A high-precision integrated navigation positioning method and device

Abstract

The present invention relates to a high-precision combined navigation and positioning method and device. The method comprises: S1. acquiring downlink navigation enhancement information from low-orbit satellites and navigation signals from medium- and high-orbit satellites, and using the observation information from the low-orbit and medium- and high-orbit satellites to initialize real-time navigation and positioning, outputting first positioning information; S2. acquiring observation signals from gyroscopes and accelerometers to perform inertial navigation and positioning, and outputting second positioning information; S3. acquiring measurement signals from the gyroscope assembly and odometer sensor to perform dead reckoning, and outputting third positioning information; and S4. fusing the first, second, and third positioning information, and using the Kalman filter method to solve the combined navigation information. The present invention can achieve continuous, high-precision, stable navigation and positioning services.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation and multi-sensor combined navigation technology, and in particular to a real-time autonomous high-precision combined navigation positioning method and device that integrates low-orbit satellite navigation signals, medium- and high-orbit satellite navigation signals, and measurement information from multiple sensors. Background Art

[0002] Global Navigation Satellite Systems (GNSS), including the U.S. Global Positioning System (GPS), Russia's GLONASS, the European Union's Galileo, China's BeiDou Navigation Satellite System (BDS), Japan's Quasi-Zenith Satellite System (QZSS), and India's Regional Navigation Satellite System (IRNSS), primarily determine the position, velocity, and time (PVT) of a moving object by measuring the distance from the satellite to the receiver using the principle of range resection. GNSS technology is one of the key technologies currently capable of providing high-precision, real-time positioning worldwide, achieving static centimeter-to-millimeter accuracy and dynamic decimeter-level positioning accuracy.

[0003] However, GNSS navigation and positioning performance is severely affected by environmental factors, and it cannot provide continuous and stable PVT services under complex conditions (such as urban canyon tunnels). Therefore, autonomous navigation systems often combine GNSS with multiple sensors, such as inertial devices, to improve the continuity and reliability of navigation and positioning. Compared with GNSS navigation and positioning systems, the unique advantage of inertial navigation systems lies in navigation continuity. Because their sensors are fully integrated with the carrier system, they do not require external signal reception or transmission and are not affected by external signal quality or environmental factors. Commonly used inertial sensors include inertial gyroscopes and accelerometers, which measure the angular and linear motion of the carrier in inertial space. Based on the carrier's kinematic differential equations, unknown parameters such as the moving carrier's position, velocity, and attitude can be accurately calculated in real time. However, because inertial navigation systems calculate changes in the carrier's velocity and position rather than the actual velocity and position, accurate acquisition of the carrier's initial position, velocity, and other state information is required before implementing inertial navigation technology. Furthermore, during the inertial navigation system's integration process, errors in the inertial measurement unit's observations accumulate and grow, making it impossible for the inertial navigation system to independently provide high-precision navigation and positioning services for an extended period of time. Integrating high-precision positioning information from satellite navigation not only facilitates rapid initialization of the inertial navigation system, but also effectively controls and corrects the accumulated errors by providing real-time, high-precision correction parameters.

[0004] Furthermore, the dead reckoning system is also an autonomous navigation system that is unaffected by external interference and provides highly accurate navigation and positioning solutions over time. In this navigation method, heading and range sensors measure displacement vectors, allowing the position of a moving vehicle at the next moment to be calculated given the current vehicle position. A dead reckoning system can be constructed using the gyroscope combination in the inertial navigation system (IAR) and an odometer. The dead reckoning system can be set to the same initial attitude error angle as the inertial navigation system (INS), which helps limit the divergence of INS errors. Combining dead reckoning with satellite navigation systems and inertial navigation systems can further compensate for the shortcomings of satellite navigation in severe line-of-sight conditions and the accumulation of INS errors, thereby ensuring continuous, high-precision, and stable navigation and positioning services. Similarly, the high-precision positioning information from the satellite navigation system facilitates the initialization of the dead reckoning system.

[0005] Using GNSS technology for inertial navigation and dead reckoning system initialization, as well as real-time inertial navigation correction, relies on the real-time navigation and positioning accuracy of GNSS. However, the existing GNSS Precision Point Positioning (PPP) convergence process ranges from 15 to 30 minutes. This long convergence time is not conducive to the real-time, high-precision error calibration of the inertial navigation system. It is also difficult to maintain continuous high-precision positioning in urban environments, where Beidou / GNSS signals are easily obstructed. This significantly hinders the ability of GNSS technology and multi-sensor combinations to maintain continuous, high-precision, real-time navigation and positioning services.

[0006] The PVT performance of GNSS systems, particularly the convergence speed of positioning, velocity measurement, and timing parameters, depends primarily on the spatial geometry of the navigation satellites. Regional augmentation technology and the joint solution of multiple navigation systems are currently commonly used to accelerate PPP convergence. Regional augmentation technology (real-time kinematic positioning RTK, RTX, and precise point positioning PPP-RTK) uses real-time precise orbit and clock error calculations and atmospheric error modeling to enhance information, thereby achieving rapid separation of ambiguity parameters from position parameters. Ambiguity parameters can be fixed within a few epochs, achieving centimeter-level positioning results and corresponding velocity and timing accuracy. Utilizing multiple navigation satellite systems can greatly increase the number of observable satellites, improve the spatial geometry of the satellites, accelerate the convergence of parameter solutions, and thus enhance PVT performance. However, regional augmentation systems are subject to geographical constraints and can generally only provide high-precision PVT services within a certain range. Beyond this range, the augmentation information is no longer available. Although the multi-navigation satellite system can improve its convergence speed, since the existing navigation satellites are all in medium and high orbits, the angle swept by the satellite at the zenith in a short period of time is small, and the satellite's spatial geometric configuration does not change significantly. This method has limited effect on accelerating the convergence of precise point positioning (PPP). When the ambiguity is fixed, its convergence time still takes at least 6 minutes.

