Navigation positioning method based on multi-source data fusion

Abstract

The invention relates to a navigation positioning method based on multi-source data fusion, and aims to improve the positioning precision and stability of mobile equipment such as an unmanned aerial vehicle. Firstly, a cylindrical preset configuration with an unmanned aerial vehicle as the center is constructed, and high-quality base station data is screened; then, by deeply analyzing the relationship between the included angle, the distance and the adjustment number quality, model dynamic evaluation is established, and the optimal adjustment number is updated; further, the method fuses various measurement data, generates comprehensive navigation information, and sends the comprehensive navigation information to a flight command center to control the flight of the unmanned aerial vehicle; besides, the concept of a minimum credible area and a dynamic continuous optimal configuration is introduced, and more accurate and more stable positioning is realized by combining a plurality of credible areas and preferentially matching data of a previous time period; according to the method, the angle, the distance, the credible area and the environment characteristics are comprehensively considered, the optimal configuration of the unmanned aerial vehicle can be dynamically constructed and optimized, the positioning performance of the unmanned aerial vehicle in a complex environment is remarkably improved, and powerful support is provided for autonomous flight and intelligent navigation of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a navigation and positioning method based on multi-source data fusion. Background Art

[0002] As drone technology develops, the positioning accuracy and flight control of drones have become key technical issues. Traditional drone positioning systems mainly rely on satellite navigation systems such as GPS, but in some complex environments, such as mountainous areas, urban high-rise areas, or areas with severe electromagnetic interference, GPS signals may be blocked or interfered with, resulting in reduced positioning accuracy or even inability to locate. This not only affects the flight safety of drones, but also limits the application of drones in more fields.

[0003] In order to improve the positioning accuracy of drones and their ability to adapt to complex environments, a variety of auxiliary positioning methods have emerged in the prior art. However, these methods often have some problems. For example, some methods rely on ground base stations or beacons for positioning, but the deployment cost of base stations or beacons is high and may not be covered in some areas; other methods attempt to use the drone's own sensors for positioning, but due to the limitations of the accuracy and stability of the sensors, the positioning effect is not ideal.

[0004] In addition, the flight control of drones also faces challenges. In a complex flight environment, drones need to be able to accurately perceive their own position, speed, attitude and other information in real time and make correct flight decisions based on this information. However, due to the problem of positioning accuracy, drones often find it difficult to obtain accurate position information, resulting in deviations in flight control and even possible flight accidents.

[0005] Therefore, there is an urgent need for a UAV positioning and flight control method that can improve the positioning accuracy of UAVs, adapt to complex environments, and achieve precise flight control. This method needs to be able to integrate multiple measurement data, including the UAV's own sensor data, ground base station or beacon data, and other available positioning information to improve the reliability and accuracy of positioning. At the same time, the method also needs to be able to dynamically adjust the positioning parameters and flight control strategies according to the UAV's flight mission and environmental characteristics to ensure that the UAV can fly safely and stably in complex environments. Summary of the invention

[0006] The present invention aims at the technical problems existing in the prior art and provides a navigation and positioning method based on multi-source data fusion. In view of the above problems, a navigation and positioning method based on multi-source data fusion collects the position measurement information of the UAV, constructs a cylindrical first configuration with the position of the UAV as the center point, and collects the coordinate information and distance adjustment number of the surrounding base stations to correct the measured distance between the UAV and the base station. At the same time, the method also calculates the relationship between the angle, the base station distance and the optimal adjustment number, obtains the quality of the adjustment number, and sets the quality value threshold range of the adjustment number to form a comprehensive distance adjustment number to calculate the current position information of the UAV. Finally, the method fuses multiple measurement data to generate comprehensive navigation information, and sends the relevant information to the flight command center to achieve precise flight control of the UAV.

[0007] The technical solution of the present invention to solve the above technical problems is as follows: A navigation positioning method based on multi-source data fusion, comprising: S101, collecting and receiving position measurement information of the UAV, and presetting a virtual first configuration based on the position of the UAV; S102, sending information to surrounding base stations during the startup phase of the drone to collect coordinate information of each base station in the first configuration; S103, collecting the distance adjustment number provided by each base station, calculating the optimal adjustment number and dynamically updating it according to a preset time length; S104, calculating the relationship between the angle, the base station distance and the optimal adjustment number to obtain the adjustment number quality; S105, setting a quality value threshold range of the adjustment number, forming a comprehensive distance adjustment number, and calculating the current position information of the UAV; S106, integrating various measurement data to generate comprehensive navigation information; S107, sending the collected UAV flight parameters, comprehensive navigation information, current location information of the UAV, and comprehensive distance adjustment number to the flight command center; S108, the flight command center confirms the position of the drone based on the flight parameters and flight navigation information of the drone, and controls the flight of the drone.

[0008] Preferably, the step S101, the first configuration, includes: The first configuration represents the construction of a cylinder with the drone’s position as the center point; The cylindrical radius R is the maximum lateral distance that the drone signal can be sent; The height H of the cylinder is the maximum vertical distance that the drone signal can be sent; The radius R and height H of the cylinder are greater than or equal to the maximum transmission distance D of the drone signal, that is, R=H=max(D).

[0009] Preferably, the step S103, dynamically updating the preset duration, includes: The preset duration is defined based on the flight mission and flight environment characteristics of the drone. When performing high-precision positioning tasks such as precision mapping and target tracking, the optimal adjustment number is updated every 5 seconds. When performing simple patrol and monitoring general positioning tasks, the optimal adjustment number is updated once a minute; In complex mountainous environments with large signal obstructions and large environmental changes, the optimal adjustment number is updated every 10 seconds; In an open environment, the optimal adjustment number is defined to be updated every 3 seconds.

