Unmanned aerial vehicle high-precision positioning method based on micro-centimeter space low-orbit constellation and Beidou PPP-B2b fusion enhancement

Abstract

The invention relates to an unmanned aerial vehicle high-precision positioning method based on micro-centimeter space low-orbit constellation and Beidou PPP-B2b fusion enhancement, and the method comprises the steps: obtaining a precise orbit correction number and a precise clock correction number of a GPS and a BDS system based on a Beidou PPP-B2b signal; calculating a precise orbit and a precise clock error of the GNSS satellite based on the precise orbit correction and the precise clock error correction; on the basis of the precise orbit and the precise clock error of the GNSS satellite, an observation value of a micro-centispace low-orbit satellite is fused, and a GNSS / LEO combined PPP observation model is constructed; and based on extended Kalman filtering, precise point positioning calculation is carried out on the GNSS / LEO combined PPP observation model, and the real-time high-precision position of the unmanned aerial vehicle is obtained. According to the invention, the real-time high-precision position of the unmanned aerial vehicle can be rapidly and stably obtained, and a reliable position reference is provided for autonomous operation of the unmanned aerial vehicle in a network-free environment.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation and high-precision positioning technology for unmanned aerial vehicles (UAVs), and in particular to a high-precision positioning method for UAVs based on the fusion enhancement of a microcentimeter-sized low-orbit constellation and the BeiDou PPP-B2b system. Background Technology

[0002] While the BeiDou PPP-B2b service can provide centimeter-level precise point positioning in network-free environments, its initial convergence time of 20-30 minutes severely limits its application in dynamic scenarios with extremely high real-time requirements, such as drones. The slow convergence is mainly due to the hysteresis caused by changes in the geometric configuration of medium- and high-orbit satellites, which restricts its practical application in agile drones.

[0003] Low Earth Orbit (LEO) satellite-enhanced navigation technology utilizes the rapidly changing observation geometry generated by the high-speed motion of LEO satellites to fundamentally improve satellite spatial configuration. This provides strong impetus for rapid ambiguity fixation and filtering convergence, accelerating PPP convergence. Internationally, communication constellations such as Iridium and Orbcomm have been explored for navigation enhancement; domestically, emerging LEO constellations, represented by Microspace, are developing rapidly. However, most existing LEO enhancement schemes either rely on precise orbit products distributed through terrestrial networks, failing to completely escape network constraints; or their constellation designs are geared towards global coverage, and their geometric enhancement effects and availability in specific regions (such as China and surrounding low- and mid-latitude regions) are not optimal.

[0004] The Microcentimeter Space low-Earth orbit (LEO) constellation, through optimized orbit design and constellation configuration, provides superior satellite visibility and geometric distribution compared to globally uniform constellations in mid-to-low latitude regions where tropospheric delay and ionospheric activity are more complex. Its large number of satellites and frequent overhead passes allow users in the region to receive signals from high-speed moving satellites from different directions more frequently, thereby continuously and rapidly enhancing the observation geometry and providing stronger spatiotemporal constraints for positioning convergence. While BeiDou PPP-B2b provides high-precision real-time orbit and clock bias references for GNSS systems, the Microcentimeter Space LEO constellation provides the dynamic observation geometry required for rapid convergence.

[0005] By deeply integrating the precise correction advantages of BeiDou PPP-B2b with the rapid geometric convergence advantages of the "Microcentimeter Space" low-Earth orbit constellation in mid- and low-latitude regions, a high-precision positioning method has been created that is completely independent of ground networks and can achieve rapid convergence. This high-precision positioning result is the foundation for realizing advanced applications such as autonomous navigation and precision operations for unmanned aerial vehicles (UAVs). Summary of the Invention

[0006] The purpose of this invention is to propose a high-precision positioning method for unmanned aerial vehicles (UAVs) based on the fusion enhancement of a micro-centimeter-level low-Earth orbit (LEO) constellation and the BeiDou PPP-B2b system, thereby addressing the problems existing in the prior art. This method obtains the precise orbit and clock error correction values ​​of the GNSS system by receiving BeiDou PPP-B2b signals, and simultaneously receives the navigation enhancement signals from the micro-centimeter-level LEO constellation to obtain its pseudorange and carrier phase observations. These two values ​​are then combined to perform a tight GNSS / LEO precise point positioning calculation, significantly improving the positioning convergence speed and accuracy, thus enabling rapid and stable acquisition of the UAV's real-time high-precision position. This method provides a reliable position reference for autonomous operation of UAVs in network-free environments.

