A method for identifying drone types

Abstract

The present invention discloses a method for identifying the type of drone, comprising: generating a time-frequency diagram template of an uplink signal of a drone to be identified based on theoretical data; scanning the signal of the drone to be detected based on preset parameters to obtain scanning data; generating a time-frequency diagram based on the scanning data, performing image processing on the time-frequency diagram to obtain a processed time-frequency diagram; and comparing the processed time-frequency diagram with the uplink signal time-frequency diagram template to obtain the type of the drone to be detected, which can significantly improve the accuracy of drone type identification.

Description

Technical Field

[0001] The present invention relates to the field of drone identification technology, and more particularly to a drone type identification method and system. Background Art

[0002] Accurately identifying and detecting drone signals is a prerequisite for drone monitoring. Existing frequency-hopping signal detection technologies primarily include methods based on power spectrum cancellation, multi-hop autocorrelation, and multi-window overlapping spectrograms. These non-blind detection algorithms require certain prior knowledge, making them quite limited in practical applications. In recent years, a growing number of blind frequency-hopping signal detection algorithms based on image processing have been proposed. Radio management departments can utilize existing monitoring technologies to achieve a certain degree of identification and tracking of drone signals. However, existing radio detection technologies are generally unable to automatically identify drone models, resulting in poor detection results and high costs.

[0003] Therefore, a better drone type recognition method is needed. Summary of the Invention

[0004] One aspect of an embodiment of the present specification provides a method for identifying the type of drone, including: generating a time-frequency diagram template of an uplink signal of a drone to be identified based on theoretical data; scanning the signal of the drone to be detected based on preset parameters to obtain scanning data; generating a time-frequency diagram based on the scanning data, performing image processing on the time-frequency diagram to obtain a processed time-frequency diagram; and comparing the processed time-frequency diagram with the uplink signal time-frequency diagram template to derive the type of the drone to be detected.

[0005] In some embodiments, the generating of the uplink signal time-frequency diagram template of the drone to be identified based on theoretical data includes: generating the corresponding uplink signal time-frequency diagram template based on the distribution pattern of the uplink frequency hopping signal points generated by the different remote-controlled drone models to be identified in the picture, and the characteristics of the image formed thereby, and finally obtaining the drone visual dictionary.

[0006] In some embodiments, the distribution pattern of the uplink frequency hopping signal points generated by different remote control models of the drones that need to be identified in the picture, and the characteristics of the images formed thereby are obtained by the following method: under preset environmental conditions, spectrum data of at least two cycles of multiple types of drones that need to be identified are collected multiple times, and the distribution pattern of the uplink frequency hopping signal points in the picture, and the characteristics of the images formed thereby are determined based on the spectrum data.

[0007] In some embodiments, the generating of a time-frequency graph based on the scanning data and performing image processing on the time-frequency graph to obtain the processed time-frequency graph include: generating a three-dimensional spectrum graph based on the scanning data; performing noise reduction processing on the three-dimensional spectrum graph to obtain a noise-reduced image; extracting the edge contours of effective signal points from the noise-reduced image; performing transformation processing on the extracted data to obtain a signal distribution vector direction; performing morphological processing and binarization processing on the signal distribution vector direction to restore the signal lost in part of the processing and obtain a binarized time-frequency graph.

[0008] In some embodiments, generating a time-frequency graph based on the scan data and performing image processing on the time-frequency graph to obtain a processed time-frequency graph includes: generating a three-dimensional spectrum graph based on the scan data; performing noise reduction processing on the three-dimensional spectrum graph to obtain a noise-reduced image; binarizing the noise-reduced image to obtain a binary time-frequency graph; finding a connected domain and obtaining a cell signal graph based on the binarized time-frequency graph; removing cells in the cell signal graph that do not conform to the frequency modulation signal to obtain a corrected time-frequency graph; and calculating the vector distribution and Manhattan distance between cells in the corrected time-frequency graph.

[0009] In some embodiments, comparing the processed time-frequency graph with the uplink signal time-frequency graph template to derive the type of the drone to be detected includes: calculating the similarity between the time-frequency graph and the uplink signal time-frequency graph template of each type of drone in the drone visual dictionary, and taking the drone model with the greatest similarity as the recognition result.

[0010] One aspect of an embodiment of the present specification provides a drone type identification system, characterized in that it includes: a template generation module, which is used to generate an uplink signal time-frequency diagram template of the drone to be identified based on theoretical data; a signal acquisition module, which is used to scan the signal of the drone to be detected based on preset parameters and obtain scanning data; an image processing module, which is used to generate a time-frequency diagram based on the scanning data, and perform image processing on the time-frequency diagram to obtain a processed time-frequency diagram; and a type matching module, which is used to compare the processed time-frequency diagram with the uplink signal time-frequency diagram template to obtain the type of the drone to be detected.

[0011] In some embodiments, the template generation module is further used to generate a corresponding uplink signal time-frequency diagram template based on the distribution pattern of uplink frequency hopping signal points generated by different remote-controlled drone models that need to be identified in the picture, as well as the characteristics of the images formed thereby, and ultimately obtain a drone visual dictionary.

[0012] One aspect of an embodiment of the present specification provides a drone type identification device, the device including a processor and a memory; the memory is used to store instructions, and when the instructions are executed by the processor, the device causes the device to implement operations corresponding to the drone type identification method.

