Ground station based drone remote identification system and method

Abstract

The application discloses a ground station-based unmanned plane remote identification system and method, relates to the field of unmanned plane positioning, and solves the problem that the anti-interference strength of an unmanned plane cannot be effectively adjusted, and comprises a signal analysis module, a frequency matching module, a signal-to-noise analysis module, an interference analysis module and an intelligent control module; the signal analysis module is used for analyzing the spectrum of a remote identification signal sent by an unmanned plane to be identified; the frequency matching module is used for matching the characteristic frequency of the remote identification signal with a preset frequency; the signal-to-noise analysis module is used for analyzing the signal-to-noise ratio of the remote identification signal sent by the target unmanned plane; the interference analysis module is used for analyzing the interference strength received by the target unmanned plane at different time nodes; and the intelligent control module is used for controlling the anti-interference level of the target unmanned plane according to the interference strength level of the remote identification signal, so that the effective adjustment of the anti-interference strength of the unmanned plane is realized.

Description

Technical Field

[0001] This invention belongs to the field of UAV positioning technology, specifically a UAV remote identification system and method based on a ground station. Background Technology

[0002] In recent years, the civilian unmanned aerial vehicle industry has developed rapidly. Due to its ease of operation and speed and flexibility, it is widely used in agriculture, forestry, power, meteorology, marine monitoring, remote sensing and mapping, logistics and emergency rescue. However, due to its ease of modification and difficulty in prevention, it is prone to illegal and disorderly flights.

[0003] At present, remote identification of drones mainly relies on active radar positioning, acoustic positioning, optical recognition tracking and passive positioning. However, during the positioning process of drones, the signals sent by drones will be affected by different interferences. Existing technologies cannot judge the intensity of interference received by drones based on the signals received by ground stations, thus failing to effectively adjust the anti-interference strength of drones, which leads to a decrease in the accuracy of remote identification of drones.

[0004] Therefore, this invention proposes a remote identification system and method for unmanned aerial vehicles (UAVs) based on a ground station. Summary of the Invention

[0005] The purpose of this invention is to propose a remote identification system and method for unmanned aerial vehicles (UAVs) based on a ground station, so as to solve the problem mentioned in the background art of the inability to effectively adjust the anti-interference strength of UAVs.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] The first aspect is a ground station-based UAV remote identification system, which includes a data acquisition module, a signal analysis module, a database, a frequency matching module, a signal-to-noise analysis module, an interference analysis module, and an intelligent control module.

[0008] The data acquisition module is used to acquire the remote identification signal sent by the UAV to be identified in real time and send it to the signal analysis module; the signal analysis module is used to analyze the spectrum of the remote identification signal sent by the UAV to be identified, obtain the characteristic frequency of the remote identification signal and send it to the spectrum matching module and the signal-to-noise analysis module, obtain the amplitude spectrum of the remote identification signal and send it to the signal-to-noise analysis module.

[0009] The frequency matching module is used to match the characteristic frequency of the remote identification signal sent by the UAV to be identified with a preset frequency to obtain the remote identification signal sent by the target UAV and send it to the signal-to-noise analysis module; the signal-to-noise analysis module is used to analyze the signal-to-noise ratio of the remote identification signal sent by the target UAV to obtain the real-time signal-to-noise ratio of the remote identification signal at different time points and send it to the interference analysis module.

[0010] The interference analysis module is used to analyze the interference intensity of the target UAV at different time points, obtain the interference intensity level of the remote identification signal at different time points, and send it to the intelligent control module; the data acquisition module is also used to collect the real-time position coordinates of the target UAV at different time points and send them to the intelligent control module; the intelligent control module is used to control the anti-interference level of the target UAV according to the interference intensity level of the remote identification signal at different time points.

[0011] Furthermore, the analysis process of the signal analysis module includes the following:

[0012] Performing a Fourier transform on the remote identification signal yields the spectrum corresponding to different frequencies in the remote identification signal;

[0013] The square root of the sum of the squares of the real part and the imaginary part of the spectrum is used to obtain the amplitude corresponding to different frequencies. Based on the amplitude corresponding to different frequencies, the amplitude spectrum of the remote identification signal is generated using a spectrum analysis tool.

[0014] The amplitude corresponding to different frequencies is compared to obtain the maximum amplitude value. The frequency corresponding to the maximum amplitude value is recorded as the characteristic frequency of the remote identification signal sent by the UAV to be identified.

[0015] Furthermore, the matching process of the frequency matching module includes the following:

[0016] Obtain the preset frequency of the remote identification signal sent by the target UAV in the database, and compare the characteristic frequency with the preset frequency;

[0017] If the characteristic frequency of the remote identification signal is the same as the preset frequency, the corresponding UAV to be identified will be recorded as the target UAV and the corresponding remote identification signal will be retained; if the characteristic frequency of the remote identification signal is different from the preset frequency, the corresponding remote identification signal will be discarded.