[0007] To achieve the above objectives, the present invention provides the following solution: A high-precision positioning method for unmanned aerial vehicles (UAVs) based on the fusion enhancement of a microcentimeter-sized low-Earth orbit constellation and BeiDou PPP-B2b includes: Based on the BeiDou PPP-B2b signal, the precise orbital correction and precise clock error correction of the GPS and BDS systems are obtained. Based on the aforementioned precise orbit corrections and precise clock error corrections, calculate the precise orbit and precise clock error of the GNSS satellite; Based on the precise orbits and precise clock errors of the GNSS satellites, and by fusing observations from microcentimeter-sized low-Earth orbit satellites, a joint GNSS / LEO PPP observation model is constructed. Based on extended Kalman filtering, precise single-point positioning calculations are performed on the GNSS / LEO joint PPP observation model to obtain the real-time high-precision position of the UAV.

[0008] Optionally, the precise orbit and precise clock bias of the GNSS satellite are as follows: in, and These are the precise orbits and precise clock biases of GNSS satellites. and These are the satellite orbits and clock biases calculated from broadcast ephemeris. and For PPP-B2b broadcast orbital corrections and clock corrections, It is the speed of light.

[0009] Optionally, the GNSS / LEO joint PPP observation model is as follows: Among them, superscript and They represent GNSS satellites and low-Earth orbit satellites, respectively. and These are pseudorange and carrier phase observations, respectively, without ionospheric assemblies. The geometric distance between the satellite and the receiver. The speed of light in a vacuum , For low-Earth orbit satellite clock bias, For GNSS satellite clock bias, For tropospheric delay, For carrier wavelength, For integer ambiguity, To observe noise.

[0010] Optionally, based on extended Kalman filtering, the precise point positioning solution for the GNSS / LEO joint PPP observation model includes: Construct the state vector of the UAV; Based on the state vector, construct the state transition matrix and the process noise covariance matrix; Based on the state transition matrix and the process noise covariance matrix, state prediction is performed; Based on the aforementioned GNSS / LEO joint PPP observation model, an observation update design matrix and an observation noise matrix are constructed. The state vector is updated based on the state prediction results, the observation update design matrix, and the observation noise matrix; Based on the updated state vector, the localization result is extracted.

[0011] Optionally, the state vector of the UAV is: in, The three-dimensional position coordinates of the UAV in the Earth-centered, Earth-fixed coordinate system. For receiver clock bias, For the first Ionospheric-free integer ambiguity of GNSS satellites i =1, , For the first Integer ambiguity of a low-Earth orbit satellite without ionospheric assemblies. =1, , For the number of GNSS satellites, For the number of low-orbit satellites, Let T be the state vector, and T be the matrix transpose symbol.

[0012] Optionally, the state transition matrix and the process noise covariance matrix are: in, Here is the state transition matrix. It is a 3-order identity matrix. The number of ambiguity parameters. The process noise covariance matrix is... , , The process noise variance of the receiver position coordinate components. The process noise variance of the receiver clock bias.

[0013] Optionally, state prediction based on the state transition matrix and the process noise covariance matrix includes: in, Let be the prior estimate of the state vector at time k. Let be the posterior estimate of the state vector at time k-1. Let be the covariance matrix estimated prior at time k. Let be the covariance matrix estimated posteriorly at time k-1.

[0014] Optionally, the observation update design matrix is: in, Let be the ionospherically unbound pseudorange observation of the i-th GNSS satellite, where , For the ionospheric-free combined carrier phase observation of the i-th GNSS satellite, Let be the ionospherically-free pseudorange observation of the j-th low-Earth orbit satellite, where , For the ionospheric combined carrier phase observation of the j-th low-Earth orbit satellite, Let represent the partial derivatives with respect to the x, y, and z coordinates, respectively. For carrier wavelength, For the number of GNSS satellites, Number of low-orbit satellites Optionally, the observation noise matrix is: in, The covariance matrix of the observed noise, The standard deviation of GNSS pseudorange observation noise. The standard deviation of GNSS carrier phase observation noise. The standard deviation of pseudorange observation noise for low-Earth orbit satellites. This represents the standard deviation of carrier phase observation noise for low-orbit satellites.

