A method for positioning a drone

Abstract

The application relates to the technical field of unmanned aerial vehicle positioning, in particular to a method for positioning an unmanned aerial vehicle. The method comprises the following steps: obtaining a first position of a target unmanned aerial vehicle at a target moment according to a message signal of the target unmanned aerial vehicle at the target moment, and obtaining a second position of the target unmanned aerial vehicle at the target moment according to an RFID signal of the target unmanned aerial vehicle at the target moment; determining an analysis area of the unmanned aerial vehicle according to the first position and the second position, and vertically projecting the analysis area of the unmanned aerial vehicle to the ground to obtain a corresponding ground projection area; adjusting an initial weight of the second position according to environmental influence information in the ground projection area to obtain an adjusted weight of the second position; and performing data fusion processing on the first position and the second position according to the adjusted weight of the second position, and determining a data fusion processing result as a final position of the target unmanned aerial vehicle at the target moment. The application improves the positioning accuracy of the unmanned aerial vehicle.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) positioning technology, and in particular to a method for UAV positioning. Background Technology

[0002] With the rapid development of the drone industry, its applications in aerial photography, surveying, logistics, and other fields are becoming increasingly widespread. However, unreported flights are frequent, posing a serious threat to airspace safety, public safety, and the protection of public facilities. The core prerequisite for low-altitude management is the accurate acquisition of drone location information. Only by clearly identifying the exact location of drones can management objectives such as behavior analysis, trajectory tracking, and deterrence be achieved.

[0003] Currently, drone positioning methods primarily rely on a single signal source. For example, they determine a drone's location by receiving and analyzing its message signals. Another example is deploying an RFID reader network on the ground and receiving signals from tags carried by the drone. However, single-signal-source positioning is susceptible to interference, resulting in relatively low accuracy. Therefore, developing a more accurate drone positioning method is a pressing issue that needs to be addressed. Summary of the Invention

[0004] The purpose of this invention is to provide a method for locating unmanned aerial vehicles (UAVs) to improve the accuracy of UAV positioning.

[0005] According to the present invention, a method for locating a drone is provided, the method comprising the following steps:

[0006] S100 acquires the target drone's message signal and RFID signal at the target time.

[0007] S200: Obtain the first position of the target drone at the target time based on the message signal of the target drone at the target time, and obtain the second position of the target drone at the target time based on the RFID signal of the target drone at the target time.

[0008] S300, determine the analysis area of ​​the UAV based on the first position and the second position, and project the analysis area of ​​the UAV vertically onto the ground to obtain the corresponding ground projection area.

[0009] S400, acquire environmental impact information within the ground projection area; the environmental impact information includes at least one of the following: building metal material density, average building height, number of radio frequency interference sources, and surface vegetation coverage.

[0010] S500, the initial weight of the second position is adjusted according to the environmental impact information within the ground projection area to obtain the adjusted weight of the second position.

[0011] S600, perform data fusion processing on the first position and the second position according to the adjusted weight of the second position, and determine the data fusion processing result as the final position of the target UAV at the target time.

[0012] Compared with the prior art, the present invention has at least the following beneficial effects:

[0013] This invention employs dual-source positioning using both message signals and RFID signals, which solves the problem of low positioning accuracy associated with positioning from a single signal source. Furthermore, after confirming the UAV's analysis area based on the first and second positions, this invention also acquires environmental impact information within the corresponding ground projection area. Based on this environmental impact information, the RFID positioning weights are optimized, allowing for less reliance on the second position when environmental interference is strong within the ground projection area, and more reliance on the second position when environmental interference is weak. This effectively avoids positioning errors introduced by environmental interference. Therefore, this invention improves the accuracy of the UAV's final position. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating a method for drone positioning provided in an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] According to this embodiment, a method for locating a drone is provided, the method including the following steps, such as... Figure 1 As shown:

[0018] S100 acquires the target drone's message signal and RFID signal at the target time.