[0013] One aspect of an embodiment of this specification provides a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer runs the drone type identification method. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, the same numbers represent the same structures, wherein:

[0015] Figure 1 This is a schematic diagram of an application scenario of drone type identification according to some embodiments of the present application;

[0016] Figure 2 is a schematic diagram of exemplary hardware and / or software components of an exemplary computing device on which a processing engine may be implemented according to some embodiments of the present application;

[0017] Figure 3 is a schematic diagram of exemplary hardware and / or software components of an exemplary mobile device on which one or more terminals may be implemented according to some embodiments of the present application;

[0018] Figure 4 is a schematic block diagram of an exemplary processing engine according to some embodiments of the present application;

[0019] Figure 5 is a flowchart of a method for identifying drone types according to some embodiments of the present application;

[0020] Figure 6 is a schematic diagram of a time-frequency graph processing flow according to some embodiments of the present application;

[0021] Figure 7 is a schematic diagram of a time-frequency graph processing flow according to some embodiments of the present application;

[0022] Figure 8 This is a DJI Phantom 3 uplink signal time-frequency diagram template according to some embodiments of the present application;

[0023] Figure 9 This is a DJI Phantom 4 uplink signal time-frequency diagram template according to some embodiments of the present application;

[0024] Figure 10 This is a DJI Phantom 4 Pro uplink signal time-frequency diagram template according to some embodiments of the present application;

[0025] Figure 11 This is a diagram showing the effect of matching the collected signals of a single DJI Phantom 3 drone according to some embodiments of the present application;

[0026] Figure 12 This is a diagram showing the effect of matching the collected signals of a single DJI Phantom 4 drone according to some embodiments of the present application;

[0027] Figure 13 This is a diagram showing the effect of matching the collected signals of a single DJI drone Phantom 4 Pro according to some embodiments of the present application;

[0028] Figure 14 This is a diagram showing the effect of matching the collected uplink remote control signals of DJI Phantom 3 and 4 drones according to some embodiments of the present application;

[0029] Figure 15 This is a diagram showing the effect of matching the collected uplink remote control signals of the DJI Phantom 3 and 4 Pro drones according to some embodiments of the present application;

[0030] Figure 16 This figure shows the effect of matching the collected uplink remote control signals of the DJI drone Phantom 4 and 4 Pro according to some embodiments of the present application. DETAILED DESCRIPTION

[0031] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0032] It should be understood that the terms "system," "device," "unit," and / or "module" used in this specification are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0033] As used in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but also include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0034] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0035] The embodiments of this application propose a method for identifying drone types. The principles of the methods of the embodiments of this application can be applied to the detection of various drone signals. It should be understood that the application scenarios of the system and method of this application are merely examples or embodiments of this application. For those of ordinary skill in the art, without inventive effort, this application can also be applied to other similar scenarios based on these figures.

[0036] Figure 1 1 is a schematic diagram of an application scenario for a drone type identification method according to some embodiments of the present application. In some embodiments, application scenario 100 may include a server 110, a network 120, a user terminal 130, a storage device 140, and a signal source 150. Server 110 may include a processing engine 112. In some embodiments, server 110, user terminal 130, storage device 140, and signal source 150 may be connected and / or communicate with each other via a wireless connection (e.g., network 120), a wired connection, or a combination thereof.

[0037] The server 110 refers to a system with computing capabilities. In some embodiments, the server 110 can be a single server or a server group. The server group can be centralized or distributed (for example, the server 110 can be a distributed system). In some embodiments, the server 110 can be local or remote. For example, the server 110 can access information and / or data stored in the user terminal 130 and / or the storage device 140 via the network 120. For another example, the server 110 can be directly connected to the user terminal 130 and / or the storage device 140 to access the stored information and / or data. In some embodiments, the server 110 can be implemented on a cloud platform. By way of example only, the cloud platform can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, etc., or any combination thereof.

[0038] Server 110 can be used to detect drone signals and determine the drone type based on the acquired signals. In some embodiments, server 110 may include a processing engine 112. Processing engine 112 can process information and / or data related to drone signals. For example, processing engine 112 can acquire the detected signal from signal source 150. In some embodiments, processing engine 112 may include one or more processing engines (e.g., a single-core processing engine or a multi-core processor). By way of example only, processing engine 112 may include one or more hardware processors, such as a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction set processor (ASIP), a graphics processing unit (GPU), a physical processing unit (PPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), a microprocessor, or any combination thereof. In some embodiments, processing engine 112 may integrate the drone type identification method disclosed in this embodiment to determine the model of the drone emitting the signal.

[0039] Network 120 can facilitate the exchange of information and / or data related to the drone type identification method. In some embodiments, one or more components in application scenario 100 (e.g., server 110, user terminal 130, storage device 140, and signal source 150) can transmit information and / or data to other components in application scenario 100 via network 120. For example, processing engine 112 can transmit information related to detected drone signals to user terminal 130 via network 120. In some embodiments, network 120 can be a wired network, a wireless network, or any combination thereof. By way of example only, network 120 can include a cable network, a wired network, a fiber optic network, a telecommunications network, an intranet, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a wide area network (WAN), a public switched telephone network (PSTN), a Bluetooth™ network, a ZigBee network, a near-field communication (NFC) network, or the like, or any combination thereof. In some embodiments, network 120 can include one or more network access points. For example, the network 120 may include wired or wireless network access points such as base stations and / or Internet exchange points 120-1, 120-2, etc., and one or more components of the application scenario 100 may be connected to the network 120 via the wired or wireless network access points to exchange data and / or information.

[0040] User terminal 130 may include a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, a desktop computer 130-4, or the like, or any combination thereof. In some embodiments, mobile device 130-1 may include a smart home device, a wearable device, a mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, smart home devices may include smart lighting devices, smart appliance control devices, smart monitoring devices, smart televisions, smart cameras, intercoms, or the like, or any combination thereof. In some embodiments, wearable devices may include wristbands, shoes, glasses, helmets, watches, clothing, backpacks, smart accessories, or the like, or any combination thereof. In some embodiments, mobile devices may include mobile phones, personal digital assistants (PDAs), gaming devices, navigation devices, point-of-sale (POS) devices, laptop computers, desktop computers, or the like, or any combination thereof. In some embodiments, virtual reality devices and / or augmented virtual reality devices may include virtual reality helmets, virtual reality glasses, virtual reality goggles, augmented reality helmets, augmented reality glasses, augmented reality goggles, or the like, or any combination thereof. For example, virtual reality devices and / or augmented reality devices may include Google Glass™, RiftCon™, Fragments™, GearVR™, or the like. In some embodiments, user terminal 130 may be part of processing engine 112 .

