Beidou differential positioning data quality evaluation method and system for unmanned aerial vehicle

Abstract

The application discloses a Beidou differential positioning data quality evaluation method and system for unmanned aerial vehicles, which comprises the following steps: evaluating various quality indexes of Beidou differential data and local observation data of the unmanned aerial vehicle, and calculating a comprehensive quality index based on the evaluation result; performing RTK resolution of the unmanned aerial vehicle, and if the resolution result is not fixed, performing ionospheric weighted model RTK resolution; if the resolution result of the RTK resolution of the unmanned aerial vehicle or the ionospheric weighted model RTK resolution is fixed and the fixed rate reaches a set threshold, it is determined that the current data has high-precision RTK positioning conditions; and if the resolution result of the ionospheric weighted model RTK resolution cannot be fixed, it is determined that the current data does not have high-precision RTK positioning conditions. The application can dynamically judge the availability of RTK resolution, guarantee the positioning precision and stability of the unmanned aerial vehicle in a complex environment, and is suitable for safety management and performance optimization of unmanned aerial vehicle high-precision application scenes such as surveying and mapping and monitoring.

Description

TECHNICAL FIELD

[0001] The application relates to the field of unmanned aerial vehicle (UAV) centimeter-level RTK high-precision positioning performance evaluation, and particularly relates to a Beidou differential positioning data quality evaluation method and system for an unmanned aerial vehicle. BACKGROUND

[0002] The navigation module in the unmanned aerial vehicle mainly uses satellite navigation technology at the absolute positioning level. Due to the need for precise navigation and mapping for accurate positions, high-precision RTK positioning technology has gradually been popularized in the unmanned aerial vehicle. By accessing ground-based augmentation services, the unmanned aerial vehicle can achieve centimeter-level positioning, so that the flight path of the unmanned aerial vehicle is more accurate, and more efficient and accurate aerial photography tasks can be achieved, improving the accuracy and reliability of data acquisition. Meanwhile, high-precision positioning can also ensure the accuracy and stability of the position of the unmanned aerial vehicle when it is hovering, so that the unmanned aerial vehicle can more stably and accurately record the changes of the target object and the information of the position in the process of aerial photography or monitoring. In addition, high-precision positioning also has a significant role and significance for the safety control and real-time supervision of the unmanned aerial vehicle.

[0003] During the real-time RTK high-precision positioning of the unmanned aerial vehicle, it may be affected by many factors, mainly including: (1) the influence of Beidou differential data quality: Beidou differential data is a prerequisite for implementing RTK positioning technology, and the quality of the data determines the performance of the terminal positioning. The source of Beidou differential data generally includes self-set short baseline base stations and access to ground-based augmentation system services. For the latter, the more the number of reference stations and the more uniform the distribution, the higher the positioning accuracy of the system, and it is also affected by factors such as the performance of the ground-based augmentation service software, the spatial atmospheric environment, etc.; (2) signal shielding: the unmanned aerial vehicle is easily blocked by surrounding buildings, mountains and other objects during flight, resulting in a decrease in signal quality and affecting the RTK positioning performance; (3) electromagnetic interference: the unmanned aerial vehicle RTK system that communicates, measures distance and navigates through modulated radio waves is easily affected by surrounding electromagnetic interference, resulting in a decrease in signal quality and a decrease in system accuracy.

[0004] As can be seen from the above analysis, the implementation of RTK positioning by the unmanned aerial vehicle is affected by many factors. The evaluation of the real-time high-precision positioning performance of the unmanned aerial vehicle is related to its safety and reliability, especially when the unmanned aerial vehicle cannot normally perform RTK high-precision and reliable positioning calculation. Clarifying the mechanism of action of each influencing factor can also effectively evaluate the source of Beidou differential data, the signal reception and calculation of the unmanned aerial vehicle terminal and its operating environment, thereby providing strong support and promoting effect for the development of high-precision applications of the unmanned aerial vehicle. SUMMARY

[0005] In order to solve the above problems, the application provides a Beidou differential positioning data quality evaluation method and system for unmanned aerial vehicles, which quantifies key factors such as Beidou differential data quality and signal shielding, combines single and comprehensive quality evaluation, fixed rate discrimination and ionospheric weighting, dynamically judges the availability of RTK resolution, and guarantees the positioning accuracy and stability of unmanned aerial vehicles in complex environments.

