A small unmanned aerial vehicle spectrum identification and direction finding system and method

Abstract

The application belongs to the technical field of unmanned aerial vehicle detection countermeasure, and discloses a small unmanned aerial vehicle spectrum identification and direction finding system and method.The system comprises an antenna array element, a radio frequency switch, a data acquisition card, a CPU processing mainboard and an upper computer.On the algorithm of the CPU processing mainboard, aiming at unmanned aerial vehicle model identification and azimuth direction finding, based on a neural network algorithm model and a uniform circular array structure model respectively, different radio frequency signal types are quickly and accurately classified through time-frequency graph analysis, the implicit advanced features of signals can be extracted from large-scale data, the result is directly output, and the unmanned aerial vehicle model is quickly identified.The DOA estimation algorithm based on deep learning is adopted, through the algorithm model based on covariance matrix decomposition, the DOA estimation value can be quickly given, the final result is obtained, and the azimuth angle can be accurately given.The shortcomings that the traditional algorithm is sensitive to multipath effect are avoided, and high-precision direction estimation can be realized.

Description

Technical Field

[0001] This invention belongs to the field of drone detection and countermeasure technology, and specifically relates to a small drone spectrum identification and direction finding system and method. Background Technology

[0002] In recent years, with the rapid development of consumer drones, their small size, low cost, and ease of operation have brought great convenience to society. However, this has also brought numerous safety issues, and incidents of unauthorized drone flights are increasing. Therefore, adopting reasonable and effective control measures to prevent unauthorized drone flights is extremely important.

[0003] Unmanned aerial vehicle (UAV) spectrum identification and direction finding technologies are crucial for monitoring unauthorized UAV flights. While UAV manufacturers restrict illegal flights by establishing no-fly zones, areas requiring temporary no-fly zones or involving national secrets are often excluded from their no-fly zone databases, leaving these areas unrestricted. Therefore, employing third-party equipment for UAV detection and direction finding is of significant practical importance. Detection technology uses spectrum identification classification algorithms to identify UAV models based on their spectrum, while direction finding technology uses DOA estimation algorithms to monitor the UAV's azimuth angle in real time, ultimately providing effective targets for jamming equipment.

[0004] Traditional drone detection methods primarily involve first acquiring drone signals, analyzing the drone's spectrum using FFT, extracting feature values, saving them to a feature library file, and then comparing the acquired signal's feature values ​​with the feature library to determine the drone model. This approach is not only slow in building the drone feature library, but also suffers from difficulties in signal extraction due to signal diversity, and a high false alarm rate in complex electromagnetic environments, making it unsuitable for practical applications. Previous direction-finding techniques mainly used amplitude or phase comparison methods, inferring the direction of the signal source based on the difference in signal amplitude or phase between the received signals from each element of the direction-finding antenna array. This method also has limitations; for example, on the side obstructed by mountains, multipath effects may occur, causing signal propagation to involve reflection, refraction, and scattering, resulting in different paths and times for the same signal source to reach different array elements, thus affecting the accuracy of the direction-finding results. Summary of the Invention

[0005] The purpose of this invention is to address the aforementioned problems by providing a spectrum identification and direction finding system and method for small unmanned aerial vehicles (UAVs). This aims to improve upon the poor accuracy and inability to adapt to complex environments of existing UAV detection methods.

[0006] The technical solution adopted in this invention is as follows: a small unmanned aerial vehicle (UAV) spectrum identification and direction finding system, the system including a radio frequency (RF) antenna array element, an RF switching switch, a signal processing unit, an Ethernet interface, and a host computer; the antenna array element receives RF signals, the RF signals are switched and selected by the RF switching switch, and the RF signals are sent to the signal processing unit for signal processing calculations; the calculation results are transmitted to the host computer through the Ethernet interface; the host computer is used to display the result data, including the UAV's center frequency band, bandwidth, power value, UAV model, and azimuth angle; wherein, the signal processing calculations include low-pass filtering, signal preprocessing, and algorithm analysis, and the resulting calculation results include the UAV model type and azimuth angle, as well as signal characteristic parameters.

[0007] Furthermore, the antenna array elements include a 5-element low-frequency antenna and an 8-element high-frequency antenna, and the antenna array elements are divided into two regions, A and B. Region A includes elements 1, 2, 3, and 4 of the 8-element array and elements 9, 10, and 11 of the 5-element array, which are switched by an 8-to-1 matrix switch (number 1), and the signal processing unit is responsible for data acquisition. Region B includes elements 5, 6, 7, and 8 of the 8-element array and elements 12 and 13 of the 5-element array, which are switched by an 8-to-1 matrix switch (number 2), and the signal processing unit is responsible for data acquisition. The antenna array elements in regions A and B operate independently and are selected by their respective 8-to-1 matrix switches. The antenna array elements are connected to the WiFi processing board, which uploads the data.

