Unmanned aerial vehicle positioning and cruising method and system based on satellite-based augmentation

Abstract

The application belongs to the technical field of satellite positioning, and discloses a kind of unmanned plane positioning cruise method and system based on satellite-based augmentation, comprising: setting unmanned plane cruise area range, high coverage cruise route and flight time.Unmanned plane software, hardware equipment are checked and take off from safety area, start automatic cruise.Through unmanned plane receiver, observation data, broadcast ephemeris and Beidou B2b and Galileo HAS product are obtained; B2b and HAS message are decoded and correction number is matched; according to the correction number provided by B2b and HAS, satellite orbit and clock difference and code and phase deviation are corrected; data is removed, clock jump is repaired and cycle slip is detected; parameter estimation is carried out using Kalman filter, and high-precision position coordinates are obtained.If emergency occurs, the operator can send the return instruction to the unmanned plane through the Beidou short message.After the cruise task is completed or the return instruction is received, the unmanned plane completes safe landing at the planned return point.

Description

Technical Field

[0001] The present invention relates to the field of satellite positioning technology, and in particular to a method and system for positioning and cruising an unmanned aerial vehicle (UAV) based on satellite-based augmentation. Background Art

[0002] The Satellite-Based Augmentation System (SBAS) is a technology that uses satellites to provide navigation signal enhancements, designed to improve the accuracy, integrity, availability, and continuity of Global Navigation Satellite System (GNSS) positioning services. SBAS receives and monitors GNSS signals through ground-based base stations, calculates errors, and generates correction information, which is then broadcast to users via geostationary satellites. The Beidou Navigation Satellite System (BDS) and the Galileo Navigation Satellite System (GALILEO) implement SBAS through PPP-B2b and HAS services, respectively, providing users with high-precision positioning services.

[0003] Beidou PPP-B2b (Precise Point Positioning Broadcast-to-Broadcast) is a high-precision positioning service provided by the Beidou global navigation satellite system, designed to support a wide range of precise positioning applications. This service broadcasts precise orbit and clock correction parameters and other assistance data directly to user terminals via the Beidou system's B2b frequency, enabling users to achieve Precise Point Positioning (PPP) without relying on ground-based reference stations or an internet connection. The PPP-B2b service operates based on the Beidou-3 satellite system. A globally deployed network of ground monitoring stations collects navigation satellite data in real time. This data is processed by a ground control center to generate precise orbit, clock, and other correction information. This data is then uploaded to Beidou satellites and broadcast to users worldwide using the B2b signal. User terminals receive the signal and, combined with their own GNSS observations, calculate and resolve it to achieve high-accuracy positioning at the centimeter to decimeter level. Unlike traditional PPP services, PPP-B2b broadcasts augmented information directly via satellite, eliminating the need for internet access. This offers global coverage, low latency, and high reliability.

[0004] The Galileo High Accuracy Service (HAS) is a global, high-precision positioning augmentation service provided by the European Galileo navigation satellite system. It aims to provide users with sub-meter or higher accuracy positioning capabilities. HAS broadcasts precise orbits, satellite clock correction parameters, and other augmentation data via the Galileo satellite's E6 frequency band, enabling user terminals to achieve precise point positioning without relying on ground base stations or internet connectivity. HAS's core technology relies on the Galileo system's high-performance satellite constellation and ground control network. Galileo ground stations deployed globally collect real-time orbit and clock data from navigation satellites. This data is processed by the ground control center to generate precise correction information. This correction information includes the satellite's precise orbit data, precise clock corrections, signal bias parameters, and auxiliary information supporting positioning solutions. This data is packaged and broadcast in real time to users worldwide via the Galileo satellite's E6 signal. User receivers can directly decode it and combine it with their own observations to perform high-precision positioning solutions.

[0005] Beidou PPP-B2b and Galileo HAS have broad application prospects, particularly in scenarios such as drone navigation, precision agriculture, unmanned driving, surveying and mapping, disaster monitoring, and traffic management. For example, in precision agriculture, using B2b and HAS services, drones can accurately execute path planning, enabling automated and precise farmland operations. In surveying and mapping, B2b and HAS can significantly improve the efficiency of high-precision measurement and terrain modeling. Furthermore, because they do not require ground base stations, B2b and HAS can achieve high-precision real-time positioning in areas without network coverage, such as remote or mountainous areas, making them a reliable option for navigation and positioning. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention provides a UAV positioning and cruising method and system based on satellite-based augmentation. By utilizing Beidou B2b and Galileo HAS correction numbers, high-precision real-time positioning of the UAV can be achieved, and high-precision automatic cruising of the UAV can also be achieved in mountainous areas without network coverage or with weak signals.