[0010] Preferably, the S104 includes: The angle is defined as the lower angle between the line connecting the drone and the base station and the preset first configuration axis; At the same time, the actual distance between the drone and the base station is measured; by analyzing the relationship between the angle, base station distance and the quality of the adjustment number, a calculation model is established to evaluate the reliability of the distance adjustment numbers provided by different base stations; Due to the change in the angle, the difference between the value sent by the same base station and the optimal adjustment value is calculated by defining the following formula:

[0011] Among them, the angle is , the base station distance is , and are the weights of the angle and base station distance, respectively, satisfying ; It is a function of the angle. It is defined by the cosine function to indicate that the angle is inversely proportional to the function value. , the function reaches its maximum value 1:

[0012] It is a function of the base station distance. It is defined as an inverse proportional function, which means that the base station distance is inversely proportional to the function value. When , the function reaches the maximum value 1:

[0013] Adjustment number quality Q calculation formula:

[0014] The adjustment number and optimal adjustment number of each base station and the reliability score of each base station adjustment number Substituting into the above formula, we get the value Q for evaluating the quality of the adjustment number.

[0015] Preferably, the S105 includes: During the dynamic adjustment period of the optimal adjustment number, if the distance adjustment number is lower than the threshold, it will not be used. If the distance adjustment number is higher than the threshold, the weight is calculated and the weighted average is taken to obtain the comprehensive distance adjustment number, which is used as the final adjustment number. The calculation formula for the comprehensive distance adjustment number is as follows: Comprehensive distance adjustment number =

[0016] Among them, is the i-th high-quality distance adjustment number, is its corresponding weight, and n is the number of high-quality distance adjustment numbers screened out.

[0017] When performing high-precision positioning tasks, the quality value is a normalized value ranging from 0 to 1, and the quality value threshold is set between 0.95 and 1.0; When performing general positioning tasks, the quality value threshold is between 0.85 and 0.95; When performing complex environment tasks, the quality value threshold is between 0.7 and 0.95; When performing open environment tasks, set the quality value threshold between 0.95 and 0.99.

[0018] Preferably, the S105 further includes: Starting from the sector to be reclaimed, traverse its adjacent sectors; S201, collecting the angle and distance data between all available base stations and the drone at the current moment, and calculating the reliability score; S202, setting a reliability score threshold St, considering base stations with scores higher than St as credible base stations, and determining the relative position relationship of the straight-line distance between the credible base stations and the UAV; S203, drawing a minimum trusted area with the drone as the center according to the location of the trusted base station; S204, combining the minimum credible areas according to their positional relationships; S205: For each base station in the combined area, a weight is assigned according to its reliability score Si and its position relationship in the combined area to obtain a final comprehensive adjustment number.

[0019] Preferably, the minimum trusted region in S203 includes: The minimum trusted area represents the location centered on the drone and connected to the trusted base station; According to the location of the trusted base station, cylindrical ring fan-shaped intervals with different radii and heights are connected and drawn, and these intervals are the minimum trusted areas.

[0020] Preferably, the S204 includes: S301, obtaining the current UAV motion state information, and collecting the angle and distance data between all available base stations and the UAV at the current moment; S302, predicting the position of the drone at the next moment based on the motion model and current state information of the drone; S303, combining the predicted point location and the base station data at the current moment, recalculating the reliability score of each base station, and selecting base stations with scores higher than a set threshold St as credible base stations according to the reliability scores; S304, taking the predicted UAV position as the center, and drawing the optimal trusted area at the next moment according to the position of the trusted base station; S305, when the UAV moves to the next actual point according to the control command, it obtains the distance adjustment number sent by the base station in the optimal trustworthy area at the previous moment; S306, identifying the smallest credible area closest to the position of the drone between the previous moment and the current moment; S307, setting the distance adjustment number sent by the base station in the minimum credible area of ​​not less than n number thresholds and the optimal credible area calculated at the last moment to obtain a comprehensive distance adjustment number.

[0021] Preferably, the S305 includes: S401, during the continuous movement of the UAV, the position of the UAV at the next moment is continuously predicted to obtain the optimal interval configuration; S402, repeating step S401 to form an optimal configuration of a dynamic continuous point angle combination; S403, preferentially obtaining base station data at locations where the combination forms match.

[0022] Preferably, the S403 includes: The drone moves to the next actual point and starts collecting data from base stations across the entire region; Analyze the base station data at the current moment and the dynamic continuous optimal configuration constructed at the previous moment, calculate the angle between each base station and the drone, and compare it with the angle combination form at the previous moment; Also consider the distance between the base station and the drone to ensure that the matched data is also close in distance; Set the matching error thresholds for angle and distance; From the matched data, filter out data of no less than n base stations; Each base station sends a distance adjustment number, which represents the deviation of the drone’s distance measured by the base station from its actual distance; The comprehensive distance adjustment number is calculated by combining the distance adjustment number sent by the base station and the position relationship in the optimal trusted area.

[0023] The beneficial effects of the present invention are: 1. By constructing a cylindrical preset configuration with the drone as the center and the maximum signal transmission distance as the radius and height, an accurate and efficient communication range is defined for drone positioning. Within this range, the drone can establish a stable connection with base stations with good signal quality and moderate location, thereby ensuring that the base station data involved in the positioning calculation is both comprehensive and accurate. This design is particularly important in a multi-base station environment because it effectively excludes base stations that are too far away, have unstable signals, or may interfere with positioning, significantly improving the accuracy and efficiency of positioning.

[0024] 2. We deeply analyzed the complex relationship between the angle, base station distance and the quality of the adjustment number, and built a scientific and accurate calculation model. This model can comprehensively consider the spatial position between the drone and the base station, the signal transmission distance and the quality of the adjustment number, so as to achieve a comprehensive evaluation of the distance adjustment number provided by each base station. This evaluation mechanism not only ensures the accuracy of the adjustment number, but also enables the drone to adjust the value of the adjustment number in real time according to the flight mission and environmental changes through the dynamically updated optimal adjustment number strategy. In a multi-base station environment, this mechanism can quickly screen out the most reliable and accurate adjustment number, effectively avoid interference, and improve the stability and accuracy of positioning.

