Positioning method, electronic equipment and system applied to unmanned aerial vehicle

Abstract

The present disclosure relates to the field of detection technology, and specifically to a positioning method, electronic equipment and system applied to drones. The technical solution provided by the present disclosure can evaluate the signal effective weight of each base station receiving the radio signal according to the energy performance in the drone frequency band in the radio signal collected by each base station, and obtain the corresponding positioning accuracy weight in combination with the signal reception time difference and positioning accuracy of each base station. The corresponding weights of each base station group are calculated by the signal effective weight and the positioning accuracy weight, so as to obtain the precise positioning coordinates of the drone, which can reduce the deviation between the real-time coordinates of the drone obtained by monitoring the base station group and the real position of the drone, eliminate the influence of electromagnetic interference of radio signals of different base stations and the equipment processing accuracy of different base stations as much as possible, and improve the accuracy of obtaining the real-time coordinates of the drone, which has a popularizable value.

Description

Background Art

[0002] Existing methods often use the TDOA algorithm for multiple base station groups to identify and locate drones in the monitoring area. There are multiple base station groups distributed in the monitoring area of ​​this method. For any base station group, when a drone flies over, each base station within it receives the radio signal from the drone and draws a positioning hyperbola between the base stations based on the time difference characteristics of the radio signal reaching the base station. The intersection of multiple positioning hyperbolas in the base station group is the suspected coordinates of the drone. Then, the suspected points of the drone coordinates calculated by each base station group are weighted and averaged to locate the final drone coordinates, thereby realizing the positioning and tracking of drones in the monitoring area.

[0003] In the actual application scenarios of the traditional TDOA algorithm for multiple base station groups, the weighted weights of the suspected coordinates of the drone calculated by each base station group are often configured to be the same. However, since the drone radio signal monitoring frequency band is in the same frequency band as electronic devices such as WiFi and Bluetooth, the degree of electromagnetic interference of radio signals received by different base stations is often different. At the same time, there are differences in the equipment processing accuracy of radio signals of each base station, which leads to different coordinate confidence levels of the suspected coordinates of the drone calculated by each base station group. As a result, the traditional TDOA algorithm for multiple base station groups uses the same weighted weights for the suspected coordinates of the drone, which has the problem of insufficient accuracy in drone positioning. There is a deviation between the monitored position and the actual position of the drone, which is not conducive to the subsequent corresponding countermeasures against the drone's "illegal flight" behavior. Summary of the invention

[0004] In order to solve the technical problems raised in the background technology, the purpose of the present disclosure is to provide a positioning method, electronic equipment and system for drones. The technical solutions adopted are as follows:

[0005] A first aspect of the present disclosure provides a positioning method for a drone, which may include the following steps:

[0006] When there are drones operating in the monitoring area, the radio signals of the drones are collected in real time based on the signal receiver of the preset base station;

[0007] According to the radio signal, a preset base station where the drone is monitored is obtained as a target base station, and multiple monitoring base station groups are constructed according to the target base station;

[0008] In each monitoring base station group, the suspected coordinates of the drone corresponding to each monitoring base station group are generated according to the radio signals obtained by each target base station in the monitoring base station group;

[0009] Evaluate the effectiveness of receiving radio signals corresponding to each monitoring base station group to obtain the effective signal weight corresponding to the monitoring base station group;

[0010] According to the time difference of the radio signal obtained by each target base station in the monitoring base station group and the suspected coordinates of the drone corresponding to each monitoring base station group, the positioning accuracy weight corresponding to the monitoring base station group is obtained;

[0011] According to the positioning accuracy weight and signal effectiveness weight, the weighted weight of the suspected coordinates of the drone corresponding to each monitoring base station group is obtained;

[0012] According to the suspected coordinates of the drone and the corresponding weighted weights, the precise location coordinates of the drone are obtained.

[0013] In a possible implementation of the first aspect, in the process of evaluating the radio signal reception validity corresponding to each monitoring base station group to obtain the signal validity weight corresponding to the monitoring base station group, the following steps are included:

[0014] According to the radio signals collected by each target base station, obtaining radio frequency domain signal data received by each target base station;

[0015] According to the radio frequency domain signal data, the drone frequency band energy saturation parameter and the electromagnetic signal interference parameter of each target base station are obtained, wherein the value of the drone frequency band energy saturation parameter is positively correlated with the value of the radio signal receiving weight of the target base station, and the value of the electromagnetic signal interference parameter is negatively correlated with the value of the radio signal receiving weight;

[0016] The radio signal reception weight of each target base station is obtained according to the energy saturation parameters of the drone frequency band and the electromagnetic signal interference parameters. The effective signal weight corresponding to the monitoring base station group is the average value of the radio signal reception weights of all target base stations in the monitoring base station group.