[0006] In order to achieve the above purpose, the application is realized by the following technical scheme:

[0007] The Beidou differential positioning data quality evaluation method for unmanned aerial vehicles comprises:

[0008] The quality indicators of Beidou differential data and local observation data of the unmanned aerial vehicle are evaluated, and the comprehensive quality indicators are calculated based on the evaluation results;

[0009] In the case that the comprehensive quality indicators reach the set judgment threshold, the RTK resolution of the unmanned aerial vehicle is carried out, if the resolution result is not fixed, the ionospheric weighted model RTK resolution is carried out; when the resolution result of the RTK resolution of the unmanned aerial vehicle or the ionospheric weighted model RTK resolution is fixed and the fixed rate reaches the set threshold, it is judged that the current Beidou differential data and the local observation data of the unmanned aerial vehicle have high-precision RTK positioning conditions; if the resolution result of the ionospheric weighted model RTK resolution cannot be fixed, it is judged that the current Beidou differential data and the local observation data of the unmanned aerial vehicle do not have high-precision RTK positioning conditions.

[0010] The further improvement of the application is that the quality indicators include observation data integrity rate, cycle slip ratio, multipath effect, pseudorange observation noise, carrier observation noise and ionospheric variation, and the calculation expression is:

[0011] The observation data integrity rate is:

[0012] (1);

[0013] (2);

[0014] Wherein, The single-frequency observation data integrity rate is represented by R; The single-system observation data integrity rate is represented by R; The total number of observed satellites in the observation time period is represented by N; The total number of actual observation epochs of the satellite No. The total number of actual observation epochs of the satellite No. The total number of theoretical epochs of the satellite No. The total number of theoretical epochs of the satellite No. The number of epochs in which all frequency points of the satellite No. The number of epochs in which all frequency points of the satellite No. represents the total number of epochs in the observation time period, and the total number of epochs of the BeiDou satellite;

[0015] cycle slip ratio:

[0016] (3);

[0017] wherein, represents the cycle slip ratio; is the total number of epochs of observation; is the number of epochs in which cycle slips occur;

[0018] multipath error:

[0019] (4);

[0020] wherein: represents the number of epochs of the sliding window; represents the epoch number of the sliding window; represents the frequency; represents the time; represents the satellite at the time the frequency of the multipath error; represents the calculation amount of the satellite at the time the frequency containing the multipath error and the integer ambiguity information;

[0021] pseudo-range and carrier observation noise:

[0022] (5);

[0023] (6);

[0024] wherein, and are the pseudo-range observation noise and the carrier observation noise, respectively, represents the number of triple differences of the adjacent epoch measurement phase carrier phase observations of the satellite at a certain frequency point; represents the epoch; and represents the pseudo-range phase observation and the carrier phase observation of the satellite at a certain frequency point at the time; represents the triple difference factor;

[0025] ionospheric variation rate:

[0026] (7);

[0027] wherein, represents the satellite at the time The rate of change of ionospheric delay at frequency, in m / s; , These respectively indicate the satellite at , Always The computational cost in terms of frequency.

[0028] A further improvement of this invention is that the calculation process of the comprehensive quality index includes:

[0029] All quality indicators were processed for trend convergence and normalized:

[0030] (8);

[0031] in: Indicates the first The quality indicator in the first Dimensionless values ​​for each observation sample; This represents the original quality index after trend convergence. and Represents the normalization constant;

[0032] The weights of each quality indicator are determined using the entropy method;

[0033] Entropy value:

[0034] (9);

[0035] Coefficient of difference:

[0036] (10);

[0037] Weighting coefficients:

[0038] (11);

[0039] in: Indicates the first The entropy value of a quality indicator; Indicates the number of samples; This represents the total number of quality indicators; Indicates the first Normalized weights of each quality indicator; Indicates the first The coefficient of variation for each quality indicator;

[0040] Calculate the overall quality index:

[0041] (12);

[0042] in: Indicates the first The overall evaluation value of each sample; represents the ideal value of the quality index.

[0043] The further improvement of the present application is that the ionosphere weighting model expression is:

[0044] (13);

[0045] wherein: represents the double difference operator; represents the carrier phase observation of the frequency; represents the pseudo-range observation of the frequency; represents the wavelength of the frequency; represents the ideal value of the frequency ambiguity; represents the ionosphere amplification factor of the frequency; represents the direction cosine coefficient matrix; represents the position parameter to be estimated; represents the ionosphere pseudo-observation value; represents the ionosphere delay parameter to be estimated;

[0046] The ionosphere pseudo-observation value measurement noise variance of the ionosphere weighting model is:

[0047] (14);

[0048] wherein: represents the constraint strength, represents the ionosphere pseudo-observation value measurement noise variance.