[0025] 3. Constructing cylindrical ring sector intervals with different radii and heights, this technical method significantly narrows the source range of distance adjustment numbers. This refined partitioning method can more accurately locate base stations that have an important impact on drone positioning, thereby effectively excluding redundant data that has little impact on the results or may introduce noise. This not only improves the efficiency of data processing, but also greatly enhances the stability and accuracy of drone positioning. Because by limiting the source of adjustment numbers, positioning deviations caused by data fluctuations or outliers can be reduced, making the drone's location information more reliable and accurate.

[0026] 4. Combining multiple minimum credible areas and further calculating the distance adjustment number at the current moment to obtain a comprehensive adjustment number is a step that has significant technical advantages. By combining minimum credible areas of different sizes and shapes, all credible base stations that contribute to the positioning of the drone can be more comprehensively covered, ensuring that the information of each base station is fully utilized. At the same time, this combination method also takes into account the relative position and reliability scores between base stations, and reflects their importance to the final adjustment number by assigning different weights. This weighted average calculation method can more accurately reflect the actual impact of each base station on the position of the drone, thereby obtaining a more accurate and stable comprehensive adjustment number. This not only improves the accuracy and robustness of drone positioning, but also provides more reliable data support for subsequent navigation and control.

[0027] 5. The embodiment of the present application dynamically and continuously predicts the next moment position of the drone, and combines the base station data at the current moment to accurately calculate the optimal trusted area at the next moment. This process not only ensures that the drone can update its positioning information in real time during flight, but also provides a solid foundation for the precise navigation and positioning of the drone by continuously optimizing the range and accuracy of the trusted area. This combination of dynamic prediction and real-time correction greatly improves the positioning accuracy and stability of the drone in complex environments, enabling it to better adapt to the needs of various flight missions.

[0028] 6. The embodiment of the present application also sets a quantity threshold to screen the base station data within no less than n minimum credible areas by combining the distance adjustment number sent by the base station within the optimal credible area obtained at the previous moment and the minimum credible area closest to the previous moment and the current moment, thereby further improving the accuracy and robustness of drone positioning. This method not only makes full use of the redundant information provided by multiple base stations, but also effectively suppresses the impact of erroneous data on positioning results through intelligent screening and weight allocation. At the same time, through the calculation of the comprehensive distance adjustment number, the fine correction of the drone position is achieved, thereby ensuring the high-precision positioning of the drone during flight, providing a strong guarantee for the safe flight and mission execution of the drone.

[0029] 7. By comprehensively considering the angle, distance, size of the trusted area and its distribution pattern, this technical solution can dynamically construct and optimize the optimal configuration of the drone. This configuration is centered on the drone, and forms a specific point angle combination form through the line connecting the center point and the center point of the trusted area, which not only improves the accuracy of drone positioning, but also enhances its adaptability to complex environments. The drone can adjust its positioning strategy in real time according to changes in the surrounding environment to ensure that it always maintains high-precision positioning during the journey.

[0030] 8. When acquiring global base station data in the next period, this technical solution gives priority to acquiring base station data that matches the combined form constructed in the previous period. This strategy significantly reduces the amount of data processing and improves positioning efficiency, while ensuring the accuracy and relevance of the data used. By giving priority to processing data that matches the current optimal configuration, the drone can respond to environmental changes more quickly and achieve more stable and reliable positioning, thus providing a solid technical guarantee for its execution of various tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A flowchart of a navigation and positioning method based on multi-source data fusion according to the present invention; Figure 2 This is a first configuration diagram of the longest distance for sending signals from the drone of the present invention; Figure 3 This is an example diagram of the minimum credible area of ​​the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0033] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0034] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0035] Embodiment 1: like Figure 1 As shown, a navigation positioning method based on multi-source data fusion includes the following steps: S101, collecting and receiving position measurement information of the UAV, and presetting a virtual first configuration based on the position of the UAV.

[0036] Among them, the first configuration represents the construction of a cylinder with the drone position as the center point, the radius R of the cylinder is the maximum horizontal distance of the drone signal transmission, the height H of the cylinder is the maximum vertical distance of the drone signal transmission, and the radius R and height H of the cylinder are greater than or equal to the maximum transmission distance D of the drone signal, that is, R=H=max(D).

[0037] S102, sending information to surrounding base stations during the startup phase of the drone to collect coordinate information of each base station in the first configuration.

[0038] S103, collecting the distance adjustment number provided by each base station, calculating the optimal adjustment number and dynamically updating it according to a preset time length.

[0039] Among them, a GPS detection receiver is installed on a base station with a precise known position. The receiver can measure and calculate the difference between the actual distance between the base station and the GPS Beidou satellite and the measured distance, that is, the distance adjustment number; The distance adjustment number is used to correct the measured distance between the drone and the base station to improve positioning accuracy.

[0040] The optimal adjustment number is calculated by using the existing weighted least square method. In the scenario of this embodiment, it is assumed that n base stations provide distance adjustment numbers, which are respectively recorded as , ,......, , the corresponding weight is , ,......, , and satisfies , then the optimal adjustment number It can be calculated by the following formula:

[0041] Since the weights have been normalized, the sum of the weights of all base stations must be equal to 1, that is, , so the denominator can be omitted; where, represents the optimal adjustment number, n is the number of base stations, is the distance adjustment number provided by the ith base station, is the weight corresponding to the i-th base station.

[0042] The preset duration is defined based on the flight mission and flight environment characteristics of the UAV. When performing high-precision positioning tasks such as precision mapping and target tracking, the optimal adjustment number is updated every 5 seconds; when performing general positioning tasks such as simple patrols and monitoring, the optimal adjustment number is updated once a minute; in complex mountainous environments with more signal obstructions and greater environmental changes, the optimal adjustment number is updated once every 10 seconds; and in open environments, the optimal adjustment number is updated once every 3 seconds.

[0043] S104, calculating the relationship between the angle, the base station distance and the optimal adjustment number to obtain the adjustment number quality.

