV y δV z [δP] represents the velocity error, δP = [δP] x δP y δP z ] represents the position error, ε = [ε x ε y ε z [This is] zero bias for the gyroscope. This is for zero bias of the accelerometer.

[0061] Furthermore, the state equation of the first Kalman filter based on inertial navigation is:

[0062]

[0063] in, White noise from a gyroscope. For accelerometer white noise,

[0064]

[0065] Where L represents latitude, ω ie This is the Earth's rotational angular velocity. v is the angular rate of rotation of the navigation coordinate system. n =[v E v N v U ] T R is the velocity in the navigation coordinate system. Mh R Nh These represent the radius of the Earth's major and minor axes, respectively.

[0066] Furthermore, the measurement equation for the first Kalman filter based on inertial navigation is:

[0067]

[0068] Where ρINS This represents the geometric pseudorange derived from the inertial navigation system, where λ is the satellite wavelength. For satellite carrier observations, N is the ambiguity parameter (known). The transformation matrix is ​​given by e, which represents the transformation from the Earth-centered Earth-fixed (ECEF) coordinate system to the geographic coordinate system (navigation coordinate system). I This is the projection vector of the satellite in the line-of-sight direction.

[0069] The present invention also provides a carrier phase-based satellite-inertial integrated navigation high-precision positioning method for a satellite-inertial integrated navigation high-precision positioning system as described in any of the preceding claims, comprising the following steps:

[0070] The second Kalman filter used for estimating the position and ambiguity parameters for satellite navigation is used to obtain carrier observations with known ambiguity.

[0071] Error estimation and compensation results are obtained through the first Kalman filter used for estimating position error, velocity error, attitude error, and zero bias in inertial navigation;

[0072] Positioning calculations are performed based on the error estimation and compensation results.

[0073] Furthermore, the second Kalman filter based on satellite navigation sends the calculated carrier observations with known ambiguity to the first Kalman filter based on inertial navigation; the first Kalman filter estimates the position error, velocity error, attitude error, and zero bias based on the carrier observations with known ambiguity.

[0074] In the technical solution of this invention, two threads can be opened in the embedded processor to process the first Kalman filter and the second Kalman filter respectively. Based on practical experience, the dimension of the two Kalman filters can be controlled within 20 dimensions. This greatly reduces the amount of computation and is more conducive to the operation of the compact combination algorithm in the embedded processor. Since this solution uses two Kalman filters, the covariance matrix of the inertial navigation system (INS) and the covariance matrix of the satellite navigation system (SVR) are independent of each other, so it will not affect the success rate of fixing the ambiguity parameters of the SVR. Furthermore, in complex environments, corresponding conditions can be set to block the mutual influence between the INS and the SVR (the deterioration of the INS position accuracy affects the fixing of the SVR ambiguity parameters, while avoiding the impact of the SVR flying points on the INS position accuracy). When the conditions are met, the INS and SVR can assist each other to eliminate gross errors, detect cycle slips, etc., and finally obtain a high-precision navigation solution.

[0075] In the description of this specification, references to terms such as "one embodiment," "another embodiment," "other embodiments," or "first embodiment to Xth embodiment," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, method steps, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0076] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0077] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A high-precision positioning system based on carrier phase GNSS tight integration navigation, characterized in that, The Kalman filter comprises two independently operating first and second Kalman filters; The first Kalman filter is used for estimating position error, velocity error, attitude error and bias of inertial navigation; The second Kalman filter is used for estimating position and ambiguity parameter of satellite navigation; The second Kalman filter based on satellite navigation sends the calculated carrier observation of known ambiguity to the first Kalman filter based on inertial navigation; The first Kalman filter estimates position error, velocity error, attitude error and bias according to the carrier observation of known ambiguity; Wherein, the state variable of the second Kalman filter based on satellite navigation is: Wherein, δP is the position error, δV is the velocity error, is the ambiguity parameter of the i-th satellite, each satellite corresponds to one ambiguity parameter, and the superscript T is the matrix transpose. The state equation of the second Kalman filter based on satellite navigation is as follows: , is a 3-dimensional unit matrix, m represents the number of satellites, and m x 3 represents a vector with m rows and 3 columns. The measurement equation of the second Kalman filter based on satellite navigation is: wherein, denotes the geometric pseudo-range of the inertial navigation recursion, λ is the satellite wavelength, is the satellite carrier observation, N is the ambiguity parameter, is the projection vector of the satellite in the line-of-sight direction, the superscripts 1, 2,..., m are the satellite numbers, respectively.

2. The carrier phase based integrated GNSS navigation high precision positioning system of claim 1, wherein, The state variables of the first Kalman filter based on inertial navigation are: wherein, is the attitude error, is the velocity error, is the position error, is the gyro bias, is the accelerometer bias.

3. The carrier phase based integrated GNSS navigation high precision positioning system of claim 2, wherein, The state equation of the first Kalman filter based on inertial navigation is: wherein, is a transformation matrix from the body coordinate system to the navigation coordinate system, is a gyro white noise, is an accelerometer white noise, ， ； wherein L represents latitude, is the earth rotation angular velocity, is the navigation coordinate system rotation angular rate, is the velocity in the navigation coordinate system, respectively represent the long semi-axis radius and the short semi-axis radius of the earth.

4. The carrier phase based integrated GNSS navigation high precision positioning system of claim 3, wherein, The measurement equation of the first Kalman filter based on inertial navigation is: wherein denotes the geometric pseudo-range of the INS recursion, λ is the satellite wavelength, is the satellite carrier observation, N is the ambiguity parameter, is the transformation matrix between the geocentric geodetic coordinate system and the geographic coordinate system, is the projection vector of the satellite in the line-of-sight direction.

5. A carrier phase based GNSS tight combination navigation high precision positioning method of a carrier phase based GNSS tight combination navigation high precision positioning system according to any one of claims 1 to 4, characterized in that, The method comprises the steps of: obtaining carrier observation of known ambiguity by the second Kalman filter used for estimating position and ambiguity parameter of satellite navigation; obtaining error estimation and compensation result by the first Kalman filter used for estimating position error, velocity error, attitude error and bias of inertial navigation; and performing positioning calculation according to the error estimation and compensation result.

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