Unmanned aerial vehicle (uav) precise point positioning (PPP) cycle slip repair method, system, device, medium and product

Abstract

This invention relates to the field of satellite positioning and navigation technology, and discloses a method, system, device, medium, and product for repairing PPP cycle slips in unmanned aerial vehicles (UAVs). The method includes: acquiring inertial navigation data and GNSS observations from the UAV; determining cycle slip observations based on the inertial navigation data and GNSS observations; performing inter-satellite and inter-epoch difference operations on the cycle slip observations to obtain cycle slip detection quantities; determining whether a cycle slip has occurred in the GNSS signal of the UAV at the current epoch based on the cycle slip detection quantities and a preset detection threshold; if a cycle slip has occurred in the GNSS signal of the UAV at the current epoch, filtering the cycle slip detection quantities using inertial navigation errors to obtain filtered cycle slip detection quantities; determining a real cycle slip quantity based on the filtered cycle slip detection quantities; acquiring the ambiguity of the previous epoch; optimizing the PPP state vector of the current epoch based on the real cycle slip quantity and the ambiguity of the previous epoch; and using the optimized PPP state vector to repair the PPP cycle slip of the current epoch, thereby solving the problems of low inspection efficiency and discontinuous positioning data caused by cycle slips.

Description

Technical Field

[0001] This invention relates to the field of satellite positioning and navigation technology, and in particular to a method, system, device, medium and product for repairing PPP cycles of unmanned aerial vehicles (UAVs). Background Technology

[0002] The advent of the Global Positioning System (GPS) has dramatically transformed industries such as positioning, navigation, and surveying, and it is now widely used in numerous fields including sea, land, air, space, and ground. GPS positioning methods are divided into Standard Point Positioning (PPP), Differential GPS (DGPS), and Precise Point Positioning (PPP). PPP technology integrates the advantages of both Standard Point Positioning and Differential GPS, freeing it from the constraints of a base station and providing a larger operational coverage area. When performing inspection missions, unmanned aerial vehicles (UAVs) often need to fly over large areas. The distance-independent nature of PPP technology allows for long-distance control of UAVs, ensuring accurate positioning information during inspections and guaranteeing efficient and accurate inspection work.

[0003] In recent years, the PPP (Plan-Do-Check-Act) technology combined with inertial navigation systems has matured and has been successfully applied to fields such as unmanned aerial vehicles (UAVs), ground-based vehicle automatic control, and photogrammetry. However, in PPP systems, receiver carrier phase observations often experience cycle slips due to factors such as high receiver dynamics, low satellite elevation angles, and signal obstruction. During UAV inspections, the high dynamic state generated by the UAV's rapid flight and signal obstructions such as buildings and trees encountered during flight increase the probability of cycle slips. The occurrence of cycle slips means that PPP requires tens of minutes to reconverge during dynamic positioning processing, a cumbersome and time-consuming process. If cycle slips are not effectively repaired, they will cause a decrease in positioning accuracy, and in severe cases, even lead to reconvergence. This will seriously affect the efficiency and data quality of UAV inspection work, which relies on high-precision positioning to accurately perform inspection tasks and obtain effective data. Summary of the Invention

[0004] In view of this, in order to solve the above-mentioned technical problems, the present invention provides a method, system, device, medium and product for repairing PPP cycles of unmanned aerial vehicles (UAVs).

[0005] The first aspect of this invention provides a method for repairing PPP cycle slips of unmanned aerial vehicles (UAVs), comprising:

[0006] Acquire inertial navigation data and GNSS observations from the UAV; determine cycle slip observations based on the inertial navigation data and GNSS observations;

[0007] The cycle slip observations are subjected to inter-satellite and inter-epoch difference calculations to obtain the cycle slip detection quantity;

[0008] Based on the cycle slip detection quantity and the preset detection quantity threshold, it is determined whether the GNSS signal of the UAV in the current epoch has experienced a cycle slip;

[0009] If it is determined that a cycle slip has occurred in the GNSS signal of the UAV at the current epoch, then the cycle slip detection quantity is filtered by inertial navigation error to obtain the filtered cycle slip detection quantity.

[0010] The real cycle slip quantity is determined based on the filtered cycle slip detection quantity;

[0011] Obtain the ambiguity of the previous epoch, optimize the PPP state vector of the current epoch based on the real cycle slip amount and the ambiguity of the previous epoch, and use the optimized PPP state vector to repair the PPP cycle slip of the current epoch.

[0012] In one embodiment, the inertial navigation data includes the receiver's position increment and the receiver's position in the previous epoch; the GNSS observations include dual-frequency carrier phase observations;

[0013] The step of determining the cycle slip observation based on the inertial navigation data and the GNSS observations includes:

[0014] Based on the receiver's position increment and the receiver's position in the previous epoch, the predicted position of the receiver in the next epoch is calculated through inertial navigation mechanical arrangement.

[0015] Acquire precise ephemeris data, and determine the satellite coordinates of each observed satellite in the next epoch based on the precise ephemeris data;

[0016] Based on the predicted position value of the receiver and the satellite coordinates of each of the observed satellites, the predicted station-satellite geometric distance from the receiver to each of the observed satellites is determined;

[0017] The geometric distance term in the dual-frequency carrier phase observation value is filtered out based on the predicted station-satellite geometric distance to obtain the filtered dual-frequency carrier phase observation value.

[0018] Based on the filtered dual-frequency carrier phase observations, the inertial navigation-assisted wide-lane combination phase observations and the inertial navigation-assisted non-ionospheric combination phase observations are determined according to the wide-lane combination rule and the non-ionospheric combination rule, respectively, and used as the cycle slip observations.

[0019] In one embodiment, performing inter-satellite and inter-epoch difference calculations on the cycle slip observations to obtain the cycle slip detection quantity includes:

[0020] Obtain a reference satellite; for each of the remaining observation satellites other than the reference satellite, perform inter-satellite difference based on the cycle slip observations of the remaining observation satellites and the cycle slip observations of the reference satellite to obtain the intermediate cycle slip detection quantity; wherein, the reference satellite is the observation satellite with the highest elevation angle, which has not experienced a cycle slip and has been continuously tracked without losing lock;

[0021] For each of the remaining observation satellites, the cycle slip detection value is obtained by differentiating between adjacent epochs based on the intermediate cycle slip detection value of the observation satellite.

[0022] In one embodiment, the cycle slip detection quantity includes a double-difference wide-lane cycle slip detection quantity and an ionosphere-free cycle slip detection quantity;

[0023] The step of filtering the cycle slip detection quantity using inertial navigation error to obtain the filtered cycle slip detection quantity includes:

[0024] The least squares ambiguity decorrelation method is used to perform least squares calculation on the double-difference wide-lane cycle slip detection quantity to obtain a floating-point cycle slip estimate. The floating-point cycle slip estimate is then fixed to an integer value to obtain an integer solution for the double-difference wide-lane cycle slip.

[0025] The inertial navigation short-time extrapolation error of the double-difference wide lane is derived from the integer solution of the double-difference wide lane cycle slip, and the inertial navigation short-time extrapolation error of the double-difference wide lane is subtracted from the double-difference wide lane cycle slip detection quantity to obtain the filtered double-difference wide lane cycle slip detection quantity.

[0026] Based on the short-time extrapolation error of the inertial navigation system for the double-difference wide lane, determine the equivalent short-time extrapolation error of the inertial navigation system for the ionospheric cycle slip detection quantity.

[0027] The filtered ionospheric cycle slip detection quantity is obtained by subtracting the equivalent inertial navigation short-time extrapolation error from the ionospheric cycle slip detection quantity.

[0028] In one embodiment, determining the real cycle slip quantity based on the filtered cycle slip detection quantity includes:

[0029] Obtain the dual-frequency carrier frequency parameters and dual-frequency carrier wavelength, and determine the first frequency combination coefficient and the second frequency combination coefficient based on the dual-frequency carrier frequency parameters;

[0030] The real cycle slip quantity is determined based on the first frequency combination coefficient, the second frequency combination coefficient, the dual-frequency carrier wavelength, and the filtered cycle slip detection quantity.

[0031] In one embodiment, optimizing the PPP state vector of the current epoch based on the real cycle slip amount and the ambiguity of the previous epoch, and using the optimized PPP state vector to repair the PPP cycle slip of the current epoch, includes:

[0032] The real number of cycle jumps is rounded down to the nearest integer to obtain the integer cycle jump value;

[0033] By combining the integer cycle jump value and the ambiguity of the previous epoch, the initial value of the ambiguity estimate for the current epoch is updated;

[0034] Obtain the PPP state parameters for the current epoch, and construct the PPP state vector based on the initial fuzzyness estimate of the current epoch and the PPP state parameters;

[0035] Obtain the pseudorange observation value and carrier phase observation value of the current epoch, and establish a linearized observation equation based on the pseudorange observation value, the carrier phase observation value and the state vector of the PPP;

[0036] The linearized observation equation is iteratively solved until the change in the PPP state vector is less than a preset convergence threshold, and the converged PPP state vector is output as the optimized PPP state vector.

[0037] The optimized PPP state vector is used as the PPP state vector after cycle slip repair in the current epoch.