[0044] Specifically, the angle is defined as the lower angle between the line connecting the UAV and the base station and the preset first configuration axis; the actual distance between the UAV and the base station is measured at the same time; by analyzing the relationship between the angle, the base station distance and the quality of the adjustment number, a calculation model is established to evaluate the reliability of the distance adjustment numbers provided by different base stations; Due to the change in the angle, the difference between the value sent by the same base station and the optimal adjustment value is calculated by defining the following formula: Assume there are n base stations and the angle is , the base station distance is , define the following formula to evaluate the reliability score of each base station adjustment number :

[0045] in, and are the weights of the angle and base station distance, respectively, satisfying ; It is a function of the angle. It is defined by the cosine function to indicate that the angle is inversely proportional to the function value. , the function reaches its maximum value 1:

[0046] It is a function of the base station distance. It is defined as an inverse proportional function, which means that the base station distance is inversely proportional to the function value. When , the function reaches the maximum value 1:

[0047] Adjustment number quality Q calculation formula:

[0048] The adjustment number and optimal adjustment number of each base station and the reliability score of each base station adjustment number Substituting into the above formula, we get the value Q for evaluating the quality of the adjustment number.

[0049] S105, setting a quality value threshold range of the adjustment number, forming a comprehensive distance adjustment number, and calculating the current position information of the drone.

[0050] Among them, during the dynamic adjustment period of the optimal adjustment number, if the distance adjustment number is lower than the threshold, it will not be used. If the distance adjustment number is higher than the threshold, the weight is calculated and the weighted average is performed to obtain the comprehensive distance adjustment number, and the comprehensive distance adjustment number is used as the final adjustment number. The calculation formula of the comprehensive distance adjustment number is as follows: Comprehensive distance adjustment number =

[0051] in, is the i-th high-quality distance adjustment number, is its corresponding weight, and n is the number of high-quality distance adjustments screened out.

[0052] When performing high-precision positioning tasks, the quality value threshold is set between 0.95 and 1.0 (assuming that the quality value is a normalized value ranging from 0 to 1); when performing general positioning tasks, the quality value threshold is between 0.85 and 0.95; when performing complex environment tasks, the quality value threshold is between 0.7 and 0.95; when performing open environment tasks, the quality value threshold is set between 0.95 and 0.99.

[0053] For example, taking the high-precision positioning task as an example, assuming that the number of high-quality distance adjustments screened out is =10.5, , Their mass values ​​are =0.96, , ,but: Weight calculation , , , distance adjustment number = 10.5×0.2+10.8×0.6+10.2×0.4=10.54.

[0054] It should be noted that in actual applications, comprehensive consideration and adjustment should be made based on factors such as the specific UAV positioning system, flight mission requirements, flight environment characteristics, and algorithm performance. For other positioning tasks, the comprehensive distance adjustment number can be calculated similarly. Due to differences in actual data distribution and quality numerical calculation methods, the values ​​and weights in the above examples may not be completely accurate, but the method itself is universal.

[0055] Among them, Figure 2 In the first configuration diagram of the farthest distance of the drone signal transmission shown, the farthest lateral distance that can be collected with the drone as the center is the diameter of the cylinder formed by drone-R1 and drone-R2; The farthest vertical distance collected with the drone as the center is the height of the cylinder formed by drone-H1 and drone-H2, which is used to construct the first configuration diagram of the drone's longest sending distance; Assuming there is a base station in the configuration, the lower angle between the line connecting the drone and the base station and the preset first configuration axis is the angle The actual distance between the UAV and the base station is r.

[0056] S106, integrating various measurement data to generate comprehensive navigation information.

[0057] Among them, the various measurement data include the flight status of the UAV in flight, not in flight, and waiting to fly, the data of the UAV under different angle measurements or different azimuth measurement mechanisms, the multi-channel homogeneous measurement data of the UAV under the same measurement system, the multi-channel homogeneous measurement data of the UAV under different measurement systems, the heterogeneous measurement data of the UAV under different measurement systems, and the joint measurement data of the UAV at different flight times.

[0058] S107, sending the collected UAV flight parameters, comprehensive navigation information, current location information of the UAV, and comprehensive distance adjustment number to the flight command center.

[0059] The flight parameters of the drone include: angular velocity, acceleration, compass heading, altitude, rotor speed, voltage and fuel usage data. The fuel usage data may be the remaining power value of the drone fuel cell, the power usage value of the drone fuel cell, the power usage percentage of the drone fuel cell, or the remaining power percentage of the drone fuel cell.

[0060] S108, the flight command center confirms the position of the drone based on the flight parameters and flight navigation information of the drone, and controls the flight of the drone.

[0061] Specifically, the planned flight navigation information will be sent to the flight command center. After receiving this information, the flight command center will control the drone in real time based on the drone's flight parameters and flight navigation information. The control instructions will be sent to the drone's actuator through the flight control computer to guide the drone to fly according to the planned flight trajectory and parameters.

[0062] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: 1. By constructing a cylindrical preset configuration with the drone as the center and the maximum signal transmission distance as the radius and height, an accurate and efficient communication range is defined for drone positioning. Within this range, the drone can establish a stable connection with base stations with good signal quality and moderate location, thereby ensuring that the base station data involved in the positioning calculation is both comprehensive and accurate. This design is particularly important in a multi-base station environment because it effectively excludes base stations that are too far away, have unstable signals, or may interfere with positioning, significantly improving the accuracy and efficiency of positioning.

[0063] 2. We deeply analyzed the complex relationship between the angle, base station distance and the quality of the adjustment number, and built a scientific and accurate calculation model. This model can comprehensively consider the spatial position between the drone and the base station, the signal transmission distance and the quality of the adjustment number, so as to achieve a comprehensive evaluation of the distance adjustment number provided by each base station. This evaluation mechanism not only ensures the accuracy of the adjustment number, but also enables the drone to adjust the value of the adjustment number in real time according to the flight mission and environmental changes through the dynamically updated optimal adjustment number strategy. In a multi-base station environment, this mechanism can quickly screen out the most reliable and accurate adjustment number, effectively avoid interference, and improve the stability and accuracy of positioning.

