CN118112611A - Multi-system fusion cycle slip detection method and device - Google Patents
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Abstract
The application provides a cycle slip detection method and device for multi-system fusion, comprising the following steps: using a first position increment constraint equation determined according to the inertial navigation observation value, a distance increment constraint equation determined according to the wheel speed observation value, a position increment observation equation determined according to the GNSS pseudo-range and the phase observation value and a second position increment constraint equation determined according to the GNSS Doppler observation value to combine, and constructing a combined observation equation set taking the correction of the coordinate increment parameter as a parameter to be estimated; performing robust least square estimation on the combined observation equation set according to the equivalent weight function until the difference value of the estimated value of the coordinate increment parameter correction meets the difference limiting requirement; and determining a cycle slip detection result according to the residual vector of the corresponding GNSS observation value when the limit difference requirement is met and the cycle slip detection criterion. According to the scheme, cycle slip detection is carried out by fusing the multisource observation data, so that the reliability of a detection result can be effectively improved.
Description
Technical Field
The application relates to the technical field of navigation, in particular to a cycle slip detection method and device for multi-system fusion.
Background
The GNSS Real-time dynamic positioning technology (Real-TIME KINEMATIC, RTK) is widely applied to the advanced fields of automatic driving, robot navigation and the like. The high-precision positioning result depends on the continuous and stable pseudo-range and carrier phase observation values output by the GNSS receiver, however, during the carrier movement process, the surrounding environment can cause interference to GNSS signals, and signal interruption is caused under serious conditions, at the moment, the pseudo-range observation values can generate great errors, the phase observation values can jump, and the precision and the usability of RTK positioning are seriously reduced, so that the research on GNSS cycle slip detection and repair is always a hotspot and a difficulty in the GNSS positioning field.
The common cycle slip detection method mainly comprises the following steps: a higher order difference method, a polynomial fitting method, a wavelet analysis method, an ionosphere residual method, a pseudo-range phase combination method and a combined inertial navigation detection method. The simplest method is a high-order difference method, wherein more than three differences are obtained among epochs, and a large cycle slip in an observed value can be detected, but the method requires high sampling rate and equal sampling interval, and cannot be detected for continuous cycle slips. The polynomial fitting method is to fit the observation values of the front epoch and the rear epoch by adopting a moving window, and the effect on small cycle slip detection is not obvious due to the influence of factors such as ionospheric delay errors, tropospheric delay errors and the like. The wavelet analysis method is to utilize multi-resolution analysis of wavelet transformation to carry out multi-scale decomposition on a carrier phase observation sequence and detect GNSS cycle slip by going deep into signal internal observation details, and the method has strict theory but poor practicality and is not suitable for cycle slip detection of dynamic positioning. The ionosphere residual method utilizes a double-frequency non-difference observation value to construct an ionosphere residual model, namely, the difference between carrier phases of two frequencies is made, and cycle slip is detected according to the variation of the ionosphere residual. The pseudo-range phase combination method adopts a double-frequency or multi-frequency observation value to combine the pseudo-range and the phase to detect cycle slip, and is limited by noise of the pseudo-range observation value, so that effective detection of small cycle slip is difficult to realize. The combined inertial navigation detection method is to take the three-dimensional absolute position recursively calculated by an inertial navigation positioning technology (INS) as a position approximate value at the next moment, then back calculate a carrier phase double difference observed value, then compare the carrier phase double difference observed value with the observed phase double difference observed value, and detect cycle slip according to the residual error, wherein the detection precision of the method depends on the precision of inertial navigation initial alignment.
In the real-time positioning of a mobile carrier, a low-cost GNSS positioning module is usually adopted, the signal-to-noise ratio of a pseudo range and a phase observation value is low, the positioning error is large, and the effect of the ionosphere detection method and the pseudo range phase combination detection method is not obvious. Meanwhile, in the dynamic positioning process, the observation environment is complex and changeable, the signal tracking continuity is poor, and accurate detection results are difficult to be given by a high-order difference method, a polynomial fitting method and a wavelet detection method. In addition, the method belongs to a detection method of a single data source, does not consider the huge advantages brought by multi-source sensor fusion, and is not suitable for vehicle-mounted dynamic positioning.
The combined inertial navigation is an effective method for cycle slip detection, but the absolute position of the inertial navigation recursion is adopted in the traditional method and is limited by the accuracy of initial alignment of the inertial navigation, the recursion three-dimensional absolute position of the absolute position has certain deviation, the reliability of cycle slip detection is directly reduced, and meanwhile, the traditional method cannot be compatible with the observation information of other sensors.
Disclosure of Invention
Therefore, the application aims to provide a cycle slip detection method and device for multi-system fusion, which can effectively improve the accuracy and reliability of detection results by carrying out cycle slip detection on multi-source observation data fusion.
The embodiment of the application provides a cycle slip detection method for multi-system fusion, which comprises the following steps:
Acquiring a first position increment constraint equation determined according to an inertial navigation observation value, a distance increment constraint equation determined according to a wheel speed observation value, a position increment observation equation determined according to a GNSS pseudo-range and a phase observation value and a second position increment constraint equation determined according to a GNSS Doppler observation value;
Combining the first position increment constraint equation, the distance increment constraint equation, the position increment observation equation and the second position increment constraint equation to construct a combined observation equation set taking a coordinate increment parameter correction as a parameter to be estimated;
Performing robust least square estimation on the joint observation equation set according to the equivalent weight function, and determining the difference value of the front and back coordinate increment parameter correction estimation values until the difference value of the front and back coordinate increment parameter correction estimation values meets the difference limiting requirement; the equivalent weight function is constructed according to a normalized residual vector of the GNSS observation value;
and determining a cycle slip detection result according to the residual vector of the corresponding GNSS observation value when the limit difference requirement is met and the cycle slip detection criterion.