[0007] Considering that low-orbit satellites move faster relative to ground stations, which will lead to rapid changes in geometric structure and rapid separation of ambiguity parameters and position parameters, thereby accelerating the PVT convergence speed, the combination of low-orbit navigation satellites and medium- and high-orbit Beidou / GNSS navigation satellites is an effective means to solve the current bottleneck of high-precision PVT services. Summary of the Invention

[0008] In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a high-precision integrated navigation and positioning method and device, which can ensure the real-time and continuous provision of high-precision stable navigation and positioning services.

[0009] To achieve the above-mentioned object, the present invention provides a high-precision integrated navigation and positioning method, comprising:

[0010] S1. Obtain downlink navigation enhancement information from low-orbit satellites and navigation signals from medium- and high-orbit satellites, and use observation information from low-orbit satellites and medium- and high-orbit satellites to initialize real-time navigation positioning and output first positioning information;

[0011] S2 obtains the observation signals of the gyroscope and accelerometer sensors for inertial navigation positioning and outputs the second positioning information;

[0012] S3 obtains the measurement signals of the gyro group and the odometer sensor for dead reckoning and outputs the third positioning information;

[0013] S4. Fuse the first positioning information, the second positioning information, and the third positioning information, and use the Kalman filter method to solve the combined navigation information.

[0014] According to one aspect of the present invention, the medium and high orbit satellite is at least one global navigation satellite system, including but not limited to the Beidou Satellite Navigation System, the Global Positioning System, the GLONASS Satellite Navigation System, the Galileo Satellite Navigation System, the Indian Regional Navigation Satellite System and the Satellite-Based Augmentation System.

[0015] According to one aspect of the present invention, step S1 includes:

[0016] S11. Acquire and recover the navigation augmentation information broadcast by low-orbit satellites and the ephemeris of medium- and high-orbit satellites, and obtain the precise orbits and clock errors of low-orbit satellites, medium- and high-orbit satellites;

[0017] S12. Obtain observation data of low-orbit satellites and medium-orbit satellites in the current epoch, and perform gross error removal and carrier phase cycle slip detection on the observation data;

[0018] S13. Using the recovered precise orbits and clock errors of the low-orbit satellites and the medium- and high-orbit satellites, correct the observation errors of the pseudorange and phase observation data of the low-orbit satellites and the medium- and high-orbit satellites in the current epoch data;

[0019] S14. Using the positioning solution of the previous epoch as the initial value, linearly expand the error-corrected observation values ​​of low-orbit satellites and medium-orbit satellites and establish observation equations. Combine all the linearized observation equations of the current epoch and the positioning solution of the previous epoch, and use filtering or adjustment methods to comprehensively estimate the positioning solution of the current epoch to obtain the first positioning information.

[0020] According to one aspect of the present invention, step S2 includes:

[0021] S21 obtains the measurement data of the gyroscope and accelerometer sensors of the carrier at the current epoch and preprocesses it to obtain inertial measurement information including the acceleration and angular velocity of the carrier;

[0022] S22. Use the inertial measurement information to perform inertial navigation mechanics arrangement to obtain the second positioning information including the attitude, velocity, and position of the carrier.

[0023] According to one aspect of the present invention, step S3 includes:

[0024] S31 obtains the attitude information output by the gyro group and the distance information measured by the odometer sensor, and accordingly measures the displacement vector of the carrier;

[0025] S32. Utilize a dead reckoning algorithm to calculate the position of the carrier according to the displacement vector and output third positioning information.

[0026] According to one aspect of the present invention, step S4 includes:

[0027] S41. Using the first positioning information, second positioning information and third positioning information of low-orbit satellites, medium-orbit satellites and high-orbit satellites, establish a fusion positioning observation equation;

[0028] S42. Use the Kalman filter method to estimate the system state error value based on the fusion positioning observation equation and correct the inertial navigation measurement information;

[0029] S43. Obtain the combined navigation positioning information of the accurate position, velocity and attitude of the current epoch carrier based on the corrected inertial navigation measurement information.

[0030] The present invention also provides a high-precision integrated navigation and positioning device, comprising:

[0031] The low-orbit satellite and medium- and high-orbit satellite navigation and positioning unit is used to obtain downlink navigation enhancement information from low-orbit satellites and navigation signals from medium- and high-orbit satellites, and initialize real-time navigation and positioning using observation information from low-orbit satellites and medium- and high-orbit satellites, and output first positioning information;

[0032] an inertial navigation and positioning unit, configured to obtain observation signals from the gyroscope and accelerometer sensors for inertial navigation and positioning, and output second positioning information;

[0033] a dead reckoning unit, configured to obtain measurement signals from the gyro group and the odometer sensor, perform dead reckoning, and output third positioning information; and

[0034] The multi-source fusion filtering processing unit is used to fuse the first positioning information, the second positioning information and the third positioning information, and use the Kalman filtering method to solve the combined navigation positioning information.

[0035] According to another aspect of the present invention, the low-orbit satellite and medium-orbit satellite navigation and positioning unit includes:

[0036] Satellite navigation receiving module, used to obtain navigation messages, navigation augmentation information and observation data from low-orbit satellites and medium- and high-orbit satellites; and

[0037] The satellite navigation processing module is used to perform real-time single-point positioning based on navigation messages, navigation augmentation information and observation data. Specifically, the navigation messages, navigation augmentation information and observation data are first processed to restore the precise orbit and clock error. Then, the observation data of low-orbit satellites and medium- and high-orbit satellites in the current epoch are subjected to gross error elimination, cycle slip detection and error correction. Combined with the positioning solution of the previous epoch, the observation values ​​are linearly expanded to establish the observation equation. The filtering or adjustment method is used to comprehensively estimate the positioning solution of the current epoch to obtain the first positioning information.