[0018] Furthermore, the analysis process of the signal-to-noise analysis module includes the following:

[0019] Obtain the standard bandwidth length of the remote identification signal sent by the target UAV in the database. Using the standard bandwidth length as the interval length, construct the first energy detection interval. Align the right endpoint of the first energy detection interval with the characteristic frequency in the amplitude spectrum. Square the amplitudes corresponding to different frequencies in the first energy detection interval and sum them to obtain the total energy in the corresponding first energy detection interval.

[0020] The first energy detection interval is shifted to the right by one frequency unit and is denoted as the second energy detection interval. The amplitudes corresponding to different frequencies in the second energy detection interval are squared and then summed to obtain the total energy in the corresponding second energy detection interval.

[0021] The right endpoint of the second energy detection interval is moved one frequency unit to the right until the left endpoint of the energy detection interval is aligned with the characteristic frequency, thereby obtaining the third energy detection interval, the fourth energy detection interval, ..., the kth energy detection interval. The amplitudes corresponding to different frequencies in any energy detection interval are squared and summed to obtain the total energy in different energy detection intervals, where k is the number of the energy detection interval.

[0022] Furthermore, the analysis process of the signal-to-noise analysis module also includes the following:

[0023] By iterating through and comparing the first energy detection interval, the second energy detection interval, ..., the total energy in the kth energy detection interval is obtained as the maximum total energy value, and the energy detection interval corresponding to the maximum total energy value is recorded as the passband frequency interval.

[0024] The frequencies within the passband frequency range are denoted as passband frequencies, the frequencies outside the passband frequency range are denoted as interference frequencies, the maximum total energy is denoted as the effective energy in the remote identification signal at the current time node, and the interference energy in the remote identification signal at the current time node is obtained by summing the squares of the amplitudes corresponding to different interference frequencies.

[0025] The effective power Ps is obtained by dividing the effective energy by the fixed duration, and the interference power Pn is obtained by dividing the interference energy by the fixed duration.

[0026] The real-time signal-to-noise ratio of the remote identification signal at the current time point is calculated, and the real-time signal-to-noise ratio of the remote identification signal at different time points is also calculated.

[0027] Furthermore, the analysis process of the interference analysis module includes the following:

[0028] Obtain the minimum signal-to-noise ratio (SNR) and standard SNR allowed when the target UAV sends remote identification signals from the database, and construct the SNR interval by taking the minimum SNR as the left endpoint and the standard SNR as the right endpoint.

[0029] Subtracting the minimum signal-to-noise ratio from the standard signal-to-noise ratio yields the interval length of the signal-to-noise ratio interval. Dividing the interval length by a fixed value yields the interval division length. Based on the interval division length, the signal-to-noise ratio interval is divided into three equally spaced intervals, which are sequentially denoted as the first signal-to-noise ratio interval, the second signal-to-noise ratio interval, and the third signal-to-noise ratio interval.

[0030] Compare the real-time signal-to-noise ratio of the remotely identified signal at the current time point with different signal-to-noise ratio ranges;

[0031] If the real-time signal-to-noise ratio of the remote identification signal at the current time point is within the first signal-to-noise ratio range, then the interference intensity level of the remote identification signal at the current time point is recorded as the first interference intensity level.

[0032] If the real-time signal-to-noise ratio of the remote identification signal at the current time point is within the second signal-to-noise ratio range, then the interference intensity level of the remote identification signal at the current time point is recorded as the second interference intensity level.

[0033] If the real-time signal-to-noise ratio of the remote identification signal at the current time point is within the third signal-to-noise ratio range, then the interference intensity level of the remote identification signal at the current time point is recorded as the third interference intensity level.

[0034] If the real-time signal-to-noise ratio of the remote identification signal at the current time point does not fall within any signal-to-noise ratio range, then the anti-interference level of the target UAV will be set to the highest anti-interference level.

[0035] The signal-to-noise ratio (SNR) of the remote identification signal at different time points is compared with different SNR ranges to obtain the interference intensity level of the remote identification signal at different time points.

[0036] Furthermore, the values ​​in the first signal-to-noise ratio interval are less than or equal to the values ​​in the second signal-to-noise ratio interval, and the values ​​in the second signal-to-noise ratio interval are less than or equal to the values ​​in the third signal-to-noise ratio interval; the interference intensity level of the first interference intensity level is higher than the interference intensity level of the second interference intensity level, and the interference intensity level of the second interference intensity level is higher than the interference intensity level of the third interference intensity level.

[0037] Furthermore, the control process of the intelligent control module includes the following:

[0038] Different time points are arranged in ascending order of time to obtain a time point sequence. The interference intensity level of the remote identification signal at different time points in the time point sequence is then obtained.