[0008] Furthermore, to improve system timeliness, the two AD9361 RF signal acquisition units operate independently. The signal processing unit includes an AD9361 RF signal acquisition unit, an FPGA preprocessor, and a CPU algorithm analyzer. RF signals enter the AD9361 RF signal acquisition unit, where it configures AD9361 parameters, acquires data, and preprocesses the IQ data, converting the RF signals into digital IQ signals. The AD9361 RF signal acquisition unit is connected to the FPGA preprocessor via a high-speed interface. After receiving the signal, the FPGA preprocessor performs signal judgment, classification, and preprocessing. The FPGA preprocessor is connected to the CPU algorithm analyzer via a gigabit switch, and the signal is uploaded to the CPU algorithm analyzer for data fusion and algorithm processing.

[0009] Furthermore, to improve the accuracy of identification, the algorithm processing includes a spectrum identification signal classification algorithm and a DOA direction finding estimation algorithm.

[0010] Furthermore, the data is processed by a CPU algorithm analyzer to obtain more accurate data parameters. The CPU algorithm analyzer performs data parsing, embeds algorithm API interfaces, and integrates and uploads the data. The data parsing process includes parsing electronic compass data, GPS module data, and WiFi module data, and integrating and packaging the parsed results and the calculated results for transmission to the host computer. The parsing and calculation results include azimuth data, UAV model, and signal characteristic parameters.

[0011] Furthermore, the AD9361 radio frequency signal acquisition device includes acquisition card 1 and acquisition card 2, which are used to acquire data respectively. The front end of acquisition card 1 and acquisition card 2 are connected to the radio frequency switching switch, and the rear end of acquisition card 1 and acquisition card 2 are connected to the gigabit switch.

[0012] Furthermore, the host computer is connected to the CPU motherboard, which performs data processing and data fusion; the CPU motherboard is also connected to the electronic compass and GPS module, which transmit data to the CPU motherboard for data processing and fusion; the data processed by the CPU motherboard is then transmitted to the host computer for display.

[0013] To better identify UAV type and azimuth data, a spectrum identification and direction finding method for small UAVs is proposed. The method includes:

[0014] S1: The host computer sends out the monitoring frequency band, divides it according to the frequency band strategy, calculates the bandwidth, number of segments and center frequency, and then selects the antenna working mode according to the sent monitoring frequency band.

[0015] S2: Data acquisition, setting the parameters of the AD9361 RF signal acquisition unit, including acquisition frequency, bandwidth, and sampling rate parameters, and acquiring data through polling switching of the matrix switch;

[0016] S3: Data Analysis. The data is analyzed in three paths. One path performs FFT preprocessing on the raw IQ data acquired by the AD9361 RF signal acquisition device to obtain signal characteristic parameter values, including center frequency, bandwidth, and power value. The acquired characteristic parameters are then packaged and uploaded. Another path slices, performs FFT, and calculates the amplitude of the raw IQ data according to the algorithm requirements, and participates in the spectrum identification algorithm. The last path adds digital filters to the center frequency band of the mixed IQ data with acquired signal characteristic parameters to obtain different raw IQ data, i.e., signal classification. Finally, the data is packaged and uploaded to participate in the DOA direction finding estimation algorithm.

[0017] S4: Upload the three data streams from S3 to the CPU motherboard via a gigabit switch; perform data fusion processing on the data from acquisition card 1 and acquisition card 2 in the AD9361 RF signal acquisition unit; then perform algorithm analysis, load the original IQ data to perform DOA estimation algorithm analysis, and obtain the direction finding angle; load the spectrum identification algorithm to remove WiFi signals and obtain the drone model;

[0018] S5: Integrate the angles and drone models measured in S4, as well as the drone's characteristic parameters, and upload them to the host computer for interface display.

[0019] Furthermore, the content displayed on the host computer includes a warning list display, a spectrum diagram and waterfall diagram display, a direction finding angle display, device positioning, and automatic north calibration.

[0020] Furthermore, the operating modes include operating mode 1 and operating mode 2. Operating mode 1 is a switching switch to select the high-frequency band antenna, and operating mode 2 is a switching switch to select the low-frequency band and the high-frequency band antenna.

