CN112629526B - Tight combination navigation method for Beidou precise single-point positioning and inertial system - Google Patents
Tight combination navigation method for Beidou precise single-point positioning and inertial system Download PDFInfo
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- 238000005259 measurement Methods 0.000 claims abstract description 17
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- 239000013598 vector Substances 0.000 claims description 8
- 230000001133 acceleration Effects 0.000 claims description 6
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- 239000005433 ionosphere Substances 0.000 claims description 3
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
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Abstract
The invention relates to a tightly combined navigation method of a Beidou precise single-point positioning and inertial system, and belongs to the technical field of combined navigation positioning. When Kalman filtering is adopted to solve a state equation and an observation equation, only the state quantity and the covariance matrix related to the INS are updated when only time is updated, parameters related to the GNSS are not updated, and the ambiguity parameters and the ambiguity covariance of the last epoch are introduced for updating when measurement is updated. According to the method, the time updating frequency is faster than the measurement updating frequency, and only the state quantity and the covariance matrix related to the INS are updated when the time updating is controlled, so that the problems of poor calculation efficiency and poor stability caused by overlarge orders of the state transition matrix when a plurality of satellites are avoided, and the calculation efficiency and the instantaneity are improved.
Description
Technical Field
The invention relates to a tightly combined navigation method of a Beidou precise single-point positioning and inertial system, and belongs to the technical field of combined navigation positioning.
Background
And at the end of 7 months in 2020, the Beidou No. three global satellite navigation system formally opens services, and marks the globalization of satellite navigation opening in China. The Beidou satellite navigation system provides all-weather, all-day and high-precision positioning navigation and time service for Chinese and global users, and is a national important space-time infrastructure. At present, the Beidou system comprehensively serves various industries such as transportation, public safety, disaster relief and reduction, agriculture, forestry, animal husbandry, urban management and the like, and is integrated with national core infrastructure construction such as electric power, finance, communication and the like. However, the "vulnerability" of satellite signals results in limited application in complex environments. The signal is easy to be blocked, multipath is complex, and the signal is easy to be disturbed and deceptively produced in an electromagnetic environment, so that the signal is interrupted, and the continuous positioning capability is lost. The inertial navigation system outputs the angular velocity and acceleration of the motion carrier by means of the gyroscope and the accelerometer, and outputs the position, velocity and attitude information of the motion carrier in an integral mode, so that the inertial navigation system has the advantages of independence, no external interference and the like, but because the inertial navigation is a recursive navigation mode, the error can be gradually increased along with time.
The satellite/inertial combination system (GNSS/INS) can realize the complementary advantages of the two, and in order to obtain high-precision position information, the common combination means are DGNSS/INS combination and PPP/INS combination. The PPP/INS combination can obtain a high-precision navigation result by using only one user receiver, and has a wider application prospect and a wider application prospect. The extended Kalman filtering algorithm is generally used in the satellite/inertial combination system to perform system fusion solution and estimate relevant parameters of the satellite navigation system (GNSS) and the Inertial Navigation System (INS). Because the satellite ambiguity parameters need to be estimated, the order of a correlation matrix is increased during Kalman filtering, the complexity of a model is increased, the data processing efficiency is reduced, and the real-time calculation is not facilitated, and the data calculation by post-processing software is inconvenient.
Disclosure of Invention
The invention aims to provide a tightly combined navigation method of a Beidou precise single-point positioning and inertial system, which aims to solve the problems of complex data processing and low instantaneity in the existing tightly combined navigation process.
The invention provides a tightly combined navigation method of a Beidou precise single-point positioning and inertial system, which aims to solve the technical problems, and comprises the following steps:
1) Acquiring satellite observation data, ephemeris data, clock error data, correction file data and inertial data;
2) Establishing a state equation and an observation equation of a tight combination according to the acquired data;
3) And solving the state equation and the observation equation by adopting Kalman filtering, wherein in the solving process, only the state quantity and covariance related to the INS are updated when the time is updated, and the ambiguity parameter and the ambiguity covariance of the last epoch are introduced when the measurement is updated.
When Kalman filtering is adopted to solve a state equation and an observation equation, only the state quantity and the covariance matrix related to the INS are updated when only time is updated, parameters related to the GNSS are not updated, and the ambiguity parameters and the ambiguity covariance of the last epoch are introduced for updating when measurement is updated. According to the method, the time updating frequency is faster than the measurement updating frequency, and only the state quantity and the covariance matrix related to the INS are updated when the time updating is controlled, so that the problems of poor calculation efficiency and poor stability caused by overlarge orders of the state transition matrix when a plurality of satellites are avoided, and the calculation efficiency and the instantaneity are improved.