[0039] The time point when the interference intensity level of the remote identification signal first reaches the second interference intensity level is recorded as the first interference enhancement time point, and the time point when the interference intensity level of the remote identification signal first reaches the first interference intensity level is recorded as the second interference enhancement time point.

[0040] For time nodes before the first interference enhancement time point, the real-time signal-to-noise ratio of the remote identification signal at any time node is subtracted from the real-time signal-to-noise ratio of the remote identification signal at the previous time node to obtain the change in the real-time signal-to-noise ratio of the remote identification signal at the corresponding time node. The time node corresponding to the change in real-time signal-to-noise ratio being less than zero is recorded as the affected node.

[0041] Using the first interference enhancement time point as the left endpoint and the second interference enhancement time point as the right endpoint, construct the interference enhancement time period;

[0042] The time point when the remotely identified signal is at the third interference intensity level during the interference enhancement period is recorded as the first time point, and the time point when the remotely identified signal is at the second interference intensity level during the interference enhancement period is recorded as the second time point.

[0043] The real-time position coordinates of the target UAV at the time of the impact on the node are designated as the first interference position coordinates, the real-time position coordinates of the target UAV at the time of the first interference enhancement are designated as the second interference position coordinates, and the real-time position coordinates of the target UAV at the time of the second interference enhancement are designated as the third interference position coordinates.

[0044] If all time points within the enhanced interference period are the first time point, then when the target UAV reaches the first interference position coordinate during its flight mission, the anti-interference level of the target UAV will be set to the first anti-interference level; when the target UAV reaches the second interference position coordinate, the anti-interference level of the target UAV will be set to the third anti-interference level.

[0045] If multiple time points are designated as second time points within the interference period, the anti-interference level of the target UAV will be set to the first anti-interference level when it reaches the first interference position coordinate during its flight mission; the anti-interference level will be set to the second anti-interference level when it reaches the second interference position coordinate; and the anti-interference level will be set to the third anti-interference level when it reaches the third interference position coordinate.

[0046] Furthermore, the first anti-interference level is lower than the second anti-interference level, and the second anti-interference level is lower than the third anti-interference level.

[0047] Secondly, a remote identification method for unmanned aerial vehicles (UAVs) based on a ground station includes the following sub-steps:

[0048] Step S101: Analyze the spectrum of the remote identification signal sent by the UAV to be identified, and obtain the characteristic frequencies in the remote identification signal.

[0049] Step S102: Match the characteristic frequency with the preset frequency to obtain the remote identification signal sent by the target UAV;

[0050] Step S103: Perform signal-to-noise analysis on the remote identification signal sent by the target UAV to obtain the real-time signal-to-noise ratio of the remote identification signal at different time points;

[0051] Step S104: Analyze the interference intensity of the remote identification signal based on the real-time signal-to-noise ratio, and obtain the interference intensity level of the remote identification signal at different time points.

[0052] Step S105: Assign different anti-interference levels to the target UAV based on different interference intensity levels.

[0053] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0054] 1. This invention first performs spectrum analysis on the remote identification signals sent by different unmanned aerial vehicles (UAVs) in the sky through a ground station, analyzes and obtains the characteristic frequencies of different UAVs, and matches them with preset frequencies in the database, thereby identifying the target UAV. This invention achieves the identification of target UAVs in the sky.

[0055] 2. The present invention then analyzes the real-time signal-to-noise ratio of the remote identification signal sent by the target UAV, and obtains the interference intensity level of the remote identification signal at different time points. At the same time, the present invention sets the corresponding anti-interference level according to the real-time position coordinates of the target UAV. The present invention realizes the effective adjustment of the anti-interference intensity of the target UAV. Attached Figure Description

[0056] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0057] Figure 1 This is an overall system block diagram of the present invention;

[0058] Figure 2 This is a schematic diagram of the energy detection range in this invention;

[0059] Figure 3 This is a schematic diagram showing the interference intensity levels corresponding to remote identification signals at different time points in this invention;

[0060] Figure 4 This is a flowchart of the method of the present invention. Detailed Implementation

[0061] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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.

[0062] Example 1: Please refer to Figures 1-3 As shown, the technical solution provided by this invention is: a remote identification system for unmanned aerial vehicles (UAVs) based on a ground station. This system first performs spectrum analysis on the remote identification signals sent by different UAVs to be identified in the sky through the ground station, analyzes and obtains the characteristic frequencies of different UAVs to be identified, and matches them with preset frequencies in the database to identify the target UAV. Then, it analyzes the real-time signal-to-noise ratio of the remote identification signals sent by the target UAVs, analyzes and obtains the interference intensity level of the remote identification signals at different time points, and sets the corresponding anti-interference level according to the real-time position coordinates of the target UAVs.