[0021] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0022] 1. By analyzing and classifying the collected data and integrating algorithm processing techniques, the specific model of the drone can be accurately identified, and the azimuth angle data of the drone can be calculated, which facilitates accurate location of the drone and makes a judgment.

[0023] 2. The system includes antenna array elements, RF switches, data acquisition cards, a CPU processing motherboard, and a host computer. For the CPU processing motherboard's algorithms, a neural network algorithm model and a uniform circular array structure model are used for UAV model identification and azimuth direction finding, respectively. The UAV RF signal classification system based on convolutional neural networks achieves rapid and accurate classification of different RF signal types through time-frequency diagram analysis. It can extract implicit high-level features of signals from large-scale data, directly output results, and quickly identify the UAV model. This model not only significantly improves the accuracy of identification but also adapts to various complex environments and UAV types, thereby improving the system's applicability. Another direction-finding algorithm upgrades the traditional direction-finding algorithm by employing a deep learning-based DOA estimation algorithm. Through an algorithm model based on covariance matrix decomposition, it can quickly provide DOA estimates and obtain the final result, accurately providing the azimuth angle. This avoids the shortcomings of traditional algorithms that are sensitive to multipath effects, achieving high-precision direction estimation. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the overall system structure of the present invention;

[0025] Figure 2 This is a system functional framework diagram of the present invention;

[0026] Figure 3 This is a detailed structural diagram of the system of the present invention;

[0027] Figure 4 This is a functional block diagram of the CPU motherboard of the present invention;

[0028] Figure 5 The method flow of the present invention Figure 1 ;

[0029] Figure 6 The method flow of the present invention Figure 2 ; Detailed Implementation

[0030] The present invention will now be described in detail with reference to the accompanying drawings.

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0032] like Figures 1 to 4 As shown, the system includes an RF antenna array, an RF switching switch, a signal processing unit, an Ethernet interface, and a host computer. In normal operation, after the radio signal is received by the RF antenna array, it is selected by the RF switching switch and sent to the signal processing unit for signal processing. The signal processing unit mainly includes an AD9361 RF signal acquisition unit, an FPGA preprocessor, and a CPU algorithm analyzer. Finally, the processing results are transmitted to the host computer via the Ethernet interface. To ensure data accuracy, real-time data processing is required, necessitating strict control over the timeliness of both the data acquisition and processing stages.

[0033] The entire system, from top to bottom, consists of several major functional modules: antenna area, array element logic selection, data acquisition, processing, transmission, algorithm processing, data fusion, and software function display. Its functional block diagram is shown below. Figure 2 As shown:

[0034] The antenna area consists of a 5-element antenna in the low-frequency band and an 8-element antenna in the high-frequency band, and is divided into two regions, A and B.

[0035] Area A mainly consists of array elements 1, 2, 3, and 4 (8 elements) and array elements 9, 10, and 11 (5 elements). It is mainly switched by an 8-to-1 matrix switch (number 1), and data acquisition is handled by acquisition card 1.

[0036] Area B mainly consists of array elements 5, 6, 7, and 8 (8 array elements) and array elements 12 and 13 (5 array elements). It is mainly switched by an 8-to-1 matrix switch (number 2), and data acquisition is handled by acquisition card 2.

[0037] The antenna array elements in areas A and B operate independently without interference, and are selected by a dedicated matrix switch. A dedicated acquisition card is responsible for data acquisition, processing, and transmission. Finally, the signals from areas A and B are uploaded to the CPU motherboard via a gigabit switch. The CPU motherboard performs data fusion and algorithm processing, and then sends the processing results to the host computer for display.

[0038] The 2.4GHz and 5.8GHz omnidirectional antennas are connected to the WiFi processing board, which then uploads the received WiFi channel data.

[0039] like Figure 3 As shown, the signal path is from left to right. The complete equipment includes multi-element antenna elements, RF switching switch, AD9361 acquisition card, gigabit switch, CPU motherboard, and host computer software.

[0040] Its main workflow is as follows: After the radio frequency signal is received by the antenna array element, it is selected by the radio frequency switch through polling and switching, and then sent to the AD9361 acquisition card. The acquisition card performs low-pass filtering, acquisition, and digital processing to convert it into a digital IQ signal, which is then sent to the FPGA preprocessor through the high-speed interface. The FPGA preprocessor receives the signal and performs signal decision, signal classification, and signal preprocessing. The processed data is then uploaded through a gigabit switch, and the CPU motherboard participates in data fusion and algorithm processing. The algorithms mainly include spectrum identification signal classification algorithm and DOA estimation algorithm. Finally, all the calculation results are sent to the host computer through the Ethernet interface.