[0038] Secondly, the present invention also provides a UAV PPP cycle slip repair system, comprising:

[0039] The cycle slip observation determination module is used to acquire inertial navigation data and GNSS observations of the UAV; and to determine the cycle slip observation based on the inertial navigation data and the GNSS observations.

[0040] The cycle slip detection and determination module is used to perform inter-satellite difference and inter-epoch difference calculations on the cycle slip observations to obtain the cycle slip detection quantity;

[0041] The cycle slip determination module is used to determine whether a cycle slip has occurred in the GNSS signal of the UAV in the current epoch based on the cycle slip detection quantity and a preset detection quantity threshold.

[0042] The cycle slip detection and filtering module is used to filter the cycle slip detection quantity by inertial navigation error if it is determined that the GNSS signal of the UAV in the current epoch has a cycle slip, so as to obtain the filtered cycle slip detection quantity.

[0043] The cycle slip determination module is used to determine the real cycle slip quantity based on the filtered cycle slip detection quantity;

[0044] The cycle slip repair module is used to obtain the ambiguity of the previous epoch, optimize the PPP state vector of the current epoch based on the real cycle slip amount and the ambiguity of the previous epoch, and use the optimized PPP state vector to repair the PPP cycle slip of the current epoch.

[0045] Thirdly, the present invention also provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, the computer program being executed by the processor causing the processor to perform the steps of the UAV PPP cycle slip repair method as described in the first aspect.

[0046] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the UAV PPP cycle slip repair method as described in the first aspect.

[0047] Fifthly, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer performs the steps of the UAV PPP cycle slip repair method as described in the first aspect.

[0048] As can be seen from the above technical solutions, this invention determines cycle slip observations by combining inertial navigation data and GNSS observations. It leverages the short-term, high-precision characteristics of inertial navigation to compensate for the susceptibility of GNSS signals to interference, providing a precise observation foundation for cycle slip detection adapted to the high-dynamic scenarios of UAVs. Furthermore, it performs inter-satellite and inter-epoch differential calculations on the cycle slip observations, eliminating common-mode errors such as receiver clock bias and ionospheric and tropospheric delays during the differential process, thereby quickly identifying cycle slip faults. When a cycle slip occurs in the GNSS signal of the UAV at the current epoch, it filters the cycle slip detection quantity using inertial navigation errors, eliminating cumulative inertial navigation error interference and solving the problem of cycle slip repair failure caused by inertial navigation errors. By calculating the real cycle slip quantity and combining it with the ambiguity of the previous epoch, it optimizes the PPP state vector of the current epoch, eliminating the need for PPP to reconverge from the beginning, significantly shortening the PPP reconvergence time, effectively repairing PPP cycle slips, ensuring high-precision positioning, and solving the problems of low inspection efficiency and discontinuous positioning data caused by cycle slips. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is an application environment diagram of a UAV PPP cycle slip repair method provided in an embodiment of the present invention;

[0051] Figure 2 A flowchart of a UAV PPP cycle slip repair method provided in an embodiment of the present invention;

[0052] Figure 3 This is a schematic diagram of the structure of a UAV PPP cycle slip repair system provided in an embodiment of the present invention;

[0053] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0054] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] In recent years, the PPP (Plan-Do-Check-Act) technology combined with inertial navigation systems has matured and has been successfully applied to fields such as unmanned aerial vehicles (UAVs), ground-based vehicle automatic control, and photogrammetry. However, in PPP systems, receiver carrier phase observations often experience cycle slips due to factors such as high receiver dynamics, low satellite elevation angles, and signal obstruction. During UAV inspections, the high dynamic state generated by the UAV's rapid flight and signal obstructions such as buildings and trees encountered during flight increase the probability of cycle slips. The occurrence of cycle slips means that PPP requires tens of minutes to reconverge during dynamic positioning processing, a cumbersome and time-consuming process. If cycle slips are not effectively repaired, they will cause a decrease in positioning accuracy, and in severe cases, even lead to reconvergence. This will seriously affect the efficiency and data quality of UAV inspection work, which relies on high-precision positioning to accurately perform inspection tasks and obtain effective data.

[0056] To address the aforementioned issues, this application proposes a method for repairing PPP cycle slips in unmanned aerial vehicles (UAVs). By combining inertial navigation (INS) data with GNSS observations, cycle slip measurements are determined. This leverages the short-term, high-precision characteristics of INS to compensate for the susceptibility of GNSS signals to interference, providing a precise observation foundation for cycle slip detection adapted to the high-dynamic scenarios of UAVs. Furthermore, inter-satellite and inter-epoch differential calculations are performed on the cycle slip measurements. By eliminating receiver clock errors and common-mode errors such as ionospheric and tropospheric delays during the differential process, cycle slip faults can be quickly identified. In the event of a cycle slip in the GNSS signal of the UAV at the current epoch, INS error filtering is applied to the cycle slip detection quantity to eliminate cumulative INS error interference, resolving the problem of cycle slip repair failure caused by INS errors. By calculating the real cycle slip quantity and combining it with the ambiguity of the previous epoch, the PPP state vector of the current epoch is optimized. This eliminates the need for PPP to reconverge from the beginning, significantly shortening the PPP reconvergence time and effectively repairing PPP cycle slips, ensuring high-precision positioning, and solving the problems of low inspection efficiency and discontinuous positioning data caused by cycle slips.

[0057] The UAV PPP cycle slip repair method provided in this application embodiment can be applied to, for example... Figure 1 The application environment shown is illustrated. Terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be integrated onto server 102, or it can be located in the cloud or on another network server. Terminal 101 or server 102 executes a UAV PPP cycle slip repair method, which includes: acquiring UAV inertial navigation data and GNSS observations; determining cycle slip observations based on the inertial navigation data and GNSS observations; performing inter-satellite and inter-epoch difference operations on the cycle slip observations to obtain cycle slip detection quantities; determining whether a cycle slip has occurred in the GNSS signal of the UAV at the current epoch based on the cycle slip detection quantities and a preset detection quantity threshold; if a cycle slip has occurred in the GNSS signal of the UAV at the current epoch, performing inertial navigation error filtering on the cycle slip detection quantities to obtain filtered cycle slip detection quantities; determining a real cycle slip quantity based on the filtered cycle slip detection quantities; acquiring the ambiguity of the previous epoch; optimizing the PPP state vector of the current epoch based on the real cycle slip quantity and the ambiguity of the previous epoch; and using the optimized PPP state vector to repair the PPP cycle slip of the current epoch.

[0058] Terminal 101 can be, but is not limited to, various personal computers, laptops, smartphones, and tablets.

[0059] Server 102 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0060] like Figure 2As shown, this application provides a method for repairing PPP cycles slip of a UAV, which is applied to... Figure 1 Taking terminal 101 or server 102 as an example, the explanation includes the following steps S1 to S6. Wherein:

[0061] Step S1: Acquire the UAV's inertial navigation data and GNSS observations; determine the cycle slip observations based on the inertial navigation data and GNSS observations.

[0062] Among them, inertial navigation data is short-term high-precision inertial navigation information continuously collected by the UAV's inertial measurement unit during flight, including the receiver's position increment, velocity increment, attitude change, position, velocity and attitude information. GNSS (Global Navigation Satellite System) observations are dual-frequency carrier phase and pseudorange observations collected in real time by the GNSS receiver carried by the UAV, including dual-frequency carrier phase observations and pseudorange observations.

[0063] Among them, the dual-frequency carrier phase observations are the carrier phase observations at frequencies L1 and L2, the pseudorange observations are C / A code or P code pseudorange observations, and the cycle slip observations are phase observations generated by fusing inertial navigation data with GNSS observations.

[0064] Step S2: Perform inter-satellite and inter-epoch difference calculations on the cycle slip observations to obtain the cycle slip detection quantity.

[0065] Inter-satellite differencing refers to differentiating observations from different satellites within the same epoch to eliminate common errors such as receiver clock bias. Inter-epoch differencing, on the other hand, involves differentiating observations from adjacent epochs on the same satellite to mitigate the impact of long-period errors such as ionospheric and tropospheric delays. Through both inter-satellite and inter-epoch differencing, receiver clock bias, ionospheric delay, and tropospheric delay in cycle slip detection are effectively suppressed, thereby significantly improving the sensitivity and stability of cycle slip identification.

[0066] Step S3: Based on the cycle slip detection quantity and the preset detection quantity threshold, determine whether a cycle slip has occurred in the GNSS signal of the UAV at the current epoch.

[0067] Specifically, by setting a detection threshold, if the absolute value of the cycle slip detection exceeds the threshold, it is determined that the GNSS signal of the UAV in the current epoch has a cycle slip; otherwise, it is considered that the signal is continuous and there is no cycle slip, and no subsequent steps are performed. Instead, the UAV's inertial navigation data and GNSS observations are re-monitored.

[0068] Step S4: If it is determined that the GNSS signal of the UAV in the current epoch has a cycle slip, then the cycle slip detection quantity is filtered by inertial navigation error to obtain the filtered cycle slip detection quantity.