Optionally, the first position delta constraint equation is determined by:
integrating the obtained inertial navigation observation value in the time interval from the last epoch to the current epoch to determine a first position increment value in a movable carrier coordinate system in the time interval from the front epoch to the rear epoch;
Performing coordinate system conversion processing on the first position increment value to determine a second position increment value under a space rectangular coordinate system;
expressing the second position increment value into a constraint equation form, and determining the first position increment constraint equation containing the front and back epoch coordinate increment parameter correction.
Optionally, the distance increment constraint equation is determined by:
integrating the obtained wheel speed observation value in the time interval from the last epoch to the current epoch to determine the distance increment value in the time interval from the front epoch to the rear epoch;
and expressing the distance increment value into a constraint equation form, and determining the distance increment constraint equation containing the front and back epoch coordinate increment parameter corrections.
Optionally, the second position delta constraint equation is determined by:
Constructing a Doppler velocity measurement equation according to the GNSS Doppler observation value of the current epoch;
expanding the Doppler velocimetry equation into an error equation, and calculating the instantaneous speed of the current epoch and the instantaneous speed of the last epoch;
Combining the instantaneous speed of the current epoch with the instantaneous speed of the last epoch to determine the position increment information in the time interval of the previous epoch and the next epoch;
Expressing the position increment information into a constraint equation form, and determining the second position increment constraint equation containing the front and back epoch coordinate increment parameter correction.
Optionally, the performing robust least square estimation on the joint observation equation set according to the equivalent weight function, determining a difference value of the incremental parameter correction estimation value until the difference value of the two previous and subsequent coordinate incremental parameter correction estimation values meets a difference-limiting requirement, including:
performing robust least square estimation on the joint observation equation set according to the equivalent weight function, and determining the difference value of the estimated value of the coordinate increment parameter correction before and after two times;
If the difference value of the estimated values of the coordinate increment parameter corrections exceeds a preset difference threshold value, continuously adopting an equivalent weight function to carry out anti-difference least square estimation on the combined observation equation set until the difference value of the estimated values of the coordinate increment parameter corrections does not exceed the preset difference threshold value;
If the difference value of the estimated values of the coordinate increment parameter correction does not exceed the preset difference limit threshold value, determining that the difference limit requirement is met, and obtaining the residual vector of the corresponding GNSS observation value.
Optionally, the equivalent weight function is constructed by:
Performing robust least square estimation on the combined observation equation set, and determining a first covariance matrix;
determining a residual vector of the GNSS observation value and a second covariance matrix according to the first covariance matrix and the position increment observation equation;
According to the residual vector of the GNSS observation value and the second covariance matrix, carrying out standardization processing, and calculating the standardized residual vector of the GNSS observation value;
and constructing the equivalent weight function according to the normalized residual vector of the GNSS observation value.
Optionally, the inertial navigation observations, the wheel speed observations, the GNSS pseudo-range and phase observations, and the GNSS doppler observations are time-aligned data.
The embodiment of the application also provides a cycle slip detection device for multi-system fusion, which comprises:
The acquisition module is used for acquiring a first position increment constraint equation determined according to the inertial navigation observation value, a distance increment constraint equation determined according to the wheel speed observation value, a position increment observation equation determined according to the GNSS pseudo-range and the phase observation value and a second position increment constraint equation determined according to the GNSS Doppler observation value;
the joint module is used for combining the first position increment constraint equation, the distance increment constraint equation, the position increment observation equation and the second position increment constraint equation to construct a joint observation equation set taking a coordinate increment parameter correction as a parameter to be estimated;
the estimation module is used for carrying out anti-difference least square estimation on the combined observation equation set according to the equivalent weight function, and determining the difference value of the front and back coordinate increment parameter correction estimation values until the difference value of the front and back coordinate increment parameter correction estimation values meets the difference limiting requirement; the equivalent weight function is constructed according to a normalized residual vector of the GNSS observation value;
and the detection module is used for determining a cycle slip detection result according to the residual vector of the corresponding GNSS observation value when the limit difference requirement is met and the cycle slip detection criterion.
Optionally, the cycle slip detection device is further configured to determine a first position increment constraint equation by:
integrating the obtained inertial navigation observation value in the time interval from the last epoch to the current epoch to determine a first position increment value in a movable carrier coordinate system in the time interval from the front epoch to the rear epoch;
Performing coordinate system conversion processing on the first position increment value to determine a second position increment value under a space rectangular coordinate system;
expressing the second position increment value into a constraint equation form, and determining the first position increment constraint equation containing the front and back epoch coordinate increment parameter correction.
Optionally, the cycle slip detection device is further configured to determine the distance increment constraint equation by:
integrating the obtained wheel speed observation value in the time interval from the last epoch to the current epoch to determine the distance increment value in the time interval from the front epoch to the rear epoch;
and expressing the distance increment value into a constraint equation form, and determining the distance increment constraint equation containing the front and back epoch coordinate increment parameter corrections.
Optionally, the cycle slip detection device is further configured to determine a second position increment constraint equation by:
Constructing a Doppler velocity measurement equation according to the GNSS Doppler observation value of the current epoch;
expanding the Doppler velocimetry equation into an error equation, and calculating the instantaneous speed of the current epoch and the instantaneous speed of the last epoch;
Combining the instantaneous speed of the current epoch with the instantaneous speed of the last epoch to determine the position increment information in the time interval of the previous epoch and the next epoch;
Expressing the position increment information into a constraint equation form, and determining the second position increment constraint equation containing the front and back epoch coordinate increment parameter correction.