[0038] According to another aspect of the present invention, the navigation enhancement information of the low-orbit satellite includes: low-orbit broadcast ephemeris used to restore the precise orbit and clock error parameters of the low-orbit satellite, and precise ephemeris correction numbers of the medium and high-orbit satellites used to restore the precise orbit and clock error parameters of the medium and high-orbit satellites.

[0039] According to another aspect of the present invention, the inertial navigation and positioning unit includes:

[0040] An inertial navigation measurement module, including gyroscope and accelerometer sensors, is used to perform inertial measurement on the carrier to obtain acceleration and rotational angular velocity of the carrier; and

[0041] The inertial navigation processing module is used to pre-process the inertial navigation measurement data, and then solve and update the state information of the carrier's attitude, speed and position through inertial navigation mechanics arrangement, and output the second positioning information.

[0042] According to another aspect of the present invention, the dead reckoning unit includes:

[0043] The dead reckoning measurement module includes a gyroscope group and an odometer sensor, which is used to measure the displacement vector of the carrier;

[0044] The dead reckoning processing module is used to calculate the position of the carrier according to the displacement vector of the carrier using a dead reckoning algorithm and output third positioning information.

[0045] According to another aspect of the present invention, the multi-source fusion filtering processing unit includes:

[0046] A fusion positioning processing module is used to jointly establish a fusion positioning observation equation using the first positioning information, second positioning information, and third positioning information of low-orbit satellites, medium-orbit satellites, and high-orbit satellites;

[0047] The filtering processing module is used to use the Kalman filtering method to estimate the fusion positioning observation equation to correct the system state error of the inertial measurement and output the integrated navigation positioning information.

[0048] Beneficial effects:

[0049] According to the solution of the present invention, by introducing a low-orbit satellite constellation as a navigation satellite and broadcasting high-precision navigation enhancement information (navigation signals of low-orbit satellites and precise ephemeris correction numbers of medium and high-orbit satellites), the low-orbit satellite and Beidou / GNSS multi-system joint real-time precise single-point positioning is realized from the two aspects of observation enhancement and information enhancement, which greatly accelerates the navigation positioning initialization time and the convergence efficiency of high-precision positioning, and integrates the measurement information of multi-source sensor information including gyroscopes, accelerometers and odometers to correct the multi-source sensor errors in real time, providing fast and high-precision initialization information for inertial navigation and dead reckoning, and making real-time corrections (online error calibration) in autonomous navigation and positioning services, thereby ensuring continuous and stable high-precision navigation and positioning services.

[0050] According to one solution of the present invention, the unique advantages of inertial navigation in terms of navigation continuity are fully utilized, integrating real-time, high-precision, precise single-point positioning information with inertial navigation to rapidly calibrate inertial navigation errors such as bias, scale factor, and cross-axis coupling online. The introduction of low-orbit satellites allows satellite navigation information to quickly initialize the inertial navigation system and even make instantaneous corrections to inertial measurements. Dead reckoning can further limit the divergence of inertial navigation errors. By integrating these three types of navigation and positioning information, continuous, high-precision autonomous navigation is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0052] Figure 1 A flowchart schematically illustrating a high-precision integrated navigation and positioning method according to an embodiment of the present invention;

[0053] Figure 2 A structural diagram schematically shows a high-precision integrated navigation and positioning device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The description of the embodiments in this specification should be combined with the corresponding drawings, which should be considered a complete part of this specification. In the drawings, the shapes and thicknesses of the embodiments may be exaggerated and indicated for simplicity or convenience. Furthermore, the various structural components in the drawings will be described separately. It is worth noting that components not shown in the drawings or not described in words are known to those of ordinary skill in the art.

[0055] The description of the embodiments herein and any references to directions and orientations are for ease of description only and are not to be construed as limiting the scope of the present invention. The following description of the preferred embodiments may involve combinations of features, which may exist independently or in combination. The present invention is not specifically limited to the preferred embodiments. The scope of the present invention is defined by the claims.

[0056] According to the concept of the present invention, the embodiments of the present invention disclose a real-time autonomous high-precision combined navigation and positioning method and device based on multi-source sensors of low-orbit satellites, medium- and high-orbit satellites, inertial navigation, and dead reckoning. By introducing a low-orbit satellite constellation, the initialization time of Beidou / GNSS satellite navigation and positioning and the convergence efficiency of high-precision positioning are greatly accelerated, fast and high-precision initialization information is provided for inertial navigation and dead reckoning, and real-time correction is provided for autonomous navigation and positioning services, thereby realizing continuous and high-precision stable navigation and positioning services.

[0057] Reference Figure 1 The embodiment of the present invention discloses a real-time autonomous high-precision integrated navigation and positioning method based on multi-source sensors of low-orbit satellites, medium-orbit satellites, inertial navigation, and dead reckoning. In the embodiment of the present invention, the medium-orbit satellites are at least one of the global navigation satellite systems, including but not limited to the BeiDou Satellite Navigation System, the Global Positioning System (GPS), the GLONASS Satellite Navigation System (GLONASS), the Galileo Satellite Navigation System (GALILEO), the Indian Regional Navigation Satellite System (IRNSS), and the Satellite-Based Augmentation System (SBAS). The high-precision integrated navigation and positioning method specifically includes the following steps:

[0058] S1. Obtain downlink navigation enhancement information from low-orbit satellites and navigation signals from medium- and high-orbit satellites, and use observation information from low-orbit satellites and medium- and high-orbit satellites to initialize real-time high-precision navigation positioning and output first positioning information;

[0059] S2 obtains the observation signals of the gyroscope and accelerometer sensors for inertial navigation positioning and outputs the second positioning information;

[0060] S3 obtains the measurement signals of the gyro group and the odometer sensor for dead reckoning and outputs the third positioning information;

[0061] S4. Fuse the first positioning information, the second positioning information, and the third positioning information, and use the Kalman filter method to solve the combined navigation information.