Further, the state equation is:
wherein the method comprises the steps ofA directional cosine matrix from the carrier b system to the geocentric earth system; />Representing accelerometer specific force output; />Representing a gyro angular rate output; δψ represents the attitude misalignment angle error; />Representing the rotation angle rate of the earth->A constructed oblique symmetrical array; epsilon a And epsilon g A system noise vector representing accelerometer and gyro noise; δr, δv, δφ, δb a 、δb g 、δT w 、δdt r And delta N IF The carrier position, carrier velocity, carrier attitude, carrier acceleration zero bias, carrier gyro zero bias, tropospheric wet delay, receiver clock bias, and error values of ambiguity parameters, respectively.
Further, the observation equation is:
wherein P is the pseudo-range observed quantity; l is the observed quantity of carrier phase; d is Doppler observed quantity; subscript IF is the ionosphere observation value and subscript INS is the inertial prediction value; e is a 1x3 matrix formed by the directional cosine of the receiver to the satellite; delta represents the error correction; t (T) w Represents tropospheric wet delay; dt (dt) r Representing receiver clock skew; n represents the ambiguity real number solution of the satellite; epsilon represents the observed noise of each observed quantity; δr, δv,δb a 、δb g 、δT w 、δdt r And delta N IF The carrier position, carrier velocity, carrier attitude, carrier acceleration zero bias, carrier gyro zero bias, tropospheric wet delay, receiver clock bias, and error values of ambiguity parameters, respectively.
Further, the time update is determined according to the output frequency of the INS, and the measurement update is determined according to the output frequency of the GNSS.
Drawings
FIG. 1 is a flow chart of a method of tightly integrated navigation of a Beidou precise single point positioning and inertial system of the present invention;
FIG. 2 is a flowchart of a specific implementation of a tightly combined model of the Beidou precise single-point positioning and inertial system of the present invention.
Detailed Description
The following describes the embodiments of the present invention further with reference to the drawings.
In the invention, only the state quantity related to INS and the covariance matrix thereof are updated in the time update of Kalman filtering, and the measurement equation is constructed by adopting PPP technology when the measurement is updated, and the related parameters of GNSS and INS are estimated. The implementation flow of the method is shown in fig. 1, and the specific implementation process is as follows.
1. And acquiring data related to the tightly combined navigation positioning, and carrying out INS mechanical arrangement.
The tightly combined navigation system formed by the GNSS and the INS comprises a Global Navigation Satellite System (GNSS) (the Beidou navigation system BDS can be adopted in the embodiment) and an Inertial Navigation System (INS), so that the acquired data comprise observation data, ephemeris data, clock error data, various correction file data and inertial measurement data of a satellite receiver.
The INS mechanics arrangement refers to a process of converting the speed increment and the angle increment output by inertial navigation into a navigation coordinate system and recursively calculating the initial position, the speed and the attitude to obtain the position, the speed and the attitude at the next moment. Because inertial navigation is essentially a gradual recursive process, it is also often referred to as INS mechanics orchestration.
2. And constructing a state equation and an observation equation, and solving by adopting Kalman filtering.
The integrated navigation system has the essential function of enabling a user to know the position, the speed and the gesture. Thus, the parameters to be solved are position r, velocity v, poseAt the same time, the inertial navigation system contains acceleration zero offset b a Zero offset b of gyro g The navigation accuracy of the inertial navigation part in the integrated navigation system is affected to a great extent, and therefore, the solution is also needed. Representing all the above parameters of interest by vector x, easily available +.>Wherein each parameter is a three-dimensional vector, and thus the state parameter x is a fifteen-dimensional vector.
The GNSS/INS tightly integrated navigation data fusion processing generally adopts an extended Kalman filtering algorithm, and in order to avoid the problem of running of an integrated navigation system caused by unstable observation coarse difference and state model, the extended Kalman filtering algorithm is provided with an robust self-adaptive algorithm, and the robustness of the integrated navigation is enhanced in a common way in engineering. A specific implementation flow of the tightly integrated navigation model is shown in fig. 2.
The satellite observation equation is linearized, and is unfolded near the initial state value, and the state parameter to be estimated becomes an error parameter of the state, namely, an error vector delta x= [ delta r, delta v, delta phi, delta b of the state parameter is used in the operation process a ,δb g ] T 。
To complete the extended Kalman filter solution, the state equation and the observation equation are first constructed, mainly the state equation of the form formula (1) and the observation equation of the form formula (2).
L＝Hδx+e (2)
And then solving according to the standard solving step of the extended Kalman filtering to obtain a state parameter delta x.
For a general integrated navigation system, the state equation is determined by a dynamics model of an inertial navigation system, and can be generally expressed as formula (3).
In the method, in the process of the invention,a directional cosine matrix from the carrier b system to the geocentric earth system; />Representing accelerometer specific force output; />Representing a gyro angular rate output; δψ represents the attitude misalignment angle error; />Representing the rotation angle rate of the earth->A constructed oblique symmetrical array; epsilon a And epsilon g Representing a system noise vector composed of accelerometer and gyro noise.