Optionally, when the estimation module is configured to perform robust least square estimation on the joint observation equation set according to an equivalent weight function, determine a difference value of the incremental parameter correction estimation value until the difference value of the two coordinate incremental parameter correction estimation values meets a difference limiting requirement, the estimation module is configured to:
performing robust least square estimation on the joint observation equation set according to the equivalent weight function, and determining the difference value of the estimated value of the coordinate increment parameter correction before and after two times;
If the difference value of the estimated values of the coordinate increment parameter corrections exceeds a preset difference threshold value, continuously adopting an equivalent weight function to carry out anti-difference least square estimation on the combined observation equation set until the difference value of the estimated values of the coordinate increment parameter corrections does not exceed the preset difference threshold value;
If the difference value of the estimated values of the coordinate increment parameter correction does not exceed the preset difference limit threshold value, determining that the difference limit requirement is met, and obtaining the residual vector of the corresponding GNSS observation value.
Optionally, the cycle slip detection device is further configured to construct the equivalent weight function by:
Performing robust least square estimation on the combined observation equation set, and determining a first covariance matrix;
determining a residual vector of the GNSS observation value and a second covariance matrix according to the first covariance matrix and the position increment observation equation;
According to the residual vector of the GNSS observation value and the second covariance matrix, carrying out standardization processing, and calculating the standardized residual vector of the GNSS observation value;
and constructing the equivalent weight function according to the normalized residual vector of the GNSS observation value.
Optionally, the inertial navigation observations, the wheel speed observations, the GNSS pseudo-range and phase observations, and the GNSS doppler observations are time-aligned data.
The embodiment of the application also provides electronic equipment, which comprises: the device comprises a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, when the electronic device is running, the processor communicates with the memory through the bus, and the machine-readable instructions are executed by the processor to perform the steps of the cycle slip detection method.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor performs the steps of the cycle slip detection method as described above.
The embodiment of the application provides a cycle slip detection method and device for multi-system fusion, wherein the cycle slip detection method comprises the following steps: acquiring a first position increment constraint equation determined according to an inertial navigation observation value, a distance increment constraint equation determined according to a wheel speed observation value, a position increment observation equation determined according to a GNSS pseudo-range and a phase observation value and a second position increment constraint equation determined according to a GNSS Doppler observation value; combining the first position increment constraint equation, the distance increment constraint equation, the position increment observation equation and the second position increment constraint equation to construct a combined observation equation set taking a coordinate increment parameter correction as a parameter to be estimated; performing robust least square estimation on the joint observation equation set according to the equivalent weight function, and determining the difference value of the front and back coordinate increment parameter correction estimation values until the difference value of the front and back coordinate increment parameter correction estimation values meets the difference limiting requirement; the equivalent weight function is constructed according to a normalized residual vector of the GNSS observation value; and determining a cycle slip detection result according to the residual vector of the corresponding GNSS observation value when the limit difference requirement is met and the cycle slip detection criterion.
Therefore, the scheme combines the characteristics of vehicle-mounted dynamic positioning and combines multisource observation data such as inertial navigation and wheel speed meters, and the like, and provides a cycle slip detection method with multisystem fusion. The innovation of the method is that: the joint observation equation set based on the increment information can effectively contain the observation data of various sensors, and the observation data of various sensors specifically comprises: and (3) performing iterative robust least square estimation by combining a GNSS observation equation, and finally determining a cycle slip detection result through an observation value residual error. Meanwhile, a strict covariance matrix is established for each type of observation data according to the accuracy of each observation value, the influence of single sensor data deviation on the overall detection performance is reduced under the overall estimation of a joint equation set, more effective observation information is used to the maximum extent, and the global GNSS cycle slip detection capability can be ensured to be optimal. The method is suitable for single-frequency, double-frequency and multi-frequency GNSS observation data, the combined detection model can be compatible with other positioning technologies such as visual positioning, UWB positioning and the like, has strong expansibility, can fully exert the huge advantages of multi-source sensor fusion, remarkably improves the detection and identification capability of GNSS cycle slip, and is suitable for dynamic positioning scenes of various mobile carriers.
In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a cycle slip detection method for multi-system fusion according to an embodiment of the present application;
FIG. 2 is a schematic process diagram of a cycle slip detection method for multi-system fusion according to the present application;
FIG. 3 is a schematic diagram of experimental results of cycle slip detection provided by the application;
Fig. 4 is a schematic structural diagram of a cycle slip detection device with multi-system fusion according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. Based on the embodiments of the present application, every other embodiment obtained by a person skilled in the art without making any inventive effort falls within the scope of protection of the present application.
The GNSS Real-time dynamic positioning technology (Real-TIME KINEMATIC, RTK) is widely applied to the advanced fields of automatic driving, robot navigation and the like. The high-precision positioning result depends on the continuous and stable pseudo-range and carrier phase observation values output by the GNSS receiver, however, during the carrier movement process, the surrounding environment can cause interference to GNSS signals, and signal interruption is caused under serious conditions, at the moment, the pseudo-range observation values can generate great errors, the phase observation values can jump, and the precision and the usability of RTK positioning are seriously reduced, so that the research on GNSS cycle slip detection and repair is always a hotspot and a difficulty in the GNSS positioning field.
The common cycle slip detection method mainly comprises the following steps: a higher order difference method, a polynomial fitting method, a wavelet analysis method, an ionosphere residual method, a pseudo-range phase combination method and a combined inertial navigation detection method. The simplest method is a high-order difference method, wherein more than three differences are obtained among epochs, and a large cycle slip in an observed value can be detected, but the method requires high sampling rate and equal sampling interval, and cannot be detected for continuous cycle slips. The polynomial fitting method is to fit the observation values of the front epoch and the rear epoch by adopting a moving window, and the effect on small cycle slip detection is not obvious due to the influence of factors such as ionospheric delay errors, tropospheric delay errors and the like. The wavelet analysis method is to utilize multi-resolution analysis of wavelet transformation to carry out multi-scale decomposition on a carrier phase observation sequence and detect GNSS cycle slip by going deep into signal internal observation details, and the method has strict theory but poor practicality and is not suitable for cycle slip detection of dynamic positioning. The ionosphere residual method utilizes a double-frequency non-difference observation value to construct an ionosphere residual model, namely, the difference between carrier phases of two frequencies is made, and cycle slip is detected according to the variation of the ionosphere residual. The pseudo-range phase combination method adopts a double-frequency or multi-frequency observation value to combine the pseudo-range and the phase to detect cycle slip, and is limited by noise of the pseudo-range observation value, so that effective detection of small cycle slip is difficult to realize. The combined inertial navigation detection method is to take the three-dimensional absolute position recursively calculated by an inertial navigation positioning technology (INS) as a position approximate value at the next moment, then back calculate a carrier phase double difference observed value, then compare the carrier phase double difference observed value with the observed phase double difference observed value, and detect cycle slip according to the residual error, wherein the detection precision of the method depends on the precision of inertial navigation initial alignment.