[0062] The above step S1 specifically includes the following steps:

[0063] S11. Acquire and recover the navigation augmentation information broadcast by low-orbit satellites and the navigation ephemeris of medium- and high-orbit satellites, and obtain the precise orbits and clock errors (precise ephemeris) of low-orbit satellites and medium- and high-orbit satellites. The specific process is: calculate the current low-orbit satellite position and satellite clock error based on the low-orbit broadcast orbit parameters and clock error coefficients broadcast by low-orbit satellites; then calculate the orbit and clock error corrections of medium- and high-orbit satellites such as Beidou / GNSS based on the broadcast ephemeris of medium- and high-orbit satellites such as Beidou / GNSS and the augmentation information of medium- and high-orbit satellites such as Beidou / GNSS broadcast by low-orbit satellites, and make corresponding corrections to the orbit clock error calculated in the navigation message, thereby recovering the precise ephemeris and clock error of medium- and high-orbit satellites such as Beidou / GNSS.

[0064] Among them, the low-orbit broadcast ephemeris is composed of the low-orbit satellite broadcast orbit parameters, and the Beidou / GNSS medium and high-orbit navigation satellite enhancement information is composed of precise orbit corrections and clock corrections. Since the low-orbit satellite orbit altitude is different from that of Beidou / GNSS, the low-orbit broadcast ephemeris model and parameters are more complex. Preferably, the low-orbit satellite broadcast orbit parameters are adjusted by the Beidou / GNSS medium and high-orbit satellite broadcast ephemeris parameters to ensure the consistency of the algorithm. Specifically, the low-orbit satellite broadcast ephemeris algorithm is described below. The algorithm adjustment adds 4 orbital parameters to the traditional 16-parameter broadcast ephemeris parameters, specifically including: the sine and cosine harmonic correction coefficients of one-third of the satellite latitude angle (C us2 and C uc2 ), and the first-order variability of the semi-major axis and the average angular acceleration For low-orbit satellites, the use of 20-parameter broadcast ephemeris requires the calculation of the semi-major axis a, mean motion n, and satellite latitude argument correction δ. u The 16 parameters are adjusted accordingly when compared. The specific formula is:

[0065]

[0066]

[0067] δ u =C uc cos2u+C us sin2u+C uc2 cos6u+C us sin6u

[0068] In the above formula, Δn, C uc 、C us is part of the original 16-parameter broadcast ephemeris parameters, t oe is the broadcast ephemeris reference time.

[0069] Among them, the precise orbit correction numbers of BeiDou / GNSS medium and high orbit navigation satellites include: the correction numbers refer to the epoch time t e , X, Y, Z direction corrections ΔX, ΔY, ΔZ, and velocity correction ΔV under ground-fixed system x , ΔV y , ΔV z ; The broadcasted precise clock corrections include: reference epoch time t c , clock bias correction Δa0, clock velocity correction Δa1. Based on the above parameters, BeiDou / GNSS precise orbit and clock error recovery are calculated as follows:

[0070]

[0071]

[0072]

[0073] δt c =δt+Δa0+Δa1·(tt c )

[0074] Among them, X j , Y j , Z j , δt are the satellite position and clock error calculated based on the broadcast ephemeris, δt c To restore the precise orbit and clock error.

[0075] S12. Obtain observation data from low-orbit satellites and medium- and high-orbit satellites in the current epoch, and perform gross error removal and carrier phase cycle slip detection on the observation data. This is achieved by: acquiring multi-frequency observations from low-orbit satellites and BeiDou / GNSS systems through receiver tracking observations, preprocessing the data, and detecting gross errors in pseudorange observations and carrier phase cycle slips by combining multi-frequency data, and marking ambiguity information.

[0076] S13. Using the recovered precise orbits and clock errors of low-orbit satellites and medium- and high-orbit satellites, the observation errors in the pseudorange and phase observation data of low-orbit satellites and medium- and high-orbit satellites in the current epoch are corrected. This is achieved by separately processing the observation errors in the pseudorange and phase observation data of low-orbit satellites and Beidou / GNSS satellites. These errors can generally be categorized into satellite-side errors, propagation-related errors, and receiver-side errors. Satellite-side errors include satellite clock error and antenna phase center error; propagation-related errors include relativistic effect corrections, Earth rotation corrections, phase wrap-around error, ionospheric delay, and tropospheric delay; and receiver-side errors include receiver antenna phase center error, station tidal displacement, and receiver clock error. These error terms can be corrected by eliminating or mitigating their effects through data combination based on their characteristics, introducing theoretical models based on their physical mechanisms and principles, or using a priori empirical models. For error terms that cannot be eliminated through the above methods, further consideration is given to estimating the relevant parameters when establishing the observation equations.