[0017] In a possible implementation of the first aspect, the drone frequency band energy saturation parameter is a ratio of a mean value of the drone frequency band signal strength collected by the target base station to a mean value of the full-band signal strength collected by the target base station, and the mean value of the drone frequency band signal strength and the mean value of the full-band signal strength are obtained based on radio frequency domain signal data;

[0018] The electromagnetic signal interference parameter is the ratio of the standard deviation of the drone frequency band signal collected by the target base station to the maximum value of the standard deviation of the full-band signal collected by all target base stations. The standard deviation of the drone frequency band signal and the standard deviation of the full-band signal are obtained based on the radio frequency domain signal data.

[0019] In a possible implementation of the first aspect, in the process of generating suspected coordinates of the drone corresponding to each monitoring base station group according to the radio signal obtained by each target base station in the monitoring base station group, the following steps are included:

[0020] Draw a positioning hyperbola based on the time difference of the radio signal reaching each target base station in the monitoring base station group;

[0021] The overlapping area formed by all positioning hyperbolas is obtained, and the geometric center of the overlapping area is selected as the suspected coordinates of the UAV corresponding to the monitoring base station group.

[0022] In a possible implementation of the first aspect, in the process of obtaining the positioning accuracy weight corresponding to the monitoring base station group according to the time difference of the radio signal obtained by each target base station in the monitoring base station group and the suspected coordinates of the drone corresponding to each monitoring base station group, the following steps are included:

[0023] According to the time difference of the radio signal reaching each target base station in the monitoring base station group, a time difference parameter is obtained, and the time difference parameter is associated with the base station combination;

[0024] Fitting based on the least square method to form a first data fitting curve associated with the base station combination and the time difference parameter;

[0025] According to the mean signal strength of the drone frequency band collected by each target base station, a relative attenuation parameter is obtained. The relative attenuation parameter is associated with the base station combination and is numerically equal to the absolute value of the difference between the mean signal strengths of the drone frequency band corresponding to the two target base stations in the base station combination;

[0026] A second data fitting curve associated with the base station combination and the relative attenuation parameter is formed based on least squares fitting;

[0027] Obtaining a Pearson correlation coefficient between the first data fitting curve and the second data fitting curve, wherein the Pearson correlation coefficient is associated with a monitoring base station group;

[0028] The Pearson correlation coefficients corresponding to all monitoring base station groups are normalized, and the normalized Pearson correlation coefficients are used as the time difference effective parameters corresponding to the monitoring base station groups. The value of the time difference effective parameter is positively correlated with the value of the positioning accuracy weight.

[0029] In a possible implementation of the first aspect, in the process of obtaining the positioning accuracy weight corresponding to the monitoring base station group according to the time difference of the radio signal obtained by each target base station in the monitoring base station group and the suspected coordinates of the drone corresponding to each monitoring base station group, the following steps are also included:

[0030] Obtain the overlapping area formed by the positioning hyperbolas corresponding to each monitoring base station group, and take the overlapping area with the largest area as the reference overlapping area;

[0031] The positioning error parameter corresponding to each monitoring base station group is obtained. The positioning error parameter is the ratio of the area of ​​the overlapping area corresponding to the monitoring base station group to the area of ​​the reference overlapping area, and the value of the positioning error parameter is inversely correlated with the value of the positioning accuracy weight.

[0032] In a possible implementation of the first aspect above, the weighted weight of the suspected coordinates of the drone corresponding to each monitoring base station group is the product of the positioning accuracy weight of each monitoring base station group and the signal effectiveness weight.

[0033] In a possible implementation of the first aspect, the following steps are also included:

[0034] When the precise location coordinates of the drone are obtained, the precise location coordinates of the drone are continuously updated;

[0035] When the precise location coordinates of the drone are within the monitoring area and the duration is greater than a preset threshold, send warning information to the drone based on the precise location coordinates; and

[0036] If the UAV fails to land according to the warning information, interference countermeasure information will be sent to the UAV based on the precise location coordinates.