[0064] Embodiment 2: The above-mentioned embodiment 1 effectively limits the communication and positioning range by designing a cylindrical preset configuration centered on the drone, and determining its radius and height according to the farthest distance of the drone signal. Secondly, the angle, base station distance and adjustment number quality are comprehensively considered, and the reliability of the adjustment number provided by the base station is evaluated through an accurate model. Combined with the dynamic optimal adjustment number, the drone can quickly obtain reliable distance adjustment numbers in a multi-base station environment, improves the positioning accuracy and adaptability to complex environments, and provides a solid technical guarantee for flight missions. In order to further improve the positioning accuracy, reduce the interference of irrelevant base stations, and optimize the positioning efficiency, the embodiment 1 is now further improved. Based on the formula of step S105, the minimum credible area is obtained, multiple minimum credible areas are combined, and the distance adjustment number at the current moment is calculated to obtain a comprehensive adjustment number.

[0065] Now, further improvements are made on the basis of step S105 in the first embodiment, specifically: S201, collect the angles between all available base stations and drones at the current moment and distance Data, calculated Reliability score.

[0066] S202, set a reliability score threshold St, regard base stations with scores higher than St as trusted base stations, and determine the relative position relationship of the straight-line distance between the trusted base stations and the drone.

[0067] Among them, the setting of the reliability score threshold St can dynamically adjust the value of St according to the flight environment of the drone, the changes in the base station layout, and the real-time requirements of positioning accuracy. For example, in areas with dense base stations and good signal quality, St can be appropriately increased to screen out better reliable base stations; while in areas with sparse base stations or poor signal quality, St can be appropriately reduced to retain more base stations to participate in positioning.

[0068] S203, based on the location of the trusted base station, draw a minimum trusted area with the drone as the center.

[0069] Among them, the minimum trusted area represents the location of the trusted base station connected with the drone as the center. According to the location of the trusted base station, cylindrical ring fan-shaped intervals with different radii and heights are connected and drawn. These intervals are the minimum trusted areas. The radius and height of each cylindrical ring fan-shaped interval can be adjusted according to actual needs to ensure that all trusted base stations are covered and the range is minimized as much as possible.

[0070] An example of the minimum credible region is shown in the figure below. Figure 3 As shown, base stations A, B, C and D are trusted base stations, and the positions and heights of base stations A, B, C and D from the drone are different. With the location of the drone as the center, the minimum trusted area is obtained by connecting the base stations.

[0071] S204: combining the minimum credible areas according to their positional relationships.

[0072] The combined area should cover all trusted base stations as much as possible and ensure that the contribution of each base station to the final adjustment number is taken into account.

[0073] S205: For each base station in the combined area, a weight is assigned according to its reliability score Si and its position relationship in the combined area. , and obtain the final comprehensive adjustment number.

[0074] Specifically, the weight The importance of the base station to the final adjustment number should be reflected. The base station with higher reliability score and closer location to the center of the drone should get a greater weight. For each combined area, the distance information provided by its internal base stations is used, combined with the weight Calculate and obtain the distance adjustment number of the area. If there are multiple combined areas, further perform weighted average or other appropriate statistical values ​​on the distance adjustment numbers of these areas as the final comprehensive adjustment number.

[0075] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: 1. Constructing cylindrical ring sector intervals with different radii and heights. This technical method significantly narrows the source range of distance adjustment numbers. This refined partitioning method can more accurately locate base stations that have an important impact on drone positioning, thereby effectively excluding redundant data that has little impact on the results or may introduce noise. This not only improves the efficiency of data processing, but also greatly enhances the stability and accuracy of drone positioning. Because by limiting the source of adjustment numbers, positioning deviations caused by data fluctuations or outliers can be reduced, making the drone's location information more reliable and accurate.

[0076] 2. Combining multiple minimum credible areas and further calculating the distance adjustment number at the current moment to obtain a comprehensive adjustment number is a step that has significant technical advantages. By combining minimum credible areas of different sizes and shapes, all credible base stations that contribute to the positioning of the drone can be more comprehensively covered, ensuring that the information of each base station is fully utilized. At the same time, this combination method also takes into account the relative position and reliability scores between base stations, and reflects their importance to the final adjustment number by assigning different weights. This weighted average calculation method can more accurately reflect the actual impact of each base station on the position of the drone, thereby obtaining a more accurate and stable comprehensive adjustment number. This not only improves the accuracy and robustness of drone positioning, but also provides more reliable data support for subsequent navigation and control.

[0077] Embodiment three: The above-mentioned second embodiment narrows the source range of the distance adjustment number by obtaining the minimum credible area, reduces redundant data, and enhances stability. In order to further improve the accuracy and stability of positioning, the second embodiment is now further improved by predicting the next moment position of the drone and calculating the optimal credible area, combining the multi-time base station data and the quantity threshold, to obtain a more accurate comprehensive distance adjustment number.

[0078] Now, further improvements are made on the basis of step S204 in the second embodiment, specifically: S301, obtaining the current UAV motion status information, and collecting the angle and distance data between all available base stations and the UAV at the current moment.

[0079] S302: predicting the position of the drone at the next moment based on the drone's motion model and current state information.

[0080] Among them, the motion model formula is x_next=x_current+vx_current Δt, x_next represents the next moment of the drone's position, x_current represents the drone's current position, vx_current represents the drone's current speed, and Δt is the time interval.

[0081] S303, combining the predicted point and the base station data at the current moment, recalculating the reliability score of each base station, and based on the reliability score, selecting base stations with scores higher than a set threshold St as credible base stations.

[0082] S304, taking the predicted UAV position as the center, draw the optimal trusted area at the next moment according to the position of the trusted base station.

[0083] Among them, the optimal credible region at the next moment is composed of cylindrical ring fan-shaped intervals with different radii and heights.