[0069] Since short-term extrapolation errors of inertial navigation systems can be mixed into cycle slip detection quantities, leading to misjudgment or correction deviations, this application estimates and eliminates the cumulative errors of inertial navigation systems in the cycle slip detection quantity. The cycle slip detection quantity with eliminated inertial navigation errors (filtered cycle slip detection quantity) is not affected by inertial navigation errors. When the satellite signal is interrupted for a long time or the accuracy of the inertial navigation device is low, this cycle slip detection quantity can still accurately identify cycle slips, thereby improving the robustness and reliability of cycle slip detection.

[0070] Step S5: Determine the real cycle slip quantity based on the filtered cycle slip detection quantity.

[0071] Among them, the real cycle jump reflects the true magnitude of the integer cycle jump of the carrier phase, thus providing accurate initial value support for subsequent ambiguity repair.

[0072] Step S6: Obtain the ambiguity of the previous epoch. Based on the real cycle slip amount and the ambiguity of the previous epoch, optimize the PPP state vector of the current epoch, and use the optimized PPP state vector to repair the PPP cycle slip of the current epoch.

[0073] Here, "epoch" refers to the time unit in GNSS observation, usually 1 second or shorter interval; in this method, the above steps are performed for each epoch to ensure the real-time and continuous nature of cycle slip detection and repair.

[0074] In GNSS carrier phase positioning, when the receiver initially acquires the satellite signal, it cannot determine the number of integer cycles the signal travels from the satellite to the receiver. This unknown integer number of cycles is the ambiguity, which is the core parameter of PPP high-precision positioning.

[0075] The PPP state vector is a set of state variables for GNSS carrier phase positioning, including receiver three-dimensional position, velocity, clock error, tropospheric delay, ionospheric delay, and ambiguities of each satellite. Through dynamic updates of this vector, ambiguities can be gradually converged in error suppression and model optimization. Each cycle slip repair is a forced reset and reconvergence guidance of the integer cycle characteristics of ambiguities, thereby ensuring the long-term high-precision positioning capability of PPP in complex dynamic environments.

[0076] It should be noted that this embodiment of the application determines cycle slip observations by using inertial navigation data and GNSS observations. This leverages the short-term high-precision characteristics of inertial navigation to compensate for the susceptibility of GNSS signals to interference, providing a precise observation foundation for cycle slip detection adapted to the high-dynamic scenarios of UAVs. Furthermore, it performs inter-satellite and inter-epoch differential calculations on the cycle slip observations. By eliminating receiver clock errors and common-mode errors such as ionospheric and tropospheric delays during the differential process, cycle slip faults can be quickly identified. In the event of a cycle slip in the GNSS signal of the UAV at the current epoch, inertial navigation error filtering is applied to the cycle slip detection quantity to eliminate cumulative inertial navigation error interference, solving the problem of cycle slip repair failure caused by inertial navigation errors. By calculating the real cycle slip quantity and combining it with the ambiguity of the previous epoch, the PPP state vector of the current epoch is optimized, eliminating the need for PPP to reconverge from the beginning, significantly shortening the PPP reconvergence time, effectively repairing PPP cycle slips, ensuring high-precision positioning, and solving the problems of low inspection efficiency and discontinuous positioning data caused by cycle slips.

[0077] In some embodiments, the inertial navigation data includes the receiver's position increment and the receiver's position in the previous epoch; the GNSS observations include dual-frequency carrier phase observations. In this case, determining the cycle slip observation based on the inertial navigation data and GNSS observations includes:

[0078] Step S101: Based on the receiver's position increment and the receiver's position in the previous epoch, calculate the predicted receiver position for the next epoch using inertial navigation mechanical arrangement.

[0079] The position increment of the receiver is obtained by solving the three-axis displacement components output in real time by the inertial measurement unit within the current epoch. Its accuracy is better than GNSS single-point positioning in the short term and is not affected by signal blockage and multipath interference.

[0080] Preferably, the position of the receiver in the previous epoch is used as a reference, and the short-time position increment output by the inertial navigation system is directly superimposed on the reference position. After position update calculation by the inertial navigation system mechanical arrangement, the position prediction value for the next epoch is obtained.

[0081] Step S102: Obtain precise ephemeris data and determine the satellite coordinates of each observed satellite in the next epoch based on the precise ephemeris data.

[0082] The precise ephemeris data is obtained from high-precision satellite orbit and clock bias products released by the International GNSS Service Organization, including satellite orbit parameters and clock bias parameters. The system first unifies and aligns the k+1 epoch, then interpolates the discrete satellite positions in the ephemeris (e.g., Lagrange interpolation) to obtain the satellite coordinates for that epoch, and finally performs clock bias correction.

[0083] Step S103: Determine the predicted station-satellite geometric distance from the receiver to each observation satellite based on the receiver's predicted position value and the satellite coordinates of each observation satellite.

[0084] Preferably, both the receiver position prediction and the satellite coordinates are transformed into an ECEF (Earth-Centered, Earth-Fixed) rectangular coordinate system. The coordinate differences between the two along the X, Y, and Z axes are calculated, and then the predicted station-satellite geometric distance is obtained by calculating using the three-dimensional Euclidean distance formula. This predicted station-satellite geometric distance can effectively isolate the geometric distance changes caused by receiver motion, thereby highlighting the abrupt changes in phase observations caused by cycle slips and improving the sensitivity of cycle slip observations to actual cycle slips.

[0085] Step S104: Filter out the geometric distance term in the dual-frequency carrier phase observation value according to the predicted station-satellite geometric distance to obtain the filtered dual-frequency carrier phase observation value.

[0086] In order to obtain phase observations that remove the influence of geometric distance, the geometric distance of the predicted station-satellite is used as prior geometric information when constructing cycle slip observations. This information is then substituted into the carrier phase observation equation to calculate the geometric distance term. This geometric distance term is then removed from the dual-frequency carrier phase observations, thereby eliminating the dominant change in geometric distance and retaining only the phase deviation caused by slow changes such as receiver clock bias, tropospheric delay, combined ambiguity, and inertial navigation extrapolation error. This significantly enhances the sensitivity of cycle slip detection to integer cycle slips.

[0087] Step S105: Based on the filtered dual-frequency carrier phase observations, determine the inertial navigation-assisted wide-lane combination phase observations and the inertial navigation-assisted non-ionospheric combination phase observations according to the wide-lane combination rule and the non-ionospheric combination rule, respectively, as cycle slip observations.

[0088] Among them, the wide-lane combination rule is formed by the linear combination of dual-frequency carrier phase observations. It is generally obtained by directly subtracting the dual-frequency phase observations and has the characteristics of a longer equivalent wavelength and sensitivity to integer cycle slip.

[0089] The ionospheric-free combination rule is formed by linearly combining the dual-frequency carrier phase observations according to the principle of eliminating the first-order term of the ionosphere. Generally, it is combined according to the first-order term cancellation coefficient of the ionosphere, which has the characteristic of weakening the effect of ionospheric delay.

[0090] The results derived from the above two rules, namely the inertial navigation-assisted wide-lane combined phase observation and the inertial navigation-assisted ionosphere-free combined phase observation, are used as cycle slip observations.

[0091] For example, by incorporating the inertial navigation-assisted wide-lane combined phase observation and the inertial navigation-assisted ionosphere-free combined phase observation together with the receiver clock error, tropospheric delay, combined ambiguity, and inertial navigation extrapolation error into the observation equation, we obtain:

[0092] (1)

[0093] In the formula, This represents the phase measurement value of the inertial navigation-assisted wide-lane combination. This represents the phase measurement value of the inertial navigation-assisted ionosphere-free combination. Represents the speed of light. Indicates receiver clock bias. The tropospheric delay on the inclined path is obtained from the tropospheric model. Specifically, the zenith tropospheric delay is first calculated, and then the zenith tropospheric delay is mapped onto the signal propagation path from the receiver to the satellite using a mapping function based on the satellite elevation angle, thus obtaining the tropospheric delay on the inclined path. The term represents the equivalent ionospheric delay term retained after the dual-frequency carrier phase observations are linearly combined using a wide-lane method. Its value is determined by the dual-frequency observation frequency and the wide-lane combination coefficient. and These are the equivalent wavelengths corresponding to the wide-lane combination and the non-ionospheric combination, respectively, both determined by the dual-frequency carrier frequencies according to the corresponding combination relationships; and These are the ambiguity parameters for the corresponding combinations, with initial values ​​given by the continuous tracking results of the previous epoch, and updated again after cycle slip repair. and These are wavelength and ambiguity, respectively. This represents the projection error of the position error generated by inertial navigation extrapolation along the line of sight from the receiver to the satellite. Its magnitude is determined by the short-time extrapolation accuracy of the inertial navigation system.

[0094] Among them, the satellite clock error is eliminated by the precision clock error product, and the carrier phase observation error is generally much smaller than the inertial navigation error, and can therefore be ignored in equation (1).

[0095] It should be noted that the embodiments of this application calculate the receiver position prediction value through inertial navigation mechanical arrangement, determine the satellite coordinates by combining precise ephemeris and calculate the predicted station-satellite geometric distance, thereby filtering out the geometric distance interference caused by receiver motion in the dual-frequency carrier phase observation value. Then, based on the wide lane and ionospheric-free combination rules, the corresponding inertial navigation-assisted combination observation is generated as the cycle slip observation, which can effectively eliminate receiver motion interference, weaken the influence of ionospheric delay, and improve the detection sensitivity and detection stability of whole cycle slips.