[0041] In some embodiments, user terminal 130 may be a mobile terminal configured to collect drone signals emitted by signal source 150. User terminal 130 may send and / or receive information related to positioning signal identification to processing engine 112 or a processor installed in user terminal 130 via a user interface. For example, user terminal 130 may transmit drone signal data captured by user terminal 130 to processing engine 112 or a processor installed in user terminal 120 via a user interface. The user interface may be in the form of an application implemented on user terminal 130 for identifying drone signals. The user interface implemented on user terminal 130 may facilitate communication between the user and processing engine 112. For example, a user may input and / or import signal data to be identified via the user interface. Processing engine 112 may receive the input signal data via the user interface. For another example, a user may input a request to identify a drone signal via the user interface implemented on user terminal 130. In some embodiments, in response to the identification request, user terminal 130 may directly process the drone signal data via the processor of user terminal 130 based on a signal acquisition device installed in user terminal 130 as described elsewhere in this application. In some embodiments, in response to the identification request, user terminal 130 may send an identification request to processing engine 112 for enabling drone signal collection based on the signal collection device. In some embodiments, a user interface may facilitate the presentation or display of information and / or data (e.g., signals) related to drone signal detection received from processing engine 112. For example, the information and / or data may include a result indicating the content of the drone signal detection, or location information corresponding to the detected drone signal. In some embodiments, the information and / or data may be further configured to enable user terminal 130 to display the identification result to the user.

[0042] The storage device 140 can store data and / or instructions. In some embodiments, the storage device 140 can store data obtained from the signal source 150. In some embodiments, the storage device 140 can store data and / or instructions that the processing engine 112 can execute or use to execute the exemplary methods described herein. In some embodiments, the storage device 140 can include a mass storage device, a removable storage device, a volatile read-write memory, a read-only memory (ROM), or the like, or any combination thereof. Exemplary mass storage devices can include magnetic disks, optical disks, solid-state drives, or the like. Exemplary removable storage devices can include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tapes, or the like. Exemplary volatile read-write memory devices can include random access memory (RAM). Exemplary RAM devices can include dynamic random access memory (DRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), static random access memory (SRAM), thyristor random access memory (T-RAM), and zero-capacitance random access memory (Z-RAM). Exemplary ROMs may include mask read-only memory (MROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), compact disk read-only memory (CD-ROM), and digital versatile disk read-only memory. In some embodiments, the storage device 140 may be executed on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, or any combination thereof.

[0043] In some embodiments, the storage device 140 can be connected to the network 120 to communicate with one or more components in the application scenario 100 (e.g., the server 110, the user terminal 130). One or more components in the application scenario 100 can access data or instructions stored in the storage device 140 via the network 120. In some embodiments, the storage device 140 can be directly connected to or communicate with one or more components in the application scenario 100 (e.g., the server 110, the user terminal 130). In some embodiments, the storage device 140 can be part of the server 110.

[0044] Signal source 150 refers to an unmanned aerial vehicle (UAV) that emits radio wave signals. In some embodiments, signal source 150 can be implemented by various types of UAVs. Unmanned aerial vehicles (UAVs) are unmanned aircraft controlled by a radio remote control device and a self-contained program control device, or operated completely or intermittently autonomously by an onboard computer. In some embodiments, corresponding signals emitted by multiple different types of UAVs can be collected simultaneously. After the signals are received by corresponding receiving devices, the type of UAV can be identified using the UAV type identification method within processing engine 112.

[0045] It should be noted that the above description is intended to be illustrative, rather than limiting the scope of the application. For those skilled in the art, many substitutions, modifications and variations will be apparent. The features, structures, methods and other characteristics of the exemplary embodiments described herein can be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, the signal source can be configured with a storage module, a processing module, a communication module, etc. However, these variations and modifications do not depart from the scope of the application.

[0046] Figure 2 FIG. 1 is a diagram of exemplary hardware and / or software components of an exemplary computing device on which a processing engine may be implemented according to some embodiments of the present application. Figure 2 As shown, computing device 200 may include processor 210 , memory 220 , input / output (I / O) 230 , and communication port 240 .

[0047] The processor 210 (e.g., a logic circuit) can execute computer instructions (e.g., program code) and perform the functions of the processing engine 112 according to the techniques described herein. In some embodiments, the processor 210 can be configured to process data and / or information related to one or more components of the application scenario 100. For example, the processor 210 can generate a time-frequency graph based on the signal data obtained from the information source 150. For another example, the processor 210 can determine the regularity of each type of drone signal based on the characteristics of each type of drone signal data. The processor 210 can also be configured to obtain a time-frequency graph template of the uplink signal of the drone to be identified based on theoretical data. The processor 210 can also send the identification information or determination result to the server 110. In some embodiments, the processor 210 can send a notification to the associated user terminal 130.

[0048] In some embodiments, the processor 210 may include an interface circuit 210-a and a processing circuit 210-b. The interface circuit may be configured to receive data from the bus ( Figure 2 The interface circuit (not shown) receives electrical signals that encode structured data and / or instructions for processing by the processing circuit. The processing circuit can perform logical calculations and then encode the conclusions, results, and / or instructions into electrical signals. The interface circuit can then transmit the electrical signals from the processing circuit via the bus.