[0063] Specifically, the target UAV is used to test the interference intensity within its flight path. When the target UAV officially performs its flight mission, it performs anti-interference at the corresponding real-time location coordinates based on the anti-interference level obtained from the test.

[0064] In this embodiment, the UAV remote identification system includes a data acquisition module, a signal analysis module, a database, a frequency matching module, a signal-to-noise analysis module, an interference analysis module, and an intelligent control module;

[0065] In specific implementation, the data acquisition module is used to acquire the remote identification signal sent by the UAV to be identified in real time, and send the remote identification signal to the signal analysis module;

[0066] Specifically, the signal analysis module is used to analyze the spectrum of the remote identification signal sent by the UAV to be identified. The analysis process includes the following:

[0067] Perform a Fourier transform on the remote identification signal to obtain the spectrum corresponding to different frequencies in the remote identification signal. The spectrum of the remote identification signal is denoted as X(f) = Re[X(f)] + Im[X(f)], where Re[X(f)] is the real part of the spectrum and Im[X(f)] is the imaginary part of the spectrum.

[0068] The square root of the sum of the squares of the real and imaginary parts is used to obtain the amplitude corresponding to different frequencies. Based on the amplitude corresponding to different frequencies, the amplitude spectrum of the remote identification signal is generated using a spectrum analysis tool.

[0069] In this embodiment, the spectrum analysis tool is an existing tool. For example, MATLAB can be used to generate the amplitude spectrum of the remotely identified signal. It should be explained that the horizontal and vertical axes of the amplitude spectrum represent frequency, and the vertical axis represents amplitude.

[0070] By iterating and comparing the amplitudes corresponding to different frequencies, the maximum amplitude value is obtained, and the frequency corresponding to the maximum amplitude value is recorded as the characteristic frequency of the remote identification signal sent by the UAV to be identified.

[0071] It should be explained that the characteristic frequency refers to the frequency at which energy is concentrated in the remote identification signal, representing the main frequency in the remote identification signal;

[0072] The signal analysis module sends the characteristic frequency of the remotely identified signal to the spectrum matching module, and the signal analysis module sends the characteristic frequency and amplitude spectrum of the remotely identified signal to the signal-to-noise analysis module.

[0073] In practical implementation, the frequency matching module is used to match the characteristic frequency of the remote identification signal sent by the UAV to be identified with a preset frequency group. The matching process includes the following:

[0074] Obtain the preset frequency of the remote identification signal sent by the target UAV in the database, and compare the characteristic frequency with the preset frequency;

[0075] If the characteristic frequency of the remote identification signal is the same as the preset frequency, the corresponding UAV to be identified will be recorded as the target UAV and the corresponding remote identification signal will be retained; if the characteristic frequency of the remote identification signal is not the same as the preset frequency, the corresponding remote identification signal will be discarded.

[0076] In this embodiment, the preset frequencies are 2.4GHz and 1090MHz. The remote identification signal sent by the target drone is a 2.4GHz Bluetooth, WiFi signal or a 1090MHz-ADS-B signal. The preset frequency is determined according to the frequency band of the remote identification signal.

[0077] The frequency matching module sends the remote identification signal sent by the target UAV to the signal-to-noise analysis module.

[0078] In practical implementation, the signal-to-noise analysis module is used to analyze the signal-to-noise ratio of the remote identification signal sent by the target UAV. The analysis process includes the following:

[0079] Obtain the standard bandwidth length of the remote identification signal transmitted by the target UAV in the database. Using this standard bandwidth length as the interval length, construct the first energy detection interval. (See [link to relevant documentation]). Figure 2As shown, the right endpoint of the first energy detection interval is aligned with the characteristic frequency in the amplitude spectrum, and the amplitudes corresponding to different frequencies in the first energy detection interval are squared and summed to obtain the total energy in the corresponding first energy detection interval.

[0080] The first energy detection interval is shifted to the right by one frequency unit and is denoted as the second energy detection interval. The amplitudes corresponding to different frequencies in the second energy detection interval are squared and then summed to obtain the total energy in the corresponding second energy detection interval.

[0081] The right endpoint of the second energy detection interval is moved one frequency unit to the right until the left endpoint of the energy detection interval is aligned with the characteristic frequency, thereby obtaining the third energy detection interval, the fourth energy detection interval, ..., the kth energy detection interval. The amplitudes corresponding to different frequencies in any energy detection interval are squared and summed to obtain the total energy in different energy detection intervals, where k is the number of the energy detection interval.

[0082] By iterating through and comparing the first energy detection interval, the second energy detection interval, ..., the total energy in the kth energy detection interval is obtained as the maximum total energy value, and the energy detection interval corresponding to the maximum total energy value is recorded as the passband frequency interval.