[0096] In some embodiments, cycle slip observations are subjected to inter-satellite and inter-epoch difference operations to obtain cycle slip detection quantities, including:

[0097] Step S201: Obtain the reference satellite; For each of the remaining observation satellites other than the reference satellite, perform inter-satellite difference based on the cycle slip observations of the remaining observation satellites and the cycle slip observations of the reference satellite to obtain the intermediate cycle slip detection quantity; wherein, the reference satellite is the observation satellite with the highest elevation angle, which has not experienced a cycle slip and has been continuously tracked without losing lock.

[0098] First, the observation satellite with the highest elevation angle, no cycle slip, and continuous tracking without loss of lock is selected as the reference satellite to maximize geometric strength. If the current reference satellite loses lock and its elevation angle drops below the threshold or a cycle slip occurs, the satellite with the highest elevation angle is selected as the reference satellite. If the selected reference satellite experiences a cycle slip, the differential detection quantity constructed based on that reference satellite may become abnormal overall. In this case, it is necessary to replace the reference satellite and re-perform cycle slip detection. Specifically, the satellite is switched to another observation satellite that meets the reference satellite conditions, and the inter-satellite differential observation quantity is reconstructed to ensure the continuity and reliability of the cycle slip detection quantity.

[0099] If the double-difference cycle slip detections of the current reference satellite and several other satellites simultaneously exceed the limit at the same epoch, it indicates that the anomaly is more likely to originate from the reference satellite, rather than all target satellites experiencing cycle slips simultaneously. In this case, the reference satellite can be marked as a suspected cycle slip satellite, and another satellite with continuous observations, stable signals, and no anomalies can be selected as the new reference satellite for verification. If the original anomaly disappears after changing the reference satellite, it can be confirmed that the original reference satellite experienced a cycle slip.

[0100] Secondly, inter-satellite differential is performed by differentiating the inertial navigation-assisted wide-lane combined phase observation of each non-reference satellite with the inertial navigation-assisted wide-lane combined phase observation of the reference satellite, and then by differentiating the inertial navigation-assisted ionospheric combined phase observation of each non-reference satellite with the inertial navigation-assisted ionospheric combined phase observation of the reference satellite, thereby eliminating receiver clock errors. After differential, the receiver clock errors are completely canceled, and only the difference information between satellites is retained, thus obtaining the intermediate cycle slip detection quantity.

[0101] Step S202: For each of the remaining observation satellites, the cycle slip detection quantity is obtained by differential calculation between adjacent epochs based on the intermediate cycle slip detection quantity of the observation satellite.

[0102] Preferably, for the same observation satellite, intermediate cycle slip detection values ​​from two adjacent epochs are used for epoch-to-epoch differential analysis to eliminate slowly changing atmospheric errors such as tropospheric delay. Tropospheric and ionospheric delays remain almost unchanged in a short time and are directly eliminated after differential analysis without affecting cycle slip detection.

[0103] The cycle slip detection quantity is expressed as:

[0104] (2)

[0105] In the formula, Indicates inter-satellite difference. Indicates the difference between epochs. This represents the wide-lane combined cycle slip detection value formed after inter-satellite and inter-epoch differentials of the inertial navigation-assisted wide-lane combined phase observation values; This represents the ionospheric cycle slip detection value formed after the inertial navigation-assisted ionospheric combination phase observation values ​​are processed by inter-satellite and inter-epoch differences. The term is the wide-lane cycle jump term, the magnitude of which is determined by the wide-lane combined equivalent wavelength and the wide-lane ambiguity jump variable. This is the cycle slip term for the non-ionospheric combination, determined by the dual-frequency carrier frequency based on the non-ionospheric combination relationship. This is the INS (Inertial Navigation System) error after double difference, the magnitude of which is determined by the short-time extrapolation accuracy of the inertial navigation system and the geometric distribution of the satellites.

[0106] For low-dynamic receivers, atmospheric delay error changes slowly over a few minutes without strong weather changes or drastic upper-level changes. Therefore, atmospheric delay error can be eliminated by using inter-epoch differential processing. In the case of continuous signal detection, the time interval between adjacent epochs is short, and the short-term extrapolation error of the inertial navigation system to the receiver position increases only slightly. Therefore, the single-epoch inertial navigation error itself is relatively small.

[0107] Furthermore, after inter-satellite double-difference and inter-epoch difference, the common part of the inertial navigation error is significantly canceled out, leaving only the residual error corresponding to the projection difference of different satellite line-of-sight directions. Therefore, the INS error after double-difference is usually small. For this reason, under continuous tracking conditions, this residual error is usually much smaller than the carrier integer abrupt change caused by cycle slips, and the INS error after double-difference is small and can be ignored during cycle slip detection.

[0108] In one example, after obtaining the cycle slip detection value, a single wide-lane combination cycle slip detection value or an ionosphere-free combination cycle slip detection value can also detect cycle slips. However, since the wide-lane combination cannot detect dual-frequency equal-cycle slips (such as 1 / 1) and the ionosphere-free combination cannot detect cycle slips with special ratios (such as 60 / 77), in order to avoid missed detections, the two methods are combined to detect various types of cycle slips.

[0109] For example, the threshold for the wide lane combined cycle slip detection is set to 0.5m, and the threshold for the non-ionospheric combined cycle slip detection is set to 0.05m. When any cycle slip detection exceeds its corresponding threshold, it is determined that a cycle slip has occurred; if neither cycle slip detection exceeds the limit, it is determined that no cycle slip has occurred.

[0110] In some embodiments, the cycle slip detection quantity includes a double-difference wide-lane cycle slip detection quantity and an ionospheric-free cycle slip detection quantity; in this case, the cycle slip detection quantity is filtered by inertial navigation error to obtain the filtered cycle slip detection quantity, including:

[0111] Step S401: Perform least squares calculation on the double-difference wide-lane cycle slip detection quantity based on the least squares ambiguity decorrelation method to obtain the floating-point cycle slip estimate. Fix the floating-point cycle slip estimate to an integer to obtain the integer solution of the double-difference wide-lane cycle slip.

[0112] Among them, the LAMBDA (Least-Squares AM Biguity Decorrelation Adjustment) method is an efficient integer ambiguity resolution method. The process is to first obtain the floating-point estimates and their covariance matrices of the double-difference wide-lane cycle slip detection quantities, and then perform decorrelation processing on the floating-point solutions to reduce the correlation between parameters through the algorithm. Then, the floating-point estimates are fixed to integers, and the integer vector that minimizes the sum of squared observation residuals is found. Finally, the integer solutions of the double-difference wide-lane cycle slip are obtained.

[0113] Preferably, the double-difference wide-lane cycle slip detection quantity satisfies the linear correspondence as shown in equation (2). The double-difference wide-lane cycle slip detection quantity is treated as an unknown floating-point parameter to be solved. Then, based on the linear correspondence, the cycle slip floating-point estimate and its covariance matrix are calculated through the cycle slip detection quantity. Then, the cycle slip floating-point estimate and its covariance matrix are input into the LAMBDA integer least squares algorithm to search for the integer vector that minimizes the sum of squared residuals in the integer space, and the integer solution of the double-difference wide-lane cycle slip is obtained.

[0114] In this scheme, the LAMBDA method further introduces a ratio test mechanism. After obtaining the optimal and suboptimal integer candidate solutions for the wide-lane cycle slip using the LAMBDA method, the corresponding objective function values ​​are calculated, denoted as... and ,in, And construct the ratio test statistic. When this ratio is greater than a preset threshold, it indicates that the optimal integer solution has a sufficiently significant advantage over the second-best integer solution, and the optimal solution can be accepted as the correct integer solution for the double-difference wide-lane cycle slip.

[0115] Step S402: Based on the integer solution of the cycle slip of the double-difference wide-lane, deduce the short-time extrapolation error of the inertial navigation system of the double-difference wide-lane, and deduct the short-time extrapolation error of the inertial navigation system of the double-difference wide-lane from the cycle slip detection quantity of the double-difference wide-lane to obtain the filtered cycle slip detection quantity of the double-difference wide-lane.

[0116] In this process, the integer solutions of cycle slips are substituted into equation (3) to estimate and eliminate the cumulative error of the inertial navigation system, thereby improving the reliability of subsequent ambiguity recovery. Equation (3) is as follows:

[0117] (3)

[0118] Step S403: Based on the short-time extrapolation error of the inertial navigation system in the double-difference wide lane, determine the equivalent short-time extrapolation error of the inertial navigation system for the ionospheric cycle slip detection quantity.

[0119] Among them, since the short-time extrapolation error of the inertial navigation of the double-difference wide lane is essentially the common double-difference station-satellite geometric distance error caused by the receiver position extrapolation deviation, this geometric error is a common term of the same source for the L1 and L2 dual-frequency phase observations, and only has a fixed mapping relationship with the linear combination coefficient of the wide lane and the ionosphere-free zone.