In the real-time positioning of the movable carrier, a low-cost GNSS positioning module is usually adopted, the signal-to-noise ratio of the pseudo-range and phase observation values is low, the positioning error is large, and the effect of the ionosphere detection method and the pseudo-range phase combination detection method is not obvious. Meanwhile, in the dynamic positioning process, the observation environment is complex and changeable, the signal tracking continuity is poor, and accurate detection results are difficult to be given by a high-order difference method, a polynomial fitting method and a wavelet detection method. In addition, the method belongs to a detection method of a single data source, does not consider the huge advantages brought by multi-source sensor fusion, and is not suitable for vehicle-mounted dynamic positioning.
The combined inertial navigation is an effective method for cycle slip detection, but the absolute position of the inertial navigation recursion is adopted in the traditional method and is limited by the accuracy of initial alignment of the inertial navigation, the recursion three-dimensional absolute position of the absolute position has certain deviation, the reliability of cycle slip detection is directly reduced, and meanwhile, the traditional method cannot be compatible with the observation information of other sensors.
Based on the above, the embodiment of the application provides a cycle slip detection method and device for multi-system fusion, which can effectively improve the accuracy of detection results by fusing multi-source observation data to perform cycle slip detection.
Referring to fig. 1, fig. 1 is a flowchart of a cycle slip detection method for multi-system fusion according to an embodiment of the present application. As shown in fig. 1, a cycle slip detection method provided by an embodiment of the present application includes:
s101, acquiring a first position increment constraint equation determined according to an inertial navigation observation value, a distance increment constraint equation determined according to a wheel speed observation value, a position increment observation equation determined according to a GNSS pseudo-range and a phase observation value and a second position increment constraint equation determined according to a GNSS Doppler observation value.
Here, the inertial navigation observations, wheel speed observations, GNSS pseudo-range and phase observations, and GNSS doppler observations are time aligned, i.e., the cycle slip detection method employs time aligned data.
Inertial navigation can be obtained by an inertial navigation system, wheel speed can be obtained by a wheel speed meter, and GNSS pseudo-range, GNSS phase and GNSS Doppler can be obtained by a receiver. The inertial navigation system, wheel speed meter, and receiver may be disposed on a movable carrier, which may be, for example, an automobile, truck, electric vehicle, etc.
In one embodiment provided by the present application, a first position delta constraint equation is determined by:
s201, integrating the obtained inertial navigation observation values in the time interval from the last epoch to the current epoch to determine a first position increment value in a movable carrier coordinate system in the time interval from the front epoch to the back epoch.
S202, performing coordinate system conversion processing on the first position increment value, and determining a second position increment value under a space rectangular coordinate system.
S203, the second position increment value is expressed into a constraint equation form, and the first position increment constraint equation containing the front epoch coordinate increment parameter correction and the back epoch coordinate increment parameter correction is determined.
For step S201, the first position increment value may be expressed as:
For step S202, the coordinate system conversion process is performed on the first position increment value in the movable carrier coordinate system through the conversion matrix from the movable carrier coordinate system to the geographic coordinate system and the conversion matrix from the geographic coordinate system to the spatial rectangular coordinate system, so as to determine the second position increment value in the spatial rectangular coordinate system.
By way of example, the coordinate system conversion processing formula is as follows:
Wherein b represents a movable carrier coordinate system, e represents a space rectangular coordinate system, Is a transformation matrix of a movable carrier coordinate system to a geographic coordinate system,/>Is a transformation matrix from a geographic coordinate system to a space rectangular coordinate system.
For step S203, the expression of the first position increment constraint equation is as follows:
Here, [ dx, dy, dz ] T is the front and back epoch coordinate increment parameter correction vector, [ Δx 0,Δy0,Δz0]T is the position increment vector of the current epoch receiver initial approximate coordinates minus the upper epoch GNSS positioning coordinates. The initial approximate coordinates are coordinate values assumed in advance.
In one embodiment provided by the present application, the distance increment constraint equation is determined by:
S301, integrating the obtained wheel speed observation value in the time interval from the last epoch to the current epoch to determine the distance increment value in the time interval from the previous epoch to the next epoch.
S302, the distance increment value is expressed into a constraint equation form, and the distance increment constraint equation containing the front epoch coordinate increment parameter correction and the rear epoch coordinate increment parameter correction is determined.
For step S302, the expression of the distance increment constraint equation is as follows:
Here, Δs ODO is a distance increment value between the front and rear epoch determined according to the wheel speed observation value, and Δs 0 is a distance increment value obtained by subtracting the upper epoch GNSS positioning coordinates from the initial approximate coordinates of the present epoch receiver.
In one embodiment provided by the present application, the second position delta constraint equation is determined by:
S401, constructing a Doppler velocity measurement equation according to the GNSS Doppler observation value of the current epoch.
S402, expanding the Doppler velocimetry equation into an error equation, and calculating the instantaneous speed of the current epoch and the instantaneous speed of the last epoch.
S403, combining the instant speed of the current epoch and the instant speed of the last epoch, and determining the position increment information in the time interval of the previous epoch and the next epoch.
