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

Abstract

The invention belongs to the technical field of satellite positioning, and discloses an unmanned aerial vehicle positioning cruise method and system based on satellite-based enhancement, and the method comprises the steps: setting an unmanned aerial vehicle cruise region range, a high-coverage cruise route and flight time; and checking software and hardware equipment of the unmanned aerial vehicle, taking off from a safe area, and starting automatic cruise. Observation data, broadcast ephemeris and Beidou B2b and Galileo HAS products are obtained through an unmanned aerial vehicle receiver. Carrying out decoding and correction number matching on the B2b and the HAS messages; correcting the satellite orbit, the clock error and the code and phase deviation according to the correction number provided by the B2b and the HAS; performing gross error elimination, clock jump repair and cycle slip detection on the data; and performing parameter estimation by using Kalman filtering to obtain high-precision position coordinates. If an emergency occurs, an operator can send a return instruction to the unmanned aerial vehicle through a Beidou short message. And after the cruise task is finished or a return instruction is received, the unmanned aerial vehicle 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 based on satellite-based enhancement. Background Art

[0002] Satellite-based augmentation system (SBAS) is a technology that uses satellites to provide navigation signal enhancement, aiming to improve the accuracy, integrity, availability and continuity of global navigation satellite system (GNSS) positioning services. SBAS receives and monitors GNSS signals through ground base stations, calculates errors and generates correction information, which is then broadcast to users via geostationary orbit satellites. Beidou Navigation Satellite System (BDS) and Galileo Navigation Satellite System (GALILEO) implement satellite-based augmentation 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 satellite navigation system, designed to support a wide range of precision positioning applications. The service broadcasts precise orbit and clock correction parameters and other auxiliary data directly to user terminals through the Beidou system's B2b frequency, allowing users to achieve precise point positioning (PPP) without relying on ground reference stations or Internet connections. The PPP-B2b service is based on the Beidou-3 satellite system. A global network of ground monitoring stations collects navigation satellite data in real time, and processes it through the ground control center to generate precise orbit, clock and other correction information. These data are then uploaded to Beidou satellites and broadcast to global users using B2b signals. After receiving the signal, the user terminal can achieve high-precision positioning at the centimeter to decimeter level after combining its own GNSS observation data. Unlike traditional PPP services, PPP-B2b directly broadcasts enhanced information through satellites without the need for Internet support, and has the advantages of global coverage, low latency and high reliability.

[0004] Galileo High Accuracy Service (HAS) is a global high-precision positioning enhancement service provided by the European Galileo satellite navigation system, aiming to provide users with sub-meter or even higher precision positioning capabilities. HAS broadcasts precise orbits, satellite clock correction parameters and other enhancement data through the E6 frequency band of the Galileo system, enabling user terminals to achieve precise single-point positioning without relying on ground base stations or Internet connections. The core technology of HAS relies on the high-performance satellite constellation and ground control network of the Galileo system. The ground stations deployed by Galileo around the world are responsible for collecting real-time orbit and clock data of navigation satellites, which are processed by the ground main control center to generate precise correction information. The correction information includes the satellite's precise orbit data, precise clock correction, signal deviation parameters and auxiliary information to support positioning solution. These data are packaged and broadcast to global users in real time through the E6 signal of the Galileo satellite. User receivers can directly decode and combine their own observation data for high-precision positioning solution.

[0005] Beidou PPP-B2b and Galileo HAS have broad application prospects, especially for UAV navigation, precision agriculture, unmanned driving, surveying and mapping, disaster monitoring and traffic management. For example, in precision agriculture, using B2b and HAS services, UAVs can accurately perform path planning and realize automated and fine operations in farmland; in the field of surveying and mapping, B2b and HAS can greatly improve the efficiency of high-precision measurement and terrain modeling. In addition, due to their characteristics of not requiring ground base stations, B2b and HAS can achieve high-precision real-time positioning in situations without network coverage, such as remote areas or mountainous areas, becoming a reliable navigation and positioning option. 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 enhancement. The Beidou B2b and Galileo HAS correction numbers can be used to achieve high-precision real-time positioning of the UAV, 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 UAV positioning and cruising method 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 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 drone receiver;