[0120] Specifically, the observations of the wide-lane combination and the ionosphere-free combination exhibit a common linear correlation. By combining the linear transformation coefficients of the wide-lane and ionosphere-free combinations of dual-frequency signals, a geometric error propagation mapping relationship between the two types of combinations is constructed. Finally, through conversion and correlation derivation, the equivalent double-difference inertial navigation error corresponding to the ionosphere-free detection quantity is obtained from the wide-lane double-difference inertial navigation error. Through conversion and correlation derivation, it can be determined that the linear transformation coefficient of the wide-lane and ionosphere-free combination is 1, meaning that the short-time extrapolation error of the double-difference wide-lane inertial navigation is equal to the equivalent short-time extrapolation error of the ionosphere-free cycle slip detection quantity.

[0121] Step S404: Subtract the equivalent inertial navigation short-time extrapolation error from the ionospheric cycle slip detection quantity to obtain the filtered ionospheric cycle slip detection quantity.

[0122] Similar to the case of short-time extrapolation error in inertial navigation for double-difference wide-lane circuits, the equivalent short-time extrapolation error of inertial navigation in the ionospheric cycle slip detection quantity is subtracted from the ionospheric cycle slip detection quantity. At this point, the cycle slip detection quantity after eliminating the short-time extrapolation error of inertial navigation, i.e., the filtered cycle slip detection quantity, is:

[0123] (4)

[0124] In the formula, To eliminate the short-time extrapolation error of the inertial navigation system, the double-difference wide-lane cycle slip detection quantity is used. To eliminate short-time extrapolation errors in inertial navigation systems, a non-ionospheric cycle slip detection quantity is required.

[0125] In some embodiments, determining the real cycle slip quantity based on the filtered cycle slip detection quantity includes:

[0126] Step S501: Obtain the dual-frequency carrier frequency parameters and dual-frequency carrier wavelength, and determine the first frequency combination coefficient and the second frequency combination coefficient based on the dual-frequency carrier frequency parameters.

[0127] Among them, the dual-frequency carrier frequency parameters f1 and f2 are the two inherent carrier signal frequencies that are fixedly transmitted by the GNSS satellite (e.g., the commonly used GPS L1 frequency f1 is 1575.42MHz and the L2 frequency f2 is 1227.60MHz), which are standard parameters of the satellite system and can be obtained directly from receiver observation information or the official GNSS interface documentation; the dual-frequency carrier wavelength can be calculated by dividing the speed of light c by the corresponding frequency; the first frequency combination coefficient is:

[0128] (5)

[0129] The second frequency combination coefficient is:

[0130] (6)

[0131] Step S502: Determine the real cycle slip quantity based on the first frequency combination coefficient, the second frequency combination coefficient, the dual-frequency carrier wavelength, and the filtered cycle slip detection quantity.

[0132] Preferably, the real cycle slip quantity is determined by combining the first frequency combination coefficient, the second frequency combination coefficient, the dual-frequency carrier wavelength, and the filtered cycle slip detection quantity using the following formula (7), through the wide-lane combination constraint and the ionosphere-free combination constraint:

[0133] (7)

[0134] In the formula, , These are the real cycle jump solutions on the original dual frequencies of L1 and L2, respectively. , It is a dual-frequency carrier wavelength.

[0135] By solving the system of two linear equations in equation (7), the real cycle jump can be obtained. , .

[0136] In some embodiments, the PPP state vector of the current epoch is optimized based on the real cycle slip amount and the ambiguity of the previous epoch, and the optimized PPP state vector is used to repair the PPP cycle slip of the current epoch, including:

[0137] Step S601: Round the real number cycle jump to the nearest integer to obtain the integer cycle jump value.

[0138] Since cycle slips essentially correspond to integer abrupt changes in carrier integer ambiguity, this application uses the nearest integer as the integer cycle slip value, that is, the estimated real cycle slip value is taken as the integer value closest to it.

[0139] Step S602: Combine the integer cycle jump value and the ambiguity of the previous epoch to update the initial value of the ambiguity estimate for the current epoch.

[0140] The integer cycle jump value is added to the ambiguity of the previous epoch to obtain the initial value of the ambiguity estimate for the current epoch.

[0141] Step S603: Obtain the PPP state parameters of the current epoch, and construct the PPP state vector based on the initial fuzzy estimation value and the PPP state parameters of the current epoch.

[0142] The PPP state parameters include receiver position, clock bias, and tropospheric delay parameters. The receiver position, clock bias, tropospheric delay parameters, and initial ambiguity estimation values ​​together constitute a complete state vector, which is used as a priori conditions.

[0143] Step S604: Obtain the pseudorange observation and carrier phase observation of the current epoch, and establish a linearized observation equation based on the pseudorange observation, carrier phase observation, and PPP state vector.

[0144] Preferably, the pseudorange and carrier phase observations of the current epoch are directly acquired and output by the GNSS dual-frequency receiver mounted on the UAV, serving as the receiver's raw observation data. Using the PPP state vector as a priori value, a first-order Taylor expansion is performed on the pseudorange and carrier phase observations to linearize them. After removing higher-order small terms, the linearized observation equation is obtained, expressed as:

[0145] Pseudo-distance linearization equation: ;

[0146] Carrier phase linearization equation: ;

[0147] In the formula, ρ is the geometric distance between the station and the satellite, and dt r For receiver clock bias, dt s λ is the satellite clock bias, T is the tropospheric delay, I is the ionospheric delay, λ is the carrier wavelength, N is the ambiguity, and ε is the spectral density. P ε L To observe noise.

[0148] Step S605: Iteratively solve the linearized observation equation until the change in the PPP state vector is less than the preset convergence threshold, and output the converged PPP state vector as the optimized PPP state vector.

[0149] This method iteratively solves the linearized observation equation using weighted least squares. The optimization objective of this iterative process is to minimize the weighted sum of squares of the observation residuals. During the iteration, the changes in the observation residuals and the PPP state vector are recalculated based on the updated state vectors. If the change in the PPP state vector does not yet meet the convergence threshold, linearization and updates continue until the change in the state vectors of each PPP is less than the convergence threshold. Finally, the optimized PPP state vector is obtained, including the receiver position, clock error, tropospheric delay parameter, and ambiguity estimate. Compared to methods using code pseudorange information to assist ambiguity convergence, this optimization method significantly shortens the PPP ambiguity reconvergence time by leveraging the high short-time accuracy of inertial navigation information.

[0150] Step S606: Use the optimized PPP state vector as the PPP state vector after the current epoch cycle slip repair.

[0151] Specifically, the optimized PPP state vector is replaced with the state vector before cycle slip detection, thereby achieving accurate positioning and numerical repair of cycle slips and ensuring that the ambiguity parameter instantly recovers its integer characteristics after a cycle slip.

[0152] This application's embodiments utilize short-time, high-precision inertial navigation information, combined with wide-lane and ionosphere-free combination rules, and employ inter-satellite and inter-epoch difference methods to establish a high-precision cycle slip detection quantity. After determining that a cycle slip has occurred, the accumulated inertial navigation error in the cycle slip detection quantity is estimated and eliminated, achieving rapid PPP ambiguity recovery. This method not only accurately detects various types of cycle slips but also eliminates the impact of inertial navigation errors on subsequent cycle slip repair, making it particularly suitable for situations involving long-term PPP signal interruptions.

[0153] Based on the same inventive concept, this application also provides a UAV PPP cycle slip repair system for implementing the UAV PPP cycle slip repair method described above.

[0154] The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more UAV PPP cycle slip repair system embodiments provided below can be found in the limitations of the UAV PPP cycle slip repair method described above, and will not be repeated here.

[0155] like Figure 3 As shown in the figure, this application provides a UAV PPP cycle slip repair system, including:

[0156] The cycle slip observation determination module 100 is used to acquire the UAV's inertial navigation data and GNSS observations; and to determine the cycle slip observation based on the inertial navigation data and GNSS observations.

[0157] The cycle slip detection determination module 200 is used to perform inter-satellite difference and inter-epoch difference calculations on the cycle slip observations to obtain the cycle slip detection quantity;

[0158] The cycle slip detection module 300 is used to determine whether a cycle slip has occurred in the GNSS signal of the UAV in the current epoch based on the cycle slip detection quantity and the preset detection quantity threshold.

[0159] The cycle slip detection and filtering module 400 is used to filter the cycle slip detection quantity by inertial navigation error if it is determined that the GNSS signal of the UAV in the current epoch has a cycle slip, so as to obtain the filtered cycle slip detection quantity.

[0160] The cycle slip determination module 500 is used to determine the real cycle slip amount based on the filtered cycle slip detection amount.

[0161] The cycle slip repair module 600 is used to obtain the ambiguity of the previous epoch, optimize the PPP state vector of the current epoch based on the real cycle slip amount and the ambiguity of the previous epoch, and use the optimized PPP state vector to repair the PPP cycle slip of the current epoch.

[0162] In some embodiments, the inertial navigation data includes the receiver's position increment and the receiver's position in the previous epoch; the GNSS observations include dual-frequency carrier phase observations.

[0163] Cycle slip observation and determination module 100 is used for:

[0164] Based on the receiver's position increment and the receiver's position in the previous epoch, the predicted position of the receiver in the next epoch is calculated through inertial navigation mechanical arrangement.

[0165] Acquire precise ephemeris data and determine the satellite coordinates of each observed satellite in the next epoch based on the precise ephemeris data;

[0166] Based on the receiver's predicted position and the satellite coordinates of each observation satellite, determine the predicted station-satellite geometric distance from the receiver to each observation satellite;

[0167] The geometric distance term in the dual-frequency carrier phase observations is filtered out based on the predicted station-satellite geometric distance to obtain the filtered dual-frequency carrier phase observations.