[0015] Optionally, updating the state of the observation update design matrix and the observation noise matrix includes: in, Here is the Kalman gain matrix. For the observation vector, It is the identity matrix. Let be the posterior estimate of the state vector at time k. Let be the covariance matrix of the posterior estimate at time k, and T be the matrix transpose.

[0016] Optionally, the positioning result is: in, The coordinates are the three-dimensional position coordinates of the UAV in the Earth-centered, Earth-fixed coordinate system.

[0017] The beneficial effects of this invention are as follows: This invention proposes a high-precision positioning method for unmanned aerial vehicles (UAVs) based on the fusion enhancement of a micro-centimeter-scale low-Earth orbit (LEO) constellation and the BeiDou PPP-B2b system. It obtains precise orbit and clock error corrections from the GNSS system by receiving BeiDou PPP-B2b signals, and simultaneously receives navigation enhancement signals from the micro-centimeter-scale LEO constellation to obtain pseudorange and carrier phase observations. These two methods are then combined to perform a tight GNSS / LEO precise point positioning calculation, significantly improving the positioning convergence speed and accuracy, thereby enabling rapid and stable acquisition of the UAV's real-time high-precision position. This method provides a reliable position reference for autonomous operation of UAVs in network-free environments. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of a high-precision UAV positioning method based on the fusion enhancement of a microcentimeter-space low-orbit constellation and BeiDou PPP-B2b according to an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] like Figure 1 As shown, this embodiment proposes a high-precision UAV positioning method based on the fusion enhancement of a microcentimeter-space low-orbit constellation and BeiDou PPP-B2b, including: Based on the BeiDou PPP-B2b signal, the precise orbital correction and precise clock error correction of the GPS and BDS systems are obtained. Based on the aforementioned precise orbit corrections and precise clock error corrections, calculate the precise orbit and precise clock error of the GNSS satellite; Based on the precise orbits and precise clock errors of the GNSS satellites, and by fusing observations from microcentimeter-sized low-Earth orbit satellites, a joint GNSS / LEO PPP observation model is constructed. Based on extended Kalman filtering, precise single-point positioning calculations are performed on the GNSS / LEO joint PPP observation model to obtain the real-time high-precision position of the UAV.

[0023] Specifically, in this embodiment, a high-precision UAV positioning method based on the fusion enhancement of microcentimeter-scale low-orbit constellation and BeiDou PPP-B2b includes the following steps: Step 1: Receive the PPP-B2b signal broadcast by the BeiDou satellite to obtain the real-time precise orbit and clock error correction values ​​of the GPS and BDS systems; at the same time, receive the navigation enhancement signal broadcast by the microcentimeter space low-Earth orbit constellation to obtain its pseudorange and carrier phase observation values.

[0024] Step 2: Based on the obtained BeiDou PPP-B2b broadcast corrections, calculate the precise orbit and clock error of the GNSS satellite used for precise point positioning.

[0025] Step 3: Apply the BeiDou PPP-B2b corrections to the GNSS observations and integrate them with the observations from the micro-centrifugal low-orbit satellites to construct a GNSS / LEO joint PPP observation model.

[0026] Step 4: Perform GNSS / LEO compact combination precise single-point positioning calculation based on extended Kalman filter to quickly obtain the real-time high-precision position of the UAV.

[0027] Step 2: Based on the obtained BeiDou PPP-B2b broadcast corrections, calculate the precise orbits and clock errors of GNSS satellites used for precise point positioning. The method is as follows: In the formula, and These are the precise orbits and precise clock biases of GNSS satellites. and These are the satellite orbits and clock biases calculated from broadcast ephemeris. and Orbit and clock corrections for PPP-B2b broadcasts It is the speed of light.

[0028] Step 3: Apply the BeiDou PPP-B2b corrections to the GNSS observations and integrate them with the observations from the micro-centrifugal low-Earth orbit satellites to construct a GNSS / LEO joint PPP observation model. The method is as follows: In the formula, superscript and They represent GNSS satellites and low-Earth orbit satellites, respectively. and These are pseudorange and carrier phase observations, respectively, for the ionospheric combination. The distance between the satellite and the Earth is the geometric distance. The speed of light in a vacuum; For receiver clock bias, For low-Earth orbit satellite clock bias, For GNSS satellite clock bias; For tropospheric delay; The carrier wavelength; For integer ambiguity; To observe noise.