[0049] Computer instructions may include, for example, routines, programs, objects, components, data structures, processes, modules, and functions that perform the specific functions described herein. For example, the processor 210 may process information related to drone signals obtained from the user terminal 130, the storage device 140, and / or any other component of the application scenario 100. In some embodiments, the processor 210 may include one or more hardware processors, such as a microcontroller, a microprocessor, a reduced instruction set computer (RISC), an application-specific integrated circuit (ASIC), an application-specific instruction set processor (ASIP), a central processing unit (CPU), a graphics processing unit (GPU), a physical processing unit (PPU), a microcontroller, a digital signal processor (DSP), a field programmable gate array (FPGA), an advanced RISC machine (ARM), a programmable logic device (PLD), any circuit or processor capable of performing one or more functions, or any combination thereof.

[0050] For illustrative purposes only, only one processor is described in computing device 200. However, it should be noted that computing device 200 in this application may also include multiple processors. Therefore, operations and / or method steps described in this application as being performed by one processor may also be performed jointly or separately by multiple processors. For example, if in this application, a processor of computing device 200 performs step A and step B simultaneously, it should be understood that step A and step B may also be performed jointly or separately by two or more different processors in computing device 200 (e.g., a first processor performs step A, a second processor performs step B, or the first processor and the second processor perform steps A and B together).

[0051] Memory 220 can store data / information obtained from user terminal 130, storage device 140, and / or any other component of application scenario 100. In some embodiments, memory 220 may include a mass storage device, a removable storage device, a volatile read-write memory, a read-only memory (ROM), or any combination thereof. For example, a mass storage device may include a magnetic disk, an optical disk, a solid-state drive, or the like. Removable storage devices may include flash memory, a floppy disk, an optical disk, a memory card, a zip disk, a magnetic tape, or the like. Volatile read-write memory may include random access memory (RAM). RAM may include dynamic RAM (DRAM), double data rate synchronous dynamic RAM (DDRSDRAM), static RAM (SRAM), thyristor RAM (T-RAM), and zero capacitor RAM (Z-RAM). ROM may include mask ROM (MROM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), compact disk ROM (CD-ROM), and digital versatile disk ROM. In some embodiments, memory 220 may store one or more programs and / or instructions for executing the exemplary methods described herein. For example, the memory 220 may store a program for the processing engine 112 to determine the type of drone.

[0052] I / O 230 can input and / or output signals, data, information, etc. In some embodiments, I / O 230 can enable a user to interact with processing engine 112. In some embodiments, I / O 230 can include input devices and output devices. Examples of input devices can include a keyboard, a mouse, a touch screen, a microphone, etc., or a combination thereof. Examples of output devices can include a display device, a speaker, a printer, a projector, etc., or a combination thereof. Examples of display devices can include a liquid crystal display (LCD), a display based on a light emitting diode (LED), a flat panel display, a curved screen, a television device, a cathode ray tube (CRT), a touch screen screen, etc., or any combination thereof.

[0053] The communication port 240 can be connected to a network (e.g., network 120) to facilitate data communication. The communication port 240 can establish a connection between the processing engine 112 and the user terminal 130, the information source 150, or the storage device 140. The connection can be a wired connection, a wireless connection, any other communication connection that can realize data transmission and / or reception, and / or any combination of these connections. The wired connection can include, for example, an electric cable, an optical cable, a telephone line, etc., or any combination thereof. The wireless connection can include, for example, a Bluetooth™ link, a Wi-Fi™ link, a WiMax™ link, a WLAN link, a ZigBee link, a mobile network link (e.g., 3G, 4G, 5G), etc., or any combination thereof. In some embodiments, the communication port 240 can be and / or include a standardized communication port, such as RS232, RS485, etc.

[0054] Figure 3 is a schematic diagram of exemplary hardware and / or software components of an exemplary mobile device on which a user terminal can be implemented according to some embodiments of the present application. In some embodiments, Figure 3 The mobile device 300 shown can be used by a user. The user can be a relevant monitoring personnel, such as a drone supervisor near an airport.

[0055] like Figure 3 As shown, mobile device 300 may include a communication platform 310, a display 320, a graphics processing unit (GPU) 330, a central processing unit (CPU) 340, an I / O 350, memory 360, and storage 390. In some embodiments, any other suitable components, including but not limited to a system bus or controller (not shown), may also be included in mobile device 300. In some embodiments, a mobile operating system 370 (e.g., iOS™, Android™, Windows Phone™) and one or more applications 380 may be loaded from storage 390 into memory 360 for execution by CPU 340. Application 380 may include a browser or any other suitable mobile application for receiving and rendering information related to image processing or other information from processing engine 112. User interaction with the information stream may be enabled via I / O 350 and provided to processing engine 112 and / or other components of application scenario 100 via network 120.

[0056] To implement the various modules, units, and their functions described herein, a computer hardware platform may be used as the hardware platform for one or more of the components described herein. A computer with user interface elements may be used to implement a personal computer (PC) or any other type of workstation or terminal device. If the computer is appropriately programmed, the computer may also be used as a server.

[0057] Those skilled in the art will appreciate that when an element of the application scenario 100 is executed, the element may be executed via electrical signals and / or electromagnetic signals. For example, when the processing engine 112 processes a task such as making a determination or identifying information, the processing engine 112 may operate the logic circuits in its processor to process the task. When the processing engine 112 sends data (e.g., the frequency of a signal to be detected and analyzed) to the user terminal 130, the processor of the processing engine 112 may generate an electrical signal that encodes the data. The processor of the processing engine 112 may then send the electrical signal to an output port. If the user terminal 130 communicates with the processing engine 112 via a wired network, the output port may be physically connected to a cable that further transmits the electrical signal to an input port of the server 110. If the user terminal 130 communicates with the processing engine 112 via a wireless network, the output port of the processing engine 112 may be one or more antennas that convert the electrical signal into an electromagnetic signal. In electronic devices such as user terminal 130 and / or server 110, when its processor processes instructions, issues instructions, and / or performs actions, the instructions and / or actions are performed via electrical signals. For example, when the processor retrieves or saves data from a storage medium (e.g., storage device 140), it can send electrical signals to a read / write device of the storage medium, which can read or write structured data in the storage medium. The structured data can be transmitted to the processor in the form of electrical signals via a bus of the electronic device. Here, the electrical signal can refer to an electrical signal, a series of electrical signals, and / or one or more discrete electrical signals.