[0007] The present invention provides a method for positioning and cruising of an unmanned aerial vehicle (UAV) based on satellite-based augmentation, the method comprising:

[0008] Step 1: Set the drone's cruising area, high-coverage cruising route, and flight time;

[0009] Step 2: Check the drone's software and hardware, take off from a safe area, and start automatic cruising;

[0010] Step 3: Obtain observation data, broadcast ephemeris, and BeiDou PPP-B2b and Galileo HAS products through the UAV receiver;

[0011] Step 4: Decode Beidou PPP-B2b and Galileo HAS messages and match corrections;

[0012] Step 5: Correct the satellite orbit and clock errors as well as the code and phase deviations based on the corrections provided by BeiDou PPP-B2b and Galileo HAS.

[0013] Step 6: Preprocess the corrected data by removing gross errors, repairing clock jumps, and detecting cycle slips;

[0014] Step 7: Use Kalman filtering to estimate the parameters of the preprocessed data to obtain high-precision position coordinates;

[0015] Step 8: In case of an emergency, the operator sends a return command to the drone via a Beidou short message;

[0016] Step 9: After the cruise mission is completed or the return command is received, the drone completes a safe landing at the planned return point.

[0017] Preferably, the Beidou PPP-B2b product comes from the Beidou-3 BDS-3 geosynchronous orbit GEO satellite and is broadcast via the B2b signal; the Galileo HAS product comes from the Galileo high-precision service and is broadcast via the satellite E6-B frequency.

[0018] Preferably, correcting the satellite orbit and clock error and the code and phase deviation according to the correction numbers provided by BeiDou PPP-B2b and Galileo HAS includes:

[0019] The steps to calculate and convert the current orbit correction number are:

[0020] Calculate the correction value corresponding to the current time t from B2b and the orbit correction value corresponding to the reference time t0 in the HAS orbit information:

[0021]

[0022] Where, Δ r , Δ a and Δ c are the radial, tangential and normal satellite position corrections; and is the corresponding speed;

[0023] Calculate the rotation matrix R between the star-fixed system and the Earth-centered Earth-fixed system:

[0024]

[0025] In the formula, r and are the satellite position and velocity vectors calculated from the broadcast ephemeris;

[0026] Convert the orbit corrections from the star-fixed system to the Earth-centered Earth-fixed system:

[0027]

[0028] Where, Δ x , Δ y and Δ z is the satellite orbit correction number in the Earth-centered Earth-fixed frame;

[0029] Combine the satellite orbit corrections in the Earth-centered Earth-fixed frame into the satellite positions calculated by broadcast ephemeris to recover the precise satellite orbit product:

[0030]

[0031] Where, X p 、Y p and Z p is the corrected coordinate of the satellite at time t in the Earth-centered fixed system; X b 、Y b and Z b are the satellite coordinates calculated from the broadcast ephemeris parameters in the correction information;

[0032] After B2b and HAS corrections, the coordinates of satellites from different systems will be unified into the ITRF framework;

[0033] The steps to calculate and restore the current real-time clock error correction are:

[0034] Calculate the correction value corresponding to the current time t using B2b and the clock correction value corresponding to the reference time t0 in the HAS clock information:

[0035] Δt C =C0+C1(t-t0)+C2(t-t0) 2 ,

[0036] Where Δt C is the real-time clock correction calculated from the clock correction information, where C0, C1, and C2 are the clock correction values;

[0037] Δt C Satellite clock error at time t incorporated into the broadcast ephemeris calculation

[0038]

[0039] Where, is the corrected satellite clock error at time t; c is the speed of light in vacuum.

[0040] Preferably, using Kalman filtering to perform parameter estimation on the preprocessed data includes:

[0041] The steps for parameter estimation using the non-differenced non-combined model and Kalman filtering are:

[0042]

[0043] Where, and are the pseudorange and carrier phase observation values ​​of different frequency bands; represents the geometric distance from the satellite to the phase center of the receiver antenna; c is the speed of light in a vacuum; dt r Indicates the receiver clock error; D r represents the receiver pseudorange hardware delay based on the ionosphere-free combination; zwd represents the tropospheric zenith wet delay, M is the projection function of zwd; γ i =f1 2 / f i 2 represents the proportional coefficient of frequency i; represents the slant ionospheric delay including the DCB effect between P1 and P2 code frequencies at the receiver; λ i represents the wavelength of frequency i; represents the frequency ambiguity affected by the phase and pseudorange hardware delays; represents the observation noise and unmodeled errors of pseudorange and phase;

[0044] The robust Kalman filter controls observations with small cycle slips or gross errors by constructing equivalent weights, reducing the impact of abnormal observations on parameter estimation:

[0045]

[0046] Where, is the equivalent weight matrix; p i is the weight corresponding to the observation; is the standardized residual; k0 and k1 are threshold constants.