[0029] Step 4: Perform GNSS / LEO compact combination precise single-point positioning calculation based on extended Kalman filtering to quickly obtain the real-time high-precision position of the UAV. The steps are as follows: (1) Construct the state vector, the formula of which is as follows: In the formula, The three-dimensional position coordinates of the UAV in the Earth-centered, Earth-fixed coordinate system. Receiver clock bias For the first Ionospheric-free integer ambiguity of GNSS satellites For the first Integer ambiguity of a low-Earth orbit satellite without ionospheric assemblies. For the number of GNSS satellites, denoted as the number of low-Earth orbit satellites, and T as the matrix transpose symbol.

[0030] (2) Construct the state transition matrix and the process noise covariance matrix, the formulas of which are as follows: In the formula, Here is the state transition matrix. It is a 3-order identity matrix. The number of ambiguity parameters. The process noise covariance matrix is... , , The process noise variance of the receiver position coordinate components. The process noise variance of the receiver clock bias is set to 0, and the process noise of the ambiguity parameter is set to 0.

[0031] (3) The state prediction is based on the system dynamics model, and the formula is as follows: In the formula, Let be the prior estimate of the state vector at time k. Let be the posterior estimate of the state vector at time k-1. Let be the covariance matrix estimated prior at time k. Let be the covariance matrix estimated posteriorly at time k-1.

[0032] (4) Construct the observation update design matrix, the formula of which is as follows: In the formula, Let be the ionospherically unbound pseudorange observation of the i-th GNSS satellite, where For the ionospheric-free combined carrier phase observation of the i-th GNSS satellite, Let be the ionospherically-free pseudorange observation of the j-th low-Earth orbit satellite, where , For the ionospheric combined carrier phase observation of the j-th low-Earth orbit satellite, Let represent the partial derivatives with respect to the x, y, and z coordinates, respectively. For carrier wavelength, For the number of GNSS satellites, Number of low-orbit satellites (5) Construct the observation noise matrix, the formula of which is as follows: In the formula, The covariance matrix of the observed noise, The standard deviation of GNSS pseudorange observation noise. The standard deviation of GNSS carrier phase observation noise. The standard deviation of pseudorange observation noise for low-Earth orbit satellites. The standard deviation of carrier phase observation noise for low-Earth orbit satellites. This proportional relationship is based on the empirical value of GNSS positioning, which is typically about two orders of magnitude more accurate than pseudorange observations.

[0033] (6) Perform state update, the formula is as follows: In the formula, Here is the Kalman gain matrix. For the observation vector, It is the identity matrix. Let be the posterior estimate of the state vector at time k. Let be the covariance matrix of the posterior estimate at time k, and T be the matrix transpose.

[0034] (7) Extract the localization result from the updated state vector, using the following formula: In the formula, The coordinates are the three-dimensional position coordinates of the UAV in the Earth-centered, Earth-fixed coordinate system.

[0035] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A high-precision positioning method for unmanned aerial vehicles (UAVs) based on the fusion enhancement of a microcentimeter-sized low-orbit constellation and BeiDou PPP-B2b, characterized in that, include: Based on the BeiDou PPP-B2b signal, the precise orbital correction and precise clock error correction of the GPS and BDS systems are obtained. Based on the aforementioned precise orbit corrections and precise clock error corrections, calculate the precise orbit and precise clock error of the GNSS satellite; Based on the precise orbits and precise clock errors of the GNSS satellites, and by fusing observations from microcentimeter-sized low-Earth orbit satellites, a joint GNSS / LEO PPP observation model is constructed. Based on extended Kalman filtering, precise single-point positioning calculations are performed on the GNSS / LEO joint PPP observation model to obtain the real-time high-precision position of the UAV.

2. The high-precision UAV positioning method based on the fusion enhancement of microcentimeter-scale low-orbit constellation and BeiDou PPP-B2b as described in claim 1, is characterized in that, The precise orbit and precise clock bias of the GNSS satellite are as follows: in, and These are the precise orbits and precise clock biases of GNSS satellites. and These are the satellite orbits and clock biases calculated from broadcast ephemeris. and For PPP-B2b broadcast orbital corrections and clock corrections, It is the speed of light.

3. The high-precision UAV positioning method based on the fusion enhancement of microcentimeter-scale low-orbit constellation and BeiDou PPP-B2b as described in claim 1, is characterized in that... The GNSS / LEO joint PPP observation model is as follows: Among them, superscript and They represent GNSS satellites and low-Earth orbit satellites, respectively. and These are pseudorange and carrier phase observations, respectively, without ionospheric assemblies. The geometric distance between the satellite and the receiver. The speed of light in a vacuum For receiver clock bias, For low-Earth orbit satellite clock bias, For GNSS satellite clock bias, For tropospheric delay, For carrier wavelength, For integer ambiguity, To observe noise.