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

[0012] Step 5: Correct the satellite orbit and clock error as well as the code and phase deviation according to the corrections provided by BeiDou PPP-B2b and Galileo HAS;

[0013] Step 6: Preprocess the corrected data by eliminating 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 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] The correction number corresponding to the current time t is calculated from the orbit correction number corresponding to the reference time t0 in B2b and the HAS orbit information:

[0021]

[0022] In the formula, 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 orbit corrections from the star-fixed system to the Earth-centered Earth-fixed system:

[0027]

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

[0029] Combine the satellite orbit corrections in the Earth-centered Earth-fixed frame into the satellite positions calculated by the 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 Earth-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 satellite coordinates of different systems will be unified under the ITRF framework;

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

[0034] The correction number corresponding to the current time t is calculated from the clock correction number corresponding to the reference time t0 in B2b and the HAS clock correction information:

[0035] Δt C =C0+C1(t-t0)+C2(t-t0) 2 ,

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

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

[0038]

[0039] In the formula, 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] In the formula, 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 proportionality coefficient of frequency i; represents the oblique ionospheric delay including the DCB effect between P1 and P2 code frequencies at the receiver; 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 the observations with small cycle slips or gross errors by constructing equivalent weights, reducing the impact of abnormal observations on parameter estimation:

[0045]

[0046] In the formula, 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 cruise system, the system is used to implement any one of the methods described, the system comprises: 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 range of the drone cruising area, the high coverage cruising route and the flight time;

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

[0050] The acquisition module is used to acquire observation data, broadcast ephemeris, and Beidou PPP-B2b and Galileo HAS products through a 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 perform parameter estimation on the preprocessed data using Kalman filtering to obtain high-precision position coordinates;

[0055] The command sending module is used for the operator 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 complete the safe landing of the UAV at the planned return point after the cruise mission is completed or the 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 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] The correction number corresponding to the current time t is calculated from the orbit correction number corresponding to the reference time t0 in B2b and the HAS orbit information:

[0061]

[0062] In the formula, 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 orbit corrections from the star-fixed system to the Earth-centered Earth-fixed system:

[0067]

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

[0069] Combine the satellite orbit corrections in the Earth-centered Earth-fixed frame into the satellite positions calculated by the 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 Earth-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 satellite coordinates of different systems will be unified under the ITRF framework;

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

[0074] The correction number corresponding to the current time t is calculated from the clock correction number corresponding to the reference time t0 in B2b and the HAS clock correction information:

[0075] Δt C =C0+C1(t-t0)+C2(t-t0) 2 ,

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

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

[0078]

[0079] In the formula, 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] In the formula, 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 proportionality coefficient of frequency i; represents the oblique ionospheric delay including the DCB effect between P1 and P2 code frequencies at the receiver; 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 the observations with small cycle slips or gross errors by constructing equivalent weights, reducing the impact of abnormal observations on parameter estimation:

[0085]

[0086] In the formula, 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 unmanned aerial vehicles based on satellite-based enhancement, comprising the following steps: Step 1, setting the range of the unmanned aerial vehicle cruising area, the high-coverage cruising route and the flight time. Step 2, checking the software and hardware equipment of the unmanned aerial vehicle 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 unmanned aerial vehicle receiver; Step 4, decoding B2b and HAS telegrams and matching correction numbers; Step 5, correcting the satellite orbit and clock error and 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 and obtain high-precision position coordinates. Step 8, in case of an emergency, the operator can send a return command to the unmanned aerial vehicle through a Beidou short message. Step 9, after the cruise mission is completed or the return command is received, the unmanned aerial vehicle completes a safe landing at the planned return point. The present invention obtains satellite signals from the Beidou and Galileo systems through the UAV receiver, increases the number of positioning satellites and does not transmit network information through ground receiving stations, so that the UAV can obtain high-precision real-time positioning without ground base station network coverage, and realizes accurate automatic cruising of the UAV in mountainous areas without network. 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 creative labor.

