Unmanned aerial vehicle countermeasure method, device and program product based on multi-target detection

Abstract

The application belongs to the technical field of unmanned aerial vehicle management and control, and specifically discloses an unmanned aerial vehicle countermeasure method, device and program product based on multi-target detection, which realizes efficient and accurate multi-target unmanned aerial vehicle detection and signal interference countermeasures by performing linkage detection on multi-target unmanned aerial vehicles, obtaining radar detection data, radio frequency monitoring data, spectral detection data and acoustic detection data of each target unmanned aerial vehicle, performing model preliminary screening based on the radar detection data, performing model preselection based on the radio frequency monitoring data and acoustic detection data, performing final matching based on the spectral detection data, determining the model of the target unmanned aerial vehicle, and then adaptively matching the corresponding unmanned aerial vehicle countermeasure strategy for signal interference.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of unmanned aerial vehicle management and control, and particularly relates to an unmanned aerial vehicle countermeasure method, device and program product based on multi-target detection. BACKGROUND

[0002] An unmanned aerial vehicle is a pilotless aircraft that is controlled by radio remote control equipment and self-provided program control devices. The unmanned aerial vehicle has no cockpit on the aircraft and is equipped with devices such as autopilots, and can be tracked, positioned and digitally transmitted by ground or mother aircraft remote control station personnel. The unmanned aerial vehicle has the advantages of small size, low cost and easy use, and is widely used in fields such as photography, aerial photography, agricultural plant protection and power inspection.

[0003] However, with the rapid increase in the number of various unmanned aerial vehicles, unmanned aerial vehicle black flight and invasion events occur frequently, which also brings many safety hazards. It is urgent to develop effective unmanned aerial vehicle countermeasures to solve the current unmanned aerial vehicle black flight and invasion situation. The common unmanned aerial vehicle countermeasure at present is to find black flight unmanned aerial vehicles through personnel observation or radar detection, and then to countermeasure by physical interception or signal interference.

[0004] Due to the differences in design purpose, communication and navigation technology and anti-interference ability of different types of unmanned aerial vehicles, the effect of adopting corresponding countermeasures by the unmanned aerial vehicle countermeasure equipment when countering each type of unmanned aerial vehicle is not the same. The consumer unmanned aerial vehicle is small in size, and has a small radar scattering cross section, plus the interference of ground clutter, which leads to low accuracy of radar detection in identifying such unmanned aerial vehicles. Therefore, it is difficult to implement a targeted countermeasure strategy, and only a unified and rough countermeasure method can be used, and the efficiency of the countermeasure needs to be improved. SUMMARY

[0005] The purpose of the present application is to provide an unmanned aerial vehicle countermeasure method, device and program product based on multi-target detection, to solve the above-mentioned problems existing in the prior art.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0007] In a first aspect, an unmanned aerial vehicle countermeasure method based on multi-target detection is provided, comprising:

[0008] obtaining multi-target unmanned aerial vehicle radar detection results, the multi-target unmanned aerial vehicle radar detection results containing the azimuth parameter, distance parameter, speed parameter and radar scattering cross section parameter of each target unmanned aerial vehicle;

[0009] determining the corresponding linked detection range according to the azimuth parameter and distance parameter of the target unmanned aerial vehicle, and performing radio frequency monitoring, spectral detection and acoustic detection on the corresponding linked detection range to obtain radio frequency monitoring data, spectral detection data and acoustic detection data of the corresponding target unmanned aerial vehicle;

[0010] extracting a radio frequency fingerprint feature from radio frequency monitoring data of the target UAV, extracting a contour feature, a texture feature and an infrared feature from spectrum detection data of the target UAV, and extracting a voiceprint feature from acoustic detection data of the target UAV;

[0011] screening an initial screening model set of the target UAV from the model library according to a speed parameter and a radar cross section area parameter of the target UAV, the initial screening model set containing a plurality of initial screening models;

[0012] screening a pre-selection model set of the target UAV from the initial screening model set according to the radio frequency fingerprint feature and the voiceprint feature of the target UAV, the pre-selection model set containing a plurality of pre-selection models;

[0013] determining an optical feature similarity between the target UAV and each pre-selection model in the pre-selection model set based on the contour feature, the texture feature and the infrared feature of the target UAV, and taking a pre-selection model with an optical feature similarity meeting a set condition as a target model of the target UAV;

[0014] matching a corresponding countermeasure strategy according to the target model of the target UAV, and performing signal interference on the target UAV according to the countermeasure strategy.

[0015] In one possible design, the determination of the corresponding linkage detection range according to the azimuth parameter and the distance parameter of the target UAV includes:

[0016] determining a reference point of the target UAV in a set spatial coordinate system according to the azimuth parameter and the distance parameter of the target UAV;

[0017] taking a line connecting the reference point and an origin of the set spatial coordinate system as a reference line, and defining a conical space region of a set size around the reference line as the linkage detection range.