[0058] Figure 4 is a schematic block diagram of an exemplary processing engine according to some embodiments of the present application.

[0059] like Figure 4 As shown, in some embodiments, the processing engine 112 may include a template generation module 410, a signal acquisition module 420, an image processing module 430, and a type matching module 440. The processing engine 110 may be implemented on various components (e.g., Figure 2 For example, at least a portion of the processing engine 110 may be implemented in a processor 210 of the computing device 200 as shown. Figure 2 The computing device shown or Figure 3 is implemented on the mobile device shown.

[0060] The template generation module 410 can be used to generate a time-frequency diagram template of the uplink signal of the drone to be identified based on theoretical data.

[0061] The signal acquisition module 420 can be used to scan the signal of the drone to be detected based on preset parameters and obtain scanning data.

[0062] The image processing module 430 may be configured to generate a time-frequency diagram based on the scan data, and perform image processing on the time-frequency diagram to obtain a processed time-frequency diagram;

[0063] The type matching module 440 can be used to compare the processed time-frequency graph with the uplink signal time-frequency graph template to obtain the type of the drone to be detected.

[0064] The modules in the processing engine 112 can be connected to each other or communicate with each other via a wired connection or a wireless connection. The wired connection can include a metal cable, an optical cable, a hybrid cable, etc. or any combination thereof. The wireless connection can include a local area network (LAN), a wide area network (WAN), Bluetooth, a ZigBee network, a near field communication (NFC), etc. or any combination thereof. Two or more modules can be combined into one module, and any one module can be split into two or more units. For example, the template generation module 410 can be integrated into the signal acquisition module 420 as a single module, and the single module can identify the mobile terminal and the target associated with the mobile terminal.

[0065] It should be understood that Figure 4 The system and its modules shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated hardware. Those skilled in the art will understand that the above-mentioned methods and systems can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the system and its modules of this specification. Not only can the hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc. be implemented, they can also be implemented using software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software (for example, firmware).

[0066] It should be noted that the above description of the processing engine and its modules is for convenience only and does not limit this specification to the scope of the embodiments. It is understandable that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the modules or form subsystems connected with other modules without deviating from the principles. For example, Figure 4The image processing module and the type matching module can be separate modules within a single system, or a single module can implement the functions of both. For another example, the modules within the processing engine can share a single storage module, or each module can have its own storage module. Such variations are within the scope of this specification.

[0067] like Figure 5 FIG. 1 is a flow chart of a method for identifying a drone type according to some embodiments of the present application. In some embodiments, Figure 5 The process 500 shown may be performed at Figure 1 For example, the process 500 may be stored in a storage medium (e.g., storage device 140 or memory 220 of computing device 200) as instructions and executed by a processor (e.g., storage device 140), a processing engine 112 of a server 110, a processor of computing device 200 or a processor of computing device 200. Figure 4 The operations of the illustrated process 500 presented below are intended to be illustrative. In some embodiments, the process 500 may be accomplished with one or more additional operations not described and / or without one or more operations discussed. Additionally, Figure 5 The order in which the operations of process 500 are illustrated and described below is not intended to be limiting.

[0068] Process 500 includes the following steps:

[0069] Step 510 : Generate a time-frequency diagram template of the uplink signal of the drone to be identified based on the theoretical data. Specifically, this step can be performed by the template generation module 410 .

[0070] In some embodiments, the signals of the type of drone that one wants to monitor can be collected and analyzed in advance, and a corresponding uplink signal time-frequency diagram template can be generated to compare the subsequently collected signals to identify whether the collected drone signals come from the type of drone that needs to be monitored.

[0071] Currently, the primary communication frequency band for drones, 2.4 GHz, belongs to the ISM open band. Communication in this band requires no license, but only compliance with certain communication standards. Consequently, the electromagnetic environment in this band is highly complex, with WiFi, wireless mice, microwave ovens, Bluetooth, and other devices present. To avoid signal interference, frequency-hopping communication technology is commonly used for uplink remote control signals in commercial drones.

[0072] DJI drones dominate the domestic market, with their remote control uplink operating frequencies primarily ranging from 2.4 to 2.483 GHz. Drone frequency hopping signals are characterized by stable hopping rates, stable frequency hopping bandwidth, stable leveling periods, stable hopping frequency sequences, and regularity in the frequency hopping signal clusters. This paper primarily identifies drone types based on these characteristics of their uplink signals. For example, based on the distribution patterns of uplink frequency hopping signal points generated by different DJI drone remote control models in an image and the characteristics of their image formation, a drone visual dictionary is generated. This dictionary is then used as a template in the drone identification algorithm framework to evaluate and match collected drone remote control uplink signals for the final matching test.

[0073] Taking DJI drones as an example, we investigated the uplink signal transmission characteristics of three DJI drone models (Phantom 3, Phantom 4, and Mavic 2). The 2400MHz to 2490MHz channel setting for these drones is 2401:2:2481, with a starting frequency of 2401MHz, a cutoff frequency of 2481MHz, and a channel bandwidth of 2MHz. DJI drones' uplink signals use frequency hopping technology, and while the signal bandwidth and hopping time are nearly identical, their hopping frequencies and hopping sequences differ.