[0083] The frequencies within the passband frequency range are denoted as passband frequencies, the frequencies outside the passband frequency range are denoted as interference frequencies, the maximum total energy is denoted as the effective energy in the remote identification signal at the current time node, and the interference energy in the remote identification signal at the current time node is obtained by summing the squares of the amplitudes corresponding to different interference frequencies.

[0084] The effective power Ps is obtained by dividing the effective energy by the fixed duration, and the interference power Pn is obtained by dividing the interference energy by the fixed duration.

[0085] The real-time signal-to-noise ratio S of the remote identification signal at the current time point is calculated using the formula S=10×lg(Ps / Pn); where lg represents the logarithm to the base 10.

[0086] Similarly, the real-time signal-to-noise ratio of the remote identification signal at different time points is calculated;

[0087] It should be explained that the effective energy and the interference energy are divided by the same fixed duration. In the formula for calculating the real-time signal-to-noise ratio, the fixed duration in the numerator and denominator will be eliminated, so there is no need to limit the value of the fixed duration.

[0088] The signal-to-noise analysis module sends the real-time signal-to-noise ratio of the remotely identified signal at different time points to the interference analysis module.

[0089] In this embodiment, the interference analysis module is used to analyze the interference intensity experienced by the target UAV at different time points. The analysis process includes the following:

[0090] Obtain the minimum signal-to-noise ratio (SNR) and standard SNR allowed when the target UAV sends remote identification signals from the database, and construct the SNR interval by taking the minimum SNR as the left endpoint and the standard SNR as the right endpoint.

[0091] It should be explained that the minimum signal-to-noise ratio (SNR) represents the minimum value allowed for the remote identification signal during propagation, while the standard SNR represents the standard value required for the remote identification signal during propagation. The smaller the SNR, the lower the propagation quality of the remote identification signal and the stronger the interference it is subjected to; the larger the SNR, the higher the propagation quality of the remote identification signal and the weaker the interference it is subjected to.

[0092] Subtracting the minimum signal-to-noise ratio from the standard signal-to-noise ratio yields the interval length of the signal-to-noise ratio interval. Dividing the interval length by a fixed value yields the interval division length. Based on the interval division length, the signal-to-noise ratio interval is divided into three equally spaced intervals, which are sequentially denoted as the first signal-to-noise ratio interval, the second signal-to-noise ratio interval, and the third signal-to-noise ratio interval.

[0093] Among them, the value in the first signal-to-noise ratio interval is less than or equal to the value in the second signal-to-noise ratio interval, and the value in the second signal-to-noise ratio interval is less than or equal to the value in the third signal-to-noise ratio interval;

[0094] In this embodiment, the fixed value is three. The value of the fixed value determines the number of intervals after dividing the signal-to-noise ratio intervals. If it is necessary to increase the division accuracy of the signal-to-noise ratio intervals, the fixed value can be increased according to the division accuracy requirements. For example, the first signal-to-noise ratio interval is [10, 15], the second signal-to-noise ratio interval is (15, 20], and the third signal-to-noise ratio interval is (20, 25].

[0095] Compare the real-time signal-to-noise ratio of the remotely identified signal at the current time point with different signal-to-noise ratio ranges;

[0096] If the real-time signal-to-noise ratio of the remote identification signal at the current time point is within the first signal-to-noise ratio range, then the interference intensity level of the remote identification signal at the current time point is recorded as the first interference intensity level.

[0097] If the real-time signal-to-noise ratio of the remote identification signal at the current time point is within the second signal-to-noise ratio range, then the interference intensity level of the remote identification signal at the current time point is recorded as the second interference intensity level.

[0098] If the real-time signal-to-noise ratio of the remote identification signal at the current time point is within the third signal-to-noise ratio range, then the interference intensity level of the remote identification signal at the current time point is recorded as the third interference intensity level.

[0099] If the real-time signal-to-noise ratio of the remote identification signal at the current time point does not fall within any signal-to-noise ratio range, then the anti-interference level of the target UAV will be set to the highest anti-interference level.

[0100] Among them, the interference intensity level of the first interference intensity level is higher than that of the second interference intensity level, and the interference intensity level of the second interference intensity level is higher than that of the third interference intensity level.

[0101] It should be explained that the interference intensity level represents the interference intensity received by the remote identification signal. The higher the interference intensity level, the stronger the interference received by the remote identification signal. When the real-time signal-to-noise ratio does not fall within any signal-to-noise ratio range, it is assumed that the real-time signal-to-noise ratio is less than the minimum signal-to-noise ratio. In this case, the real-time signal-to-noise ratio is less than the minimum value allowed for the remote identification signal during propagation.

[0102] The signal-to-noise ratio of the remote identification signal at different time points is compared with different signal-to-noise ratio ranges to obtain the interference intensity level of the remote identification signal at different time points.

[0103] The interference analysis module sends the interference intensity level of the remotely identified signal at different time points to the intelligent control module.