[0049] The present application is a Beidou differential positioning data quality evaluation system of a UAV, comprising:

[0050] A single quality index evaluation module is used to evaluate each quality index of the Beidou differential data and the local observation data of the UAV;

[0051] A comprehensive quality index evaluation module is used to calculate the comprehensive quality index of the Beidou differential data and the comprehensive quality index of the local observation data of the UAV;

[0052] The RTK solving module is configured to perform the RTK solving of the UAV when the comprehensive quality index reaches the set determination threshold, perform the ionosphere weighted model RTK solving if the solving result is not fixed, determine that the current Beidou differential data and the local observation data of the UAV have the high-precision RTK positioning condition when the solving result of the RTK solving of the UAV or the ionosphere weighted model RTK solving is fixed and the fixing rate reaches the set threshold, and determine that the current Beidou differential data and the local observation data of the UAV do not have the high-precision RTK positioning condition when the solving result of the ionosphere weighted model RTK solving cannot be fixed.

[0053] Further improvement of the application is that each quality index includes an observation data integrity rate, a cycle slip ratio, a multipath effect, pseudorange and carrier observation noise, and ionosphere variation, and the calculation expression is:

[0054] The observation data integrity rate is:

[0055] (1);

[0056] (2);

[0057] wherein, represents the single-frequency observation data integrity rate; represents the single-system observation data integrity rate; represents the total number of observed satellites in the observation time period; represents the total number of actual observation epochs of the i-th satellite at a certain frequency point in the observation time period; represents the total number of theoretical epochs of the i-th satellite at a certain frequency point in the observation time period; represents the number of epochs in which the i-th satellite has valid observation data at all frequency points in the observation time period; represents the total number of theoretical epochs of the i-th satellite in the observation time period; represents the total number of theoretical epochs of the i-th satellite in the observation time period; The cycle slip ratio is:

[0058]

[0059] (3);

[0060] wherein, represents the cycle slip ratio; is the total number of observation epochs; is the number of epochs in which cycle slip occurs;

[0061] The multipath error is:

[0062] (4);

[0063] wherein:​​​ represents the number of epochs of the sliding window; represents the epoch number of the sliding window; represents the frequency; represents the time; represents the evaluation value of the multipath error of the satellite at frequency; represents the calculation amount of the multipath error and the integer ambiguity information of the satellite at time on frequency;

[0064] pseudo-range and carrier observation noise:

[0065] (5);

[0066] (6);

[0067] wherein, and are pseudo-range observation noise and carrier observation noise respectively; represents the number of triple difference values of the adjacent epoch measurement phase carrier phase observation of the satellite at a frequency point; represents the epoch; and represent the pseudo-range phase observation and the carrier phase observation of the satellite at a frequency point at time;

[0068] ionospheric change rate:

[0069] (7);

[0070] wherein, represents the ionospheric delay change rate of the satellite at frequency, unit m / s; , represent the calculation amount of the satellite at , time on frequency respectively.

[0071] Further improvement of the present application is that the calculation process of the comprehensive quality index includes:

[0072] trend processing and normalization of each quality index:

[0073] (8);

[0074] wherein: represents the first quality index in the first The non-dimensional value under the observation sample; The original quality index after homogenization is represented as And The normalization constant is represented as

[0075] The weight of each quality index is determined by using the entropy value method;

[0076] Entropy value:

[0077] (9);

[0078] Difference coefficient:

[0079] (10);

[0080] Weight coefficient:

[0081] (11);

[0082] Wherein: The entropy value of the i-th quality index is represented as The sample number is represented as The total number of quality indexes is represented as The normalization weight of the i-th quality index is represented as The difference coefficient of the i-th quality index is represented as The comprehensive quality index is calculated as

[0083]

[0084] (12);

[0085] Wherein: The comprehensive evaluation value of the i-th sample is represented as The ideal value of the i-th quality index is represented as

[0086] Further improvement of the application is that the ionosphere weighted model expression is:

[0087] (13);

[0088] Wherein: The double difference operator is represented as The carrier phase observation of the i-th frequency is represented as The pseudo-range observation of the i-th frequency is represented as The wavelength of the i-th frequency is represented as ​​​​​​​​​Ambiguity of frequency; Indicates Ionospheric amplification factor of frequency; Indicates the direction cosine coefficient matrix; Indicates the position parameter to be estimated; Indicates ionospheric pseudo-observation value; Indicates ionospheric delay estimated parameter;

[0089] The ionospheric pseudo-observation value measurement noise variance of the ionospheric weighting model is:

[0090] (14);

[0091] Wherein: Indicates the constraint strength, Indicates the ionospheric pseudo-observation value measurement noise variance.