[0037] The second aspect of the present disclosure provides a positioning system applied to a drone. The positioning system is applied to the positioning method applied to a drone provided in the first aspect, and may specifically include:

[0038] The drone sensing unit is used to collect the drone's radio signal in real time based on the signal receiver of the preset base station when there is a drone operating in the monitoring area;

[0039] A base station group construction unit is used to obtain a preset base station that monitors the operation of the drone as a target base station according to the radio signal, and to construct a plurality of monitoring base station groups according to the target base station;

[0040] A preliminary positioning unit is used to generate the suspected coordinates of the UAV corresponding to each monitoring base station group according to the radio signal obtained by each target base station in the monitoring base station group in each monitoring base station group;

[0041] A signal effective weight acquisition unit, used to evaluate the effectiveness of receiving radio signals corresponding to each monitoring base station group, so as to obtain the signal effective weight corresponding to the monitoring base station group;

[0042] A positioning accuracy weight acquisition unit, used to acquire the positioning accuracy weight corresponding to the monitoring base station group according to the time difference of the radio signal obtained by each target base station in the monitoring base station group and the suspected coordinates of the drone corresponding to each monitoring base station group;

[0043] The base station group weighting unit is used to obtain the weighted weight of the suspected coordinates of the drone corresponding to each monitoring base station group according to the positioning accuracy weight and the signal effectiveness weight;

[0044] The precise positioning unit is used to obtain the precise position coordinates of the drone according to the suspected coordinates of the drone and the corresponding weighted weights.

[0045] A third aspect of the present disclosure provides an electronic device, including:

[0046] processor;

[0047] a memory in which executable instructions for the processor are stored;

[0048] Among them, the processor is configured to execute the steps of the positioning method applied to the drone provided in the first aspect by executing executable instructions.

[0049] Compared with the background technology, the present invention has the following beneficial effects:

[0050] The technical solution provided by the present disclosure can evaluate the signal effective weight of each base station receiving the radio signal according to the energy performance in the drone frequency band of the radio signal collected by each base station, and obtain the corresponding positioning accuracy weight in combination with the signal reception time difference and positioning accuracy of each base station. The corresponding weights of each base station group are calculated by the signal effective weight and the positioning accuracy weight, so as to obtain the precise positioning coordinates of the drone, which can reduce the deviation between the real-time coordinates of the drone obtained by monitoring the base station group and the real position of the drone, eliminate the influence of electromagnetic interference of radio signals of different base stations and the equipment processing accuracy of different base stations as much as possible, and improve the accuracy of obtaining the real-time coordinates of the drone, which has a value for promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 According to an embodiment of the present disclosure, a flow chart of a positioning method applied to a drone is provided.

[0053] Figure 2 According to an embodiment of the present disclosure, a schematic diagram of the principle of using positioning hyperbolas to locate drones in a single monitoring base station group is provided.

[0054] Figure 3 According to an embodiment of the present disclosure, a schematic diagram of a process for obtaining effective parameters of a time difference corresponding to a monitoring base station group is provided.

[0055] Figure 4 According to an embodiment of the present disclosure, a schematic diagram of a process of performing warning countermeasures on a drone is provided.

[0056] Figure 5 According to an embodiment of the present disclosure, a structural schematic diagram of a positioning system applied to a drone is provided.

[0057] Figure 6 According to an embodiment of the present disclosure, a structural schematic diagram of an electronic device is provided. DETAILED DESCRIPTION

[0058] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a positioning method applied to a drone proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0059] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0060] In response to the problems raised in the background technology, the present disclosure provides a positioning method and system for drones, which can reduce the deviation between the real-time coordinates of drones obtained by monitoring a base station group and the actual position of the drone, eliminate the electromagnetic interference of radio signals from different base stations and the equipment processing accuracy of different base stations as much as possible, and improve the accuracy of obtaining the real-time coordinates of drones. Specifically, in some embodiments of the present disclosure, Figure 1 FIG. 1 is a flow chart showing a positioning method applied to a UAV. Figure 1 As shown, this method may specifically include the following steps:

[0061] Step 101: When there is a drone operating in the monitoring area, the drone's radio signal is collected in real time based on a signal receiver of a preset base station. In an embodiment of the present disclosure, the collected radio signal can be converted to frequency domain analysis by a spectrum analyzer in the base station to facilitate the calculation of subsequent weights.

[0062] Step 102: According to the radio signal, a preset base station that monitors the operation of the drone is obtained as a target base station, and multiple monitoring base station groups are constructed based on the target base stations. It can be understood that in the embodiment of the present disclosure, when the signal strength of the frequency band commonly used by drones in the radio signal spectrum reaches the monitoring threshold, it can be considered that the base station monitors the operation of the drone and is used as a target base station, and all target base stations are randomly grouped into three groups to form multiple monitoring base station groups.

[0063] Step 103: In each monitoring base station group, based on the radio signal obtained by each target base station in the monitoring base station group, generate the suspected coordinates of the drone corresponding to each monitoring base station group. Specifically, the specific acquisition of the suspected coordinates of the drone will be described later and will not be repeated here.