[0074] Take the DJI Phantom 3 and Phantom 4 drones as examples. Figure 8 This is a time-frequency diagram template of the uplink signal of the DJI Phantom 3 shown in some embodiments of the present application; the frequency hopping sequence of the Phantom 3 is as follows Figure 8 As shown, the channel can be expressed as 40->20->39->19...->21->1, a total of 40 frequency hopping points, each frequency hopping point has a bandwidth of 2MHz, each frequency hopping point appears for about 2.167ms, a single frequency hopping time is about 11.868ms, and the total frequency hopping cycle time is about 11.868*40=474.72ms. The Phantom 4 frequency hopping sequence pattern can be seen in Figure 9 , which can be expressed in terms of channels as 35->34->33->…->3->2, with a total of 34 frequency hopping points. The bandwidth of each frequency hopping point is 2MHz, the duration of each frequency hopping point is approximately 2.167ms, the single frequency hopping time is approximately 11.868ms, and the total frequency hopping cycle time is approximately 11.868*34=403.512ms.

[0075] In a clean, noise-free and unobstructed environment (such as a shielded room), we collected spectrum data of several types of drones for more than two cycles and generated a drone visual dictionary based on the distribution pattern of their frequency hopping signal points in the image and the characteristics of their image formation. This dictionary serves as a template for evaluating and matching the subsequently collected drone signals. Figure 9 This is a DJI Phantom 4 uplink signal time-frequency diagram template according to some embodiments of the present application. Figure 10This is a template for the DJI Phantom 4 Pro uplink signal time-frequency diagram according to some embodiments of this application. The DJI Phantom 3 remote control model is GL300B, the DJI Phantom 4 remote control model is GL300F, and the DJI Phantom 4 Pro remote control model is GL300E.

[0076] Step 520 : Scan the signal of the drone to be detected based on the preset parameters to obtain scanning data. Specifically, this step can be performed by the signal acquisition module 420 .

[0077] In some embodiments, the MR3300A receiver's frequency band scanning function can be used to scan the 2400MHz-2500MHz frequency band in 100kHz steps, and the scanned data can be saved. For example, in a clean, noise-free environment, the MR3300A receiver can be used to collect spectrum data from a Phantom 3 in the 2.4GHz-2.48GHz (80MHz) frequency band with 100kHz steps (the sampled data should contain more than two cycles of valid data, and the signal-to-noise ratio should be above 40dB). For another example, in a clean, low-noise, unobstructed, and free of other strong interfering signals, continuous, valid spectrum data can be collected for at least five cycles of visible drones (the maximum cycle of the tested drone type is the sampling period). The frequency range is 2.4GHz-2.5GHz (minimum frequency range 2.4GHz-2.48GHz), with a 100kHz step and a signal-to-noise ratio (SNR) of 25-30dB or above.

[0078] Step 530 : Generate a time-frequency graph based on the scan data, and perform image processing on the time-frequency graph to obtain a processed time-frequency graph. Specifically, this step can be performed by the image processing module 430 .

[0079] In some embodiments, in order to better perform image processing on the time-frequency graph and obtain a processed time-frequency graph that is easier to compare, there are multiple processing schemes for processing the time-frequency graph.

[0080] like Figure 6 FIG. 6 is a schematic diagram of a time-frequency image processing flow according to some embodiments of the present application. In some embodiments, the specific process of image processing 600 is as follows:

[0081] Step 610: Generate a three-dimensional spectrum diagram based on the scan data.

[0082] Step 620: performing noise reduction processing on the three-dimensional spectrum image to obtain a noise-reduced image;

[0083] Step 630: extracting effective signal point edge contours from the noise-reduced image;

[0084] Step 640, transforming the extracted data to obtain a signal distribution vector direction;

[0085] Step 650: Perform morphological processing and binarization processing on the signal distribution vector direction to restore the signal lost in the partial processing and obtain a binarized time-frequency graph.

[0086] like Figure 7 FIG. 7 is a schematic diagram of a time-frequency image processing flow according to some embodiments of the present application. In some embodiments, the specific process of image processing 700 is as follows:

[0087] Step 710, generating a three-dimensional spectrum diagram based on the scan data;

[0088] Step 720: performing noise reduction processing on the three-dimensional spectrum image to obtain a noise-reduced image;

[0089] Step 730: binarize the denoised image to obtain a binarized time-frequency graph;

[0090] Step 740: Find a connected domain and obtain a cell signal map based on the binarized time-frequency map;

[0091] Step 750, removing cells that do not conform to the frequency modulation signal in the cell signal map to obtain a corrected time-frequency map;

[0092] Step 760: Calculate the vector distribution and Manhattan distance between cells in the corrected time-frequency graph.

[0093] Image processing 600 is more suitable for signal processing of DJI Phantom 3 and DJI Phantom 4, while image processing 700 is more suitable for signal processing of DJI Phantom 4 Pro.

[0094] In step 540 , the processed time-frequency graph is compared with the uplink signal time-frequency graph template to determine the type of the drone to be detected. Specifically, this step can be performed by the type matching module 440 .

[0095] In some embodiments, the three-dimensional spectrogram can be denoised using a total variation adaptive TV model. The total variation adaptive TV model is an anisotropic model that relies on gradient descent to smooth images. It aims to smooth the image as much as possible within the image (where the difference between adjacent pixels is small) while minimizing smoothing at image edges (image contours). This allows for edge preservation while smoothing noise.

[0096] In some embodiments, if it is necessary to extract features, such as extracting the edge contours of effective signal points from the denoised image, this can be achieved through the Hough Transform. The Hough Transform is a feature extraction technology in image processing that detects objects with specific shapes through a voting algorithm. This process calculates the local maximum of the cumulative results in a parameter space to obtain a set that conforms to the specific shape as the Hough Transform result. The Hough Transform transforms a given curve in the image space into a point in the parameter space according to the parameter expression of the curve, and then achieves the purpose of finding the curve in the image space by finding the peak in the parameter space. The Hough Transform can be used to find straight lines in an image.