[0084] S305, when the UAV moves to the next actual point according to the control instruction, it obtains the distance adjustment number sent by the base station in the optimal trusted area at the previous moment.

[0085] Specifically, when the drone moves to the next actual point according to the control command, it sends a request to all base stations in the optimal trusted area determined at the previous moment through the communication link, requesting to obtain the distance adjustment number between the drone position and the base station position measured by each of them based on the current moment (that is, the moment after the drone moves to the actual point). Receive responses from each base station, which contain the distance adjustment numbers calculated by each of them. Verify and organize the received distance adjustment numbers to ensure the accuracy and integrity of the data.

[0086] S306, identifying the smallest credible area closest to the drone's position between the previous moment and the current moment.

[0087] Among them, between the previous moment and the current moment, the smallest credible area closest to the actual position of the drone is identified. The "closest" here can be determined by calculating the Euclidean distance between the actual position of the drone and the center point of each smallest credible area. The smallest distance is the closest smallest credible area. The minimum trusted area is a cylindrical ring sector with different radius and height drawn with the predicted position of the drone as the center based on the location of the trusted base station. Therefore, when identifying the nearest minimum trusted area, it is actually comparing the distance between the actual position of the drone and the center point of these cylindrical ring sector intervals.

[0088] S307, setting the distance adjustment number sent by the base station in the minimum credible area of ​​not less than n number thresholds and the optimal credible area calculated at the last moment to obtain a comprehensive distance adjustment number.

[0089] Specifically, the distance adjustment numbers sent by no less than n base stations are selected from the optimal trustworthy area determined at the last moment. According to the reliability scores of these base stations and their positional relationships in the optimal trustworthy area (such as the distance from the center point, the size of the angle, etc.), corresponding weights are assigned to them. The calculated comprehensive distance adjustment number is used as one of the bases for the drone position correction to improve the accuracy and stability of the drone positioning.

[0090] The specific value of n should be considered comprehensively based on factors such as the actual application scenario, base station distribution density, drone positioning accuracy requirements, and the reliability of the communication link. In practical applications, a suitable range of n values ​​can be determined through simulation tests or experimental verification. For example, a smaller n value can be set for preliminary testing, and then the value of n can be gradually increased to observe the trend of positioning accuracy, and finally an n value that can meet the positioning accuracy requirements without increasing the computational complexity too much can be found.

[0091] For example, the drone is currently performing a forest fire detection mission. The current position of the drone (x_current) = (100, 100, 50) meters (three-dimensional coordinates, assuming the height is 50 meters), the current speed of the drone (vx_current) = (5, 3, 0) meters / second (three-dimensional speed vector, assuming that the drone moves in the horizontal direction and the height remains unchanged), the time interval (Δt) = 1 second, and the motion model formula x_next = x_current + vx_current is used. Δt is calculated: x_next=(100,100,50)+(5,3,0) 1=(105,103,50) meters; Assume that there are 5 base stations. According to their angles, distances and historical data with the drone, the reliability score of each base station is calculated. The threshold St is set to 0.8, and base stations with scores higher than 0.8 are selected as credible base stations. Assume that 3 credible base stations are obtained after the selection, which are marked as A, B and C respectively. Taking the predicted drone position (105, 103, 50) as the center, according to the positions of the trusted base stations A, B, and C, a cylindrical ring sector with different radii and heights is drawn as the optimal trusted area; Assume that base station A is closer to the drone, the radius of the corresponding cylindrical ring sector is smaller; base stations B and C are farther away, and the radius of the corresponding cylindrical ring sector is larger; The drone moves to the next actual point according to the control command. Assume that the actual point is (104, 102, 50). It sends a request to the base stations A, B, and C in the optimal trusted area determined at the previous moment through the communication link to obtain the distance adjustment numbers between the drone position and the base station position measured by them. Assume that the received distance adjustment numbers are: -1 meter (base station A), +2 meters (base station B), and 0 meters (base station C). Calculate the Euclidean distance between the actual position of the drone (104, 102, 50) and the center point of each minimum credible area. Assuming that the center point of the minimum credible area corresponding to base station A is closest to the drone, the minimum credible area corresponding to base station A is identified as the closest minimum credible area. Set the number threshold n to 2 (based on actual application scenarios and base station distribution density and other factors), select the distance adjustment numbers sent by two base stations (A and B) from the optimal trustworthy area determined at the last moment, and assign weights to them based on the reliability scores of the base stations and their positional relationships in the optimal trustworthy area. Assuming that the weight of base station A is 0.6 and the weight of base station B is 0.4, the comprehensive distance adjustment number = -1 0.6+2 0.4=0.2.

[0092] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: 1. The embodiment of the present application dynamically and continuously predicts the next moment position of the drone, and combines the base station data at the current moment to accurately calculate the optimal trusted area at the next moment. This process not only ensures that the drone can update its positioning information in real time during flight, but also provides a solid foundation for the precise navigation and positioning of the drone by continuously optimizing the range and accuracy of the trusted area. This combination of dynamic prediction and real-time correction greatly improves the positioning accuracy and stability of the drone in complex environments, enabling it to better adapt to the needs of various flight missions.

[0093] 2. The embodiment of the present application also sets a quantity threshold to screen the base station data within no less than n minimum credible areas by combining the distance adjustment number sent by the base station within the optimal credible area obtained at the previous moment and the minimum credible area closest to the previous moment and the current moment, thereby further improving the accuracy and robustness of drone positioning. This method not only makes full use of the redundant information provided by multiple base stations, but also effectively suppresses the impact of erroneous data on positioning results through intelligent screening and weight allocation. At the same time, through the calculation of the comprehensive distance adjustment number, the fine correction of the drone position is achieved, thereby ensuring the high-precision positioning of the drone during flight, and providing a strong guarantee for the safe flight and mission execution of the drone.