4. The high-precision UAV positioning method based on the fusion enhancement of microcentimeter-scale low-orbit constellation and BeiDou PPP-B2b as described in claim 1, characterized in that, Based on extended Kalman filtering, the precise single-point positioning solution of the GNSS / LEO joint PPP observation model includes: Construct the state vector of the UAV; Based on the state vector, construct the state transition matrix and the process noise covariance matrix; Based on the state transition matrix and the process noise covariance matrix, state prediction is performed; Based on the aforementioned GNSS / LEO joint PPP observation model, an observation update design matrix and an observation noise matrix are constructed. The state vector is updated based on the state prediction results, the observation update design matrix, and the observation noise matrix; Based on the updated state vector, the localization result is extracted.

5. The high-precision UAV positioning method based on the fusion enhancement of microcentimeter-scale low-orbit constellation and BeiDou PPP-B2b as described in claim 4, characterized in that, The state vector of the UAV is: in, The three-dimensional position coordinates of the UAV in the Earth-centered, Earth-fixed coordinate system. For receiver clock bias, For the first Ionospheric-free integer ambiguity of GNSS satellites i =1, , For the first Integer ambiguity of a low-Earth orbit satellite without ionospheric assemblies. =1, , For the number of GNSS satellites, For the number of low-orbit satellites, Let T be the state vector, and T be the matrix transpose symbol.

6. The high-precision UAV positioning method based on the fusion enhancement of microcentimeter-scale low-orbit constellation and BeiDou PPP-B2b as described in claim 4, is characterized in that... The state transition matrix and the process noise covariance matrix are: in, Here is the state transition matrix. It is a 3-order identity matrix. The number of ambiguity parameters. The process noise covariance matrix is... , , The process noise variance of the receiver position coordinate components. The process noise variance of the receiver clock bias.

7. The high-precision UAV positioning method based on the fusion enhancement of microcentimeter-scale low-orbit constellation and BeiDou PPP-B2b as described in claim 6, is characterized in that... Based on the state transition matrix and the process noise covariance matrix, state prediction includes: in, Let be the prior estimate of the state vector at time k. Let be the posterior estimate of the state vector at time k-1. Let be the covariance matrix estimated prior at time k. Let be the covariance matrix estimated posteriorly at time k-1.

8. The high-precision UAV positioning method based on the fusion enhancement of microcentimeter-scale low-orbit constellation and BeiDou PPP-B2b as described in claim 7, is characterized in that... The observation update design matrix is: in, Let be the ionospherically unbound pseudorange observation of the i-th GNSS satellite, where For the ionospheric-free combined carrier phase observation of the i-th GNSS satellite, Let be the ionospherically-free pseudorange observation of the j-th low-Earth orbit satellite, where , For the ionospheric combined carrier phase observation of the j-th low-Earth orbit satellite, They represent the values ​​for x respectively y Partial derivative of the z-coordinate, For carrier wavelength, For the number of GNSS satellites, This refers to the number of low-orbit satellites. The observation noise matrix is: in, The covariance matrix of the observed noise, The standard deviation of GNSS pseudorange observation noise. The standard deviation of GNSS carrier phase observation noise. The standard deviation of pseudorange observation noise for low-Earth orbit satellites. This represents the standard deviation of carrier phase observation noise for low-orbit satellites.

9. The high-precision positioning method for unmanned aerial vehicles (UAVs) based on the fusion enhancement of microcentimeter-scale low-orbit constellation and BeiDou PPP-B2b as described in claim 8, is characterized in that... The state update of the observation update design matrix and the observation noise matrix includes: in, Here is the Kalman gain matrix. For the observation vector, It is the identity matrix. Let be the posterior estimate of the state vector at time k. Let be the covariance matrix of the posterior estimate at time k, and T be the matrix transpose.

10. The high-precision positioning method for unmanned aerial vehicles (UAVs) based on the fusion enhancement of microcentimeter-scale low-orbit constellation and BeiDou PPP-B2b as described in claim 9, is characterized in that... The location result is as follows: in, The coordinates are the three-dimensional position coordinates of the UAV in the Earth-centered, Earth-fixed coordinate system.

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