[0077] S14. Using the positioning solution of the previous epoch as the initial value, linearly expand the error-corrected observation values ​​of low-orbit satellites and medium- and high-orbit satellites and establish observation equations. Combine all the linearized observation equations of the current epoch and the positioning solution of the previous epoch, and use filtering or adjustment methods to comprehensively estimate the positioning solution of the current epoch to obtain the first positioning information. The specific process includes: jointly utilizing the measurement signals of low-orbit satellites and Beidou / GNSS medium- and high-orbit navigation satellites. Their basic positioning principles are the same. Low-orbit satellites can be used as a new navigation system and jointly solved with traditional Beidou / GNSS measurement data. By combining the observation signals of low-orbit satellites and Beidou / GNSS satellites, the high dynamic characteristics of low-orbit navigation can be brought into play, so that the carrier position and velocity parameters can converge quickly. The following unified observation model is established according to different systems, different frequency combinations, and different observation types:

[0078]

[0079]

[0080] In the above formula, ρ and φ represent pseudorange and phase observation values ​​respectively, i, s, and a represent frequency, satellite, and receiver respectively, LC represents the ionosphere-free combination, G represents the current satellite system, and G0 represents the reference system. is the geometric distance between the satellite and the receiver, T a is the tropospheric delay in the zenith direction of the station, is the tropospheric mapping function, c is the speed of light, δt s and δt a are the satellite and receiver clock errors, is the inter-system bias ISB of the receiver, is the satellite inter-frequency bias IFB, is the integer ambiguity parameter, Δ ρ is other pseudorange errors, mainly including antenna phase deviation and change, relativistic effect of satellite clock error, etc., Δ φ Then it is the phase error, relative to Δ ρ Phase wrapping is further corrected. The above formula ignores errors such as multipath and observation noise.

[0081] For navigation satellite systems using code division multiple access technology such as low-orbit satellites, GPS, Galileo, QZSS and BeiDou, after selecting a reference system, the inter-system bias of the system is 0, other systems need to estimate For the GLONASS system, which uses frequency division multiple access technology, the pseudorange and phase hardware delays are also related to the satellite (frequency). Different GLONASS satellites (frequencies) correspond to different receiver hardware delays. Therefore, in the observation model, the GLONASS system inter-system bias 0, but needs to be estimated by satellite

[0082] The above observation model is a second-order nonlinear equation. To facilitate estimation, the predicted value of the carrier state equation at the previous epoch is used as the initial state parameter. The observation equation is expanded according to the Taylor formula, and its second-order and higher-order terms are discarded. The linearized observation equation is as follows:

[0083]

[0084]

[0085] Where, is the station-satellite geometric distance calculated based on the initial state parameters of the receiver, l, m, n are the partial derivatives of the observation equation with respect to the receiver coordinates, respectively And x s 、y s and z s is the satellite coordinate, x a 、y a and z a is the initial coordinate of the measuring station, Δx a , Δy a and Δz a are their correction values ​​respectively. The partial differentials of the observation equation for receiver clock error, ISB, and IFB are all c, and the partial differentials for tropospheric parameters are The partial derivative of the carrier observation value to the ambiguity parameter is λ LC .in addition, as well as They are also called pseudorange and carrier prior observation residuals respectively.

[0086] At this point, the observation equations for all satellites in the current epoch can be constructed satellite by satellite. The following describes the Kalman filter and the estimation process based on the observation equations.

[0087] To facilitate state estimation, the parameters to be estimated at the current epoch are recorded as The partial differential matrix of all satellites is denoted as C k , the observation residual is recorded as z k , then the observation equations of all satellites can be expressed as:

[0088] z k =C k x k +v k

[0089] Where, v k represents the observation system noise.

[0090] Without loss of generality, the motion state equation of the carrier motion is described using a discrete linear state space model as follows:

[0091] x k =A k，k-1 x k-1 +B k，k-1 w k-1

[0092] Where A k.k-1 is the state transfer matrix, which represents the conversion mode from the previous epoch to the current epoch and is related to the motion state of the carrier; B k，k-1 is the noise input matrix, C k is the observation matrix; w k-1 is the process noise.

[0093] Combined with the observation information of the previous epoch, including the state parameter vector x k-1 and its covariance matrix information P k-1 Perform Kalman filtering on the observation equation of the current epoch to estimate the state solution of the current epoch and its covariance matrix:

[0094]

[0095]

[0096] In the formula, the relevant intermediate quantity is calculated as follows:

[0097]

[0098]

[0099]

[0100] Q k-1 =E[w k-1 (w k-1 ) T ], R k =E[v k (v k ) T ].

[0101] The above step S2 specifically includes the following steps:

[0102] S21. Obtain and preprocess measurement data from the carrier's gyroscope and accelerometer sensors for the current epoch to obtain inertial measurement information including the carrier's acceleration and rotational angular velocity. Specifically, inertial sensor devices such as gyroscopes and accelerometers measure the carrier's angular motion and linear motion in inertial space to obtain carrier acceleration information and rotational angular velocity information, which are then preprocessed.

[0103] S22. Use inertial measurement information to perform inertial navigation mechanics and solve for secondary positioning information containing the carrier's attitude, velocity, and position. This primarily involves: integrating the angular velocity measured by the gyroscope to obtain the carrier's attitude matrix relative to the ground-fixed system, completing the attitude update; using the calculated attitude matrix to convert the specific force measured by the accelerometer to the ground-fixed system, performing gravity compensation, and integrating to obtain velocity, completing the velocity update; further integrating the velocity to obtain position, completing the position update. The attitude update algorithm is the core, and its solution accuracy plays a decisive role in the accuracy of the entire inertial navigation system.