[0047] The present invention also provides a satellite-based augmented UAV positioning and cruising system, the system being used to implement any one of the methods described above, the system comprising: a setting module, a checking module, an acquisition module, a matching module, a correction module, a preprocessing module, an estimation module, an instruction sending module, and an instruction completion module;

[0048] The setting module is used to set the drone's cruising area, high coverage cruising route and flight time;

[0049] The inspection module is used to check the drone software and hardware equipment, take off from a safe area, and start automatic cruising;

[0050] The acquisition module is used to obtain observation data, broadcast ephemeris, and Beidou PPP-B2b and Galileo HAS products through the UAV receiver;

[0051] The matching module is used to decode Beidou PPP-B2b and Galileo HAS messages and match correction numbers;

[0052] The correction module is used to correct the satellite orbit and clock error as well as the code and phase deviation according to the correction numbers provided by Beidou PPP-B2b and Galileo HAS;

[0053] The preprocessing module is used to perform gross error elimination, clock jump repair and cycle slip detection preprocessing on the corrected data;

[0054] The estimation module is used to use Kalman filtering to perform parameter estimation on the preprocessed data to obtain high-precision position coordinates;

[0055] The command sending module is used to send a return command to the UAV via a Beidou short message in case of an emergency;

[0056] The command completion module is used to ensure that the UAV lands safely at the planned return point after the cruise mission is completed or a return command is received.

[0057] Preferably, the Beidou PPP-B2b product comes from the Beidou-3 BDS-3 geosynchronous orbit GEO satellite and is broadcast via the B2b signal; the Galileo HAS product comes from the Galileo high-precision service and is broadcast via the satellite E6-B frequency.

[0058] Preferably, correcting the satellite orbit and clock error and the code and phase deviation according to the correction numbers provided by BeiDou PPP-B2b and Galileo HAS includes:

[0059] The steps to calculate and convert the current orbit correction number are:

[0060] Calculate the correction value corresponding to the current time t from B2b and the orbit correction value corresponding to the reference time t0 in the HAS orbit information:

[0061]

[0062] Where, Δ r , Δ a and Δ c are the radial, tangential and normal satellite position corrections; and is the corresponding speed;

[0063] Calculate the rotation matrix R between the star-fixed system and the Earth-centered Earth-fixed system:

[0064]

[0065] In the formula, r and are the satellite position and velocity vectors calculated from the broadcast ephemeris;

[0066] Convert the orbit corrections from the star-fixed system to the Earth-centered Earth-fixed system:

[0067]

[0068] Where, Δ x , Δ y and Δ z is the satellite orbit correction number in the Earth-centered Earth-fixed frame;

[0069] Combine the satellite orbit corrections in the Earth-centered Earth-fixed frame into the satellite positions calculated by broadcast ephemeris to recover the precise satellite orbit product:

[0070]

[0071] Where, X p 、Y p and Z p is the corrected coordinate of the satellite at time t in the Earth-centered fixed system; X b 、Y b and Z b are the satellite coordinates calculated from the broadcast ephemeris parameters in the correction information;

[0072] After B2b and HAS corrections, the coordinates of satellites from different systems will be unified into the ITRF framework;

[0073] The steps to calculate and restore the current real-time clock error correction are:

[0074] Calculate the correction value corresponding to the current time t using B2b and the clock correction value corresponding to the reference time t0 in the HAS clock information:

[0075] Δt C =C0+C1(t-t0)+C2(t-t0) 2 ,

[0076] Where Δt C is the real-time clock correction calculated from the clock correction information, where C0, C1, and C2 are the clock correction values;

[0077] Δt C Satellite clock error at time t incorporated into the broadcast ephemeris calculation

[0078]

[0079] Where, is the corrected satellite clock error at time t; c is the speed of light in vacuum.

[0080] Preferably, using Kalman filtering to perform parameter estimation on the preprocessed data includes:

[0081] The steps for parameter estimation using the non-differenced non-combined model and Kalman filtering are:

[0082]

[0083] Where, and are the pseudorange and carrier phase observation values ​​of different frequency bands; represents the geometric distance from the satellite to the phase center of the receiver antenna; c is the speed of light in a vacuum; dt r Indicates the receiver clock error; D r represents the receiver pseudorange hardware delay based on the ionosphere-free combination; zwd represents the tropospheric zenith wet delay, M is the projection function of zwd; γ i =f1 2 / f i 2 represents the proportional coefficient of frequency i; represents the slant ionospheric delay including the DCB effect between P1 and P2 code frequencies at the receiver; λ i represents the wavelength of frequency i; represents the frequency ambiguity affected by the phase and pseudorange hardware delays; represents the observation noise and unmodeled errors of pseudorange and phase;

[0084] The robust Kalman filter controls observations with small cycle slips or gross errors by constructing equivalent weights, reducing the impact of abnormal observations on parameter estimation:

[0085]

[0086] Where, is the equivalent weight matrix; p i is the weight corresponding to the observation; is the standardized residual; k0 and k1 are threshold constants.