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

[0091] Figure 2 The present invention is a flowchart 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 be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work 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 be 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. "Including" or "comprising" and similar words mean that the elements or objects appearing before the word cover 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 can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional 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] Embodiment 1

[0096] like Figure 1 , 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. The method comprises 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 software and hardware equipment and take off from a safe area to start automatic cruising. The safe area includes: the take-off and landing point is open, the ground is flat, there is no debris within a radius of 2.5 meters from the take-off and landing point; there is no obstruction above the take-off and landing area; the area is cleared before take-off or landing, and no one is 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 B2b and HAS messages and match correction numbers;

[0101] Step 5: Correct the satellite orbit and clock error as well as the code and phase deviation according to the correction numbers 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 is to obtain observation data, broadcast ephemeris and high-precision service products through the drone receiver, which is an important step for accurate positioning. Among them, the PPP-B2b product of the Beidou system is broadcast by the Beidou-3 (BDS-3) geosynchronous orbit (GEO) satellite through the B2b signal, providing high-precision positioning, navigation and timing services. The high-precision service (HAS) product of the Galileo system is transmitted through the E6-B frequency point to provide users with reliable high-precision positioning information. These data are efficiently received and processed by the drone 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, convert their contents 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 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 reference, 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 requirements 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 deviations are comprehensively corrected for high-precision positioning. In order to convert the real-time orbit correction numbers of B2b and HAS into standard precise orbits 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 geocentric 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 to calculate and convert the orbit correction numbers at the current moment are as follows:

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

[0110]

[0111] In the formula, 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 correction value in the satellite-fixed system to the Earth-centered Earth-fixed system:

[0116]

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

[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 Earth-fixed system; X b , Y b and Z b The satellite coordinates are calculated from the broadcast ephemeris parameters in the correction information. After B2b and HAS correction, 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 restoring 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 correction information:

[0123] Δt C =C0+C1(t-t0)+C2(t-t0) 2 ,

[0124] In the formula, Δt C It is the real-time clock correction calculated from the clock correction information. C0, C1, and C2 are the clock correction numbers.

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

[0126]

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

[0128] In this embodiment, step six, in the high-precision positioning data processing, in order to ensure the reliability and accuracy of the solution results, the data needs to be processed for gross error elimination, clock jump repair and cycle jump detection. The outliers in the observation data are identified and removed by the gross error elimination method. These anomalies may come from hardware failure, signal shielding or multipath effects. The elimination process uses statistical analysis methods to set the tolerance range and perform residual detection, mark and exclude abnormal observations. Clock jump repair is performed for discontinuous changes in the receiver clock error. When the internal clock of the receiver changes suddenly, it will cause obvious jumps in the measured value. The repair process analyzes the time series characteristics of the observed data, identifies the location where the clock jump occurs, and uses the interpolation compensation method to restore normal data continuity. Then cycle jump detection and correction are performed. The cycle jump is an integer cycle deviation caused by discontinuity in the receiver signal processing, usually manifested as a sudden change in the carrier phase observation value. Through double difference observation value analysis, sliding window detection and high-order difference methods, the location and amplitude of the cycle jump can be accurately identified, and the model calculation and constraint solution method are used for correction to ensure the continuity and consistency of the phase observation data.

[0129] In this embodiment, step seven, use Kalman filtering to perform parameter estimation and obtain high-precision position coordinates: Kalman filtering optimizes parameter estimation to the greatest extent while reducing the impact of noise by fusing observed data and system model prediction values. First, the state variables are predicted using the system's motion model. According to the state estimate value at the previous moment and the system dynamic equation, the prior state and its covariance matrix at the current moment are deduced. Then, the prediction results are corrected by introducing observed data. The observed values ​​collected by the receiver are compared with the predicted values, the residual is calculated, and the filter gain is used to perform weighted correction on the prior state in combination with the statistical characteristics of the observation noise, thereby obtaining a posterior state estimate that is closer to the true value. Kalman filtering can update the system state in real time and gradually improve the estimation accuracy of the position coordinates. Especially in multi-system fusion positioning, Kalman filtering further improves the accuracy and stability of the solution results by reasonably processing the noise characteristics and spatiotemporal deviations of data from different sources. The method for parameter estimation using a non-difference non-combination model and using Kalman filtering is as follows:

[0130]

[0131] In the formula, 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 proportionality coefficient of frequency i; represents the oblique ionospheric delay including the DCB effect between P1 and P2 code frequencies at the receiver; 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 the observations with small cycle slips or gross errors by constructing equivalent weights, reducing the impact of abnormal observations on parameter estimation:

[0133]

[0134] In the formula, 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 eight, in case of an emergency, the operator can send a return command to the drone via a Beidou short message. Beidou short message is a unique communication function of the Beidou navigation system, which allows users to perform two-way data transmission through Beidou satellites and provide reliable communication in an environment without a ground network. The operator sends the return command to the Beidou satellite via a Beidou short message, and then the satellite relays the information to the drone. The receiver terminal on the drone receives the short message information, parses it into recognizable instructions, and returns according to the instruction information.