[0019] In this embodiment, the message signal refers to the structured data containing the UAV's own status sent through the communication module, including information such as location, time, and accuracy factor; the RFID signal refers to the radio frequency signal actively / passively emitted by the RFID tag carried by the UAV, which is received by the ground RFID reader and used to assist in positioning.

[0020] As a specific implementation, message information is received through ground receiving equipment (such as data receivers or communication base stations); the coverage area is continuously scanned by RFID readers deployed on the ground. When the drone enters the coverage area, the reader reads the tag ID and measures physical layer parameters such as received signal strength and signal-to-noise ratio, and sends this data to the backend server through the network.

[0021] In this embodiment, the target time is any time. By acquiring the message signal and RFID signal of the target UAV at the target time, dual-source positioning can be achieved.

[0022] S200: Obtain the first position of the target drone at the target time based on the message signal of the target drone at the target time, and obtain the second position of the target drone at the target time based on the RFID signal of the target drone at the target time.

[0023] Those skilled in the art will recognize that the process of obtaining the location of a drone based on its message data is prior art and will not be described in detail here; similarly, the process of obtaining the location of a drone based on its RFID information is prior art. For example, when there are at least three readers, the trilateration method can be used, which will not be described in detail here.

[0024] S300, determine the analysis area of ​​the UAV based on the first position and the second position, and project the analysis area of ​​the UAV vertically onto the ground to obtain the corresponding ground projection area.

[0025] In this embodiment, the analysis area refers to the reliable range of the target UAV's true location. As a specific implementation, the process of determining the UAV's analysis area includes: determining a first confidence area based on the accuracy factor of the message signal, and determining a second confidence area based on the signal quality of the RFID signal; the intersection of the first and second confidence areas is then determined as the UAV's analysis area. The intersection area represents a high-confidence range recognized by both positioning methods, eliminating obviously erroneous positioning results, narrowing the candidate range of the UAV's true location, reducing the computational load of subsequent environmental analysis, and improving positioning efficiency. As an optional implementation, if no intersection exists, a preset error message is output.

[0026] As a specific implementation, determining the first confidence region based on the precision factor of the message signal includes:

[0027] S310, calculate the first confidence radius based on the horizontal accuracy factor in the message signal and the predefined pseudorange measurement error.

[0028] As a specific implementation method, the horizontal precision factor is obtained directly from the message information.

[0029] In this embodiment, the pseudorange measurement error is an empirical value, which is related to the receiver chip performance and the current ionospheric and tropospheric conditions. Optionally, the pseudorange measurement error is between 1 and 3 meters.

[0030] In one specific implementation, the first confidence radius is the product of the horizontal accuracy factor and the pseudorange measurement error.

[0031] S311, with the first position as the center and the first confidence radius as the radius, a circular first confidence region is determined.

[0032] In this embodiment, based on S310-S311, a first confidence region can be defined according to the reliability of the message signal.

[0033] As an optional specific implementation, determining the second confidence region based on the signal quality of the RFID signal includes:

[0034] S320, acquire signal quality parameters from at least one RFID reader, the signal quality parameters including received signal strength and signal-to-noise ratio.

[0035] In this embodiment, the received signal strength is the power of the RFID signal received by the reader; the larger the value, the stronger the signal. The signal-to-noise ratio is the ratio of signal power to noise power; the larger the value, the purer the signal and the stronger the anti-interference capability.

[0036] S321, based on the received signal strength and signal-to-noise ratio, query the predefined confidence radius mapping table to determine the second confidence radius.

[0037] In this embodiment, the confidence radius mapping table is a pre-constructed table that includes the correspondence between received signal strength, signal-to-noise ratio, and the second confidence radius. Optionally, in a test environment, the distribution of RFID positioning errors is statistically analyzed under different received signal strengths and signal-to-noise ratios, and the error value corresponding to a higher confidence level (e.g., 95%) is taken as the second confidence radius.

[0038] S322, with the second position as the center and the second confidence radius as the radius, a circular second confidence region is determined.