[0064] Step 104: Evaluate the effectiveness of receiving radio signals corresponding to each monitoring base station group to obtain the signal effective weight corresponding to the monitoring base station group. The specific acquisition of the signal effective weight will be described in detail later and will not be repeated here.

[0065] Step 105: According to the time difference of the radio signal obtained by each target base station in the monitoring base station group and the suspected coordinates of the drone corresponding to each monitoring base station group, the positioning accuracy weight corresponding to the monitoring base station group is obtained. The specific acquisition of the positioning accuracy weight will be described in detail later and will not be repeated here.

[0066] Step 106: According to the positioning accuracy weight and the signal validity weight, the weighted weight of the suspected coordinates of the drone corresponding to each monitoring base station group is obtained. The specific acquisition of the weighted weight will be described in detail later and will not be repeated here.

[0067] Step 107: Obtain the precise position coordinates of the drone according to the suspected coordinates of the drone and the corresponding weighted weights.

[0068] It can be understood that through the above steps 101 to 107, the real-time and accurate position coordinates of the drone can be obtained, the deviation between the real-time coordinates of the drone obtained by monitoring the base station group and the actual position of the drone can be reduced, and the influence of electromagnetic interference of radio signals of different base stations and the equipment processing accuracy of different base stations are eliminated as much as possible, thereby improving the accuracy of obtaining the real-time coordinates of the drone. The specific implementation of the above steps 101 to 107 will be further elaborated and illustrated in combination with specific embodiments below.

[0069] In the above embodiment, further, in the specific implementation of the above step 103, Figure 2 The schematic diagram shows the principle of using positioning hyperbola to locate UAV in a single monitoring base station group. Figure 2 In the figure, t1, t2 and t3 represent three different base stations. Figure 2 As shown in the figure, it is possible to draw a positioning hyperbola based on the time difference between the radio signal reaching each target base station in the monitoring base station group; then obtain the overlapping area formed by all the positioning hyperbolas, and select the geometric center of the overlapping area as the suspected coordinates of the drone corresponding to the monitoring base station group.

[0070] In the above embodiment, further, in the specific implementation of the aforementioned step 104, the signal effective weight includes the drone frequency band energy saturation parameter and the electromagnetic signal interference parameter. For the drone frequency band energy saturation parameter, since the energy amplitude of the radio signal received by the target base station in the drone commonly used frequency band is significantly higher than that in other frequency bands, the drone frequency band energy saturation parameter can be used to reflect the existence probability of the radio signal monitoring drone. In a specific implementation of the above embodiment, the radio signal collected by each target base station can be first converted through spectrum analysis to obtain the radio frequency domain signal data received by each target base station; and then the drone frequency band energy saturation parameter of each target base station is obtained based on the radio frequency domain signal data. Specifically, the drone frequency band energy saturation parameter is the ratio of the mean drone frequency band signal strength collected by the target base station to the mean signal strength of the full frequency band collected by the target base station, and its mathematical expression can be as follows:

[0071]

[0072] in, For the The first of the monitoring base station groups The energy saturation parameters of the drone frequency band of the target base station, For the The first of the monitoring base station groups The average signal strength of the drone frequency band of the target base station, For the The first of the monitoring base station groups The average signal strength of all frequency bands of target base stations and the average signal strength of drone frequency bands And the average signal strength of the entire frequency band All of them can be directly obtained through radio frequency domain signal data. It can be understood that since the drone frequency band energy saturation parameter can reflect the existence probability of the radio signal monitoring drone, the value of the drone frequency band energy saturation parameter is positively correlated with the value of the radio signal reception weight of the target base station.

[0073] In the specific implementation of the aforementioned step 104, the electromagnetic signal interference parameter is used to reflect the degree of electromagnetic interference in the frequency band commonly used by drones. In a specific implementation of the above embodiment, it is also possible to first obtain the radio frequency domain signal data received by each target base station through spectrum analysis conversion based on the radio signal collected by each target base station; and then obtain the electromagnetic signal interference parameter of each target base station based on the radio frequency domain signal data. Specifically, the electromagnetic signal interference parameter is the ratio of the standard deviation of the drone frequency band signal collected by the target base station to the maximum value of the standard deviation of the full-band signal collected by all target base stations, and its mathematical expression can be as follows:

[0074]

[0075] in, For the The first of the monitoring base station groups The electromagnetic signal interference parameters of the target base stations, For the The first of the monitoring base station groups The standard deviation of the drone frequency band signal of the target base station, is the maximum value of the standard deviation of the full-band signals collected by all target base stations, and the standard deviation of the drone frequency band signal And the maximum value of the standard deviation of the full-band signal All of them can be directly obtained through radio frequency domain signal data. It can be understood that since the electromagnetic signal interference parameter reflects the degree of electromagnetic interference of the commonly used frequency band of the drone, the value of the electromagnetic signal interference parameter is inversely correlated with the value of the radio signal reception weight.