[0092] The electronic device of the application comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to realize the steps of the above-mentioned unmanned aerial vehicle Beidou differential positioning data quality evaluation method.

[0093] The computer readable storage medium of the application stores a computer program, and the computer program is executed by the processor to realize the steps of the above-mentioned unmanned aerial vehicle Beidou differential positioning data quality evaluation method.

[0094] The beneficial effects of the application are: the application quantifies the key factors such as Beidou differential data quality and signal shielding, combines single and comprehensive quality evaluation, fixed rate discrimination and ionospheric weighting modeling, dynamically judges the availability of RTK solution, guarantees the positioning accuracy and stability of unmanned aerial vehicle in complex environment, and is suitable for safety management and performance optimization of unmanned aerial vehicle high-precision application scene such as surveying and mapping and monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0095] Figure 1 It is the method flowchart in the embodiment of the application;

[0096] Figure 2 It is the implementation flowchart in the embodiment of the application;

[0097] Figure 3 It is the multipath error of unmanned aerial vehicle in open observation environment in the embodiment of the application;

[0098] Figure 4 It is the multipath error of unmanned aerial vehicle in complex observation environment in the embodiment of the application;

[0099] Figure 5Data availability and cycle slip ratio in an open observation environment of a UAV in an embodiment of the present application;

[0100] Figure 6 Data availability and cycle slip ratio in a complex observation environment of a UAV in an embodiment of the present application;

[0101] Figure 7 Schematic diagram of positioning error sequence in a complex scenario using an ionosphere fixed model in an embodiment of the present application;

[0102] Figure 8 Schematic diagram of positioning error sequence in a complex scenario using an ionosphere floating model in an embodiment of the present application. DETAILED DESCRIPTION

[0103] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0104] As shown in Figure 1 and Figure 2 , the present embodiment discloses a Beidou differential positioning data quality evaluation method for a UAV. The UAV uploads GPGGA format positioning results locally, and requests Beidou differential data from a continuous operation reference station (CORS) center. The CORS center generates virtual observation values (Beidou differential data) according to the approximate position uploaded by the UAV, and broadcasts the virtual observation values to the UAV for local use through a network, wherein the Beidou differential data includes pseudo-range observation values and carrier observation values. The quality indicators of the local observation data of the UAV and the Beidou differential data are calculated. When the quality indicator of any single item does not meet the set condition, it is determined that the current data quality is abnormal and cannot be used for high-precision positioning calculation of the UAV. When all the quality indicators meet the set conditions, the comprehensive quality indicators of the Beidou differential data and the local observation data of the UAV are calculated respectively, and it is further determined whether the current data (including the Beidou differential data and the local observation data of the UAV) is feasible. In the case where the current data quality is determined to be feasible, the UAV RTK calculation is performed. When the calculation result can be fixed and the fixing rate meets the set threshold, it is further indicated that, in addition to the availability of the current data quality, the residual ionospheric error after differential is also not significant. When the calculation result cannot be fixed, the ionosphere weighted model is switched to. After switching to the ionosphere weighted model, the calculation is performed using ionosphere weighted constraint strengths (10 cm, 15 cm, 20 cm) from small to large. When the fixing rate meets the requirements, it is indicated that the ionospheric error is the main factor affecting the fixing performance. If it still cannot be fixed, it is determined that the current data cannot be used for high-precision positioning of the UAV. The specific implementation steps are as follows:

[0105] Step 1, evaluate the quality indicators of Beidou differential data and UAV local observation data, including observation data integrity, cycle slip ratio, multipath effect, pseudorange and carrier observation noise, ionospheric variation rate. When any indicator does not meet the set threshold, it is determined that the current data quality is abnormal and cannot be used for high-precision positioning of UAV. The local observation data of UAV is the local observation data received by the positioning module GNSS carried on the UAV. The local observation data includes pseudorange observation data and carrier observation data.