[0104] In the specific implementation process, the data acquisition module is also used to collect the real-time position coordinates of the target UAV at different time points and send them to the intelligent control module;

[0105] In this embodiment, the intelligent control module is used to control the anti-interference level of the target UAV based on the interference intensity level of the remote identification signal at different time points. The control process includes the following:

[0106] Different time points are arranged in ascending order of time to obtain a time point sequence. The interference intensity level of the remote identification signal at different time points in the time point sequence is then obtained.

[0107] The time point when the interference intensity level of the remote identification signal first reaches the second interference intensity level is recorded as the first interference enhancement time point, and the time point when the interference intensity level of the remote identification signal first reaches the first interference intensity level is recorded as the second interference enhancement time point.

[0108] For time nodes before the first interference enhancement time point, the real-time signal-to-noise ratio of the remote identification signal at any time node is subtracted from the real-time signal-to-noise ratio of the remote identification signal at the previous time node to obtain the change in the real-time signal-to-noise ratio of the remote identification signal at the corresponding time node. The time node corresponding to the change in real-time signal-to-noise ratio being less than zero is recorded as the affected node.

[0109] Using the first interference enhancement time point as the left endpoint and the second interference enhancement time point as the right endpoint, construct the interference enhancement time period;

[0110] The time point when the remotely identified signal is at the third interference intensity level during the interference enhancement period is recorded as the first time point, and the time point when the remotely identified signal is at the second interference intensity level during the interference enhancement period is recorded as the second time point.

[0111] It should be explained that the left end of the interference enhancement period is the first interference enhancement time point, and the right end is the second interference enhancement time point. The second interference enhancement time point represents the time point when the remote identification signal first reaches the first interference intensity level. Therefore, before the second interference enhancement time point, the remote identification signal cannot be at the first interference intensity level.

[0112] The real-time position coordinates of the target UAV at the time of the impact on the node are designated as the first interference position coordinates, the real-time position coordinates of the target UAV at the time of the first interference enhancement are designated as the second interference position coordinates, and the real-time position coordinates of the target UAV at the time of the second interference enhancement are designated as the third interference position coordinates.

[0113] If all time points within the enhanced interference period are the first time point, then when the target UAV reaches the first interference position coordinate during its flight mission, the anti-interference level of the target UAV will be set to the first anti-interference level; when the target UAV reaches the second interference position coordinate, the anti-interference level of the target UAV will be set to the third anti-interference level.

[0114] If multiple time points within the interference period are designated as second time points, then when the target UAV reaches the first interference location coordinate during its flight mission, its anti-interference level will be set to the first anti-interference level; when the target UAV reaches the second interference location coordinate, its anti-interference level will be set to the second anti-interference level; and when the target UAV reaches the third interference location coordinate, its anti-interference level will be set to the third anti-interference level.

[0115] Among them, the first anti-interference level is lower than the second anti-interference level, and the second anti-interference level is lower than the third anti-interference level;

[0116] For example, please refer to Figure 3 As shown, solid circles indicate that the remote identification signal is at the third interference level at the corresponding time point, dashed circles indicate that the remote identification signal is at the second interference level at the corresponding time point, and solid circles indicate that the remote identification signal is at the first interference level at the corresponding time point. Figure 3In the upper-middle timeline, all time points within the interference period are considered the first time point. In the lower-middle timeline, multiple second time points exist within the interference period. In this embodiment, the higher the anti-interference level, the stronger the anti-interference capability.

[0117] Example 2: Please refer to Figure 4 As shown, based on another concept of the same invention, a remote identification method for unmanned aerial vehicles (UAVs) based on a ground station is proposed. The method includes the following steps:

[0118] Step S101: Analyze the spectrum of the remote identification signal sent by the UAV to be identified, and obtain the characteristic frequencies in the remote identification signal.

[0119] Step S102: Match the characteristic frequency with the preset frequency to obtain the remote identification signal sent by the target UAV;

[0120] Step S103: Perform signal-to-noise analysis on the remote identification signal sent by the target UAV to obtain the real-time signal-to-noise ratio of the remote identification signal at different time points;

[0121] Step S104: Analyze the interference intensity of the remote identification signal based on the real-time signal-to-noise ratio, and obtain the interference intensity level of the remote identification signal at different time points.

[0122] Step S105: Assign different anti-interference levels to the target UAV based on different interference intensity levels.