[0076] In the above embodiment, further, the radio signal reception weight of each target base station can be obtained according to the drone frequency band energy saturation parameter and the electromagnetic signal interference parameter, and the relevant mathematical expression can be as follows:

[0077]

[0078] in, For the The first of the monitoring base station groups The radio signal reception weight of the target base station, that is, the energy saturation of the target base station receiving signal in the frequency band commonly used by drones The higher the signal, the greater the degree of electromagnetic interference to the received signal. The lower the value, the higher the effectiveness of the target base station receiving the signal. It can be expressed as the average value of the radio signal reception weights of all target base stations in the monitoring base station group.

[0079] In the above embodiment, further, in the specific implementation of the aforementioned step 106, it can be understood that the radio signal will diverge and attenuate during the propagation process, and the time difference of receiving signals at different base stations should have a positive correlation with the relative attenuation of the received signal. However, due to the differences in the processing accuracy of different base station equipment, the read time difference does not conform to the positive correlation with the relative attenuation of the signal, which in turn leads to inaccurate positioning hyperbolas drawn according to the time difference, resulting in a large area of ​​the public area of ​​the hyperbolas under the base station combination, that is, the hyperbolic positioning error is too large. Therefore, the positioning accuracy of the base station group is obtained according to the effectiveness of the time difference combined with the hyperbolic positioning error of the base station combination, and then the weighted weights of each base station group are adaptively adjusted in combination with the effectiveness of each base station receiving the radio signal. Specifically, Figure 3 FIG. 1 shows a flow chart of obtaining effective time difference parameters corresponding to a monitoring base station group. Figure 3 As shown, the following steps may be specifically included:

[0080] Step 301: Obtain a time difference parameter based on the time difference of the radio signal reaching each target base station in the monitoring base station group. Specifically, the time difference parameter is associated with the base station combination. For example, for the first A monitoring base station group is formed, and the average signal strength of the radio signals received by each target base station in the monitoring base station group in the frequency band commonly used by drones is subtracted and the absolute value is taken, which is recorded as the relative attenuation between the two target base stations, and then the time difference parameters of multiple groups of base stations in the base station group are obtained.

[0081] Step 302: Based on the least squares method, a first data fitting curve associated with the base station combination and the time difference parameter is formed. That is, the order of the base station combinations in the monitoring base station group is used as the horizontal coordinate, and the time difference parameter of each base station combination is used as the vertical coordinate to place each sample data in the base station group coordinate system, and fit to generate the first data fitting curve.

[0082] Step 303: Obtain a relative attenuation parameter based on the average signal strength of the drone frequency band collected by each target base station. Specifically, the relative attenuation parameter is associated with the base station combination, and is numerically equal to the absolute value of the difference between the average signal strengths of the drone frequency bands corresponding to the two target base stations in the base station combination.

[0083] Step 304: Fitting based on the least square method to form a second data fitting curve associated with the base station combination and the relative attenuation parameter. It can be understood that the above steps 301 to 302 and steps 303 to 304 can be performed synchronously or asynchronously, which is not limited here.

[0084] Step 305: Obtain the Pearson correlation coefficient between the first data fitting curve and the second data fitting curve, wherein the Pearson correlation coefficient is associated with the monitoring base station group.

[0085] Step 306: normalize the Pearson correlation coefficients corresponding to all monitoring base station groups, and use the normalized Pearson correlation coefficients as the time difference effective parameters corresponding to the monitoring base station groups. It can be understood that the value of the time difference effective parameter is positively correlated with the value of the positioning accuracy weight.

[0086] In the above embodiment, further, in the specific implementation of the aforementioned step 106, in the process of obtaining the positioning error parameter, the overlapping area formed by the positioning hyperbola corresponding to each monitoring base station group can be first obtained, and the overlapping area with the largest area is used as the reference overlapping area; then the positioning error parameter corresponding to each monitoring base station group is obtained, and the positioning error parameter is the ratio of the area of ​​the overlapping area corresponding to the monitoring base station group to the area of ​​the reference overlapping area, and its mathematical expression can be as follows:

[0087]

[0088] in, For the The positioning error parameters of the monitoring base station group, For the The overlapping area of ​​the monitoring base station groups, It can be understood that the value of the positioning error parameter is inversely correlated with the value of the positioning accuracy weight.