[0097] In some embodiments, edge contour detection can be implemented based on the Prewitt operator. For example, before extracting the edge contours of valid signal points in the denoised image, edge contour detection needs to be performed first. This can be achieved using the Prewitt operator. The Prewitt operator is a first-order differential operator for edge detection. It uses the grayscale difference between the upper and lower, left and right neighbors of a pixel to detect the edge at the extreme value, remove some pseudo-edges, and smooth noise. This is achieved by performing neighborhood convolution with the image in image space using two directional templates: one for detecting horizontal edges and the other for detecting vertical edges.

[0098] In some embodiments, the morphological processing in the above process can be implemented based on mathematical morphological processing of image dilation. Image dilation refers to the process of analyzing the geometric structure of an object, which is to bring the subject and the object closer to each other. The principle of dilation is to merge all background points that are in contact with the object into the object, and expand the boundary outward, which can be used to fill the holes in the object. The dilation method is to compare the center point of the image with the points around it one by one. If the operation conditions are met, the color will be added around the points in the original image.

[0099] In some embodiments, the comparison of the processed time-frequency graph with the uplink signal time-frequency graph template can be achieved based on binary image matching, specifically by calculating the connected domain through the binary image, finding the intersecting area in the image, and calculating the similarity of the matched images.

[0100] like Figure 11The following figure demonstrates the matching results of a single DJI Phantom 3 signal collected using this solution. As can be seen, when only the uplink signal time-frequency diagram of the Phantom 3 was collected, the Phantom 3 signal was identified in all 10 matches, with a match rate of over 70% with the Phantom 3 time-frequency dictionary each time, successfully identifying the uplink signal as coming from the Phantom 3. However, the match rate with the dictionary for the other two drones was very low, less than 10%, resulting in a relatively good recognition result.

[0101] like Figure 12 This figure demonstrates the matching results of a single DJI Phantom 4 signal collected using this solution. As can be seen, when only the uplink signal time-frequency diagram of the Phantom 4 was collected, the Phantom 4 signal was identified in all 10 matches, with a match rate of over 70% with the Phantom 4 time-frequency dictionary each time, successfully identifying the uplink signal as coming from the Phantom 4. However, the match rate with the dictionary for the other two drones was very low, less than 10%, resulting in a relatively good recognition result.

[0102] like Figure 13 The following figure demonstrates the matching results of a single DJI Phantom 4 Pro signal collected using this solution. As can be seen, when only the uplink signal time-frequency diagram of the DJI Phantom 4 Pro was collected, the DJI Phantom 4 Pro signal was identified in all 10 matches, with a match rate of over 70% with the DJI Phantom 4 Pro time-frequency dictionary each time, successfully identifying the uplink signal as coming from the DJI Phantom 4 Pro. The match rate with the dictionary for the other two drones was very low, less than 10%, resulting in a relatively good recognition result.

[0103] The following table shows the error rate statistics of the Manhattan distance between the best effective points of the DJI Phantom 4 Pro drone signal in the above test based on this solution:

[0104] It can be seen that the average error rate is less than 0.02, the error rate is low, and the recognition result is better.

[0105] like Figure 14 The following figure demonstrates the matching results of the uplink remote control signals collected from the DJI Phantom 3 and 4 drones using this solution. As can be seen from the figure, for the time-frequency diagrams of the uplink signals collected from the DJI Phantom 3 and DJI Phantom 4 simultaneously, the signals of the DJI Phantom 3 and DJI Phantom 4 were successfully identified in all 10 matches. The dictionary matching rate of the time-frequency diagrams of the two signals exceeded 70% in each test, indicating that the recognition results are excellent.

[0106] like Figure 15The following figure shows the effect of matching the uplink remote control signals collected from the DJI Phantom 3 and 4Pro drones based on this solution. As can be seen from the figure, for the time-frequency diagrams of the uplink signals collected from the DJI Phantom 3 and DJI Phantom 4Pro drones, the signals of the DJI Phantom 3 and DJI Phantom 4Pro drones were successfully identified in 10 matches. The dictionary matching rate of the time-frequency diagrams of the two signals was above 70% in each test, so the recognition results were good. Among them, although the matching rate of the Phantom 3 dropped by several percentage points compared to the single detection and recognition, and the matching degree of some detections dropped below 90%, the matching rate of most detections was still above 90%, and the overall recognition effect still met the design requirements.

[0107] like Figure 16 The following diagram demonstrates the matching results of the uplink remote control signals collected from the DJI Phantom 4 and 4 Pro drones using this solution. As can be seen from the figure, when simultaneously collecting the uplink signal time-frequency diagrams of the Phantom 4 and Phantom 4 Pro drones, the Phantom 4 and Phantom 4 Pro signals were successfully identified in all 10 matches. The dictionary matching rate of the Phantom 4 Pro and Phantom 4 signals exceeded 70% in each test, indicating successful recognition. Furthermore, the matching rates were similar to those obtained when detecting only the signals. This indicates that the mutual interference between the Phantom 4 and Phantom 4 Pro from a signal and image perspective is relatively weak, barely affecting the final matching rate. The recognition results are excellent.

[0108] The beneficial effects of the drone type identification method of the embodiments of this specification include but are not limited to the following: 1. The recognition and identification of drone uplink signals based on visual images combines image recognition technology and frequency hopping signal feature analysis. By "seeing" to recognize wireless communication channels, it can better compress the amount of data transmission than conventional signal analysis methods while fully displaying the characteristics of the radio channel. Channel recognition and drone uplink signal identification are achieved through analysis of radio channel characteristics; 2. The methods and technologies for obtaining visual information and the theory and technology of multi-domain collaborative perception based on visual images are studied, which can be used not only for drone identification, but also for signal analysis and identification of large-scale monitoring networks; 3. The accuracy of drone type identification is significantly improved. It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other possible beneficial effects.

[0109] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0110] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0111] In addition, it will be understood by those skilled in the art that various aspects of this specification may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of this specification may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of this specification may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0112] A computer storage medium may include a propagated data signal embodying the computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, or any suitable combination thereof. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium that can be connected to an instruction execution system, apparatus, or device to communicate, propagate, or transfer the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of these.