S404, expressing the position increment information into a constraint equation form, and determining the second position increment constraint equation containing the front and rear epoch coordinate increment parameter corrections.
For step S401, a doppler velocity equation is constructed according to the GNSS doppler observed value of the current epoch, which specifically includes: and constructing a Doppler velocity measurement equation according to the GNSS Doppler frequency shift observation value of the current epoch. The expression of the Doppler velocimetry equation is as follows:
here, λ is wavelength information of a doppler corresponding carrier phase; r is the receiver identification; s is a satellite identifier; Is a Doppler shift observed value; /(I) For satellite-to-receiver range rate; c is the speed of light; /(I)For the clock speed of the receiver; /(I)Is the clock speed of the satellite; /(I)Is the rate of change of the ionosphere; /(I)Is the rate of change of the troposphere; /(I)Is the residual observed error.
For step S402, the expression for developing the doppler velocimetry equation into an error equation is as follows:
here, V s is the correction of the doppler observation, The vectors respectively representing the partial derivatives of the satellite distance change rate and the velocity component are shown, v x,vy,vz]T is the velocity correction vector of the epoch, and other symbols are the same as the above.
For step S402, the doppler velocimetry equation is developed into an error equation, and the instantaneous velocity of the current epoch is calculated, which specifically includes: and summarizing Doppler error equations of a plurality of satellites, and calculating the speed correction of the current epoch receiver by using least square estimation, so as to obtain the instantaneous speed of the current epoch receiver. It will be appreciated that the receiver is arranged on the movable carrier and is fixed relative to the movable carrier, so that the instantaneous speed of the receiver coincides with the instantaneous speed of the movable carrier.
The principle of determining the instantaneous speed of the last epoch receiver is the same as that of the current epoch receiver, and will not be described in detail herein.
The expression of the instantaneous speed of the current epoch receiver is as follows:
In the method, in the process of the invention, The initial velocity value of the epoch receiver is a preset value.
For step S403, the expression for determining the position increment information in the time interval of the previous and subsequent epochs is as follows, in combination with the instantaneous speed of the current epoch and the instantaneous speed of the previous epoch:
Here, [ Vel' x,Vel′y,Vel′z]T is the instantaneous speed of the last epoch receiver. 0.5 represents an average of the instantaneous speed of the current epoch receiver and the instantaneous speed of the last epoch receiver. Δt is the time interval from the last epoch to the current epoch.
For step S404, the expression of the second position increment constraint equation is as follows:
In one embodiment of the present application, the incremental positional observation equation is: acquiring GNSS pseudo-range and phase observation values of a current epoch and a last epoch, and establishing a position increment observation equation taking a coordinate increment parameter correction of a previous epoch and a later epoch as a parameter to be estimated, wherein the expression of the position increment observation equation is as follows:
Where the superscripts i, j, k each denote a satellite, where i denotes a reference satellite, the other satellites differ from the reference satellite, [ a, b, c ] T is the vector of the satellite-to-receiver distance partial derivative of the coordinate difference parameter, Δp is the satellite epoch-to-epoch pseudorange variation, The phase change value between satellite epochs; lambda is the wavelength of the satellite carrier phase, deltaρ is the change value of the satellite ground distance between the satellites, deltaN is the change value of the ambiguity between the satellites, and other symbols are the same.
S102, combining the first position increment constraint equation, the distance increment constraint equation, the position increment observation equation and the second position increment constraint equation to construct a combined observation equation set taking a coordinate increment parameter correction as a parameter to be estimated.
Here, the first position increment constraint equation, the distance increment constraint equation, the position increment observation equation, and the second position increment constraint equation are combined, that is, the equations (2) (3) (9) (10) are combined.
The matrix expression of the joint observation equation set is as follows:
Here, x= [ dx, dy, dz ] T is a front and rear epoch coordinate increment parameter correction vector, and [ a 1,A2,A3,A4 ] and [ L 1,L2,L3,L4 ] are coefficient matrices and constant matrices of a first position increment constraint equation, a distance increment constraint equation, a second position increment constraint equation, and a position increment observation equation, respectively. [ P INS,PODO,PDOP,PPC ] is the weight matrix corresponding to the four equations respectively.
S103, performing robust least square estimation on the combined observation equation set according to the equivalent weight function, and determining the difference value of the front and back coordinate increment parameter correction estimation values until the difference value of the front and back coordinate increment parameter correction estimation values meets the difference limiting requirement.
Here, the equivalent weight function is constructed from normalized residual vectors of GNSS observations.
In one embodiment provided by the present application, the equivalent weight function is constructed by:
S501, performing robust least square estimation on the combined observation equation set, and determining a first covariance matrix.
S502, determining a residual vector of the GNSS observation value and a second covariance matrix according to the first covariance matrix and the position increment observation equation.
S503, performing standardization processing according to the residual vector of the GNSS observation value and the second covariance matrix, and calculating the standardized residual vector of the GNSS observation value.
S504, constructing the equivalent weight function according to the normalized residual vector of the GNSS observation value.
For step S501, robust least square estimation is performed on the joint observation equation set, and a first covariance matrix and a coordinate increment parameter correction estimation value are determined, where the corresponding expression is as follows:
Here the number of the elements is the number, For the coordinate increment parameter correction estimation value,/>Is the first covariance matrix.
For step S502, according to the first covariance matrix and the position increment observation equation, a calculation formula of determining a residual vector of the GNSS observation value and the second covariance matrix is as follows:
Here, V PC is the residual vector of the GNSS observations, Q PC is the second covariance matrix.
For step S503, the normalization process is performed according to the residual vector of the GNSS observation value and the second covariance matrix, and the normalized residual vector of the GNSS observation value is calculated, where the following formula is adopted:
Here, the superscript s is a satellite identification, Is a normalized residual vector.