[0106] The calculation of each quality indicator specifically includes:

[0107] (1) Observation data integrity:

[0108] Single frequency point observation data integrity:

[0109] (1);

[0110] Single system observation data integrity:

[0111] (2);

[0112] Wherein, represents the single frequency point observation data integrity; represents the single system observation data integrity; represents the total number of satellites observed in the observation period; represents the total number of actual observation epochs of the th satellite at a certain frequency point in the observation period; represents the total number of theoretical epochs of the th satellite at a certain frequency point in the observation period; represents the number of epochs in which the th satellite has valid observation data at all frequency points in the observation period; represents the total number of theoretical epochs of the th satellite in the observation period.

[0113] Threshold: According to engineering application experience, set the integrity < 90% when the data is abnormal.

[0114] (2) Cycle slip ratio

[0115] The main steps of cycle slip ratio calculation are as follows:

[0116] (a) Read Beidou differential data and UAV local observation data, and count the actual epoch data of Beidou differential data and UAV local observation data respectively;

[0117] (b) Combine gross error detection method, cycle slip detection method and receiver clock slip detection method to determine the epoch of cycle slip occurrence and collect data on the epoch of cycle slip occurrence;

[0118] (c) Calculate the evaluation value according to the definition of cycle slip ratio.

[0119] (3);

[0120] in, Indicates the cycle jump ratio; The total number of epochs observed; This is the epoch number at which the cycle slip occurred.

[0121] Judgment threshold: When the average week jump ratio is less than or equal to 200, the current data is judged to be unreliable.

[0122] (3) Multipath error

[0123] Calculating pseudorange multipath error requires dual-frequency observation data. A combination of pseudorange and carrier phase observation equations is used to eliminate the effects of tropospheric and ionospheric delays, and the calculation is performed using the following formula.

[0124] (4);

[0125] in: and These represent the multipath errors at frequency point 1 and frequency point 2, respectively. frequency, Multipath error at frequency , express frequency, Frequency pseudorange observation, in meters (m). frequency, The frequency is the drone navigation signal; , express frequency, The frequency of the frequency carrier, measured in MHz; , express frequency, Frequency carrier phase observations, in meters (m).

[0126] The ambiguity parameters of the same satellite will not change when observed continuously without cycle slips. The multipath error is calculated using the following formula across multiple epochs without cycle slips.

[0127] (5);

[0128] in: Epoch number of sliding window, default is 50; Epoch number of sliding window; Frequency; Time; Evaluation value of multipath error of satellite at Frequency; Time; Frequency; Calculation amount of multipath error and integer ambiguity information of satellite at

[0129] Decision threshold: when > 0.5 m (meter), it is determined that the multipath error is large.

[0130] (4) Pseudorange and carrier observation noise

[0131] It is calculated by third-order difference, and the expression is:

[0132] (6);

[0133] (7);

[0134] Wherein, and are the pseudorange observation noise and the carrier observation noise respectively, represents the number of third-order difference values of adjacent epoch measurement phase carrier phase observation of satellite at a frequency point, represents epoch, and represent Pseudorange phase observation and carrier phase observation of satellite at a frequency point at represents the third-order difference factor;

[0135] Decision threshold: the pseudorange observation noise is greater than 1.5 m, which indicates that the current data quality is poor. The carrier observation noise is greater than 0.005 m, which indicates that the current data noise is large.

[0136] (5) Ionospheric variation rate

[0137] The calculation expression is:

[0138] (8);

[0139] Wherein, represents the ionospheric delay variation rate of satellite at Frequency, with the unit of meter per second (m / s); , represent the satellite at , Time at The computational cost in terms of frequency includes ionospheric delay, multipath and integer ambiguity information.

[0140] Judgment threshold: When the rate of change of the ionosphere is greater than 0.07 m / s, an ionospheric jump is determined to have occurred, and the current data is not used for high-precision positioning.

[0141] In this embodiment, the multipath errors of the UAV in open, undisturbed scenarios and complex, undisturbed scenarios are as follows: Figure 3 and Figure 4 As shown, the data integrity rate and cycle slip of the drone in open, undisturbed scenarios and in complex, undisturbed scenarios are as follows: Figure 5 and Figure 6 As shown in Table 1, various quality indicators of the two sets of data are statistically analyzed.

[0142] Table 1: Statistical Results of Data Quality Indicators for UAVs in Open and Complex Observation Environments

[0143]

[0144] Step 2: If all individual quality indicators meet the corresponding judgment thresholds, the comprehensive quality indicator is further calculated. This embodiment combines normalization, entropy weight method, and Euclidean distance to calculate the comprehensive quality indicator.