[0113] The computer program code required for the operation of the various parts of this specification can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, Visual Basic, Fortran2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages. The program code can be run entirely on the user's computer, or as a separate software package on the user's computer, or partly on the user's computer and partly on a remote computer, or entirely on a remote computer or processing device. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0114] In addition, unless expressly stated in the claims, the order of the processing elements and sequences, the use of alphanumeric characters, or the use of other names described in this specification are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some embodiments of the invention that are currently considered useful through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the spirit and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing processing device or mobile device.

[0115] Similarly, it should be noted that, in order to simplify the presentation of this specification and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this specification sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of this specification requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single disclosed embodiment.

[0116] In some embodiments, numbers are used to describe the quantity of components and attributes. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise stated, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the description and claims are approximate values, which may change according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the settings of such numerical values ​​are as accurate as possible within the feasible range.

[0117] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, and documents, cited in this specification is hereby incorporated by reference in its entirety. This includes application history documents that are inconsistent with or conflict with the content of this specification, as well as documents (currently or subsequently attached to this specification) that limit the broadest scope of the claims of this specification. It should be noted that if the descriptions, definitions, and / or terminology used in the accompanying materials are inconsistent or conflicting with the content of this specification, the descriptions, definitions, and / or terminology used in this specification will control.

[0118] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. A method for identifying drone types, characterized in that: include: Generate the time-frequency diagram template of the uplink signal of the drone to be identified based on theoretical data; Scanning the uplink frequency hopping signal of the drone to be detected based on preset parameters to obtain scanning data; the uplink frequency hopping signal corresponds to the 2400MHz-2500MHz frequency band; generating a time-frequency graph based on the scan data, and performing image processing on the time-frequency graph to obtain a processed time-frequency graph; Comparing the processed time-frequency graph with the uplink signal time-frequency graph template to obtain the type of the drone to be detected; The uplink signal time-frequency diagram template of the drone to be identified based on theoretical data includes: Based on the distribution patterns of uplink frequency-hopping signal points generated by the different remote-controlled drone models to be identified in the image and the characteristics of the images they form, the corresponding uplink signal time-frequency graph template is generated, and finally a drone visual dictionary is obtained; The distribution pattern of uplink frequency hopping signal points generated by different remote control models of the drone to be identified in the image and the characteristics of the image formed thereby are obtained by the following method: Under preset environmental conditions, spectrum data of at least two cycles of multiple types of drones that need to be identified are collected multiple times, and the distribution pattern of the uplink frequency hopping signal points in the image and the characteristics of the image formed thereby are determined based on the spectrum data.

2. A method for identifying drone types according to claim 1, characterized in that: Generating a time-frequency graph based on the scan data and performing image processing on the time-frequency graph to obtain a processed time-frequency graph includes: generating a three-dimensional spectrogram based on the scan data; Performing noise reduction processing on the three-dimensional spectrum image to obtain a noise-reduced image; Extracting effective signal point edge contours from the noise-reduced image; The extracted data is transformed to obtain the signal distribution vector direction; Morphological processing and binarization processing are performed on the signal distribution vector direction to restore the signal lost in the partial processing and obtain a binarized time-frequency graph.

3. The method for identifying the type of drone according to claim 1, wherein: Generating a time-frequency graph based on the scan data and performing image processing on the time-frequency graph to obtain a processed time-frequency graph includes: generating a three-dimensional spectrogram based on the scan data; Performing noise reduction processing on the three-dimensional spectrum image to obtain a noise-reduced image; Binarizing the denoised image to obtain a binary time-frequency graph; Finding a connected domain, and obtaining a cell signal map based on the binarized time-frequency map; Remove cells that do not conform to the frequency modulation signal in the cell signal map to obtain a corrected time-frequency map; The vector distribution and Manhattan distance between cells in the corrected time-frequency map are calculated.

4. A method for identifying drone types according to claim 2 or 3, characterized in that: The processing time-frequency graph is compared with the uplink signal time-frequency graph template to obtain the type of the drone to be detected, including: The similarity between the time-frequency graph and the uplink signal time-frequency graph templates of each type of drone in the drone visual dictionary is calculated, and the drone model with the greatest similarity is taken as the recognition result.

5. A drone type identification system, characterized in that: include: The template generation module is used to generate the time-frequency diagram template of the uplink signal of the drone to be identified based on theoretical data; A signal acquisition module is used to scan the uplink frequency hopping signal of the drone to be detected based on preset parameters and obtain scanning data; the uplink frequency hopping signal corresponds to the 2400MHz-2500MHz frequency band; an image processing module, configured to generate a time-frequency diagram based on the scan data, and perform image processing on the time-frequency diagram to obtain a processed time-frequency diagram; a type matching module, configured to compare the processed time-frequency graph with the uplink signal time-frequency graph template to determine the type of the drone to be detected; The template generation module is further used to generate a corresponding uplink signal time-frequency graph template based on the distribution pattern of uplink frequency hopping signal points generated by different remote-controlled drone models to be identified in the image and the characteristics of the image formed thereby, thereby ultimately obtaining a drone visual dictionary; The distribution pattern of uplink frequency hopping signal points generated by different remote control models of the drone to be identified in the image and the characteristics of the image formed thereby are obtained by the following method: Under preset environmental conditions, spectrum data of at least two cycles of multiple types of drones that need to be identified are collected multiple times, and the distribution pattern of the uplink frequency hopping signal points in the image and the characteristics of the image formed thereby are determined based on the spectrum data.

6. A drone type identification device, comprising a processor and a memory; the memory is used to store instructions, characterized in that: When the processor executes the instruction, the device implements operations corresponding to the method for identifying the type of drone according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer runs the drone type identification method according to any one of claims 1 to 4.

Images