It will be appreciated that V PC is a residual vector comprising the residuals of the plurality of satellites and Q PC is a second covariance matrix comprising the second covariance of the plurality of satellites. When calculating the normalized residual vector of the GNSS observation value of one satellite, selecting the residual of the satellite from V PC and the second covariance of the satellite from Q PC, substituting the second covariance into formula (14) to obtain the normalized residual vector of the GNSS observation value corresponding to the satellite. When the normalized residual vector of the GNSS observation value corresponding to another satellite needs to be calculated, the calculation method is the same, and will not be described here again.
For step S504, the expression of the equivalence weight function is as follows:
Here, c 0 and c 1 are set parameters, and 1.5 and 3.0 are preferable as examples.
One of the satellites can be obtained according to formula (15)It will be appreciated that by repeating steps S503 and S504, a plurality of satellites/>/>, Of multiple satellitesP PC can be constructed.
In one embodiment of the present application, the performing robust least square estimation on the joint observation equation set according to the equivalent weight function, determining a difference value of the two front and rear coordinate increment parameter correction estimation values until the difference value of the two front and rear coordinate increment parameter correction estimation values meets a differential limiting requirement, includes:
s1031, performing robust least square estimation on the combined observation equation set according to the equivalent weight function, and determining the difference value of the estimated value of the coordinate increment parameter correction before and after two times.
S1032, if the difference value of the estimated values of the coordinate increment parameter corrections exceeds a preset difference threshold, continuously adopting an equivalent weight function to carry out anti-difference least square estimation on the combined observation equation set until the difference value of the estimated values of the coordinate increment parameter corrections does not exceed the preset difference threshold.
S1033, if the difference value of the two coordinate increment parameter correction estimation values does not exceed the preset difference threshold value, determining that the difference requirement is met, and obtaining the residual vector of the corresponding GNSS observation value.
For step S1031, the coordinate increment parameter correction estimation value is determined by expression (12), thereby determining the difference value of the coordinate increment parameter correction estimation values of the two times before and after.
For step S1032, if the difference between the estimated values of the coordinate increment parameter corrections exceeds the preset difference threshold, the iterative calculation is repeated according to equation (12) -equation (15) until the difference between the estimated values of the coordinate increment parameter corrections meets the difference-limiting requirement. In particular, according to the newly acquiredAnd formulas (13) - (15) complete the iterative computation of P PC, and complete the iterative computation of/>, according to the newly acquired P PC and formula (12)And/>Is performed in a computer system. After iteration, two times/>Is compared with a preset limit difference threshold, if the difference exceeds the preset difference threshold, continuing to perform iterative calculation.
For example, the preset difference threshold may be set to 1cm.
S104, determining a cycle slip detection result according to the residual vector of the GNSS observation value corresponding to the limit difference requirement and the cycle slip detection criterion.
Here, the cycle slip detection criterion is shown in the following formula:
here, round () is a rounding function. If H 0 is true, it is determined that cycle slip occurs in the satellite phase observation, and if H 1 is true, it is determined that cycle slip does not occur in the satellite phase observation.
For example, referring to fig. 2, fig. 2 is a schematic process diagram of a cycle slip detection method with multi-system fusion according to the present application. As shown in fig. 2, 201: acquiring an inter-epoch inertial navigation observation value; 202: acquiring an inter-epoch wheel speed observation value; 203: acquiring a GNSS pseudo-range and phase observation value between epochs; 204: acquiring an inter-epoch GNSS Doppler observation value; 205: determining position increment information according to the inter-epoch inertial navigation observation value; 206: constructing a first position increment constraint equation according to the position increment information determined in the step 205; 207: determining distance increment information according to the inter-epoch wheel speed observation value; 208: constructing a distance increment constraint equation according to the distance increment information determined in the step 207; 209: constructing a position increment observation equation; 210: calculating an instantaneous speed; 211: constructing a second position increment constraint equation; 212: the equations are combined to obtain a combined observation equation set; 213: robust least squares estimation; 214: judging whether the difference value of the estimated value of the coordinate increment parameter correction meets the difference limiting requirement or not; if yes, go to step 215, if no, go to step 216;215: detecting cycle slip according to residual vector of GNSS observation value; 216: an equivalence weight function; 217: and outputting a cycle slip result.
For example, referring to fig. 3, fig. 3 is a schematic diagram of an experimental result of cycle slip detection provided by the present application. Here, a set of measured GNSS/INS/ODO multisource sensor observations is used for verification calculations, the total epoch of the observations is about 1270, and no cycle slip exists in the original GNSS observations. The verification scheme is as follows: starting from the 1 st epoch, selecting 1 satellite every 10 epochs to add 2 weeks of simulated cycle slip, and then counting floating point values of the satellite observation value correction, wherein the upper graph of FIG. 3 is an observation value correction time sequence without inertial navigation, wheel speed and Doppler speed constraint, and is hereinafter referred to as scheme 1; the lower graph of fig. 3 is a time series of observed value corrections incorporating inertial navigation, wheel speed and doppler speed constraints, hereinafter referred to as scheme 2. Analysis showed that the observed value correction sequence always fluctuates up and down around the simulated cycle slip by 2 weeks, the correction sequence of scheme 1 fluctuates more, the RMS is about 2.07 weeks, the correction sequence of scheme 2 fluctuates less, and the RMS is about 2.01 weeks. According to formula (16), the simulated cycle slip detection success rate of scheme 1 is about 73.2%, while the simulated cycle slip detection success rate of scheme 2 is 98.4%, so the result shows that the method has better cycle slip detection capability.