[0104] Attitude update is the process of updating the carrier's attitude using angular velocity data measured by the gyroscope. Since the gyroscope can only measure the carrier's rotational angular velocity in the carrier coordinate system (the b system) relative to the Earth's center inertial coordinate system (the i system), and attitude updates must be output to the Earth-fixed system, the main considerations are the multiple coordinate system conversions of the attitude data and the attitude changes caused by the Earth's rotation. The specific attitude update process is as follows:

[0105]

[0106] in It represents the change of the station center system (n system) from time T-1 to time T, Indicates the change of the carrier coordinate system (b system) from time T to time T-1, Then the posture matrix at time T-1 is known to be the posture matrix at the previous moment. The following can be calculated based on the gyroscope observations, earth related parameters and the initial velocity and attitude of the carrier: and So as to further calculate the posture matrix at time T

[0107] The speed update is mainly to integrate the accelerations such as specific force, gravity acceleration and Coriolis acceleration measured by the accelerometer to get the current speed. The specific calculation equation is:

[0108]

[0109] in is the acceleration increment of the navigation system during the period [T, T-1], is the harmful acceleration increment. The approximate discretization expressions of the two are:

[0110]

[0111]

[0112] Where, and are the rotation error compensation and the paddling error compensation of the speed, Δv m is the accelerometer sampling specific force velocity increment, I is the unit matrix, Δt is the time interval, is the specific force measured by the accelerometer, is the Coriolis acceleration caused by the motion of the carrier and the rotation of the Earth, is the centripetal acceleration caused by the carrier motion, g n is the acceleration due to Earth's gravity.

[0113] Using the updated velocity value, the position is updated according to the trapezoidal integral as follows:

[0114]

[0115] Where M pv (t) represents the transformation matrix at time T, It can be obtained by linear extrapolation algorithm.

[0116] The above step S3 specifically includes the following steps:

[0117] S31. Obtain the attitude information output by the gyro assembly and the distance information measured by the odometer sensor, and measure the displacement vector of the vehicle based on this information. Specifically, this includes using the attitude information output by the inertial navigation module and the distance information measured by the odometer to form a dead reckoning algorithm to measure the displacement vector. In the dead reckoning algorithm, the initial attitude matrix can be the same as the initial attitude of the inertial navigation module self-alignment. That is, at the initial moment, the dead reckoning module and the inertial navigation module have the same initial attitude error angle. In subsequent solution processes, the dead reckoning system does not perform a separate attitude update, but directly uses the attitude matrix of the inertial navigation module. Dead reckoning can help limit the divergence of inertial navigation errors.

[0118] S32. Use the dead reckoning algorithm to calculate the position of the carrier based on the displacement vector and output the third positioning information. Specifically, the principle of dead reckoning technology is to know the position of the carrier at the current moment, measure the distance and direction of movement, and then calculate the position of the carrier at the next moment. In a short period of time, dead reckoning can maintain a high positioning accuracy and is not affected by external environmental interference; however, its position estimation accuracy is greatly limited by the accuracy of the initial state, and the influence of the constant drift error in the attitude update will cause the estimation error to accumulate over time. The carrier is at t k The calculation method of the position at a moment can be expressed as:

[0119]

[0120]

[0121] Where (x0, y0) is the position of the carrier at the initial time t0, S i and θ i The carriers are t i Time to t i+1 The length of the displacement vector at the moment and the angle between the displacement vector and the east direction are the heading angle.

[0122] The above step S4 specifically includes the following steps:

[0123] S41. Use the first positioning information, second positioning information and third positioning information of low-orbit satellites, medium-orbit satellites and high-orbit satellites to establish a fusion positioning observation equation. Specifically, when the low-orbit, Beidou / GNSS satellite navigation system, inertial navigation system and dead reckoning system are jointly solved, the difference between the carrier position and velocity output by the low-orbit, Beidou / GNSS medium-orbit satellite navigation positioning and the position and velocity solved by the inertial navigation is used, as well as the difference between the position output by the low-orbit and medium-orbit satellite navigation positioning and the position solved by the dead reckoning is used as the observation value, and the attitude error, velocity error, position error, gyroscope error and accelerometer of the inertial navigation system are used as state quantities. The state parameters of the combined system are recorded as:

[0124]

[0125] in, is the attitude error, [δv nx ,δv ny ,δv nz ] is the speed error, [δp x ,δp y ,δp z ] is the position error, is the gyroscope error, is the accelerometer error.

[0126] The difference between the speed and position output by the low-orbit, Beidou / GNSS satellite navigation and inertial navigation systems, as well as the difference between the position output by inertial navigation and the position output by dead reckoning are taken as observation values ​​to construct the observation equation and record it in matrix form as follows:

[0127]

[0128] Among them, the coefficient matrix

[0129] S42. Use the Kalman filter method to estimate the system state error value based on the fused positioning observation equation and correct the inertial navigation measurement information. Specifically, it includes: using the above-mentioned fused observation equation and combining it with the carrier state equation to estimate the state error through Kalman filtering. The detailed process of Kalman filtering can be referred to S14. During implementation, it is necessary to make corresponding adjustments based on the estimated state vector and take into account the process noise configuration of the state parameters. Specifically, the state transfer matrix is ​​adjusted to:

[0130]

[0131] The system noise matrix is ​​adjusted to:

[0132]

[0133] The noise input matrix is ​​adjusted to:

[0134]

[0135] Where, and are the noise of the gyroscope and accelerometer respectively. The remaining matrices are expressed as follows:

[0136] M aa =-(ω×)

[0137]

[0138] M ap =M1+M2

[0139]

[0140]

[0141] M vp =(v n ×)(2M1+M2)+M3

[0142]

[0143]

[0144] Among them, β1 and β3 are related parameters of the Earth's gravitational oblateness.

[0145]

[0146]

[0147]

[0148] S43. Obtain the combined navigation positioning information of the accurate position, velocity, and attitude of the carrier at the current epoch based on the corrected inertial navigation measurement information. Specifically, this includes: correcting the inertial navigation system through closed-loop feedback or open-loop feedback based on the inertial navigation velocity error, position error, attitude error, gyro drift, and acceleration zero deviation state quantities calculated in S42; then re-arranging the inertial navigation mechanics and outputting the corrected carrier state information; thus, completing the closed loop of real-time, high-precision autonomous navigation positioning that integrates low-orbit, Beidou / GNSS navigation satellite information sources, and multi-sensor measurement signals.