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

[0137] It should be noted that some embodiments of the present disclosure are described above. Other embodiments are within the scope of the attached claims. In some cases, it should be understood that the size of the serial number of each step in the above embodiment 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 embodiment and still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0138] Embodiment 2

[0139] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present invention also provides a satellite-based augmented UAV positioning and cruising system, the system is used to implement any of the methods described, 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;

[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 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 according to 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 for the operator to send a return command to the drone via Beidou short message in case of an emergency;

[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 through the B2b signal; the Galileo HAS product comes from the Galileo high-precision service and is broadcast through the satellite E6-B frequency.

[0150] In this embodiment, the correction of 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:

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

[0152] The correction number corresponding to the current time t is calculated from the orbit correction number corresponding to the reference time t0 in B2b and the HAS orbit information:

[0153]

[0154] In the formula, 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 orbit corrections from the star-fixed system to the Earth-centered Earth-fixed system:

[0159]

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

[0161] Combine the satellite orbit corrections in the Earth-centered Earth-fixed frame into the satellite positions calculated by the 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 Earth-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 satellite coordinates of different systems will be unified under the ITRF framework;

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

[0166] The correction number corresponding to the current time t is calculated from the clock correction number corresponding to the reference time t0 in B2b and the HAS clock correction information:

[0167] Δt C =C0+C1(t-t0)+C2(t-t0) 2 ,

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

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

[0170]

[0171] In the formula, 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] In the formula, 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 proportionality coefficient of frequency i; represents the oblique ionospheric delay including the DCB effect between P1 and P2 code frequencies at the receiver; 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 the observations with small cycle slips or gross errors by constructing equivalent weights, reducing the impact of abnormal observations on parameter estimation:

[0177]

[0178] In the formula, 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 embodiments, which will not be repeated here.

[0180] It should be noted that the satellite-based augmented UAV positioning cruise system is embodied in the form of a functional unit. The term "module" here can be implemented in the form of software and / or hardware, 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 above functions. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a dedicated processor, or a group processor, etc.) and a memory for executing one or more software or firmware programs, a combined logic circuit, and / or other suitable components that support the described functions.

[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 method for positioning and cruising of unmanned aerial vehicles 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 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 drone receiver; Step 4: Decode Beidou PPP-B2b and Galileo HAS messages and match correction numbers; Step 5: Correct the satellite orbit and clock error as well as the code and phase deviation according to the corrections provided by BeiDou PPP-B2b and Galileo HAS; Step 6: Preprocess the corrected data by eliminating 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.

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 Correction of satellite orbit and clock error as well as code and phase deviation based on corrections provided by BeiDou PPP-B2b and Galileo HAS includes: The steps to calculate and convert the current orbit correction number are: The correction number corresponding to the current time t is calculated from the orbit correction number corresponding to the reference time t0 in B2b and the HAS orbit information: In the formula, Δ r , Δ a and Δ c 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: In the formula, r and are the satellite position and velocity vectors calculated from the broadcast ephemeris; Convert orbit corrections from the star-fixed system to the Earth-centered Earth-fixed system: In the formula, Δ x , Δ y and Δ z is the satellite orbit correction number in the Earth-centered Earth-fixed system; Combine the satellite orbit corrections in the Earth-centered Earth-fixed frame into the satellite positions calculated by the broadcast ephemeris to recover the precise satellite orbit product: Where, X p , Y p and Z p is the corrected coordinate of the satellite at time t in the Earth-centered Earth-fixed frame; X b , Y b and Z b are the satellite coordinates calculated from the broadcast ephemeris parameters in the correction information; After B2b and HAS corrections, the satellite coordinates of different systems will be unified under the ITRF framework; The steps to calculate and restore the current real-time clock error correction are: The correction number corresponding to the current time t is calculated from the clock correction number corresponding to the reference time t0 in B2b and the HAS clock correction information: Δt C =C0+C1(t-t0)+C2(t-t0) 2 , In the formula, Δt C is the real-time clock correction calculated from the clock correction information, where C0, C1, and C2 are the clock correction numbers; Δt C Satellite clock error at time t incorporated into the broadcast ephemeris calculation In the formula, is the corrected satellite clock error at time t; c is the speed of light in vacuum.