[0039] Based on S320-S322, the second confidence region can be quickly determined based on a pre-established mapping table.

[0040] As another optional implementation, determining the second confidence region based on the signal quality of the RFID signal includes:

[0041] S301, acquire signals from at least three RFID readers.

[0042] S302: Based on the signal strength received by each reader, obtain the distance estimate from each reader to the drone.

[0043] As a specific implementation method, distance estimates from each reader to the UAV are obtained based on a signal strength and distance attenuation model (such as a logarithmic distance path loss model).

[0044] S303, Calculate the standard deviation of all the distance estimates.

[0045] S304, Calculate the second confidence radius based on the standard deviation using a predefined functional relationship; wherein the value of the standard deviation is positively correlated with the value of the second confidence radius.

[0046] In this embodiment, the predefined functional relationship can be pre-calibrated experimentally. For example, the second confidence radius is the product of the standard deviation and a preset scaling factor, which is pre-calibrated experimentally.

[0047] In this embodiment, if the standard deviation is smaller, the observation results of all readers are highly consistent, indicating that the positioning is reliable; otherwise, it indicates that the environmental interference is more serious, the positioning is unreliable, and the confidence region needs to be expanded.

[0048] S305, with the second position as the center and the second confidence radius as the radius, a circular second confidence region is determined.

[0049] Based on S301-S305, precise positioning of the second confidence region can be achieved.

[0050] In one specific implementation, when the number of readers is less than 3, S320-S322 are executed; when the number of readers is greater than or equal to 3, S301-S305 are executed.

[0051] S400, Obtain environmental impact information within the ground projection area; the environmental impact information includes at least one of the following: building metal material density, average building height, distribution of known radio frequency interference sources, and surface vegetation coverage.

[0052] As a specific implementation method, the density of building metal materials is represented by the proportion of metal materials per unit area.

[0053] As a specific implementation method, an environmental information database is established in advance, which includes the environmental attributes of each grid cell, such as building material, building height, number of radio frequency interference sources, and surface vegetation coverage. After determining the ground projection area, the system uses this area as a spatial query condition to send a request to the environmental information database to obtain the environmental attributes of all grid cells in the area and perform statistics.

[0054] S500, the initial weight of the second position is adjusted according to the environmental impact information within the ground projection area to obtain the adjusted weight of the second position.

[0055] In this embodiment, the initial weight of the second position is the initial proportion of the second position in the fusion calculation, reflecting its basic reliability (e.g., 0.5 indicates that it is initially as important as the first position). Optionally, the initial weight of the second position is a preset empirical value.

[0056] As one specific implementation, S500 includes:

[0057] S510, construct a target environmental feature vector based on the environmental impact information within the ground projection area.

[0058] As a specific implementation, the environmental information obtained in S400 (such as building metal material density = 0.7, average height = 15m, number of interference sources = 0, and surface vegetation coverage = 0.2) is combined into a digital vector, for example [0.7, 15, 0, 0.2...].

[0059] S520, the target environment feature vector is input into the trained prediction model to obtain the RFID target positioning error; the trained prediction model is used to infer the RFID positioning error of the corresponding location based on the environment feature vector corresponding to each location.

[0060] As a specific implementation method, the prediction model is a regression model (such as a gradient boosting tree or a neural network).

[0061] As a specific implementation method, the training process of the prediction model includes:

[0062] S521, Collect data from multiple sample points within a preset work area; the data for each sample point includes: the environmental feature vector of the sample point, and the RFID positioning error between the location obtained through RFID positioning and the actual location of the sample point.

[0063] S522, using the environmental feature vector as input and the corresponding RFID positioning error as output, supervised training is performed on the prediction model to obtain the trained prediction model.

[0064] In this embodiment, the trained prediction model has the function of predicting the corresponding RFID positioning error based on the input environmental feature vector. Those skilled in the art will understand that the specific training process is prior art and will not be described in detail here.