[0168] Based on the filtered dual-frequency carrier phase observations, the inertial navigation-assisted wide-lane combination phase observations and the inertial navigation-assisted non-ionospheric combination phase observations are determined according to the wide-lane combination rule and the non-ionospheric combination rule, respectively, and used as cycle slip observations.

[0169] In some embodiments, the cycle slip detection and determination module 200 is configured to:

[0170] Obtain the reference satellite; for each of the remaining observation satellites other than the reference satellite, perform inter-satellite difference based on the cycle slip observations of the remaining observation satellites and the cycle slip observations of the reference satellite to obtain the intermediate cycle slip detection quantity; among them, the reference satellite is the observation satellite with the highest elevation angle, which has not experienced a cycle slip and has been continuously tracked without losing lock;

[0171] For each of the remaining observation satellites, the cycle slip detection value is obtained by differentiating the intermediate cycle slip detection value between adjacent epochs based on the intermediate cycle slip detection value of the observation satellite.

[0172] In some embodiments, cycle slip detection quantities include double-difference wide-lane cycle slip detection quantities and ionosphere-free cycle slip detection quantities;

[0173] Cycle slip detection and filtering module 400 is used for:

[0174] The least squares ambiguity decorrelation method is used to perform least squares calculation on the double-difference wide lane cycle slip detection quantity to obtain a floating-point cycle slip estimate. The floating-point cycle slip estimate is then fixed to an integer value to obtain an integer solution for the double-difference wide lane cycle slip.

[0175] The integer solution of the cycle slip of the double-difference wide lane is used to deduce the short-time extrapolation error of the inertial navigation system of the double-difference wide lane. The short-time extrapolation error of the inertial navigation system of the double-difference wide lane is then deducted from the cycle slip detection quantity of the double-difference wide lane to obtain the filtered cycle slip detection quantity of the double-difference wide lane.

[0176] Based on the short-time extrapolation error of the inertial navigation system in the double-difference wide lane, the equivalent short-time extrapolation error of the inertial navigation system for the ionospheric cycle slip detection quantity is determined.

[0177] The filtered ionospheric cycle slip detection quantity is obtained by subtracting the equivalent inertial navigation short-time extrapolation error from the ionospheric cycle slip detection quantity.

[0178] In some embodiments, the cycle slip determination module 500 is used for:

[0179] Obtain the dual-frequency carrier frequency parameters and dual-frequency carrier wavelength, and determine the first frequency combination coefficient and the second frequency combination coefficient based on the dual-frequency carrier frequency parameters;

[0180] The real cycle slip quantity is determined based on the first frequency combination coefficient, the second frequency combination coefficient, the dual-frequency carrier wavelength, and the filtered cycle slip detection quantity.

[0181] In some embodiments, the cycle slip repair module 600 is used for:

[0182] Round the real cycle jump to the nearest integer to obtain the integer cycle jump value;

[0183] The initial ambiguity estimate for the current epoch is updated by combining the integer cycle jump value and the ambiguity of the previous epoch.

[0184] Obtain the PPP state parameters for the current epoch, and construct the PPP state vector based on the initial fuzzy estimation value and the PPP state parameters for the current epoch.

[0185] Obtain the pseudorange and carrier phase observations for the current epoch, and establish a linearized observation equation based on the pseudorange, carrier phase, and PPP state vector.

[0186] The linearized observation equation is iteratively solved until the change in the PPP state vector is less than the preset convergence threshold, and the converged PPP state vector is output as the optimized PPP state vector.

[0187] The optimized PPP state vector is used as the PPP state vector after cycle slip repair in the current epoch.

[0188] like Figure 4 As shown, this application provides an electronic device 10, which includes a memory 20 and a processor 30. The memory 20 stores a computer program. When the computer program is executed by the processor 30, the processor 30 performs the following steps:

[0189] Acquire inertial navigation data and GNSS observations from the UAV; determine cycle slip observations based on the inertial navigation data and GNSS observations;

[0190] The cycle slip detection quantity is obtained by performing inter-satellite and inter-epoch difference calculations on the cycle slip observations.

[0191] Based on the cycle slip detection quantity and the preset detection quantity threshold, determine whether the GNSS signal of the UAV in the current epoch has experienced a cycle slip;

[0192] If it is determined that a cycle slip has occurred in the GNSS signal of the UAV at the current epoch, then the cycle slip detection quantity is filtered by inertial navigation error to obtain the filtered cycle slip detection quantity.

[0193] The real cycle slip quantity is determined based on the filtered cycle slip detection quantity;

[0194] Obtain the ambiguity of the previous epoch, optimize the PPP state vector of the current epoch based on the real cycle slip and the ambiguity of the previous epoch, and use the optimized PPP state vector to repair the PPP cycle slip of the current epoch.

[0195] In some embodiments, the inertial navigation data includes the receiver's position increment and the receiver's position in the previous epoch; the GNSS observations include dual-frequency carrier phase observations.

[0196] When the computer program is executed by the processor 30, the processor 30 also performs the following steps:

[0197] Based on the receiver's position increment and the receiver's position in the previous epoch, the predicted position of the receiver in the next epoch is calculated through inertial navigation mechanical arrangement.

[0198] Acquire precise ephemeris data and determine the satellite coordinates of each observed satellite in the next epoch based on the precise ephemeris data;

[0199] Based on the receiver's predicted position and the satellite coordinates of each observation satellite, determine the predicted station-satellite geometric distance from the receiver to each observation satellite;

[0200] The geometric distance term in the dual-frequency carrier phase observations is filtered out based on the predicted station-satellite geometric distance to obtain the filtered dual-frequency carrier phase observations.

[0201] Based on the filtered dual-frequency carrier phase observations, the inertial navigation-assisted wide-lane combination phase observations and the inertial navigation-assisted non-ionospheric combination phase observations are determined according to the wide-lane combination rule and the non-ionospheric combination rule, respectively, and used as cycle slip observations.

[0202] In some embodiments, when the computer program is executed by the processor 30, the processor 30 also performs the following steps:

[0203] Obtain the reference satellite; for each of the remaining observation satellites other than the reference satellite, perform inter-satellite difference based on the cycle slip observations of the remaining observation satellites and the cycle slip observations of the reference satellite to obtain the intermediate cycle slip detection quantity; among them, the reference satellite is the observation satellite with the highest elevation angle, which has not experienced a cycle slip and has been continuously tracked without losing lock;

[0204] For each of the remaining observation satellites, the cycle slip detection value is obtained by differentiating the intermediate cycle slip detection value between adjacent epochs based on the intermediate cycle slip detection value of the observation satellite.

[0205] In some embodiments, cycle slip detection quantities include double-difference wide-lane cycle slip detection quantities and ionosphere-free cycle slip detection quantities;

[0206] When the computer program is executed by the processor 30, the processor 30 also performs the following steps:

[0207] The least squares ambiguity decorrelation method is used to perform least squares calculation on the double-difference wide lane cycle slip detection quantity to obtain a floating-point cycle slip estimate. The floating-point cycle slip estimate is then fixed to an integer value to obtain an integer solution for the double-difference wide lane cycle slip.

[0208] The integer solution of the cycle slip of the double-difference wide lane is used to deduce the short-time extrapolation error of the inertial navigation system of the double-difference wide lane. The short-time extrapolation error of the inertial navigation system of the double-difference wide lane is then deducted from the cycle slip detection quantity of the double-difference wide lane to obtain the filtered cycle slip detection quantity of the double-difference wide lane.

[0209] Based on the short-time extrapolation error of the inertial navigation system in the double-difference wide lane, the equivalent short-time extrapolation error of the inertial navigation system for the ionospheric cycle slip detection quantity is determined.

[0210] The filtered ionospheric cycle slip detection quantity is obtained by subtracting the equivalent inertial navigation short-time extrapolation error from the ionospheric cycle slip detection quantity.

[0211] In some embodiments, when the computer program is executed by the processor 30, the processor 30 also performs the following steps:

[0212] Obtain the dual-frequency carrier frequency parameters and dual-frequency carrier wavelength, and determine the first frequency combination coefficient and the second frequency combination coefficient based on the dual-frequency carrier frequency parameters;

[0213] The real cycle slip quantity is determined based on the first frequency combination coefficient, the second frequency combination coefficient, the dual-frequency carrier wavelength, and the filtered cycle slip detection quantity.

[0214] In some embodiments, when the computer program is executed by the processor 30, the processor 30 also performs the following steps:

[0215] Round the real cycle jump to the nearest integer to obtain the integer cycle jump value;

[0216] The initial ambiguity estimate for the current epoch is updated by combining the integer cycle jump value and the ambiguity of the previous epoch.

[0217] Obtain the PPP state parameters for the current epoch, and construct the PPP state vector based on the initial fuzzy estimation value and the PPP state parameters for the current epoch.

[0218] Obtain the pseudorange and carrier phase observations for the current epoch, and establish a linearized observation equation based on the pseudorange, carrier phase, and PPP state vector.