[0089] Correspondingly, the mathematical expression of the positioning accuracy weight can be shown as follows:

[0090]

[0091] in, For the The positioning accuracy weight of the monitoring base station group, For the The effective parameters of the time difference of the monitoring base station group, For the It can be seen that if the effective parameter of the time difference of the monitoring base station group is The larger the value, the more positioning error parameters of the base station group will be monitored. The smaller it is, the higher the positioning accuracy of the monitoring base station group, that is, the suspected coordinates of the drone calculated by the monitoring base station group have a higher credibility, and a higher weight should be given.

[0092] In the above embodiment, further, in the specific implementation of the aforementioned step 106, the weighted weight of the suspected coordinates of the drone corresponding to each monitoring base station group is configured as the product of the positioning accuracy weight of each monitoring base station group and the signal effective weight, and the mathematical expression of the weighted weight can be as follows:

[0093]

[0094] in, For the The weight of the monitoring base station group, For the The positioning accuracy weight of the monitoring base station group, For the The effective weight of the signal of each monitoring base station group. Correspondingly, the final precise coordinate position of the drone can be obtained according to the weighted weight corresponding to each monitoring base station group, and its mathematical expression can be shown as follows:

[0095]

[0096] in, To monitor the number of base station clusters, For the The weight of the monitoring base station group, For the The suspected coordinates of the drone monitoring the base station cluster, The precise location coordinates of the drone.

[0097] In some embodiments of the present disclosure, after obtaining the precise location information of the drone, it is also necessary to carry out targeted warnings and countermeasures against the drone suspected of "illegal flight". Figure 4 A schematic diagram of a process of warning and countering a drone is shown. Figure 4 As shown, the following steps may be specifically included:

[0098] Step 401: When the precise location coordinates corresponding to the drone are obtained, the precise location coordinates of the drone are continuously updated. It is understandable that the real-time precise location coordinates of the drone can be obtained based on the technical solution provided in the above-mentioned embodiment, which is not limited here; by continuously updating the precise location coordinates of the drone, it is possible to obtain whether the drone is within the monitoring area and the flight trajectory of the drone, so as to better monitor the "illegal flight" behavior of the drone.

[0099] Step 402: When the precise location coordinates of the drone are all located in the monitoring area and the duration is greater than the preset threshold, send an early warning message to the drone based on the precise location coordinates. It is understandable that when the duration of the drone in the monitoring area is greater than the preset threshold, it means that the drone stays in the prohibited flight monitoring area for a long time, which is likely to be a malicious "illegal flight" behavior, and it is necessary to send a corresponding early warning message to the drone to prompt it to leave as soon as possible.

[0100] Step 403: If the drone fails to land according to the warning information, interference countermeasure information is sent to the drone according to the precise location coordinates. If the drone fails to land according to the warning information, it can be determined that the drone is performing malicious "illegal flight" behavior, and corresponding countermeasures are required, such as sending interference countermeasure information, so as to prevent the further implementation of the relevant "illegal flight" behavior as much as possible.

[0101] In some embodiments of the present disclosure, Figure 5 A schematic diagram of the structure of a positioning system for UAVs is provided. Figure 5 As shown in the figure, it can be seen that this positioning system is used to implement the positioning method applied to the drone provided in the above embodiment, which may specifically include:

[0102] The drone sensing unit 001 is used to collect the drone's radio signal in real time based on a signal receiver of a preset base station when there is a drone operating in the monitoring area.

[0103] The base station group construction unit 002 is used to obtain a preset base station that monitors the operation of the drone as a target base station based on a radio signal, and to construct a plurality of monitoring base station groups based on the target base station.

[0104] The preliminary positioning unit 003 is used to generate the suspected coordinates of the drone corresponding to each monitoring base station group in each monitoring base station group according to the radio signals obtained by each target base station in the monitoring base station group.

[0105] The signal effective weight acquisition unit 004 is used to evaluate the effectiveness of receiving radio signals corresponding to each monitoring base station group to obtain the signal effective weight corresponding to the monitoring base station group.

[0106] The positioning accuracy weight acquisition unit 005 is used to obtain the positioning accuracy weight corresponding to the monitoring base station group according to the time difference of the radio signal obtained by each target base station in the monitoring base station group and the suspected coordinates of the drone corresponding to each monitoring base station group.

[0107] The base station group weighting unit 006 is used to obtain the weighted weight of the suspected coordinates of the drone corresponding to each monitoring base station group according to the positioning accuracy weight and the signal effectiveness weight.