[0123] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A ground-station-based unmanned aerial vehicle (UAV) remote identification system, characterized in that, It includes a data acquisition module, a signal analysis module, a database, a frequency matching module, a signal-to-noise analysis module, an interference analysis module, and an intelligent control module. The data acquisition module is used to acquire the remote identification signal sent by the UAV to be identified in real time and send it to the signal analysis module. The signal analysis module is used to analyze the spectrum of the remote identification signal sent by the UAV to be identified, obtain the characteristic frequency of the remote identification signal and send it to the frequency matching module and the signal-to-noise analysis module, obtain the amplitude spectrum of the remote identification signal and send it to the signal-to-noise analysis module. The frequency matching module is used to match the characteristic frequency of the remote identification signal sent by the UAV to be identified with a preset frequency to obtain the remote identification signal sent by the target UAV and send it to the signal-to-noise analysis module; the signal-to-noise analysis module is used to analyze the signal-to-noise ratio of the remote identification signal sent by the target UAV to obtain the real-time signal-to-noise ratio of the remote identification signal at different time points and send it to the interference analysis module. The signal-to-noise analysis module's analysis process includes the following: Obtain the standard bandwidth length of the remote identification signal sent by the target UAV in the database. Using the standard bandwidth length as the interval length, construct the first energy detection interval. Align the right endpoint of the first energy detection interval with the characteristic frequency in the amplitude spectrum. Square the amplitudes corresponding to different frequencies in the first energy detection interval and sum them to obtain the total energy in the corresponding first energy detection interval. The first energy detection interval is shifted to the right by one frequency unit and is denoted as the second energy detection interval. The amplitudes corresponding to different frequencies in the second energy detection interval are squared and then summed to obtain the total energy in the corresponding second energy detection interval. The right endpoint of the second energy detection interval is moved one frequency unit to the right until the left endpoint of the energy detection interval is aligned with the characteristic frequency, thereby obtaining the third energy detection interval, the fourth energy detection interval, ..., the kth energy detection interval. The amplitudes corresponding to different frequencies in any energy detection interval are squared and summed to obtain the total energy in different energy detection intervals, where k is the number of the energy detection interval. By iterating through and comparing the first energy detection interval, the second energy detection interval, ..., the total energy in the kth energy detection interval is obtained as the maximum total energy value, and the energy detection interval corresponding to the maximum total energy value is recorded as the passband frequency interval. The frequencies within the passband frequency range are denoted as passband frequencies, the frequencies outside the passband frequency range are denoted as interference frequencies, the maximum total energy is denoted as the effective energy in the remote identification signal at the current time node, and the interference energy in the remote identification signal at the current time node is obtained by summing the squares of the amplitudes corresponding to different interference frequencies. The effective power Ps is obtained by dividing the effective energy by the fixed duration, and the interference power Pn is obtained by dividing the interference energy by the fixed duration. The real-time signal-to-noise ratio of the remote identification signal at the current time point is calculated, and the real-time signal-to-noise ratio of the remote identification signal at different time points is also calculated. The interference analysis module is used to analyze the interference intensity of the target UAV at different time points, obtain the interference intensity level of the remote identification signal at different time points, and send it to the intelligent control module; the data acquisition module is also used to acquire the real-time position coordinates of the target UAV at different time points and send them to the intelligent control module; the intelligent control module is used to control the anti-interference level of the target UAV according to the interference intensity level of the remote identification signal at different time points. The analysis process of the interference analysis module includes the following: Obtain the minimum signal-to-noise ratio (SNR) and standard SNR allowed when the target UAV sends remote identification signals from the database, and construct the SNR interval by taking the minimum SNR as the left endpoint and the standard SNR as the right endpoint. Subtracting the minimum signal-to-noise ratio from the standard signal-to-noise ratio yields the interval length of the signal-to-noise ratio interval. Dividing the interval length by a fixed value yields the interval division length. Based on the interval division length, the signal-to-noise ratio interval is divided into three equally spaced intervals, which are sequentially denoted as the first signal-to-noise ratio interval, the second signal-to-noise ratio interval, and the third signal-to-noise ratio interval. Compare the real-time signal-to-noise ratio of the remotely identified signal at the current time point with different signal-to-noise ratio ranges; If the real-time signal-to-noise ratio of the remote identification signal at the current time point is within the first signal-to-noise ratio range, then the interference intensity level of the remote identification signal at the current time point is recorded as the first interference intensity level. If the real-time signal-to-noise ratio of the remote identification signal at the current time point is within the second signal-to-noise ratio range, then the interference intensity level of the remote identification signal at the current time point is recorded as the second interference intensity level. If the real-time signal-to-noise ratio of the remote identification signal at the current time point is within the third signal-to-noise ratio range, then the interference intensity level of the remote identification signal at the current time point is recorded as the third interference intensity level. If the real-time signal-to-noise ratio of the remote identification signal at the current time point does not fall within any signal-to-noise ratio range, then the anti-interference level of the target UAV will be set to the highest anti-interference level. The signal-to-noise ratio (SNR) of the remote identification signal at different time points is compared with different SNR ranges to obtain the interference intensity level of the remote identification signal at different time points.