[0087] Compared with the prior art, the present invention has the following beneficial effects:

[0088] The present invention discloses a method and system for positioning and cruising of a UAV based on satellite-based enhancement, comprising the following steps: Step 1, setting the range of the UAV cruising area, the high-coverage cruising route and the flight time. Step 2, checking the UAV software and hardware equipment and taking off from a safe area to start automatic cruising. Step 3, obtaining observation data, broadcast ephemeris and Beidou B2b and Galileo HAS products through the UAV receiver; Step 4, decoding the B2b and HAS telegrams and matching the correction numbers; Step 5, correcting the satellite orbit and clock error as well as the code and phase deviation according to the correction numbers provided by B2b and HAS; Step 6, performing gross error elimination, clock jump repair and cycle slip detection on the data; Step 7, using Kalman filtering to perform parameter estimation to obtain high-precision position coordinates. Step 8, in case of an emergency, the operator can send a return command to the UAV via a Beidou short message. Step 9, after the cruise mission is completed or the return command is received, the UAV completes a safe landing at the planned return point. The present invention obtains satellite signals from the Beidou and Galileo systems through the drone receiver, increases the number of positioning satellites, and does not transmit network information through ground receiving stations. This allows the drone to obtain high-precision real-time positioning without ground base station network coverage, and realizes accurate automatic cruising of the drone in mountainous areas without network coverage. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0090] Figure 1 This is a flowchart of real-time positioning of a UAV according to an embodiment of the present invention;

[0091] Figure 2 The figure is a flow chart of a method for positioning and cruising a UAV based on satellite-based augmentation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0092] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0093] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should have the usual meanings understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the described object changes, the relative position relationship may also change accordingly.

[0094] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0095] Example 1

[0096] like Figure 1 、 Figure 2 FIG. 1 is a flow chart of a method for positioning and cruising a UAV based on satellite-based augmentation according to an embodiment of the present invention. The method includes the following steps:

[0097] Step 1: Set the drone's cruising area, high-coverage cruising route, and flight time.

[0098] Step 2: Check the drone's software and hardware, take off from a safe area, and begin automatic cruising. A safe area includes: an open, flat surface with no debris within a 2.5-meter radius of the takeoff and landing area; no obstructions above the takeoff and landing area; and a clear area before takeoff or landing, with no personnel allowed to enter.

[0099] Step 3: Obtain observation data, broadcast ephemeris, and BeiDou B2b and Galileo HAS products through the UAV receiver;

[0100] Step 4: Decode the B2b and HAS messages and match the correction numbers;

[0101] Step 5: Correct the satellite orbit and clock error as well as the code and phase deviations based on the corrections provided by B2b and HAS;

[0102] Step 6: Eliminate gross errors, repair clock jumps, and detect cycle slips on the data;

[0103] Step 7: Use Kalman filtering to estimate parameters and obtain high-precision position coordinates.

[0104] Step 8. In case of emergency, the operator can send a return command to the drone via Beidou short message.

[0105] Step 9: After the cruise mission is completed or the return command is received, the drone completes a safe landing at the planned return point.

[0106] In this embodiment, step three, obtaining observation data, broadcast ephemeris, and high-precision service products through the UAV receiver, is an important step in precise positioning. Among them, the Beidou system's PPP-B2b product is broadcast by the Beidou-3 (BDS-3) geosynchronous orbit (GEO) satellite via the B2b signal, providing high-precision positioning, navigation, and timing services. The Galileo system's high-precision service (HAS) product is transmitted via the E6-B frequency point, providing users with reliable high-precision positioning information. These data are efficiently received and processed by the UAV receiver.

[0107] In this embodiment, step four, in the high-precision positioning process, it is necessary to decode the Beidou B2b and Galileo HAS messages and match the correction numbers. First, by decoding the B2b and HAS messages, their contents are converted into the standardized SSR format of the International GNSS Service (IGS) to ensure seamless compatibility with the existing precise point positioning (PPP) algorithm. At the same time, the correction numbers provided by B2b and HAS are strictly time-matched and aligned. When performing time matching, it is first necessary to ensure that the time scales of the two are consistent. HAS needs to be converted from UTC time to coordinated time UTC+8 to eliminate the time deviation between different systems and ensure that the correction data can be applied under a unified time base, thereby improving the accuracy and stability of high-precision positioning; secondly, effective time alignment is required. The correction numbers of B2b and HAS usually take effect within a certain time period, and there may be delays or update cycles, so it is necessary to ensure that the correction numbers received from both are in the same valid time period. Provide a high-quality data foundation for subsequent positioning solutions and support the high-precision positioning needs of multi-system integration.

[0108] In this embodiment, step five, based on the correction numbers provided by Beidou B2b and Galileo HAS, the satellite's orbit, clock error, and code and phase deviation are comprehensively corrected for high-precision positioning. In order to convert the real-time orbit correction numbers of B2b and HAS into a standard precise orbit suitable for precise point positioning (PPP), it is necessary to first convert them from the satellite-fixed coordinate system (star-fixed system) to the Earth-centered Earth-fixed coordinate system (ITRF). This conversion process ensures that the correction numbers can accurately characterize the changes in the satellite orbit in the Earth-centered coordinate system, laying the foundation for the precise correction of the orbit and clock error. At the same time, by accurately applying the correction numbers, the code deviation and phase deviation are synchronously corrected, which effectively improves the accuracy and reliability of the positioning solution. The steps for calculating and converting the orbit correction numbers at the current moment are as follows:

[0109] (1) Calculate the correction value corresponding to the current time t from B2b and the orbit correction value corresponding to the reference time t0 in the HAS orbit information:

[0110]

[0111] Where, Δ r , Δ a and Δ c are the radial, tangential and normal satellite position corrections; and is the corresponding speed.