δx＝[δr,δv,δφ,δb a ,δb g ] T Is the state quantity related to INS, and the covariance isEssentially, a diagonal matrix is formed by the state quantities, and uncertainty of each quantity is represented. In the integrated navigation model, it is necessary to pass all the way to the next time. In the time update, only the covariance of the INS-related state quantity is updated, while the covariance of the GNSS-related state quantity remains unchanged, and is updated again at the time of measurement update.
(3) The formula is constructed according to an error equation of the INS, can be classified as a state quantity related to the INS, and can be abbreviated as:
in the PPP/INS tight combination, the receiver clock error, tropospheric wet delay and ambiguity parameters are also estimated, so the state equation needs to be extended, and the specific form can be expressed as:
after expanding the state quantity of the GNSS part, obtaining a shorthand form of a tightly combined state equation:
based on the above analysis, the observation equation for the PPP/INS tight combination is shown as follows:
also can be abbreviated as:
wherein P is the pseudo-range observed quantity; l is the observed quantity of carrier phase; d is Doppler observed quantity; subscript IF is the ionosphere observation value and subscript INS is the inertial prediction value; e is a 1x3 matrix formed by the directional cosine of the receiver to the satellite; delta represents the error correction; t (T) w Represents tropospheric wet delay; dt (dt) r Representing receiver clock skew; n represents the ambiguity real number solution of the satellite; epsilon represents the observed noise of each observed quantity.
The observation equation of PPP/INS tight combination is solved by Kalman filtering, in Kalman filtering, the time update is calculated according to the output frequency of INS, the general frequency is 100Hz, the measurement update is calculated according to the output frequency of GNSS, the general receiver frequency is 1Hz, namely, the measurement update is carried out for 100 times under the general condition. If the update is performed according to equation (6) in the time update, the state transition matrix dimension is very large, and the calculation efficiency of 100 times is relatively low. Therefore, in the time update, only the state quantity and covariance related to the INS are updated, and the ambiguity parameter and the ambiguity covariance of the last epoch are introduced during measurement update, so that the operation efficiency is improved. The specific implementation process is as follows:
assume that the measurement update of the k-1 epoch filter period yields the state parametersAnd its covariance matrix P k-1 。
Wherein:
performing Kalman filtering time updating: calculating state prediction vectors and covariance matrixes of the k-1 to k periods:
in the method, in the process of the invention,representing an INS-related system state transition matrix for periods k-1 through k; q (Q) k-1 A covariance matrix representing system noise, wherein:
and (3) performing time updating according to the above, and substituting the ambiguity state quantity and covariance of the k-1 epoch when the k epoch is measured and updated, so as to expand the state quantity and covariance of the k epoch:
update Kalmam filtered measurements:
wherein:
through the process, the method and the device can avoid the problems of poor calculation efficiency and stability caused by overlarge orders of the state transition matrix when a plurality of satellites are used, and can meet the requirement of instantaneity.
Claims (1)
1. The tightly combined navigation method of the Beidou precise single-point positioning and inertial system is characterized by comprising the following steps of:
1) Acquiring satellite observation data, ephemeris data, clock error data, correction file data and inertial data;
2) Establishing a state equation and an observation equation of a tight combination according to the acquired data; the state equation is as follows:
wherein the method comprises the steps ofA directional cosine matrix from the carrier b system to the geocentric earth system; />Representing accelerometer specific force output; />Representing a gyro angular rate output; δψ represents the attitude misalignment angle error; />Representing the rotation angle rate of the earth->A constructed oblique symmetrical array; epsilon a And epsilon g A system noise vector representing accelerometer and gyro noise; δr, δv, δφ, δb a 、δb g 、δT w 、δdt r And delta N IF The error values of carrier position, carrier speed, carrier posture, carrier acceleration zero bias, carrier gyro zero bias, tropospheric wet delay, receiver clock error and ambiguity parameters are respectively;
the observation equation is as follows:
wherein P is the pseudo-range observed quantity; l is the observed quantity of carrier phase; d is Doppler observed quantity; subscript IF is the ionosphere observation value and subscript INS is the inertial prediction value; e is a 1x3 matrix formed by the directional cosine of the receiver to the satellite; delta representsError correction; t (T) w Represents tropospheric wet delay; dt (dt) r Representing receiver clock skew; n represents the ambiguity real number solution of the satellite; epsilon represents the observed noise of each observed quantity; δr, δv,δb a 、δb g 、δT w 、δdt r And delta N IF The error values of carrier position, carrier speed, carrier posture, carrier acceleration zero bias, carrier gyro zero bias, tropospheric wet delay, receiver clock error and ambiguity parameters are respectively;
3) Solving the state equation and the observation equation by adopting Kalman filtering, wherein in the solving process, only the state quantity and covariance related to the INS are updated when the time is updated, and the ambiguity parameter and the ambiguity covariance of the last epoch are introduced when the measurement is updated;
the time update is determined according to the output frequency of the INS, and the measurement update is determined according to the output frequency of the GNSS.
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