[0108] The precise positioning unit 007 is used to obtain the precise position coordinates of the drone according to the suspected coordinates of the drone and the corresponding weighted weights.

[0109] It can be understood that the functions implemented by the drone perception unit 001 to the precise positioning unit 007 in the above functional modules correspond one-to-one to the actions performed in the aforementioned steps 101 to 107, and will not be elaborated here.

[0110] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0111] Specifically, Figure 6 According to an embodiment of the present disclosure, a schematic diagram of the structure of an electronic device is shown. Figure 6 The electronic device 600 according to this embodiment of the present disclosure is described. Figure 6 The electronic device 600 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0112] like Figure 6 As shown, the electronic device 600 is in the form of a general computing device. The components of the electronic device 600 may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0113] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present disclosure. For example, the processing unit 610 can perform the following steps: Figure 1 The relevant steps of the copy generation method shown in .

[0114] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0115] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0116] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0117] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or may communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. In addition, the electronic device 600 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0118] In summary, the technical solution provided by the present disclosure can evaluate the signal effective weight of each base station receiving the radio signal according to the energy performance in the drone frequency band of the radio signal collected by each base station, and obtain the corresponding positioning accuracy weight in combination with the signal reception time difference and positioning accuracy of each base station. The corresponding weights of each base station group are calculated by the signal effective weight and the positioning accuracy weight, so as to obtain the precise positioning coordinates of the drone, which can reduce the deviation between the real-time coordinates of the drone obtained by monitoring the base station group and the actual position of the drone, eliminate the influence of electromagnetic interference of radio signals of different base stations and the equipment processing accuracy of different base stations as much as possible, and improve the accuracy of obtaining the real-time coordinates of the drone, which has a popularizable value.

[0119] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0120] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A positioning method applied to an unmanned aerial vehicle, characterized in that: The positioning method comprises the following steps: When there is a UAV operating in the monitoring area, the radio signal of the UAV is collected in real time based on the signal receiver of the preset base station; According to the radio signal, the preset base station that monitors the operation of the drone is obtained as a target base station, and a plurality of monitoring base station groups are constructed according to the target base station; In each of the monitoring base station groups, generating suspected coordinates of the drone corresponding to each of the monitoring base station groups according to the radio signals obtained by each of the target base stations in the monitoring base station group; Evaluate the radio signal reception effectiveness corresponding to each monitoring base station group to obtain the signal effectiveness weight corresponding to the monitoring base station group; According to the time difference of the radio signal obtained by each target base station in the monitoring base station group and the suspected coordinates of the drone corresponding to each monitoring base station group, the positioning accuracy weight corresponding to the monitoring base station group is obtained; According to the positioning accuracy weight and the signal effectiveness weight, obtaining the weighted weight of the suspected coordinates of the drone corresponding to each monitoring base station group; According to the suspected coordinates of the drone and the corresponding weighted weights, the precise position coordinates of the drone are obtained; The process of evaluating the radio signal reception validity corresponding to each monitoring base station group to obtain the signal validity weight corresponding to the monitoring base station group includes the following steps: Acquire radio frequency domain signal data received by each target base station according to the radio signal collected by each target base station; According to the radio frequency domain signal data, obtaining a drone frequency band energy saturation parameter and an electromagnetic signal interference parameter of each target base station, wherein the value of the drone frequency band energy saturation parameter is positively correlated with the value of the radio signal receiving weight of the target base station, and the value of the electromagnetic signal interference parameter is inversely correlated with the value of the radio signal receiving weight; The radio signal receiving weight of each target base station is obtained according to the energy saturation parameter of the drone frequency band and the electromagnetic signal interference parameter, and the effective signal weight corresponding to the monitoring base station group is the average value of the radio signal receiving weights of all the target base stations in the monitoring base station group; The drone frequency band energy saturation parameter is a ratio of the drone frequency band signal strength mean value collected by the target base station to the full-band signal strength mean value collected by the target base station, and the drone frequency band signal strength mean value and the full-band signal strength mean value are obtained based on the radio frequency domain signal data; The electromagnetic signal interference parameter is a ratio of a standard deviation of the drone frequency band signal collected by the target base station to a maximum value of a standard deviation of full-frequency band signals collected by all the target base stations, and the standard deviation of the drone frequency band signal and the standard deviation of the full-frequency band signal are obtained based on the radio frequency domain signal data; The process of obtaining the positioning accuracy weight corresponding to the monitoring base station group according to the time difference of the radio signal obtained by each target base station in the monitoring base station group and the suspected coordinates of the drone corresponding to each monitoring base station group includes the following steps: According to the time difference of the radio signal reaching each of the target base stations in the monitoring base station group, a time difference parameter is obtained, wherein the time difference parameter is associated with a base station combination; Fitting based on the least square method to form a first data fitting curve associated with the base station combination and the time difference parameter; According to the mean value of the drone frequency band signal strength collected by each of the target base stations, a relative attenuation parameter is obtained, where the relative attenuation parameter is associated with the base station combination and is numerically equal to the absolute value of the difference between the mean values ​​of the drone frequency band signal strength corresponding to the two target base stations in the base station combination; A second data fitting curve associated with the base station combination and the relative attenuation parameter is formed based on least squares fitting; Obtaining a Pearson correlation coefficient between the first data fitting curve and the second data fitting curve, wherein the Pearson correlation coefficient is associated with the monitoring base station group; Normalizing the Pearson correlation coefficients corresponding to all the monitoring base station groups, and using the normalized Pearson correlation coefficients as the time difference effective parameters corresponding to the monitoring base station group, wherein the value of the time difference effective parameter is positively correlated with the value of the positioning accuracy weight; Obtaining the overlapping area formed by the positioning hyperbolas corresponding to each monitoring base station group, and taking the overlapping area with the largest area as the reference overlapping area; Obtaining a positioning error parameter corresponding to each of the monitoring base station groups, wherein the positioning error parameter is a ratio of an area of ​​the overlapping region corresponding to the monitoring base station group to an area of ​​the reference overlapping region, and a value of the positioning error parameter is inversely correlated with a value of the positioning accuracy weight; The mathematical expression of positioning accuracy weight is: ;in, For the The positioning accuracy weight of the monitoring base station group, For the The effective parameters of the time difference of the monitoring base station group, For the The positioning error parameters of a group of monitoring base stations.