[0112] (2) Calculate the rotation matrix R between the star-fixed system and the Earth-centered Earth-fixed system:

[0113]

[0114] In the formula, r and are the satellite position and velocity vectors calculated from the broadcast ephemeris.

[0115] (3) Convert the orbit corrections in the satellite-fixed system to the Earth-centered Earth-fixed system:

[0116]

[0117] Where, Δ x , Δ y and Δ z is the satellite orbit correction number in the Earth-centered Earth-fixed frame.

[0118] (4) Incorporate the satellite orbit corrections in the Earth-centered Earth-fixed frame into the satellite positions calculated using the broadcast ephemeris to recover the precise satellite orbit product:

[0119]

[0120] Where, X p 、Y p and Z p is the corrected coordinate of the satellite at time t in the Earth-centered fixed system; X b 、Y b and Z b Satellite coordinates are calculated from the broadcast ephemeris parameters in the correction information. After B2b and HAS corrections, the satellite coordinates of different systems will be unified into the ITRF (International Terrestrial Reference Frame) framework.

[0121] The real-time clock correction is defined as the difference between the precise satellite clock error and the clock error calculated by broadcast ephemeris, and is usually broadcast in the form of quadratic polynomial coefficients. Calculating and recovering the real-time clock correction at the current moment is divided into the following two steps:

[0122] (1) Calculate the correction corresponding to the current time t from B2b and the SSR clock correction corresponding to the reference time t0 in the HAS clock information:

[0123] Δt C =C0+C1(t-t0)+C2(t-t0) 2 ,

[0124] Where Δt C It is the real-time clock correction calculated from the clock correction information, where C0, C1, and C2 are the clock correction values.

[0125] (2) Δt C Satellite clock error at time t incorporated into the broadcast ephemeris calculation

[0126]

[0127] Where, is the corrected satellite clock error at time t; c is the speed of light in vacuum.

[0128] In this embodiment, step six, during high-precision positioning data processing, to ensure the reliability and accuracy of the solution, the data must undergo gross error removal, clock slip repair, and cycle slip detection. Gross error removal identifies and removes outliers in the observation data. These anomalies may be caused by hardware failures, signal obstruction, or multipath effects. The removal process utilizes statistical analysis methods to set a tolerance range and perform residual detection, marking and eliminating anomalous observations. Clock slip repair is performed for discontinuous changes in the receiver clock error. Sudden changes in the receiver's internal clock can cause significant jumps in the measured values. The repair process analyzes the time series characteristics of the observation data to identify the location of the clock slip and uses interpolation compensation to restore normal data continuity. Cycle slip detection and correction are then performed. A cycle slip is an integer cycle deviation caused by discontinuities in receiver signal processing, typically manifesting as a sudden change in the carrier phase observation. Double-difference observation analysis, sliding window detection, and high-order differencing methods can accurately identify the location and magnitude of the cycle slip. Correction is then performed using model inference and constraint solution methods to ensure the continuity and consistency of the phase observation data.

[0129] In this embodiment, step seven uses a Kalman filter for parameter estimation to obtain high-precision position coordinates. Kalman filtering fuses observed data with system model predictions to optimize parameter estimation while minimizing the impact of noise. First, the system's motion model is used to predict state variables. Based on the state estimate at the previous moment and the system's dynamic equations, the current prior state and its covariance matrix are inferred. The predictions are then corrected by introducing observed data. The observed values ​​collected by the receiver are compared with the predicted values, and the residual is calculated. The filter gain is then used to weight the prior state, taking into account the statistical characteristics of the observation noise, to obtain a posterior state estimate that is closer to the true value. Kalman filtering can update the system state in real time, gradually improving the accuracy of position coordinate estimation. In particular, in multi-system fusion positioning, Kalman filtering further improves the accuracy and stability of the solution by rationally addressing the noise characteristics and spatiotemporal deviations of data from different sources. The method for parameter estimation using a non-differenced, non-combined model and Kalman filtering is as follows:

[0130]

[0131] Where, and are the pseudorange and carrier phase observation values ​​of different frequency bands; represents the geometric distance from the satellite to the phase center of the receiver antenna; c is the speed of light in a vacuum; dt r Indicates the receiver clock error; D r represents the receiver pseudorange hardware delay based on the ionosphere-free combination; zwd represents the tropospheric zenith wet delay, M is the projection function of zwd; γ i =f1 2 / f i 2 represents the proportional coefficient of frequency i; represents the slant ionospheric delay including the DCB effect between P1 and P2 code frequencies at the receiver; λ i represents the wavelength of frequency i; represents the frequency ambiguity affected by the phase and pseudorange hardware delays; represents the observation noise and unmodeled errors of pseudorange and phase.