[0219] The linearized observation equation is iteratively solved until the change in the PPP state vector is less than the preset convergence threshold, and the converged PPP state vector is output as the optimized PPP state vector.

[0220] The optimized PPP state vector is used as the PPP state vector after cycle slip repair in the current epoch.

[0221] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed, performs the following steps:

[0222] Acquire inertial navigation data and GNSS observations from the UAV; determine cycle slip observations based on the inertial navigation data and GNSS observations;

[0223] The cycle slip detection quantity is obtained by performing inter-satellite and inter-epoch difference calculations on the cycle slip observations.

[0224] Based on the cycle slip detection quantity and the preset detection quantity threshold, determine whether the GNSS signal of the UAV in the current epoch has experienced a cycle slip;

[0225] If it is determined that a cycle slip has occurred in the GNSS signal of the UAV at the current epoch, then the cycle slip detection quantity is filtered by inertial navigation error to obtain the filtered cycle slip detection quantity.

[0226] The real cycle slip quantity is determined based on the filtered cycle slip detection quantity;

[0227] Obtain the ambiguity of the previous epoch, optimize the PPP state vector of the current epoch based on the real cycle slip and the ambiguity of the previous epoch, and use the optimized PPP state vector to repair the PPP cycle slip of the current epoch.

[0228] In some embodiments, the inertial navigation data includes the receiver's position increment and the receiver's position in the previous epoch; the GNSS observations include dual-frequency carrier phase observations.

[0229] When a computer program is executed, it also performs the following steps:

[0230] Based on the receiver's position increment and the receiver's position in the previous epoch, the predicted position of the receiver in the next epoch is calculated through inertial navigation mechanical arrangement.

[0231] Acquire precise ephemeris data and determine the satellite coordinates of each observed satellite in the next epoch based on the precise ephemeris data;

[0232] Based on the receiver's predicted position and the satellite coordinates of each observation satellite, determine the predicted station-satellite geometric distance from the receiver to each observation satellite;

[0233] The geometric distance term in the dual-frequency carrier phase observations is filtered out based on the predicted station-satellite geometric distance to obtain the filtered dual-frequency carrier phase observations.

[0234] Based on the filtered dual-frequency carrier phase observations, the inertial navigation-assisted wide-lane combination phase observations and the inertial navigation-assisted non-ionospheric combination phase observations are determined according to the wide-lane combination rule and the non-ionospheric combination rule, respectively, and used as cycle slip observations.

[0235] In some embodiments, when a computer program is executed, it also performs the following steps:

[0236] Obtain the reference satellite; for each of the remaining observation satellites other than the reference satellite, perform inter-satellite difference based on the cycle slip observations of the remaining observation satellites and the cycle slip observations of the reference satellite to obtain the intermediate cycle slip detection quantity; among them, the reference satellite is the observation satellite with the highest elevation angle, which has not experienced a cycle slip and has been continuously tracked without losing lock;

[0237] For each of the remaining observation satellites, the cycle slip detection value is obtained by differentiating the intermediate cycle slip detection value between adjacent epochs based on the intermediate cycle slip detection value of the observation satellite.

[0238] In some embodiments, cycle slip detection quantities include double-difference wide-lane cycle slip detection quantities and ionosphere-free cycle slip detection quantities;

[0239] When a computer program is executed, it also performs the following steps:

[0240] The least squares ambiguity decorrelation method is used to perform least squares calculation on the double-difference wide lane cycle slip detection quantity to obtain a floating-point cycle slip estimate. The floating-point cycle slip estimate is then fixed to an integer value to obtain an integer solution for the double-difference wide lane cycle slip.

[0241] The integer solution of the cycle slip of the double-difference wide lane is used to deduce the short-time extrapolation error of the inertial navigation system of the double-difference wide lane. The short-time extrapolation error of the inertial navigation system of the double-difference wide lane is then deducted from the cycle slip detection quantity of the double-difference wide lane to obtain the filtered cycle slip detection quantity of the double-difference wide lane.

[0242] Based on the short-time extrapolation error of the inertial navigation system in the double-difference wide lane, the equivalent short-time extrapolation error of the inertial navigation system for the ionospheric cycle slip detection quantity is determined.

[0243] The filtered ionospheric cycle slip detection quantity is obtained by subtracting the equivalent inertial navigation short-time extrapolation error from the ionospheric cycle slip detection quantity.

[0244] In some embodiments, when a computer program is executed, it also performs the following steps:

[0245] Obtain the dual-frequency carrier frequency parameters and dual-frequency carrier wavelength, and determine the first frequency combination coefficient and the second frequency combination coefficient based on the dual-frequency carrier frequency parameters;

[0246] The real cycle slip quantity is determined based on the first frequency combination coefficient, the second frequency combination coefficient, the dual-frequency carrier wavelength, and the filtered cycle slip detection quantity.

[0247] In some embodiments, when a computer program is executed, it also performs the following steps:

[0248] Round the real cycle jump to the nearest integer to obtain the integer cycle jump value;

[0249] The initial ambiguity estimate for the current epoch is updated by combining the integer cycle jump value and the ambiguity of the previous epoch.

[0250] Obtain the PPP state parameters for the current epoch, and construct the PPP state vector based on the initial fuzzy estimation value and the PPP state parameters for the current epoch.

[0251] Obtain the pseudorange and carrier phase observations for the current epoch, and establish a linearized observation equation based on the pseudorange, carrier phase, and PPP state vector.

[0252] The linearized observation equation is iteratively solved until the change in the PPP state vector is less than the preset convergence threshold, and the converged PPP state vector is output as the optimized PPP state vector.

[0253] The optimized PPP state vector is used as the PPP state vector after cycle slip repair in the current epoch.

[0254] This application provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the following steps:

[0255] Acquire inertial navigation data and GNSS observations from the UAV; determine cycle slip observations based on the inertial navigation data and GNSS observations;

[0256] The cycle slip detection quantity is obtained by performing inter-satellite and inter-epoch difference calculations on the cycle slip observations.

[0257] Based on the cycle slip detection quantity and the preset detection quantity threshold, determine whether the GNSS signal of the UAV in the current epoch has experienced a cycle slip;

[0258] If it is determined that a cycle slip has occurred in the GNSS signal of the UAV at the current epoch, then the cycle slip detection quantity is filtered by inertial navigation error to obtain the filtered cycle slip detection quantity.

[0259] The real cycle slip quantity is determined based on the filtered cycle slip detection quantity;

[0260] Obtain the ambiguity of the previous epoch, optimize the PPP state vector of the current epoch based on the real cycle slip and the ambiguity of the previous epoch, and use the optimized PPP state vector to repair the PPP cycle slip of the current epoch.

[0261] In some embodiments, the inertial navigation data includes the receiver's position increment and the receiver's position in the previous epoch; the GNSS observations include dual-frequency carrier phase observations.

[0262] When program instructions are executed by the computer, the computer also performs the following steps:

[0263] Based on the receiver's position increment and the receiver's position in the previous epoch, the predicted position of the receiver in the next epoch is calculated through inertial navigation mechanical arrangement.

[0264] Acquire precise ephemeris data and determine the satellite coordinates of each observed satellite in the next epoch based on the precise ephemeris data;

[0265] Based on the receiver's predicted position and the satellite coordinates of each observation satellite, determine the predicted station-satellite geometric distance from the receiver to each observation satellite;

[0266] The geometric distance term in the dual-frequency carrier phase observations is filtered out based on the predicted station-satellite geometric distance to obtain the filtered dual-frequency carrier phase observations.

[0267] Based on the filtered dual-frequency carrier phase observations, the inertial navigation-assisted wide-lane combination phase observations and the inertial navigation-assisted non-ionospheric combination phase observations are determined according to the wide-lane combination rule and the non-ionospheric combination rule, respectively, and used as cycle slip observations.

[0268] In some embodiments, when program instructions are executed by a computer, the computer also performs the following steps:

[0269] Obtain the reference satellite; for each of the remaining observation satellites other than the reference satellite, perform inter-satellite difference based on the cycle slip observations of the remaining observation satellites and the cycle slip observations of the reference satellite to obtain the intermediate cycle slip detection quantity; among them, the reference satellite is the observation satellite with the highest elevation angle, which has not experienced a cycle slip and has been continuously tracked without losing lock;

[0270] For each of the remaining observation satellites, the cycle slip detection value is obtained by differentiating the intermediate cycle slip detection value between adjacent epochs based on the intermediate cycle slip detection value of the observation satellite.

[0271] In some embodiments, cycle slip detection quantities include double-difference wide-lane cycle slip detection quantities and ionosphere-free cycle slip detection quantities;

[0272] When program instructions are executed by the computer, the computer also performs the following steps:

[0273] The least squares ambiguity decorrelation method is used to perform least squares calculation on the double-difference wide lane cycle slip detection quantity to obtain a floating-point cycle slip estimate. The floating-point cycle slip estimate is then fixed to an integer value to obtain an integer solution for the double-difference wide lane cycle slip.

[0274] The integer solution of the cycle slip of the double-difference wide lane is used to deduce the short-time extrapolation error of the inertial navigation system of the double-difference wide lane. The short-time extrapolation error of the inertial navigation system of the double-difference wide lane is then deducted from the cycle slip detection quantity of the double-difference wide lane to obtain the filtered cycle slip detection quantity of the double-difference wide lane.