2. The positioning method for unmanned aerial vehicle according to claim 1, characterized in that: The process of generating the suspected coordinates of the drone corresponding to each monitoring base station group according to the radio signal obtained by each target base station in the monitoring base station group includes the following steps: Draw a positioning hyperbola according to the time difference between the radio signal reaching each of the target base stations in the monitoring base station group; The overlapping area formed by all the positioning hyperbolas is obtained, and the geometric center of the overlapping area is selected as the suspected coordinates of the drone corresponding to the monitoring base station group.

3. The positioning method for unmanned aerial vehicle according to claim 1, characterized in that: The weighted weight of the suspected coordinates of the UAV corresponding to each monitoring base station group is the product of the positioning accuracy weight of each monitoring base station group and the signal effective weight.

4. The positioning method for unmanned aerial vehicle according to claim 3, characterized in that: The following steps are also included: When the precise position coordinates corresponding to the drone are obtained, the precise position coordinates of the drone are continuously updated; When the precise location coordinates of the UAV are all located in the monitoring area and the duration is greater than a preset threshold, sending warning information to the UAV according to the precise location coordinates; as well as In a case where the UAV fails to perform a landing operation according to the early warning information, interference countermeasure information is sent to the UAV according to the precise position coordinates.

5. A positioning system for an unmanned aerial vehicle, characterized in that: A positioning method for a drone as claimed in any one of claims 1 to 4; The positioning system comprises: A drone sensing unit is used to collect the radio signal of the drone in real time based on the signal receiver of the preset base station when there is a drone operating in the monitoring area; A base station group construction unit is used to obtain the preset base station that monitors the operation of the drone as a target base station according to the radio signal, and to construct a plurality of monitoring base station groups according to the target base station; A preliminary positioning unit is used to generate the suspected coordinates of the drone corresponding to each monitoring base station group according to the radio signal obtained by each target base station in the monitoring base station group in each monitoring base station group; A signal effective weight acquisition unit, used to evaluate the effectiveness of receiving radio signals corresponding to each monitoring base station group, so as to obtain the signal effective weight corresponding to the monitoring base station group; A positioning accuracy weight acquisition unit, configured to acquire the positioning accuracy weight corresponding to the monitoring base station group according to the time difference of the radio signal obtained by each of the target base stations in the monitoring base station group and the suspected coordinates of the drone corresponding to each of the monitoring base station group; A base station group weighting unit, configured to obtain a weighted weight of the suspected coordinates of the drone corresponding to each monitoring base station group according to the positioning accuracy weight and the signal effectiveness weight; The precise positioning unit is used to obtain the precise position coordinates of the drone according to the suspected coordinates of the drone and the corresponding weighted weights.

6. An electronic device, characterized in that: include: processor; a memory storing executable instructions of the processor; Wherein, the processor is configured to execute the steps of the positioning method applied to a drone as described in any one of claims 1 to 4 by executing the executable instructions.

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