[0094] Embodiment 4: The above-mentioned embodiment 3 predicts the next position of the drone and calculates the optimal trusted area in combination with the base station data. After the drone is in place, the adjustment number of at least n base stations and the nearest trusted area are combined to obtain an accurate correction number. In order to further solve the problem of efficiently and accurately determining the position of the drone during continuous movement and optimize the positioning accuracy, the embodiment 1 is now further improved. During continuous flight, the drone dynamically constructs the optimal interval, integrates the angle, area size and distribution, and forms a continuous optimal configuration. The drone is the center of the circle and the line connecting the center of the trusted area forms an angle combination; in the next period of time, the base station data matching this configuration is collected first.

[0095] Now, further improvements are made on the basis of step S305 in the third embodiment, specifically: S401, during the continuous movement of the UAV, the position of the UAV at the next moment is continuously predicted to obtain the optimal interval configuration.

[0096] Specifically, using the motion model x_next=x_current+vx_current Δt, calculate the next position of the drone, set x_next to the new x_current, and update vx_current (if the speed changes); Collect the angle and distance data between all available base stations and the drone at the current moment. Calculate the reliability score of each base station based on the base station data, select the base station with a score higher than St as the trusted base station, and draw a cylindrical ring sector interval with different radii and heights as the optimal trusted area based on the location of the trusted base station and the predicted location of the drone; Among them, the configuration is optimized by considering the angle (the direction of the connection between the base station and the drone), the size of the trusted area (radius and height), and the distribution law (such as base station density, distribution uniformity, etc.), specifically: Calculate the direction (angle) of the connection between all trusted base stations and the drone, and count the number of base stations in each direction. By adjusting the flight trajectory or predicted position of the drone, the number of base stations in each direction is balanced as much as possible to avoid too many or too few base stations in a certain direction. According to the flight mission or environmental characteristics of the drone, determine some key flight directions. When optimizing the configuration, give priority to the base stations in these key directions to ensure that they can cover the predicted position of the drone. The radius and height of the trusted area are dynamically adjusted according to the flight speed and altitude of the drone and the distribution density of base stations. For example, when the drone is flying at a faster speed, the radius can be appropriately increased to cover more base stations; when the drone is flying at a higher altitude, the height of the trusted area should be increased accordingly; When drawing a trusted area, you should refer to the coverage of the base station (such as signal strength, transmission distance, etc.). Make sure that the trusted area covers the effective coverage of all trusted base stations; When selecting a trusted base station, in addition to considering its reliability score, its distribution pattern should also be considered. Base stations that are evenly distributed and can cover the area around the predicted location of the drone should be given priority.

[0097] S402, repeat step S401 to form an optimal configuration of a dynamic and continuous point angle combination.

[0098] Specifically, as the UAV continues to move, the UAV is used as the center point, the center point is connected with the center point of the trusted area, and the angles between these lines are calculated to form a point angle combination pattern.

[0099] S403, preferentially obtaining base station data at locations where the combination forms match.

[0100] Specifically, the drone travels to the next actual point and starts collecting data from base stations across the entire region; Analyze the base station data at the current moment and the dynamic continuous optimal configuration constructed at the previous moment, calculate the angle between each base station and the drone, and compare it with the angle combination form at the previous moment; At the same time, the distance between the base station and the drone is considered to ensure that the matched data is also close in distance; the matching error thresholds of angle and distance are set, such as angle error less than 5 degrees and distance error less than 10 meters; From the matched data, data of no less than n base stations are screened out (n is a parameter determined according to the actual application scenario, such as 5); Each base station sends a distance adjustment number, which represents the deviation of the drone’s distance measured by the base station from its actual distance; The distance adjustment numbers sent by these base stations and their positional relationships in the optimal trusted area are combined to calculate a comprehensive distance adjustment number.

[0101] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: 1. By comprehensively considering the angle, distance, size of the trusted area and its distribution pattern, this technical solution can dynamically construct and optimize the optimal configuration of the drone. This configuration takes the drone as the center, and forms a specific point angle combination form through the line connecting the center point and the center point of the trusted area, which not only improves the accuracy of the drone positioning, but also enhances its adaptability to complex environments. The drone can adjust its positioning strategy in real time according to changes in the surrounding environment to ensure that it always maintains high-precision positioning during the journey.

[0102] 2. When acquiring global base station data in the next period, this technical solution gives priority to acquiring base station data that matches the combined form constructed in the previous period. This strategy significantly reduces the amount of data processing and improves positioning efficiency, while ensuring the accuracy and relevance of the data used. By giving priority to processing data that matches the current optimal configuration, the drone can respond to environmental changes more quickly and achieve more stable and reliable positioning, thus providing a solid technical guarantee for it to perform various tasks.

[0103] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0104] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0105] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0106] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0108] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0109] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A navigation and positioning method based on multi-source data fusion, characterized in that: include: S101, collecting and receiving position measurement information of the UAV, and presetting a virtual first configuration based on the position of the UAV; S102, sending information to surrounding base stations during the startup phase of the drone to collect coordinate information of each base station in the first configuration; S103, collecting the distance adjustment number provided by each base station, calculating the optimal adjustment number and dynamically updating it according to a preset time length; S104, calculating the relationship between the angle, the base station distance and the optimal adjustment number to obtain the adjustment number quality; S105, setting a quality value threshold range of the adjustment number, forming a comprehensive distance adjustment number, and calculating the current position information of the UAV; S106, integrating various measurement data to generate comprehensive navigation information; S107, sending the collected UAV flight parameters, comprehensive navigation information, current location information of the UAV, and comprehensive distance adjustment number to the flight command center; S108, the flight command center confirms the position of the drone based on the flight parameters and flight navigation information of the drone, and controls the flight of the drone.

2. A navigation and positioning method based on multi-source data fusion according to claim 1, characterized in that: The S101, first configuration, includes: The first configuration represents the construction of a cylinder with the drone’s position as the center point; The cylindrical radius R is the maximum lateral distance that the drone signal can be sent; The height H of the cylinder is the maximum vertical distance that the drone signal can be sent; The radius R and height H of the cylinder are greater than or equal to the maximum transmission distance D of the drone signal, that is, R=H=max(D).