[0132] The robust Kalman filter controls observations with small cycle slips or gross errors by constructing equivalent weights, reducing the impact of abnormal observations on parameter estimation:

[0133]

[0134] Where, is the equivalent weight matrix; p i is the weight corresponding to the observation; is the standardized residual; k0 and k1 are threshold constants.

[0135] In this embodiment, in step 8, in the event of an emergency, the operator can send a return command to the drone via a Beidou short message. Beidou short messages are a unique communication feature of the Beidou navigation system. They allow users to conduct two-way data transmission via Beidou satellites, providing reliable communication even in environments without a terrestrial network. The operator sends the return command to the Beidou satellite via a Beidou short message, which then relays the information to the drone. The drone's receiver terminal receives the short message, interprets it as a recognizable command, and then returns to its destination accordingly.

[0136] It should be noted that the method of the embodiments of the present disclosure can be performed by a single device, such as a computer or server. The method of the embodiments of the present disclosure can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the multiple devices will interact with each other to complete the method.

[0137] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, it should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution, and the execution order of each process 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. The actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

[0138] Example 2

[0139] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present invention further provides a satellite-based augmented UAV positioning and cruising system, the system being used to implement any of the above-mentioned methods, the system comprising: a setting module, a checking module, an acquisition module, a matching module, a correction module, a preprocessing module, an estimation module, a command sending module, and a command completion module;

[0140] The setting module is used to set the drone's cruising area, high-coverage cruising route, and flight time;

[0141] The inspection module is used to check the drone's software and hardware equipment, take off from a safe area, and start automatic cruising;

[0142] The acquisition module is used to obtain observation data, broadcast ephemeris, and Beidou PPP-B2b and Galileo HAS products through the UAV receiver;

[0143] The matching module is used to decode Beidou PPP-B2b and Galileo HAS messages and match correction numbers;

[0144] The correction module is used to correct the satellite orbit and clock error as well as the code and phase deviation based on the correction numbers provided by BeiDou PPP-B2b and Galileo HAS.

[0145] The preprocessing module is used to perform gross error elimination, clock jump repair and cycle slip detection preprocessing on the corrected data;

[0146] The estimation module is used to use Kalman filtering to perform parameter estimation on the preprocessed data to obtain high-precision position coordinates;

[0147] The command sending module is used to send return instructions to the drone via Beidou short messages in case of emergencies;

[0148] The command completion module is used to ensure that the drone lands safely at the planned return point after the cruise mission is completed or the return command is received.

[0149] In this embodiment, the Beidou PPP-B2b product comes from the Beidou-3 BDS-3 geosynchronous orbit GEO satellite and is broadcast via the B2b signal; the Galileo HAS product comes from the Galileo high-precision service and is broadcast via the satellite E6-B frequency.

[0150] In this embodiment, correcting the satellite orbit and clock error as well as the code and phase deviations according to the corrections provided by BeiDou PPP-B2b and Galileo HAS includes:

[0151] The steps to calculate and convert the current orbit correction number are:

[0152] Calculate the correction value corresponding to the current time t from B2b and the orbit correction value corresponding to the reference time t0 in the HAS orbit information:

[0153]

[0154] Where, Δ r , Δ a and Δ c are the radial, tangential and normal satellite position corrections; and is the corresponding speed;

[0155] Calculate the rotation matrix R between the star-fixed system and the Earth-centered Earth-fixed system:

[0156]

[0157] In the formula, r and are the satellite position and velocity vectors calculated from the broadcast ephemeris;

[0158] Convert the orbit corrections from the star-fixed system to the Earth-centered Earth-fixed system:

[0159]

[0160] Where, Δ x , Δ y and Δ z is the satellite orbit correction number in the Earth-centered Earth-fixed frame;

[0161] Combine the satellite orbit corrections in the Earth-centered Earth-fixed frame into the satellite positions calculated by broadcast ephemeris to recover the precise satellite orbit product:

[0162]

[0163] Where, X p 、Y p and Z p is the corrected coordinate of the satellite at time t in the Earth-centered fixed system; X b 、Y b and Z b are the satellite coordinates calculated from the broadcast ephemeris parameters in the correction information;

[0164] After B2b and HAS corrections, the coordinates of satellites from different systems will be unified into the ITRF framework;

[0165] The steps to calculate and restore the current real-time clock error correction are:

[0166] Calculate the correction value corresponding to the current time t using B2b and the clock correction value corresponding to the reference time t0 in the HAS clock information:

[0167] Δt C =C0+C1(t-t0)+C2(t-t0) 2 ,

[0168] Where Δt C is the real-time clock correction calculated from the clock correction information, where C0, C1, and C2 are the clock correction values;

[0169] Δt C Satellite clock error at time t incorporated into the broadcast ephemeris calculation

[0170]

[0171] Where, is the corrected satellite clock error at time t; c is the speed of light in vacuum.