[0041] The auxiliary workflow also includes GPS module data parsing, electronic compass data parsing, and WiFi module board data parsing, which, along with the CPU's calculation results, are transmitted to the host computer via an Ethernet interface.

[0042] The spectrum recognition signal classification algorithm includes the following process: First, define the problem to be solved, collect data by the acquisition device, preprocess the collected data, select a model architecture, train the selected model, send the data to the model for testing and evaluation, when the performance indicators are met, deploy and test, when the performance indicators are not met, the data is re-verified and adjusted, and then tested and evaluated again until the data meets the performance indicators.

[0043] The DOA estimation algorithm includes two processes: training and identification. The data output from both processes are fed into the weight model to calculate more accurate azimuth data using the DOA estimation algorithm.

[0044] like Figure 5-6 As shown, a method for spectrum identification and direction finding of a small unmanned aerial vehicle (UAV) includes the following workflow: First, the host computer sends out monitoring frequency bands. Through frequency band strategy allocation, the bandwidth, number of bands, and center frequency are calculated. Then, the antenna's operating mode is selected based on the sent monitoring frequency bands. Operating mode 1 involves switching to select a high-frequency antenna, while operating mode 2 involves switching between low-frequency and high-frequency antennas.

[0045] Data acquisition involves setting parameters for the acquisition card, including acquisition frequency, bandwidth, and sampling rate, and then switching the matrix switch for data acquisition.

[0046] The collected data undergoes data analysis. One path preprocesses the raw IQ data acquired by the acquisition unit using FFT to obtain parameters, including center frequency, bandwidth, and power values, and then packages and uploads the collected feature parameters. Another path slices the raw IQ data according to requirements, performs FFT, and calculates amplitudes for use in the spectrum recognition algorithm. Finally, the mixed IQ data with acquired signal feature parameters is processed by adding digital filters to its center frequency band to obtain different raw IQ data, i.e., signal classification, and then packaged and uploaded for use in the DOA direction-finding estimation algorithm.

[0047] The three data streams collected above are uploaded to the CPU motherboard via a gigabit switch. The front ends of acquisition cards 1 and 2 are connected to an RF switch, and their rear ends are connected to the gigabit switch. Data fusion processing is performed on the data from acquisition cards 1 and 2. Then, algorithm analysis is performed: raw IQ data is loaded for DOA estimation algorithm analysis to obtain the direction-finding angle; a spectrum recognition algorithm is loaded to eliminate WiFi signals and obtain the drone model.

[0048] Finally, the measured angles, drone model, and characteristic parameters of the drone are integrated and uploaded to the host computer for display. This includes displaying a warning list, spectrum graphs, waterfall charts, direction-finding angles, device positioning, and automatic north calibration.

[0049] This embodiment has a detection range of up to 3 kilometers or even 5 kilometers compared to existing technologies. Moreover, this system is easy to operate, has lower costs, is more adaptable, and is easy to promote and use on a large scale.

[0050] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A spectrum identification and direction finding system for small unmanned aerial vehicles (UAVs), characterized in that, The system includes radio frequency antenna array elements, radio frequency switching switches, signal processing units, Ethernet interfaces, and a host computer; The antenna array element receives radio frequency (RF) signals, which are switched and selected by an RF switching switch. The RF signals are then sent to the signal processing unit for signal processing and calculation. The calculation results are transmitted to the host computer via an Ethernet interface. The host computer is used to display the result data, including the UAV's center frequency band, bandwidth, power value, UAV model, and azimuth angle. The signal processing operations include low-pass filtering, signal preprocessing, and algorithm analysis. The resulting operations include the UAV model and azimuth angle, as well as signal characteristic parameters. The antenna array includes a 5-element antenna for the low-frequency band and an 8-element antenna for the high-frequency band, and the antenna array is divided into two regions, A and B. Region A includes elements 1, 2, 3, and 4 of the 8-element antenna and elements 9, 10, and 11 of the 5-element antenna, which are switched by an 8-to-1 matrix switch (number 1), and the data acquisition is handled by the signal processing unit. Region B includes elements 5, 6, 7, and 8 of the 8-element antenna and elements 12 and 13 of the 5-element antenna, which are switched by an 8-to-1 matrix switch (number 2), and the data acquisition is handled by the signal processing unit. Among them, the antenna array elements in areas A and B operate independently and are selected by their respective 8-to-1 matrix switches; Furthermore, the antenna array elements are connected to the WiFi processing board, which then uploads the data. The signal processing unit includes an AD9361 RF signal acquisition unit, an FPGA preprocessor, and a CPU algorithm analyzer. RF signals enter the AD9361 RF signal acquisition unit, where it performs parameter configuration, data acquisition, and IQ data preprocessing to convert the RF signals into digital IQ signals. The AD9361 RF signal acquisition unit is connected to the FPGA preprocessor via a high-speed interface. Upon receiving the signal, the FPGA preprocessor performs signal decision, classification, and preprocessing. The FPGA preprocessor is connected to the CPU algorithm analyzer via a gigabit switch. The signal is uploaded to the CPU algorithm analyzer via the gigabit switch, where it performs data fusion and algorithm processing. The algorithm processing includes a spectrum recognition signal classification algorithm and a DOA direction finding estimation algorithm.