The scheme combines the dynamic positioning characteristics of the movable carrier and combines multisource observation data such as inertial navigation and wheel speed meters, and provides a cycle slip detection method with multisystem fusion. The innovation of the method is that: the joint observation equation set based on the increment information can effectively contain the observation data of various sensors, and the observation data of various sensors specifically comprises: and (3) performing iterative robust least square estimation by combining a GNSS observation equation, and finally determining a cycle slip detection result through an observation value residual error. Meanwhile, a strict covariance matrix is established for each type of observation data according to the accuracy of each observation value, the influence of single sensor data deviation on the overall detection performance is reduced under the overall estimation of a joint equation set, more effective observation information is used to the maximum extent, and the global GNSS cycle slip detection capability can be ensured to be optimal. The method is suitable for single-frequency, double-frequency and multi-frequency GNSS observation data, the combined detection model can be compatible with other positioning technologies such as visual positioning, UWB positioning and the like, has strong expansibility, can fully exert the huge advantages of multi-source sensor fusion, remarkably improves the detection and identification capability of GNSS cycle slip, and is suitable for dynamic positioning scenes of various movable carriers.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a multi-system integrated cycle slip detection device according to an embodiment of the present application. As shown in fig. 4, the cycle slip detection apparatus 400 includes:
An acquisition module 410 configured to acquire a first position increment constraint equation determined according to the inertial navigation observations, a distance increment constraint equation determined according to the wheel speed observations, a position increment observation equation determined according to the GNSS pseudo-ranges and the phase observations, and a second position increment constraint equation determined according to the GNSS doppler observations;
A combination module 420, configured to combine the first position increment constraint equation, the distance increment constraint equation, the position increment observation equation, and the second position increment constraint equation to construct a combined observation equation set with a coordinate increment parameter correction as a parameter to be estimated;
The estimation module 430 is configured to perform robust least square estimation on the joint observation equation set according to an equivalent weight function, determine a difference value of the two previous and subsequent coordinate increment parameter correction estimation values, until the difference value of the two previous and subsequent coordinate increment parameter correction estimation values meets a difference limiting requirement; the equivalent weight function is constructed according to a normalized residual vector of the GNSS observation value;
the detection module 440 is configured to determine a cycle slip detection result according to a cycle slip detection criterion according to a residual vector of the corresponding GNSS observation value when the difference constraint requirement is satisfied.
Optionally, the cycle slip detection device 400 is further configured to determine a first position increment constraint equation by:
integrating the obtained inertial navigation observation value in the time interval from the last epoch to the current epoch to determine a first position increment value in a movable carrier coordinate system in the time interval from the front epoch to the rear epoch;
Performing coordinate system conversion processing on the first position increment value to determine a second position increment value under a space rectangular coordinate system;
expressing the second position increment value into a constraint equation form, and determining the first position increment constraint equation containing the front and back epoch coordinate increment parameter correction.
Optionally, the cycle slip detection device 400 is further configured to determine the distance increment constraint equation by:
integrating the obtained wheel speed observation value in the time interval from the last epoch to the current epoch to determine the distance increment value in the time interval from the front epoch to the rear epoch;
and expressing the distance increment value into a constraint equation form, and determining the distance increment constraint equation containing the front and back epoch coordinate increment parameter corrections.
Optionally, the cycle slip detection device 400 is further configured to determine the second position increment constraint equation by:
Constructing a Doppler velocity measurement equation according to the GNSS Doppler observation value of the current epoch;
expanding the Doppler velocimetry equation into an error equation, and calculating the instantaneous speed of the current epoch and the instantaneous speed of the last epoch;
Combining the instantaneous speed of the current epoch with the instantaneous speed of the last epoch to determine the position increment information in the time interval of the previous epoch and the next epoch;
Expressing the position increment information into a constraint equation form, and determining the second position increment constraint equation containing the front and back epoch coordinate increment parameter correction.
Optionally, when the estimation module 430 is configured to perform robust least square estimation on the joint observation equation set according to an equivalent weight function, determine a difference value of the incremental parameter correction estimation value until the difference value of the two coordinate incremental parameter correction estimation values meets a difference-limiting requirement, the estimation module 430 is configured to:
performing robust least square estimation on the joint observation equation set according to the equivalent weight function, and determining the difference value of the estimated value of the coordinate increment parameter correction before and after two times;
If the difference value of the estimated values of the coordinate increment parameter corrections exceeds a preset difference threshold value, continuously adopting an equivalent weight function to carry out anti-difference least square estimation on the combined observation equation set until the difference value of the estimated values of the coordinate increment parameter corrections does not exceed the preset difference threshold value;
If the difference value of the estimated values of the coordinate increment parameter correction does not exceed the preset difference limit threshold value, determining that the difference limit requirement is met, and obtaining the residual vector of the corresponding GNSS observation value.
Optionally, the cycle slip detection apparatus 400 is further configured to construct the equivalent weight function by:
Performing robust least square estimation on the combined observation equation set, and determining a first covariance matrix;
determining a residual vector of the GNSS observation value and a second covariance matrix according to the first covariance matrix and the position increment observation equation;
According to the residual vector of the GNSS observation value and the second covariance matrix, carrying out standardization processing, and calculating the standardized residual vector of the GNSS observation value;
and constructing the equivalent weight function according to the normalized residual vector of the GNSS observation value.
Optionally, the inertial navigation observations, the wheel speed observations, the GNSS pseudo-range and phase observations, and the GNSS doppler observations are time-aligned data.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the application. As shown in fig. 5, the electronic device 500 includes a processor 510, a memory 520, and a bus 530.
The memory 520 stores machine-readable instructions executable by the processor 510, and when the electronic device 500 is running, the processor 510 communicates with the memory 520 through the bus 530, and when the machine-readable instructions are executed by the processor 510, the steps in the method embodiments shown in fig. 1 to 3 can be executed, and the specific implementation can be referred to the method embodiments, which are not repeated herein.