[0065] S530, the initial weight of the second position is adjusted according to the target positioning error of the RFID to obtain the adjusted weight of the second position; the target positioning error of the RFID is negatively correlated with the adjusted weight of the second position.

[0066] In one specific implementation, the sum of 1 and the RFID target positioning error u is obtained. Then, the adjusted weight of the second position is the initial weight of the second position divided by (u×k), where k is the influence coefficient of the RFID positioning error on the weight, and k>0. k is an empirical value, or it can be obtained by fitting historical data.

[0067] As a preferred embodiment, considering the impact of dynamic obstacles on RFID signals, S500 also includes a dynamic degradation process, which includes:

[0068] S531, monitor the quality parameters of the RFID signal in real time, including the signal-to-noise ratio and the received signal strength.

[0069] S532, determine whether the real-time quality parameter has undergone a negative mutation exceeding a preset threshold compared to the pre-established signal quality baseline.

[0070] As a specific implementation, when the system is initialized or the drone enters an open area, the signal-to-noise ratio and signal strength of the RFID signal are continuously monitored for a period of time (e.g., 10 seconds), and the moving average of SNR and RSSI during this period is calculated as the signal quality baseline for this flight phase in the current static environment.

[0071] As a specific implementation, a simple threshold judgment is set: if the instantaneous signal quality drops sharply compared to the baseline (i.e., the drop exceeds the preset signal quality change threshold, such as the signal-to-noise ratio drop exceeding the preset signal-to-noise ratio change threshold, or the received signal strength drop exceeding the preset signal strength change threshold), then a negative mutation exceeding the preset threshold is determined to have occurred.

[0072] S533, if so, ignore the weight calculated based on the environmental impact information, and set the adjusted weight of the second position as the specified weight.

[0073] In this embodiment, the specified weight is a preset small value, for example, the specified weight is less than or equal to 0.2 (or 0.1). As a specific implementation, after the instantaneous signal quality recovers to the previous baseline level (or recovers to above the threshold) and remains there for a period of time (e.g., 3 seconds), the system switches back to the normal intelligent weight adjustment mode driven by static environmental data.

[0074] Based on S531-S53, when the above-mentioned sudden change is detected, it is determined that a dynamic obstacle (such as a bird, other drones, or a temporarily appearing vehicle) has appeared. In this case, the adjusted weight of the second position is directly determined to be a smaller weight to reduce the influence of the second position on the final position.

[0075] S600, perform data fusion processing on the first position and the second position according to the adjusted weight of the second position, and determine the data fusion processing result as the final position of the target UAV at the target time.

[0076] In this embodiment, data fusion refers to superimposing two independent positioning results (first location and second location) according to weights to obtain a positioning result with higher overall accuracy. As a specific implementation, S600 includes:

[0077] S610, obtain the weight of the first position; the weight of the first position is the difference between 1 and the adjusted weight of the second position.

[0078] S620, the product of the weight of the first position and the first position is determined as the first result.

[0079] S630, the product of the adjusted weight of the second position and the second position is determined as the second result.

[0080] S640, the sum of the first result and the second result is determined as the final position of the target UAV at the target time.

[0081] Based on S610-S640, and using the weighted average concept that reflects credibility, the more credible the positioning result (the greater the weight), the higher its proportion in the fusion, thereby balancing the advantages of the two positioning methods and improving the accuracy of the final UAV location.

[0082] This embodiment employs dual-source positioning using both message signals and RFID signals, which solves the problem of low positioning accuracy associated with positioning from a single signal source. Furthermore, after confirming the UAV's analysis area based on the first and second positions, this embodiment also acquires environmental impact information within the corresponding ground projection area. Based on this environmental impact information, the RFID positioning weights are optimized, allowing for less reliance on the second position when environmental interference is strong within the ground projection area, and more reliance on the second position when environmental interference is weak. This effectively avoids positioning errors introduced by environmental interference. Therefore, this embodiment improves the accuracy of the UAV's final position.