[0149] Reference Figure 2 An embodiment of the present invention discloses a high-precision integrated navigation and positioning device, including: a low-orbit satellite and medium- and high-orbit satellite navigation and positioning unit M210, which is used to obtain downlink navigation enhancement information from low-orbit satellites and navigation signals from medium- and high-orbit satellites, and use the observation information of low-orbit satellites and medium- and high-orbit satellites to initialize real-time navigation and positioning, and output first positioning information; an inertial navigation and positioning unit M220, which is used to obtain observation signals from gyroscopes and accelerometer sensors to perform inertial navigation and positioning, and output second positioning information; a dead reckoning unit M230, which is used to obtain measurement signals from a gyroscope group and an odometer sensor to perform dead reckoning, and output third positioning information; and a multi-source fusion filtering processing unit M240, which is used to fuse the first positioning information, the second positioning information, and the third positioning information, and solve the integrated navigation and positioning information using a Kalman filtering method.

[0150] Among them, the low-orbit satellite and medium- and high-orbit satellite navigation and positioning unit M210 includes:

[0151] The satellite navigation receiving module M211 is used to obtain navigation messages, navigation enhancement information and observation data from low-orbit satellites and medium- and high-orbit satellites; and the satellite navigation processing module M212 is used to perform real-time single-point positioning based on the navigation messages, navigation enhancement information and observation data. Specifically, the navigation messages, navigation enhancement information and observation data are first processed to restore the precise orbit and clock error, and then the observation data of the low-orbit satellites and medium- and high-orbit satellites in the current epoch are subjected to gross error elimination, cycle slip detection and error correction. Combined with the positioning solution of the previous epoch, the observation values ​​are linearly expanded to establish the observation equation, and the filtering or adjustment method is used to comprehensively estimate the positioning solution of the current epoch to obtain the first positioning information.

[0152] Preferably, the navigation enhancement information of the above-mentioned low-orbit satellite includes: low-orbit broadcast ephemeris used to restore the precise orbit and clock error parameters of the low-orbit satellite, and precise ephemeris correction numbers of the medium and high-orbit satellites used to restore the precise orbit and clock error parameters of the medium and high-orbit satellites.

[0153] The inertial navigation and positioning unit M220 comprises an inertial navigation measurement module M221, which includes gyroscopes and accelerometers and is used to perform inertial measurements on the carrier, acquiring information including acceleration and rotational angular velocity; and an inertial navigation processing module M222, which preprocesses the inertial navigation measurement data and then calculates and updates the carrier's attitude, velocity, and position status information using inertial navigation mechanics, outputting secondary positioning information. This process primarily involves the following steps: integrating the angular velocity measured by the gyroscope to obtain the carrier's attitude matrix relative to the ground-fixed system, completing the attitude update; using the calculated attitude matrix, converting the specific force measured by the accelerometer to the ground-fixed system, performing gravity compensation, and integrating to obtain velocity, completing the velocity update; and further integrating the velocity to obtain position, completing the position update.

[0154] The dead reckoning unit M230 includes a dead reckoning measurement module M231, which includes a gyroscope assembly and an odometer sensor and is used to measure the displacement vector of the carrier; and a dead reckoning processing module M232, which is used to use a dead reckoning algorithm to solve the position of the carrier based on the displacement vector of the carrier and output third positioning information.

[0155] The multi-source fusion filtering processing unit M240 includes: a fusion positioning processing module M241, which is used to jointly establish a fusion positioning observation equation based on the first positioning information, second positioning information and third positioning information of low-orbit satellites, medium-orbit satellites and high-orbit satellites; a filtering processing module M242, which is used to use the Kalman filtering method to estimate the fusion positioning observation equation to correct the system state error of inertial measurement and output combined navigation positioning information.

[0156] The serial numbers of the above-mentioned steps involved in the method of the present invention do not mean the order of execution of the method. The execution order of each step should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0157] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A high-precision integrated navigation and positioning method, comprising: S1. Obtain downlink navigation enhancement information from low-orbit satellites and navigation signals from medium- and high-orbit satellites, and use observation information from low-orbit satellites and medium- and high-orbit satellites to initialize real-time navigation positioning and output first positioning information; S2 obtains the observation signals of the gyroscope and accelerometer sensors for inertial navigation positioning and outputs the second positioning information; S3 obtains the measurement signals of the gyro group and the odometer sensor for dead reckoning, and outputs a third positioning information; the initial moment of the inertial navigation positioning and the dead reckoning has the same initial attitude error angle; S4. Fusion of the first positioning information, the second positioning information and the third positioning information, and the use of Kalman filtering to solve the combined navigation positioning information; The S4 includes: S41. Use the first positioning information, second positioning information, and third positioning information of low-orbit satellites, medium-orbit satellites, and high-orbit satellites to establish a fused positioning observation equation; wherein, when the low-orbit, Beidou / GNSS satellite navigation system, inertial navigation system, and dead reckoning system jointly solve, use the difference between the carrier position and velocity output by the low-orbit, Beidou / GNSS medium-orbit satellite navigation positioning and the position and velocity solved by the inertial navigation, as well as the difference between the position output by the low-orbit and medium-orbit satellite navigation positioning and the position solved by the dead reckoning as observation values, and use the attitude error, velocity error, position error, gyroscope error, and accelerometer of the inertial navigation system as state quantities to construct a state vector; take the difference between the velocity and position output by the low-orbit, Beidou / GNSS satellite navigation, and inertial navigation system, as well as the difference between the position output by the inertial navigation and the position output by the dead reckoning as observation values ​​to construct the observation equation; S42. Use the Kalman filter method to estimate the system state error value based on the fusion positioning observation equation and correct the inertial navigation measurement information; S43. Obtain the combined navigation positioning information of the accurate position, velocity and attitude of the current epoch carrier based on the corrected inertial navigation measurement information.