[0275] Based on the short-time extrapolation error of the inertial navigation system in the double-difference wide lane, the equivalent short-time extrapolation error of the inertial navigation system for the ionospheric cycle slip detection quantity is determined.

[0276] The filtered ionospheric cycle slip detection quantity is obtained by subtracting the equivalent inertial navigation short-time extrapolation error from the ionospheric cycle slip detection quantity.

[0277] In some embodiments, when program instructions are executed by a computer, the computer also performs the following steps:

[0278] Obtain the dual-frequency carrier frequency parameters and dual-frequency carrier wavelength, and determine the first frequency combination coefficient and the second frequency combination coefficient based on the dual-frequency carrier frequency parameters;

[0279] The real cycle slip quantity is determined based on the first frequency combination coefficient, the second frequency combination coefficient, the dual-frequency carrier wavelength, and the filtered cycle slip detection quantity.

[0280] In some embodiments, when program instructions are executed by a computer, the computer also performs the following steps:

[0281] Round the real cycle jump to the nearest integer to obtain the integer cycle jump value;

[0282] The initial ambiguity estimate for the current epoch is updated by combining the integer cycle jump value and the ambiguity of the previous epoch.

[0283] Obtain the PPP state parameters for the current epoch, and construct the PPP state vector based on the initial fuzzy estimation value and the PPP state parameters for the current epoch.

[0284] Obtain the pseudorange and carrier phase observations for the current epoch, and establish a linearized observation equation based on the pseudorange, carrier phase, and PPP state vector.

[0285] The linearized observation equation is iteratively solved until the change in the PPP state vector is less than the preset convergence threshold, and the converged PPP state vector is output as the optimized PPP state vector.

[0286] The optimized PPP state vector is used as the PPP state vector after cycle slip repair in the current epoch.

[0287] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, electronic devices, computer storage media, and computer program products described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0288] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products or devices.

[0289] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0290] In the several embodiments provided by this invention, it should be understood that the disclosed systems, electronic devices, computer storage media, computer program products, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0291] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0292] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0293] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of the present invention through a computer device (which may be a personal computer, a server, or a network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0294] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for repairing PPP cycle slips in unmanned aerial vehicles (UAVs), characterized in that, include: Acquire inertial navigation data and GNSS observations from the UAV; Based on the inertial navigation data and the GNSS observations, determine the cycle slip observations; The cycle slip observations are subjected to inter-satellite and inter-epoch difference calculations to obtain the cycle slip detection quantity; Based on the cycle slip detection quantity and the preset detection quantity threshold, it is determined whether the GNSS signal of the UAV in the current epoch has experienced a cycle slip; If it is determined that a cycle slip has occurred in the GNSS signal of the UAV at the current epoch, then the cycle slip detection quantity is filtered by inertial navigation error to obtain the filtered cycle slip detection quantity. The real cycle slip quantity is determined based on the filtered cycle slip detection quantity; Obtain the ambiguity of the previous epoch, optimize the PPP state vector of the current epoch based on the real cycle slip amount and the ambiguity of the previous epoch, and use the optimized PPP state vector to repair the PPP cycle slip of the current epoch.

2. The UAV PPP cycle slip repair method according to claim 1, characterized in that, The inertial navigation data includes the receiver's position increment and the receiver's position in the previous epoch; The GNSS observations include dual-frequency carrier phase observations; The step of determining cycle slip observations based on the inertial navigation data and the GNSS observations includes: Based on the receiver's position increment and the receiver's position in the previous epoch, the predicted position of the receiver in the next epoch is calculated through inertial navigation mechanical arrangement. Acquire precise ephemeris data, and determine the satellite coordinates of each observed satellite in the next epoch based on the precise ephemeris data; Based on the predicted position value of the receiver and the satellite coordinates of each of the observed satellites, the predicted station-satellite geometric distance from the receiver to each of the observed satellites is determined; The geometric distance term in the dual-frequency carrier phase observation value is filtered out based on the predicted station-satellite geometric distance to obtain the filtered dual-frequency carrier phase observation value. Based on the filtered dual-frequency carrier phase observations, the inertial navigation-assisted wide-lane combination phase observations and the inertial navigation-assisted non-ionospheric combination phase observations are determined according to the wide-lane combination rule and the non-ionospheric combination rule, respectively, and used as the cycle slip observations.

3. The UAV PPP cycle slip repair method according to claim 1 or 2, characterized in that, The process of performing inter-satellite and inter-epoch difference calculations on the cycle slip observations to obtain the cycle slip detection quantity includes: Obtain a reference satellite; for each of the remaining observation satellites other than the reference satellite, perform inter-satellite difference based on the cycle slip observations of the remaining observation satellites and the cycle slip observations of the reference satellite to obtain the intermediate cycle slip detection quantity; wherein, the reference satellite is the observation satellite with the highest elevation angle, which has not experienced a cycle slip and has been continuously tracked without losing lock; For each of the remaining observation satellites, the cycle slip detection value is obtained by differentiating between adjacent epochs based on the intermediate cycle slip detection value of the observation satellite.

4. The UAV PPP cycle slip repair method according to claim 3, characterized in that, The cycle slip detection quantity includes double-difference wide-lane cycle slip detection quantity and ionosphere-free cycle slip detection quantity; The step of filtering the cycle slip detection quantity using inertial navigation error to obtain the filtered cycle slip detection quantity includes: The least squares ambiguity decorrelation method is used to perform least squares calculation on the double-difference wide-lane cycle slip detection quantity to obtain a floating-point cycle slip estimate. The floating-point cycle slip estimate is then fixed to an integer value to obtain an integer solution for the double-difference wide-lane cycle slip. The inertial navigation short-time extrapolation error of the double-difference wide lane is derived from the integer solution of the double-difference wide lane cycle slip, and the inertial navigation short-time extrapolation error of the double-difference wide lane is subtracted from the double-difference wide lane cycle slip detection quantity to obtain the filtered double-difference wide lane cycle slip detection quantity. Based on the short-time extrapolation error of the inertial navigation system for the double-difference wide lane, determine the equivalent short-time extrapolation error of the inertial navigation system for the ionospheric cycle slip detection quantity. The filtered ionospheric cycle slip detection quantity is obtained by subtracting the equivalent inertial navigation short-time extrapolation error from the ionospheric cycle slip detection quantity.

5. The UAV PPP cycle slip repair method according to claim 1, characterized in that, Determining the real cycle slip quantity based on the filtered cycle slip detection quantity includes: Obtain the dual-frequency carrier frequency parameters and dual-frequency carrier wavelength, and determine the first frequency combination coefficient and the second frequency combination coefficient based on the dual-frequency carrier frequency parameters; The real cycle slip quantity is determined based on the first frequency combination coefficient, the second frequency combination coefficient, the dual-frequency carrier wavelength, and the filtered cycle slip detection quantity.

6. The UAV PPP cycle slip repair method according to claim 1, characterized in that, The step of optimizing the PPP state vector of the current epoch based on the real cycle slip amount and the ambiguity of the previous epoch, and using the optimized PPP state vector to repair the PPP cycle slip of the current epoch, includes: The real number of cycle jumps is rounded down to the nearest integer to obtain the integer cycle jump value; By combining the integer cycle jump value and the ambiguity of the previous epoch, the initial value of the ambiguity estimate for the current epoch is updated; Obtain the PPP state parameters for the current epoch, and construct the PPP state vector based on the initial fuzzyness estimate of the current epoch and the PPP state parameters; Obtain the pseudorange observation value and carrier phase observation value of the current epoch, and establish a linearized observation equation based on the pseudorange observation value, the carrier phase observation value and the state vector of the PPP; The linearized observation equation is iteratively solved until the change in the PPP state vector is less than a preset convergence threshold, and the converged PPP state vector is output as the optimized PPP state vector. The optimized PPP state vector is used as the PPP state vector after cycle slip repair in the current epoch.

7. A UAV PPP cycle slip repair system, characterized in that, include: The cycle slip observation determination module is used to acquire inertial navigation data and GNSS observations from the UAV. Based on the inertial navigation data and the GNSS observations, determine the cycle slip observations; The cycle slip detection and determination module is used to perform inter-satellite difference and inter-epoch difference calculations on the cycle slip observations to obtain the cycle slip detection quantity; The cycle slip determination module is used to determine whether a cycle slip has occurred in the GNSS signal of the UAV in the current epoch based on the cycle slip detection quantity and a preset detection quantity threshold. The cycle slip detection and filtering module is used to filter the cycle slip detection quantity by inertial navigation error if it is determined that the GNSS signal of the UAV in the current epoch has a cycle slip, so as to obtain the filtered cycle slip detection quantity. The cycle slip determination module is used to determine the real cycle slip quantity based on the filtered cycle slip detection quantity; The cycle slip repair module is used to obtain the ambiguity of the previous epoch, optimize the PPP state vector of the current epoch based on the real cycle slip amount and the ambiguity of the previous epoch, and use the optimized PPP state vector to repair the PPP cycle slip of the current epoch.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the UAV PPP cycle slip repair method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the steps of the UAV PPP cycle slip repair method as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the steps of the UAV PPP cycle slip repair method as described in any one of claims 1-6.

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