3. A navigation and positioning method based on multi-source data fusion according to claim 1, characterized in that: The step S103, dynamically updating the preset duration, includes: The preset duration is defined based on the flight mission and flight environment characteristics of the drone. When performing high-precision positioning tasks such as precision mapping and target tracking, the optimal adjustment number is updated every 5 seconds. When performing simple patrol and monitoring general positioning tasks, the optimal adjustment number is updated once a minute; In complex mountainous environments with large signal obstructions and large environmental changes, the optimal adjustment number is updated every 10 seconds; In an open environment, the optimal adjustment number is defined to be updated every 3 seconds.

4. A navigation and positioning method based on multi-source data fusion according to claim 1, characterized in that: The S104 includes: The angle is defined as the lower angle between the line connecting the drone and the base station and the preset first configuration axis; At the same time, the actual distance between the drone and the base station is measured; by analyzing the relationship between the angle, base station distance and the quality of the adjustment number, a calculation model is established to evaluate the reliability of the distance adjustment numbers provided by different base stations; Due to the change in the angle, the difference between the value sent by the same base station and the optimal adjustment value is calculated by defining the following formula: Among them, the angle is , the base station distance is , and are the weights of the angle and base station distance, respectively, satisfying ; It is a function of the angle. It is defined by the cosine function to indicate that the angle is inversely proportional to the function value. , the function reaches its maximum value 1: It is a function of the base station distance. It is defined as an inverse proportional function, which means that the base station distance is inversely proportional to the function value. When , the function reaches the maximum value 1: Adjustment number quality Q calculation formula: The adjustment number and optimal adjustment number of each base station and the reliability score of each base station adjustment number Substituting into the above formula, we get the value Q for evaluating the quality of the adjustment number.

5. A navigation and positioning method based on multi-source data fusion according to claim 1, characterized in that: The S105 includes: During the dynamic adjustment period of the optimal adjustment number, if the distance adjustment number is lower than the threshold, it will not be used. If the distance adjustment number is higher than the threshold, the weight is calculated and the weighted average is taken to obtain the comprehensive distance adjustment number, which is used as the final adjustment number. The calculation formula for the comprehensive distance adjustment number is as follows: Comprehensive distance adjustment number = ; Among them, is the i-th high-quality distance adjustment number, is its corresponding weight, and n is the number of high-quality distance adjustment numbers screened; When performing high-precision positioning tasks, the quality value is a normalized value ranging from 0 to 1, and the quality value threshold is set between 0.95 and 1.0; When performing general positioning tasks, the quality value threshold is between 0.85 and 0.95; When performing complex environment tasks, the quality value threshold is between 0.7 and 0.95; When performing open environment tasks, set the quality value threshold between 0.95 and 0.

99.

6. A navigation and positioning method based on multi-source data fusion according to claim 1, characterized in that: The S105 further includes: Starting from the sector to be reclaimed, traverse its adjacent sectors; S201, collecting the angle and distance data between all available base stations and the drone at the current moment, and calculating the reliability score; S202, setting a reliability score threshold St, considering base stations with scores higher than St as credible base stations, and determining the relative position relationship of the straight-line distance between the credible base stations and the UAV; S203, drawing a minimum trusted area with the drone as the center according to the location of the trusted base station; S204, combining the minimum credible areas according to their positional relationships; S205: For each base station in the combined area, a weight is assigned according to its reliability score Si and its position relationship in the combined area to obtain a final comprehensive adjustment number.

7. A navigation and positioning method based on multi-source data fusion according to claim 6, characterized in that: The minimum trusted area in S203 includes: The minimum trusted area represents the location centered on the drone and connected to the trusted base station; According to the location of the trusted base station, cylindrical ring fan-shaped intervals with different radii and heights are connected and drawn, and these intervals are the minimum trusted areas.

8. A navigation and positioning method based on multi-source data fusion according to claim 6, characterized in that: The S204 includes: S301, obtaining the current UAV motion state information, and collecting the angle and distance data between all available base stations and the UAV at the current moment; S302, predicting the position of the drone at the next moment based on the motion model and current state information of the drone; S303, combining the predicted point location and the base station data at the current moment, recalculating the reliability score of each base station, and selecting base stations with scores higher than a set threshold St as credible base stations according to the reliability scores; S304, taking the predicted UAV position as the center, and drawing the optimal trusted area at the next moment according to the position of the trusted base station; S305, when the UAV moves to the next actual point according to the control command, it obtains the distance adjustment number sent by the base station in the optimal trustworthy area at the previous moment; S306, identifying the smallest credible area closest to the position of the drone between the previous moment and the current moment; S307, setting the distance adjustment number sent by the base station in the minimum credible area of ​​not less than n number thresholds and the optimal credible area calculated at the last moment to obtain a comprehensive distance adjustment number.

9. A navigation and positioning method based on multi-source data fusion according to claim 8, characterized in that: The S305 includes: S401, during the continuous movement of the UAV, the position of the UAV at the next moment is continuously predicted to obtain the optimal interval configuration; S402, repeating step S401 to form an optimal configuration of a dynamic continuous point angle combination; S403, preferentially obtaining base station data at locations where the combination forms match.

10. A navigation and positioning method based on multi-source data fusion according to claim 8, characterized in that: The S403 includes: The drone moves to the next actual point and starts collecting data from base stations across the entire region; Analyze the base station data at the current moment and the dynamic continuous optimal configuration constructed at the previous moment, calculate the angle between each base station and the drone, and compare it with the angle combination form at the previous moment; Also consider the distance between the base station and the drone to ensure that the matched data is also close in distance; Set the matching error thresholds for angle and distance; From the matched data, filter out data of no less than n base stations; Each base station sends a distance adjustment number, which represents the deviation of the drone’s distance measured by the base station from its actual distance; The comprehensive distance adjustment number is calculated by combining the distance adjustment number sent by the base station and the position relationship in the optimal trusted area.

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