2. The spectrum identification and direction finding system for a small unmanned aerial vehicle (UAV) according to claim 1, characterized in that, The CPU algorithm analyzer parses and processes the data, embeds algorithm API interfaces, and integrates and uploads the data. Data parsing and processing includes parsing electronic compass data, GPS module data, and WiFi module data, and then integrating and packaging the parsed results and the calculated results before transmitting them to the host computer. The analysis and calculation results include azimuth data, UAV model, and characteristic parameters of the signal.

3. The small unmanned aerial vehicle (UAV) spectrum identification and direction finding system according to claim 1, characterized in that, The AD9361 RF signal acquisition device includes acquisition card 1 and acquisition card 2, which are used to acquire data respectively. The front end of acquisition card 1 and acquisition card 2 are connected to an RF switching switch, and the rear end of acquisition card 1 and acquisition card 2 are connected to a gigabit switch.

4. The spectrum identification and direction finding system for a small unmanned aerial vehicle (UAV) according to claim 1, characterized in that, The host computer is connected to the CPU motherboard, which performs data processing and data fusion. The CPU motherboard is also connected to the electronic compass and GPS module, which transmit data to the CPU motherboard for data processing and fusion. The data processed by the CPU motherboard is then transmitted to the host computer for display.

5. A method for spectrum identification and direction finding of a small unmanned aerial vehicle (UAV), comprising a spectrum identification and direction finding system for a small UAV according to any one of claims 1-4, characterized in that, The method includes: S1: The host computer sends out the monitoring frequency band, divides it according to the frequency band strategy, calculates the bandwidth, number of segments and center frequency, and then selects the antenna working mode according to the sent monitoring frequency band. S2: Data acquisition, setting the parameters of the AD9361 RF signal acquisition unit, including acquisition frequency, bandwidth, and sampling rate parameters, and acquiring data through polling switching of the matrix switch; S3: Data Analysis. The data is analyzed in three paths. One path performs FFT preprocessing on the raw IQ data acquired by the AD9361 RF signal acquisition device to obtain signal characteristic parameter values, including center frequency, bandwidth, and power value. The acquired characteristic parameters are then packaged and uploaded. Another path slices, performs FFT, and calculates the amplitude of the raw IQ data according to the algorithm requirements, and participates in the spectrum identification algorithm. The last path adds digital filters to the center frequency band of the mixed IQ data with acquired signal characteristic parameters to obtain different raw IQ data, i.e., signal classification. Finally, the data is packaged and uploaded to participate in the DOA direction finding estimation algorithm. S4: Upload the three data streams from S3 to the CPU motherboard via a gigabit switch; perform data fusion processing on the data from acquisition card 1 and acquisition card 2 in the AD9361 RF signal acquisition unit; then perform algorithm analysis, load the original IQ data to perform DOA estimation algorithm analysis, and obtain the direction finding angle; load the spectrum identification algorithm to remove WiFi signals and obtain the drone model; S5: Integrate the angles and drone models measured in S4, as well as the drone's characteristic parameters, and upload them to the host computer for interface display.

6. The method for spectrum identification and direction finding of a small unmanned aerial vehicle (UAV) according to claim 5, characterized in that, The content displayed on the host computer includes a warning list, a spectrum diagram, a waterfall diagram, a direction finding angle, device positioning, and automatic north calibration.

7. The method for spectrum identification and direction finding of a small unmanned aerial vehicle (UAV) according to claim 6, characterized in that, The operating modes include operating mode 1 and operating mode 2. Operating mode 1 is a switch that selects the high-frequency band antenna, and operating mode 2 is a switch that selects the low-frequency band and the high-frequency band antenna.

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