[0172] In this embodiment, using Kalman filtering to perform parameter estimation on the preprocessed data includes:

[0173] The steps for parameter estimation using the non-differenced non-combined model and Kalman filtering are:

[0174]

[0175] Where, and are the pseudorange and carrier phase observation values ​​of different frequency bands; represents the geometric distance from the satellite to the phase center of the receiver antenna; c is the speed of light in a vacuum; dt r Indicates the receiver clock error; D r represents the receiver pseudorange hardware delay based on the ionosphere-free combination; zwd represents the tropospheric zenith wet delay, M is the projection function of zwd; γ i =f1 2 / f i 2 represents the proportional coefficient of frequency i; represents the slant ionospheric delay including the DCB effect between P1 and P2 code frequencies at the receiver; λ i represents the wavelength of frequency i; represents the frequency ambiguity affected by the phase and pseudorange hardware delays; represents the observation noise and unmodeled errors of pseudorange and phase.

[0176] The robust Kalman filter controls observations with small cycle slips or gross errors by constructing equivalent weights, reducing the impact of abnormal observations on parameter estimation:

[0177]

[0178] Where, is the equivalent weight matrix; p i is the weight corresponding to the observation; is the standardized residual; k0 and k1 are threshold constants.

[0179] The system of the above embodiment is used to implement a corresponding satellite-based enhanced UAV positioning and cruising method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0180] It should be noted that the satellite-based augmented UAV positioning and cruise system is implemented in the form of functional units. The term "module" here can be implemented in software and / or hardware form, and is not specifically limited to this.

[0181] For example, a "module" may be a software program, a hardware circuit, or a combination of the two that implements the aforementioned functionality. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (e.g., a shared processor, a dedicated processor, or a group of processors) and memory for executing one or more software or firmware programs, combined logic circuits, and / or other suitable components that support the described functionality.

[0182] The embodiments of the present disclosure are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A UAV positioning and cruising method based on satellite-based augmentation, characterized in that: The method comprises: Step 1: Set the drone's cruising area, high-coverage cruising route, and flight time; Step 2: Check the drone's software and hardware, take off from a safe area, and start automatic cruising; Step 3: Obtain observation data, broadcast ephemeris, and BeiDou PPP-B2b and Galileo HAS products through the UAV receiver; Step 4: Decode Beidou PPP-B2b and Galileo HAS messages and match corrections; Step 5: Correct the satellite orbit and clock errors as well as the code and phase deviations based on the corrections provided by BeiDou PPP-B2b and Galileo HAS. Step 6: Preprocess the corrected data by removing gross errors, repairing clock jumps, and detecting cycle slips; Step 7: Use Kalman filtering to estimate the parameters of the preprocessed data to obtain high-precision position coordinates; Step 8: In case of an emergency, the operator sends a return command to the drone via a Beidou short message; Step 9: After the cruise mission is completed or the return command is received, the drone completes a safe landing at the planned return point; Using Kalman filtering to estimate parameters of preprocessed data includes: The steps for parameter estimation using the non-differenced non-combined model and Kalman filtering are: ， Where, 、 and 、 are the pseudorange and carrier phase observation values ​​of different frequency bands; Indicates the geometric distance from the satellite end to the phase center of the receiver antenna; is the speed of light in a vacuum; Indicates the receiver clock error; represents the receiver pseudorange hardware delay based on the ionosphere-free combination; represents the tropospheric zenith wet delay, for The projection function of Indicates frequency The proportionality coefficient of represents the slant ionospheric delay including the DCB effect between P1 and P2 code frequencies at the receiver; Indicates frequency wavelength; Indicates the phase and pseudorange hardware delay frequency ambiguity; 、 represents the observation noise and unmodeled errors of pseudorange and phase; The robust Kalman filter controls observations with small cycle slips or gross errors by constructing equivalent weights, reducing the impact of abnormal observations on parameter estimation: ， Where, is the equivalent weight matrix; is the weight corresponding to the observation; is the standardized residual; and is the threshold constant.

2. The method according to claim 1, characterized in that Beidou PPP-B2b products come from the Beidou-3 BDS-3 geosynchronous orbit GEO satellite and are broadcast via the B2b signal; Galileo HAS products come from the Galileo high-precision service and are broadcast via the satellite E6-B frequency.

3. The method according to claim 1, characterized in that Corrections for satellite orbits, clock errors, code and phase deviations are performed based on the corrections provided by BeiDou PPP-B2b and Galileo HAS. These corrections include: The steps to calculate and convert the current orbit correction number are: Calculate the correction value corresponding to the current time t from B2b and the orbit correction value corresponding to the reference time t0 in the HAS orbit information: ， Where, 、 and are the radial, tangential and normal satellite position corrections; 、 and is the corresponding speed; Calculate the rotation matrix R between the star-fixed system and the Earth-centered Earth-fixed system: ， Where, and are the satellite position and velocity vectors calculated from the broadcast ephemeris; Convert the orbit corrections from the star-fixed system to the Earth-centered Earth-fixed system: ， Where, 、 and is the satellite orbit correction number in the Earth-centered Earth-fixed frame; Combine the satellite orbit corrections in the Earth-centered Earth-fixed frame into the satellite positions calculated by broadcast ephemeris to recover the precise satellite orbit product: ， Where, 、 and is the corrected coordinate of the satellite at time t in the Earth-centered Earth-fixed frame; 、 and are the satellite coordinates calculated from the broadcast ephemeris parameters in the correction information; After B2b and HAS corrections, the coordinates of satellites from different systems will be unified into the ITRF framework; The steps to calculate and restore the current real-time clock error correction are: Calculate the correction value corresponding to the current time t using B2b and the clock correction value corresponding to the reference time t0 in the HAS clock information: ， Where, is the real-time clock correction calculated from the clock correction information, 、 、 is the clock correction number; Will Satellite clock error at time t incorporated into the broadcast ephemeris calculation : ， Where, is the corrected satellite clock error at time t; c is the speed of light in vacuum.