The embodiment of the present application further provides a computer readable storage medium, where a computer program is stored, where the computer program may execute the steps in the method embodiments shown in the foregoing fig. 1 to 3 when the computer program is executed by a processor, and a specific implementation manner may refer to the method embodiments and is not repeated herein.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above examples are only specific embodiments of the present application, and are not intended to limit the scope of the present application, but it should be understood by those skilled in the art that the present application is not limited thereto, and that the present application is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (10)
1. The cycle slip detection method for multi-system fusion is characterized by comprising the following steps of:
Acquiring a first position increment constraint equation determined according to an inertial navigation observation value, a distance increment constraint equation determined according to a wheel speed observation value, a position increment observation equation determined according to a GNSS pseudo-range and a phase observation value and a second position increment constraint equation determined according to a GNSS Doppler observation value;
Combining the first position increment constraint equation, the distance increment constraint equation, the position increment observation equation and the second position increment constraint equation to construct a combined observation equation set taking a coordinate increment parameter correction as a parameter to be estimated;
Performing robust least square estimation on the joint observation equation set according to the equivalent weight function, and determining the difference value of the front and back coordinate increment parameter correction estimation values until the difference value of the front and back coordinate increment parameter correction estimation values meets the difference limiting requirement; the equivalent weight function is constructed according to a normalized residual vector of the GNSS observation value;
and determining a cycle slip detection result according to the residual vector of the corresponding GNSS observation value when the limit difference requirement is met and the cycle slip detection criterion.
2. The cycle slip detection method of claim 1, wherein the first position delta constraint equation is determined by:
integrating the obtained inertial navigation observation value in the time interval from the last epoch to the current epoch to determine a first position increment value in a movable carrier coordinate system in the time interval from the front epoch to the rear epoch;
Performing coordinate system conversion processing on the first position increment value to determine a second position increment value under a space rectangular coordinate system;
expressing the second position increment value into a constraint equation form, and determining the first position increment constraint equation containing the front and back epoch coordinate increment parameter correction.
3. The cycle slip detection method of claim 1, wherein the distance increment constraint equation is determined by:
integrating the obtained wheel speed observation value in the time interval from the last epoch to the current epoch to determine the distance increment value in the time interval from the front epoch to the rear epoch;
and expressing the distance increment value into a constraint equation form, and determining the distance increment constraint equation containing the front and back epoch coordinate increment parameter corrections.
4. The cycle slip detection method of claim 1, wherein the second position delta constraint equation is determined by:
Constructing a Doppler velocity measurement equation according to the GNSS Doppler observation value of the current epoch;
expanding the Doppler velocimetry equation into an error equation, and calculating the instantaneous speed of the current epoch and the instantaneous speed of the last epoch;
Combining the instantaneous speed of the current epoch with the instantaneous speed of the last epoch to determine the position increment information in the time interval of the previous epoch and the next epoch;
Expressing the position increment information into a constraint equation form, and determining the second position increment constraint equation containing the front and back epoch coordinate increment parameter correction.
5. The cycle slip detection method according to claim 1, wherein the performing robust least square estimation on the joint observation equation set according to the equivalent weight function, determining a difference value of the incremental parameter correction estimation value until the difference value of the two coordinate incremental parameter correction estimation values satisfies a difference limit requirement, includes:
performing robust least square estimation on the joint observation equation set according to the equivalent weight function, and determining the difference value of the estimated value of the coordinate increment parameter correction before and after two times;
If the difference value of the estimated values of the coordinate increment parameter corrections exceeds a preset difference threshold value, continuously adopting an equivalent weight function to carry out anti-difference least square estimation on the combined observation equation set until the difference value of the estimated values of the coordinate increment parameter corrections does not exceed the preset difference threshold value;
If the difference value of the estimated values of the coordinate increment parameter correction does not exceed the preset difference limit threshold value, determining that the difference limit requirement is met, and obtaining the residual vector of the corresponding GNSS observation value.
6. The cycle slip detection method of claim 1, wherein the equivalence weight function is constructed by:
Performing robust least square estimation on the combined observation equation set, and determining a first covariance matrix;
determining a residual vector of the GNSS observation value and a second covariance matrix according to the first covariance matrix and the position increment observation equation;
According to the residual vector of the GNSS observation value and the second covariance matrix, carrying out standardization processing, and calculating the standardized residual vector of the GNSS observation value;
and constructing the equivalent weight function according to the normalized residual vector of the GNSS observation value.
7. The cycle slip detection method of claim 1, wherein the inertial navigation observations, wheel speed observations, GNSS pseudorange and phase observations, and GNSS doppler observations are time aligned data.
8. A cycle slip detection device for multi-system fusion, the cycle slip detection device comprising:
The acquisition module is used for acquiring a first position increment constraint equation determined according to the inertial navigation observation value, a distance increment constraint equation determined according to the wheel speed observation value, a position increment observation equation determined according to the GNSS pseudo-range and the phase observation value and a second position increment constraint equation determined according to the GNSS Doppler observation value;
the joint module is used for combining the first position increment constraint equation, the distance increment constraint equation, the position increment observation equation and the second position increment constraint equation to construct a joint observation equation set taking a coordinate increment parameter correction as a parameter to be estimated;
the estimation module is used for carrying out anti-difference least square estimation on the combined observation equation set according to the equivalent weight function, and determining the difference value of the front and back coordinate increment parameter correction estimation values until the difference value of the front and back coordinate increment parameter correction estimation values meets the difference limiting requirement; the equivalent weight function is constructed according to a normalized residual vector of the GNSS observation value;
and the detection module is used for determining a cycle slip detection result according to the residual vector of the corresponding GNSS observation value when the limit difference requirement is met and the cycle slip detection criterion.
9. An electronic device, comprising: a processor, a memory and a bus, said memory storing machine readable instructions executable by said processor, said processor and said memory communicating via said bus when the electronic device is running, said machine readable instructions when executed by said processor performing the steps of the cycle slip detection method according to any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the cycle slip detection method according to any one of claims 1 to 7.
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