[0145] (1) Homogeneity and dimensionlessness

[0146] To facilitate a unified comparison of indicators with different dimensions, we first compare each individual quality indicator. Perform trend reversal and normalization:

[0147] (9);

[0148] in: Indicates the first The quality indicator in the first Dimensionless values ​​for each observation sample; This represents the original quality index after trend convergence. and As a normalization constant, c=0 and d=1 are taken in this embodiment.

[0149] (2) Determining index weights using the entropy method

[0150] Entropy value:

[0151] (10);

[0152] Coefficient of difference:

[0153] (11);

[0154] Weighting coefficients:

[0155] (12);

[0156] in: Indicates the first The entropy value of a quality indicator; Indicates the number of samples; This represents the total number of quality indicators; Indicates the first Normalized weights of each quality indicator; Indicates the first The coefficient of variation of each quality indicator.

[0157] (3) Calculate the comprehensive quality index

[0158] Calculate the first using the Euclidean distance method. Overall evaluation value of each sample :

[0159] (13);

[0160] in: Indicates the first Ideal values ​​for each quality indicator; Indicates the first The overall evaluation value of each sample The smaller the value, the better the overall data quality.

[0161] (4) Threshold setting

[0162] Based on reference standards, experience, and engineering practice: An empirical threshold is set; in this embodiment, the threshold is set to 0.3. If... If the current data quality is acceptable, then RTK calculation for the UAV is performed, i.e., conventional RTK model positioning.

[0163] If the solution result can be fixed and the fixation rate is higher than the threshold, it indicates that the residual error has no significant impact on positioning performance. If it cannot be fixed, switch to the ionospheric weighted model and dynamically adjust the constraint strength to further determine the degree of influence of ionospheric error. Finally, if it still cannot be fixed, it is determined that the current data does not meet the conditions for high-precision RTK positioning.

[0164] Step 3: Perform ionosphere weighted calculation using different constraint strengths. In this embodiment, the constraint strengths are 10 cm, 15 cm, and 20 cm.

[0165] The ionospheric weighting is as follows:

[0166] (14);

[0167] in: Represents the double difference operator; denotes carrier phase observation of frequency; denotes pseudo-range observation of frequency; denotes wavelength of frequency; denotes ambiguity of frequency; denotes ionospheric amplification factor of frequency; denotes direction cosine matrix; denotes position parameter to be estimated; denotes ionospheric pseudo-observation, in the embodiment ; denotes ionospheric delay parameter to be estimated. The measurement noise variance of ionospheric pseudo-observation restricts the fluctuation range of ionospheric residual error. When the measurement noise variance value is zero, the ionospheric residual error is zero, which has been completely eliminated, equivalent to ionospheric fixed model. When the measurement noise variance value is infinite, the ionospheric residual error is not restricted, and the ionospheric residual error is estimated by the carrier observation and the pseudo-range observation, equivalent to ionospheric float model. Compared with the ionospheric fixed model, the ionospheric float model increases one parameter to be estimated for each satellite under the condition of the same number of observations, which leads to the weakening of the model strength. Therefore, in the case of a small number of satellites, the ionospheric float model will be difficult to converge to a fixed solution. Compared with the ionospheric float model, the ionospheric weighted model increases one ionospheric pseudo-observation for each satellite to restrict the ionospheric delay, so the model strength is slightly stronger than the ionospheric float model. The ionospheric float model and the ionospheric fixed model can be considered as two special cases of the ionospheric weighted model.

[0168] The measurement noise variance of ionospheric pseudo-observation of the ionospheric weighted model is:

[0169] (15);

[0170] wherein: denotes restriction strength, denotes measurement noise variance of ionospheric pseudo-observation.

[0171] The positioning error of the ionospheric fixed model and the positioning error of the ionospheric float model of the unmanned aerial vehicle in a complex environment in the embodiment are shown in Figure 7 and Figure 8 , wherein AVE represents mean value, STD represents standard deviation, RMS represents root mean square error, Fixed represents fixed solution, Float represents float solution, and Fix represents fixing rate. If the fixing rate meets the requirements, it indicates that the ionospheric error is the main factor affecting the fixed solution; if it still cannot be fixed, the current data does not have the condition of high-precision RTK positioning.

[0172] As used herein, unless defined otherwise, all technical and scientific terms have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It is also to be understood that terms such as those defined in commonly used dictionaries should be given their ordinary and customary meaning, unless implicitly defined in context or expressly stated to the contrary herein.