4. A satellite-based augmented UAV positioning and cruising system, characterized in that: The system includes: a setting module, a checking module, an acquisition module, a matching module, a correction module, a pre-processing module, an estimation module, an instruction sending module, and an instruction completion module; The setting module is used to set the drone's cruising area, high coverage cruising route and flight time; The inspection module is used to check the drone software and hardware equipment, take off from a safe area, and start automatic cruising; The acquisition module is used to obtain observation data, broadcast ephemeris, and Beidou PPP-B2b and Galileo HAS products through the UAV receiver; The matching module is used to decode Beidou PPP-B2b and Galileo HAS messages and match correction numbers; The correction module is used to correct the satellite orbit and clock error as well as the code and phase deviation according to the correction numbers provided by Beidou PPP-B2b and Galileo HAS; The preprocessing module is used to perform gross error elimination, clock jump repair and cycle slip detection preprocessing on the corrected data; The estimation module is used to use Kalman filtering to perform parameter estimation on the preprocessed data to obtain high-precision position coordinates; The command sending module is used to send a return command to the UAV via a Beidou short message in case of an emergency; The command completion module is used to ensure that the UAV lands safely at the planned return point after the cruise mission is completed or a return command is received; Using Kalman filtering to estimate parameters of preprocessed data includes: The steps for parameter estimation using the non-differenced non-combined model and Kalman filtering are: ， Where, 、 and 、 are the pseudorange and carrier phase observation values ​​of different frequency bands; Indicates the geometric distance from the satellite end to the phase center of the receiver antenna; is the speed of light in a vacuum; Indicates the receiver clock error; represents the receiver pseudorange hardware delay based on the ionosphere-free combination; represents the tropospheric zenith wet delay, for The projection function of Indicates frequency The proportionality coefficient of represents the slant ionospheric delay including the DCB effect between P1 and P2 code frequencies at the receiver; Indicates frequency wavelength; Indicates the phase and pseudorange hardware delay frequency ambiguity; 、 represents the observation noise and unmodeled errors of pseudorange and phase; The robust Kalman filter controls observations with small cycle slips or gross errors by constructing equivalent weights, reducing the impact of abnormal observations on parameter estimation: ， Where, is the equivalent weight matrix; is the weight corresponding to the observation; is the standardized residual; and is the threshold constant.

5. The system according to claim 4, characterized in that Beidou PPP-B2b products come from the Beidou-3 BDS-3 geosynchronous orbit GEO satellite and are broadcast via the B2b signal; Galileo HAS products come from the Galileo high-precision service and are broadcast via the satellite E6-B frequency.

6. The system according to claim 4, characterized in that Corrections for satellite orbits, clock errors, code and phase deviations are performed based on the corrections provided by BeiDou PPP-B2b and Galileo HAS. These corrections include: The steps to calculate and convert the current orbit correction number are: Calculate the correction value corresponding to the current time t from B2b and the orbit correction value corresponding to the reference time t0 in the HAS orbit information: ， Where, 、 and are the radial, tangential and normal satellite position corrections; 、 and is the corresponding speed; Calculate the rotation matrix R between the star-fixed system and the Earth-centered Earth-fixed system: ， Where, and are the satellite position and velocity vectors calculated from the broadcast ephemeris; Convert the orbit corrections from the star-fixed system to the Earth-centered Earth-fixed system: ， Where, 、 and is the satellite orbit correction number in the Earth-centered Earth-fixed frame; Combine the satellite orbit corrections in the Earth-centered Earth-fixed frame into the satellite positions calculated by broadcast ephemeris to recover the precise satellite orbit product: ， Where, 、 and is the corrected coordinate of the satellite at time t in the Earth-centered Earth-fixed frame; 、 and are the satellite coordinates calculated from the broadcast ephemeris parameters in the correction information; After B2b and HAS corrections, the coordinates of satellites from different systems will be unified into the ITRF framework; The steps to calculate and restore the current real-time clock error correction are: Calculate the correction value corresponding to the current time t using B2b and the clock correction value corresponding to the reference time t0 in the HAS clock information: ， Where, is the real-time clock correction calculated from the clock correction information, 、 、 is the clock correction number; Will Satellite clock error at time t incorporated into the broadcast ephemeris calculation : ， Where, is the corrected satellite clock error at time t; c is the speed of light in vacuum.

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