2. The method according to claim 1, characterized in that The medium and high orbit satellites are at least one global navigation satellite system, including but not limited to the Beidou Satellite Navigation System, the Global Positioning System, the GLONASS Satellite Navigation System, the Galileo Satellite Navigation System, the Indian Regional Navigation Satellite System and the Satellite-Based Augmentation System.

3. The method according to claim 1 or 2, characterized in that Said S1 comprises: S11. Acquire and recover the navigation augmentation information broadcast by low-orbit satellites and the ephemeris of medium- and high-orbit satellites, and obtain the precise orbits and clock errors of low-orbit satellites, medium- and high-orbit satellites; S12. Obtain observation data of low-orbit satellites and medium-orbit satellites in the current epoch, and perform gross error removal and carrier phase cycle slip detection on the observation data; S13. Using the recovered precise orbits and clock errors of the low-orbit satellites and the medium- and high-orbit satellites, correct the observation errors of the pseudorange and phase observation data of the low-orbit satellites and the medium- and high-orbit satellites in the current epoch data; S14. Using the positioning solution of the previous epoch as the initial value, linearly expand the error-corrected observation values ​​of low-orbit satellites and medium-orbit satellites and establish observation equations. Combine all the linearized observation equations of the current epoch and the positioning solution of the previous epoch, and use filtering or adjustment methods to comprehensively estimate the positioning solution of the current epoch to obtain the first positioning information.

4. The method according to claim 1 or 2, characterized in that The S2 includes: S21 obtains the measurement data of the gyroscope and accelerometer sensors of the carrier at the current epoch and preprocesses it to obtain inertial measurement information including the acceleration and angular velocity of the carrier; S22. Use the inertial measurement information to perform inertial navigation mechanics arrangement to obtain the second positioning information including the attitude, velocity, and position of the carrier.

5. The method according to claim 1 or 2, characterized in that The S3 includes: S31 obtains the attitude information output by the gyro group and the distance information measured by the odometer sensor, and accordingly measures the displacement vector of the carrier; S32. Utilize a dead reckoning algorithm to calculate the position of the carrier according to the displacement vector and output third positioning information.

6. A high-precision integrated navigation and positioning device, used to implement the method according to any one of claims 1 to 5, characterized in that: include: The low-orbit satellite and medium- and high-orbit satellite navigation and positioning unit (M210) is used to obtain downlink navigation enhancement information from low-orbit satellites and navigation signals from medium- and high-orbit satellites, initialize real-time navigation and positioning using observation information from low-orbit satellites and medium- and high-orbit satellites, and output first positioning information; Inertial navigation and positioning unit (M220), used to obtain observation signals from gyroscope and accelerometer sensors for inertial navigation and positioning, and output second positioning information; The dead reckoning unit (M230) is used to obtain the measurement signals of the gyro group and the odometer sensor to perform dead reckoning and output the third positioning information; as well as The multi-source fusion filtering processing unit (M240) is used to fuse the first positioning information, the second positioning information and the third positioning information, and use the Kalman filtering method to solve the combined navigation positioning information.

7. The device according to claim 6, characterized in that The low-orbit satellite and medium- and high-orbit satellite navigation and positioning unit (M210) includes: Satellite navigation receiving module (M211), used to obtain navigation messages, navigation augmentation information and observation data from low-orbit satellites and medium- and high-orbit satellites; and The satellite navigation processing module (M212) is used to perform real-time single-point positioning based on navigation messages, navigation augmentation information and observation data. Specifically, the navigation messages, navigation augmentation information and observation data are first processed to restore the precise orbit and clock error. Then, the observation data of low-orbit satellites and medium- and high-orbit satellites in the current epoch are subjected to gross error elimination, cycle slip detection and error correction. Combined with the positioning solution of the previous epoch, the observation values ​​are linearly expanded to establish the observation equation. The filtering or adjustment method is used to comprehensively estimate the positioning solution of the current epoch to obtain the first positioning information.

8. The device according to claim 6 or 7, characterized in that The navigation enhancement information of the low-orbit satellite includes: low-orbit broadcast ephemeris used to restore the precise orbit and clock error parameters of the low-orbit satellite, and precise ephemeris correction numbers of the medium- and high-orbit satellites used to restore the precise orbit and clock error parameters of the medium- and high-orbit satellites.

9. The device according to claim 6, characterized in that The inertial navigation and positioning unit (M220) includes: An inertial navigation measurement module (M221), including gyroscope and accelerometer sensors, is used to perform inertial measurement of the carrier, including the acceleration and rotational angular velocity of the carrier; and The inertial navigation processing module (M222) is used to pre-process the inertial navigation measurement data, and then calculate and update the state information of the carrier's attitude, velocity and position through inertial navigation mechanics arrangement, and output the second positioning information.

10. The device according to claim 6, characterized in that The dead reckoning unit (M230) includes: The dead reckoning measurement module (M231), which includes a gyroscope group and an odometer sensor, is used to measure the displacement vector of the vehicle; The dead reckoning processing module (M232) is configured to calculate the position of the carrier according to the displacement vector of the carrier using a dead reckoning algorithm and output third positioning information.

11. The device according to claim 6, characterized in that The multi-source fusion filtering processing unit (M240) includes: The fusion positioning processing module (M241) is used to combine the first positioning information, second positioning information and third positioning information of low-orbit satellites, medium-orbit satellites and high-orbit satellites to establish a fusion positioning observation equation; The filtering processing module (M242) is used to use the Kalman filtering method to estimate the fusion positioning observation equation to correct the system state error of the inertial measurement and output the integrated navigation positioning information.

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