[0083] While specific embodiments of the invention have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of the invention. It should also be understood that various modifications can be made to the embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

Claims

1. A method for positioning a drone, the method comprising: The method comprises the following steps: S100, obtaining a message signal and an RFID signal of a target unmanned aerial vehicle at a target time; the message signal refers to structured data containing the state of the unmanned aerial vehicle sent by the unmanned aerial vehicle through a communication module, containing position, time, and precision factor information; S200, obtaining a first position of the target unmanned aerial vehicle at the target time according to the message signal of the target unmanned aerial vehicle at the target time, and obtaining a second position of the target unmanned aerial vehicle at the target time according to the RFID signal of the target unmanned aerial vehicle at the target time; S300, determining an analysis area of the unmanned aerial vehicle according to the first position and the second position, and vertically projecting the analysis area of the unmanned aerial vehicle to the ground to obtain a corresponding ground projection area; the determination process of the analysis area of the unmanned aerial vehicle comprises: determining a first confidence area according to the precision factor of the message signal, and determining a second confidence area according to the signal quality of the RFID signal, and determining the intersection area of the first confidence area and the second confidence area as the analysis area of the unmanned aerial vehicle; wherein determining the first confidence area according to the precision factor of the message signal comprises: S310, calculating a first confidence radius according to the horizontal precision factor in the message signal and a predefined pseudo-range measurement error; S311, determining the first confidence area in the form of a circle with the first position as the center and the first confidence radius as the radius; determining the second confidence area according to the signal quality of the RFID signal comprises: S320, obtaining signal quality parameters from at least one RFID reader, the signal quality parameters comprising received signal strength and signal-to-noise ratio; S321, querying a predefined confidence radius mapping table based on the received signal strength and signal-to-noise ratio to determine a second confidence radius; S322, determining the second confidence area in the form of a circle with the second position as the center and the second confidence radius as the radius; S400, obtaining environmental influence information in the ground projection area; the environmental influence information comprises at least one of the following information: building metal material density, average building height, number of radio frequency interference sources, and ground vegetation coverage; S500, adjusting the initial weight of the second position according to the environmental influence information in the ground projection area to obtain an adjusted weight of the second position; S500 comprises: S531, monitoring the quality parameters of the RFID signal in real time, the quality parameters comprising signal-to-noise ratio and received signal strength; S532, judging whether the real-time quality parameters have a negative mutation beyond a preset threshold compared with a pre-established signal quality baseline; S533, if yes, ignoring the adjusted weight of the second position calculated based on the environmental influence information, and setting the adjusted weight of the second position as a specified weight; S600, performing data fusion processing on the first position and the second position according to the adjusted weight of the second position, and determining the data fusion processing result as the final position of the target unmanned aerial vehicle at the target time.

2. The method of claim 1, wherein, S500 comprises: S510, constructing a target environment feature vector according to the environmental influence information in the ground projection area; S520, input the target environment feature vector into a trained prediction model to obtain a target positioning error of the RFID; the trained prediction model is used to infer the RFID positioning error of a corresponding position according to an environment feature vector corresponding to each position; S530, adjust the initial weight of the second position according to the target positioning error of the RFID to obtain an adjusted weight of the second position; the target positioning error of the RFID is negatively correlated with the adjusted weight of the second position.

3. The method of claim 1, wherein, S600 includes: S610, obtain the weight of the first position; the weight of the first position is the difference between 1 and the adjusted weight of the second position; S620, determine the product of the weight of the first position and the first position as a first result; S630, determine the product of the adjusted weight of the second position and the second position as a second result; S640, determine the sum of the first result and the second result as the final position of the target UAV at the target time.

4. The method of claim 2, wherein, The training process of the prediction model includes: S521, collect data of a plurality of sample points in a preset work area; the data of each sample point includes: an environment feature vector of the sample point, and an RFID positioning error between a position obtained by RFID positioning and a real position of the sample point; S522, supervise the training of the prediction model with the environment feature vector as input and the corresponding RFID positioning error as output to obtain the trained prediction model.

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