[0173] The above description is only specific embodiments of the present application. It is to be understood that the above description is intended to be illustrative and not restrictive. Many modifications and changes can come to mind of one skilled in the art having the benefit of the teachings of the present disclosure without departing from the spirit and scope of the application. It is therefore intended that the application not be limited to the preferred embodiments described above, but that the application be able to extend to all appropriate structures that come within the scope of the appended claims, and that their equivalents, when interpreted in accordance with the spirit and principles of the application, also fall within the scope of the application.

Claims

1. A method for evaluating the quality of UAV BeiDou differential positioning data, characterized in that: include: Evaluate the quality indicators of BeiDou differential data and UAV local observation data, and calculate the corresponding comprehensive quality indicators based on the evaluation results; If the comprehensive quality index reaches the set judgment threshold, the UAV RTK solution is performed. If the solution result is not fixed, the ionospheric weighted model RTK solution is performed. When the solution result of the UAV RTK solution or the ionospheric weighted model RTK solution is fixed and the fixation rate reaches the set threshold, it is determined that the current BeiDou differential data and the UAV local observation data have the conditions for high-precision RTK positioning. If the solution result of the ionospheric weighted model RTK solution cannot be fixed, it is determined that the current BeiDou differential data and the UAV local observation data do not have the conditions for high-precision RTK positioning. The quality indicators include observation data integrity rate, cycle slip ratio, multipath effect, pseudorange observation noise, carrier observation noise, and ionospheric variation; Observational data completeness rate: （1）； （2）； in, This indicates the completeness rate of single-frequency observation data; This indicates the completeness rate of observation data for a single system; This indicates the total number of satellites observed during the observation period; Indicates the first [number]th ...time period] during the observation period. The total number of actual observation epochs of a satellite at a certain frequency; Indicates the first [number]th ...time period] during the observation period. The theoretical total number of epochs for a given satellite at a specific frequency; Indicates the first [number]th ...time period] during the observation period. The number of epochs for which all frequencies of the satellite have valid observation data; Indicates the first [number]th ...time period] during the observation period. The total theoretical epochs of the satellites; Weekly jump ratio: （3）； in, Indicates the cycle jump ratio; The total number of epochs observed; The epoch number in which the cycle slip occurred; Multipath error: （4）； in: Indicates the number of epochs in the sliding window; Indicates the epoch number of the sliding window; Indicates frequency; Indicates time; Indicates that the satellite is Evaluation value of multipath error in frequency; Indicates in Time Satellite The computational cost includes multipath error and integer ambiguity information in terms of frequency. Pseudorange and carrier observation noise: （5）； （6）； in, and These are pseudorange observation noise and carrier observation noise, respectively. This indicates the number of three differences in the phase carrier phase observations of a satellite at a certain frequency point between adjacent epochs; Indicates the epoch; and express The pseudorange phase observations and carrier phase observations of a satellite at a certain frequency point at a given time; Indicates the cubic difference factor; Ionospheric change rate: （7）； in, Indicates that the satellite is The rate of change of ionospheric delay at frequency; , These respectively indicate the satellite at , Always The computational cost in frequency measurement; The calculation process for the comprehensive quality index includes: Normalize all quality indicators: （8）； in: Indicates the first The quality indicator in the first Dimensionless values ​​for each observation sample; This represents the original quality index after trend convergence. and Represents the normalization constant; The weights of each quality indicator are determined using the entropy method; Entropy value: （9）； Coefficient of difference: （10）； Weighting coefficients: （11）； in: Indicates the first The entropy value of a quality indicator; Indicates the number of samples; This represents the total number of quality indicators; Indicates the first Normalized weights of each quality indicator; Indicates the first The coefficient of variation for each quality indicator; Calculate the overall quality index: （12）； in: Indicates the first The overall evaluation value of each sample; Indicates the first The ideal value of each quality indicator.

2. The method for evaluating the quality of UAV BeiDou differential positioning data according to claim 1, characterized in that: The weighted model expression for the ionosphere is: （13）； in: Represents the double difference operator; express Frequency carrier phase observations; express Pseudorange observations at frequency; express Wavelength of frequency; express Frequency ambiguity; express The ionospheric amplification factor of the frequency; Represents the direction cosine coefficient matrix; Indicates the location parameter to be estimated; This indicates spurious observations of the ionosphere; This indicates the parameters to be estimated regarding the ionospheric delay; The variance of ionospheric pseudo-observation measurement noise in the ionospheric weighted model is: （14）； in: Indicates constraint strength. This represents the variance of noise in ionospheric pseudo-observations.