2. The UAV remote identification system based on a ground station according to claim 1, characterized in that, The analysis process of the signal analysis module includes the following: Performing a Fourier transform on the remote identification signal yields the spectrum corresponding to different frequencies in the remote identification signal; The square root of the sum of the squares of the real part and the imaginary part of the spectrum is used to obtain the amplitude corresponding to different frequencies. Based on the amplitude corresponding to different frequencies, the amplitude spectrum of the remote identification signal is generated using a spectrum analysis tool. The amplitude corresponding to different frequencies is compared to obtain the maximum amplitude value. The frequency corresponding to the maximum amplitude value is recorded as the characteristic frequency of the remote identification signal sent by the UAV to be identified.

3. The UAV remote identification system based on a ground station according to claim 1, characterized in that, The matching process of the frequency matching module includes the following: Obtain the preset frequency of the remote identification signal sent by the target UAV in the database, and compare the characteristic frequency with the preset frequency; If the characteristic frequency of the remote identification signal is the same as the preset frequency, the corresponding drone to be identified will be recorded as the target drone, and the corresponding remote identification signal will be retained. If the characteristic frequency of the remote identification signal is different from the preset frequency, the corresponding remote identification signal will be discarded.

4. The UAV remote identification system based on a ground station according to claim 1, characterized in that, The values ​​in the first signal-to-noise ratio interval are less than or equal to the values ​​in the second signal-to-noise ratio interval, and the values ​​in the second signal-to-noise ratio interval are less than or equal to the values ​​in the third signal-to-noise ratio interval; the interference intensity level of the first interference intensity level is higher than the interference intensity level of the second interference intensity level, and the interference intensity level of the second interference intensity level is higher than the interference intensity level of the third interference intensity level.

5. The UAV remote identification system based on a ground station according to claim 1, characterized in that, The control process of the intelligent control module includes the following: Different time points are arranged in ascending order of time to obtain a time point sequence. The interference intensity level of the remote identification signal at different time points in the time point sequence is then obtained. The time point when the interference intensity level of the remote identification signal first reaches the second interference intensity level is recorded as the first interference enhancement time point, and the time point when the interference intensity level of the remote identification signal first reaches the first interference intensity level is recorded as the second interference enhancement time point. For time nodes before the first interference enhancement time point, the real-time signal-to-noise ratio of the remote identification signal at any time node is subtracted from the real-time signal-to-noise ratio of the remote identification signal at the previous time node to obtain the change in the real-time signal-to-noise ratio of the remote identification signal at the corresponding time node. The time node corresponding to the change in real-time signal-to-noise ratio being less than zero is recorded as the affected node. Using the first interference enhancement time point as the left endpoint and the second interference enhancement time point as the right endpoint, construct the interference enhancement time period; The time point when the remotely identified signal is at the third interference intensity level during the interference enhancement period is recorded as the first time point, and the time point when the remotely identified signal is at the second interference intensity level during the interference enhancement period is recorded as the second time point. The real-time position coordinates of the target UAV at the time of the impact on the node are designated as the first interference position coordinates, the real-time position coordinates of the target UAV at the time of the first interference enhancement are designated as the second interference position coordinates, and the real-time position coordinates of the target UAV at the time of the second interference enhancement are designated as the third interference position coordinates. If all time points within the enhanced interference period are the first time point, then when the target UAV reaches the first interference position coordinate during its flight mission, the anti-interference level of the target UAV will be set to the first anti-interference level; when the target UAV reaches the second interference position coordinate, the anti-interference level of the target UAV will be set to the third anti-interference level. If multiple time points are designated as second time points within the interference period, the anti-interference level of the target UAV will be set to the first anti-interference level when it reaches the first interference position coordinate during its flight mission; the anti-interference level will be set to the second anti-interference level when it reaches the second interference position coordinate; and the anti-interference level will be set to the third anti-interference level when it reaches the third interference position coordinate.

6. The UAV remote identification system based on a ground station according to claim 5, characterized in that, The first level of anti-interference is lower than the second level of anti-interference, and the second level of anti-interference is lower than the third level of anti-interference.

7. A remote identification method for unmanned aerial vehicles based on a ground station, characterized in that, The method of the ground station-based unmanned aerial vehicle (UAV) remote identification system according to any one of claims 1-6 includes the following sub-steps: Step S101: Analyze the spectrum of the remote identification signal sent by the UAV to be identified, and obtain the characteristic frequencies in the remote identification signal. Step S102: Match the characteristic frequency with the preset frequency to obtain the remote identification signal sent by the target UAV; Step S103: Perform signal-to-noise analysis on the remote identification signal sent by the target UAV to obtain the real-time signal-to-noise ratio of the remote identification signal at different time points; Step S104: Analyze the interference intensity of the remote identification signal based on the real-time signal-to-noise ratio, and obtain the interference intensity level of the remote identification signal at different time points. Step S105: Assign different anti-interference levels to the target UAV based on different interference intensity levels.

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