4. The method according to claim 1, characterized in that 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: In the formula, 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 proportionality coefficient of frequency i; represents the oblique ionospheric delay including the DCB effect between P1 and P2 code frequencies at the receiver; 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; The robust Kalman filter controls the observations with small cycle slips or gross errors by constructing equivalent weights, reducing the impact of abnormal observations on parameter estimation: In the formula, is the equivalent weight matrix; p i is the weight corresponding to the observation; is the standardized residual; k0 and k1 are threshold constants.

5. A satellite-based augmented UAV positioning cruise system, the system being used to implement the method described in any one of claims 1 to 4, characterized in that: The system comprises: a setting module, a checking module, an acquiring module, a matching module, a correction module, a preprocessing module, an estimating module, an instruction sending module, and an instruction completing module; The setting module is used to set the range of the drone cruising area, the high coverage cruising route and the flight time; The inspection module is used to inspect the software and hardware of the drone, take off from a safe area, and start automatic cruising; The acquisition module is used to acquire observation data, broadcast ephemeris, and Beidou PPP-B2b and Galileo HAS products through a 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 perform parameter estimation on the preprocessed data using Kalman filtering to obtain high-precision position coordinates; The command sending module is used for the operator 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 complete the safe landing of the UAV at the planned return point after the cruise mission is completed or the return command is received.

6. The system according to claim 5, 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.

7. The system according to claim 5, characterized in that Correction of satellite orbit and clock error as well as code and phase deviation based on corrections provided by BeiDou PPP-B2b and Galileo HAS includes: The steps to calculate and convert the current orbit correction number are: The correction number corresponding to the current time t is calculated from the orbit correction number corresponding to the reference time t0 in B2b and the HAS orbit information: In the formula, Δ r , Δ a and Δ c 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: In the formula, r and are the satellite position and velocity vectors calculated from the broadcast ephemeris; Convert orbit corrections from the star-fixed system to the Earth-centered Earth-fixed system: In the formula, Δ x , Δ y and Δ z is the satellite orbit correction number in the Earth-centered Earth-fixed system; Combine the satellite orbit corrections in the Earth-centered Earth-fixed frame into the satellite positions calculated by the broadcast ephemeris to recover the precise satellite orbit product: Where, X p , Y p and Z p is the corrected coordinate of the satellite at time t in the Earth-centered Earth-fixed frame; X b , Y b and Z b are the satellite coordinates calculated from the broadcast ephemeris parameters in the correction information; After B2b and HAS corrections, the satellite coordinates of different systems will be unified under the ITRF framework; The steps to calculate and restore the current real-time clock error correction are: The correction number corresponding to the current time t is calculated from the clock correction number corresponding to the reference time t0 in B2b and the HAS clock correction information: Δt C =C0+C1(t-t0)+C2(t-t0) 2 , In the formula, Δt C is the real-time clock correction calculated from the clock correction information, where C0, C1, and C2 are the clock correction numbers; Δt C Satellite clock error at time t incorporated into the broadcast ephemeris calculation In the formula, is the corrected satellite clock error at time t; c is the speed of light in vacuum.

8. The system according to claim 5, characterized in that 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: In the formula, 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 proportionality coefficient of frequency i; represents the oblique ionospheric delay including the DCB effect between P1 and P2 code frequencies at the receiver; 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; The robust Kalman filter controls the observations with small cycle slips or gross errors by constructing equivalent weights, reducing the impact of abnormal observations on parameter estimation: In the formula, is the equivalent weight matrix; p i is the weight corresponding to the observation; is the standardized residual; k0 and k1 are threshold constants.

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