3. A UAV BeiDou differential positioning data quality evaluation system, characterized in that: include: The single-item quality indicator evaluation module is used to evaluate various quality indicators of BeiDou differential data and UAV local observation data; The comprehensive quality index evaluation module is used to calculate the comprehensive quality index of BeiDou differential data and the comprehensive quality index of UAV local observation data. The RTK calculation module is used to perform UAV RTK calculation when the comprehensive quality index reaches the set judgment threshold. If the calculation result is not fixed, the ionospheric weighted model RTK calculation is performed. When the calculation result of UAV RTK calculation or ionospheric weighted model RTK calculation is fixed and the fixation rate reaches the set threshold, it is determined that the current BeiDou differential data and UAV local observation data have high-precision RTK positioning conditions. If the calculation result of ionospheric weighted model RTK calculation cannot be fixed, it is determined that the current BeiDou differential data and UAV local observation data do not have high-precision RTK positioning conditions. The quality indicators include observation data integrity rate, cycle slip ratio, multipath effect, pseudorange observation noise, carrier observation noise, and ionospheric variation; Observational data completeness rate: （1）； （2）； in, This indicates the completeness rate of single-frequency observation data; This indicates the completeness rate of observation data for a single system; This indicates the total number of satellites observed during the observation period; Indicates the first [number]th ...time period] during the observation period. The total number of actual observation epochs of a satellite at a certain frequency; Indicates the first [number]th ...time period] during the observation period. The theoretical total number of epochs for a given satellite at a specific frequency; Indicates the first [number]th ...time period] during the observation period. The number of epochs for which all frequencies of the satellite have valid observation data; Indicates the first [number]th ...time period] during the observation period. The total theoretical epochs of the satellites; Weekly jump ratio: （3）； in, Indicates the cycle jump ratio, To observe the total epoch number, The epoch number in which the cycle slip occurred; Multipath error: （4）； in: Indicates the number of epochs in the sliding window; Indicates the epoch number of the sliding window; Indicates frequency; Indicates time; Indicates that the satellite is Evaluation value of multipath error in frequency; express Time Satellite The computational cost includes multipath error and integer ambiguity information in terms of frequency. Pseudorange and carrier observation noise: （5）； （6）； in, and These are pseudorange observation noise and carrier observation noise, respectively. This indicates the number of three differences in the phase carrier phase observations of a satellite at a certain frequency point between adjacent epochs; Indicates the epoch; and express The pseudorange phase observations and carrier phase observations of a satellite at a certain frequency point at a given time; Indicates the cubic difference factor; Ionospheric change rate: （7）； in, Indicates that the satellite is The rate of change of ionospheric delay at frequency; , These respectively indicate the satellite at , Always The computational cost in frequency measurement; The calculation process for the comprehensive quality index includes: Normalize all quality indicators: （8）； in: Indicates the first The quality indicator in the first Dimensionless values ​​for each observation sample; This represents the original quality index after trend convergence. and Represents the normalization constant; The weights of each quality indicator are determined using the entropy method; Entropy value: （9）； Coefficient of difference: （10）； Weighting coefficients: （11）； in: Indicates the first The entropy value of a quality indicator; Indicates the number of samples; This represents the total number of quality indicators; Indicates the first Normalized weights of each quality indicator; Indicates the first The coefficient of variation for each quality indicator; Calculate the overall quality index: （12）； in: Indicates the first The overall evaluation value of each sample; Indicates the first The ideal value of each quality indicator.

4. The UAV BeiDou differential positioning data quality evaluation system according to claim 3, characterized in that: The weighted model expression for the ionosphere is: （13）； in: Represents the double difference operator; express Frequency carrier phase observations; express Pseudorange observations at frequency; express Wavelength of frequency; express Frequency ambiguity; express The ionospheric amplification factor of the frequency; Represents the direction cosine coefficient matrix; Indicates the location parameter to be estimated; This indicates spurious observations of the ionosphere; This indicates the parameters to be estimated regarding the ionospheric delay; The variance of ionospheric pseudo-observation measurement noise in the ionospheric weighted model is: （14）； in: Indicates constraint strength. This represents the variance of noise in ionospheric pseudo-observations.

5. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the steps of the method for evaluating the quality of UAV BeiDou differential positioning data as described in claim 1 or 2.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, it implements the steps of the UAV BeiDou differential positioning data quality evaluation method as described in claim 1 or 2.

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