[0018] In one possible design, the model library contains a plurality of UAV models, and each UAV model is associated with a corresponding attribute feature set, the attribute feature set including a speed parameter interval, a radar cross section area parameter interval, a template radio frequency fingerprint feature, a template voiceprint feature, a template contour feature, a template texture feature and a template infrared feature.

[0019] In one possible design, the screening of the initial screening model set of the target UAV from the model library according to the speed parameter and the radar cross section area parameter of the target UAV includes:

[0020] comparing the speed parameter and the radar cross section area parameter of the target UAV with a speed parameter interval and a radar cross section area parameter interval associated with each UAV model in the model library;

[0021] If the speed parameter of the target UAV is in the speed parameter interval of the corresponding UAV model, and the radar cross-section parameter of the target UAV is in the radar cross-section parameter interval of the corresponding UAV model, the corresponding UAV model is taken as the initial screening model of the target UAV.

[0022] The initial screening models of the target UAV screened from the model library are summarized to obtain the initial screening model set of the target UAV.

[0023] In one possible design, the countermeasure strategy includes a signal interference mode, a signal interference frequency band, and a signal interference intensity, the signal interference mode includes communication interference, navigation interference, or electromagnetic shielding, and the matching of the corresponding countermeasure strategy according to the target model of the target UAV includes:

[0024] The model number corresponding to the target model of the target UAV is determined.

[0025] The model number is substituted into the preset countermeasure strategy table to match the signal interference mode, the signal interference frequency band, and the signal interference intensity corresponding to the model number, and the countermeasure strategy table contains a plurality of model numbers and the signal interference mode, the signal interference frequency band, and the signal interference intensity associated with each model number.

[0026] The signal interference mode, the signal interference frequency band, and the signal interference intensity matched by the model number are used to form the corresponding countermeasure strategy.

[0027] In a second aspect, an unmanned aerial vehicle countermeasure device based on multi-target detection is provided, which includes a radar detection unit, a linkage detection unit, a feature extraction unit, a model initial screening unit, a model preselection unit, a model determination unit, and a target countermeasure unit, wherein:

[0028] The radar detection unit is configured to obtain multi-target unmanned aerial vehicle radar detection results, and the multi-target unmanned aerial vehicle radar detection results include the azimuth parameter, the distance parameter, the speed parameter, and the radar cross-section parameter of each target unmanned aerial vehicle.

[0029] The linkage detection unit is configured to determine the corresponding linkage detection range according to the azimuth parameter and the distance parameter of the target unmanned aerial vehicle, and perform radio frequency monitoring, spectral detection, and acoustic detection on the corresponding linkage detection range to obtain the radio frequency monitoring data, the spectral detection data, and the acoustic detection data of the corresponding target unmanned aerial vehicle.

[0030] The feature extraction unit is configured to extract the radio frequency fingerprint feature from the radio frequency monitoring data of the target unmanned aerial vehicle, extract the contour feature, the texture feature, and the infrared feature from the spectral detection data of the target unmanned aerial vehicle, and extract the voiceprint feature from the acoustic detection data of the target unmanned aerial vehicle.

[0031] The model preliminary screening unit is configured to screen a preliminary model set of the target UAV from the model library according to the speed parameter and the radar cross section parameter of the target UAV, and the preliminary model set contains a plurality of preliminary models;

[0032] The model preselection unit is configured to screen a preselected model set of the target UAV from the preliminary model set according to the radio frequency fingerprint feature and the voiceprint feature of the target UAV, and the preselected model set contains a plurality of preselected models;

[0033] The model determination unit is configured to determine the optical feature similarity between the target UAV and each preselected model in the preselected model set based on the contour feature, the texture feature and the infrared feature of the target UAV, and take the preselected model that meets a set condition with the optical feature similarity of the target UAV as the target model of the target UAV.

[0034] The target countermeasure unit is configured to match a corresponding countermeasure strategy according to the target model of the target UAV, and perform signal interference on the target UAV according to the countermeasure strategy.

[0035] In a third aspect, a UAV countermeasure device based on multi-target detection is provided, which comprises:

[0036] A memory is configured to store instructions.

[0037] A processor is configured to read the instructions stored in the memory, and execute the UAV countermeasure method based on multi-target detection according to the instructions.

[0038] In a fourth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores instructions. When the instructions are run on a computer, the computer is caused to execute the UAV countermeasure method based on multi-target detection. Meanwhile, a computer program product is also provided, and the computer program product is run on a computer to execute the UAV countermeasure method based on multi-target detection.

[0039] Beneficial effects: the present application obtains radar detection data, radio frequency monitoring data, spectral detection data and acoustic detection data of each target unmanned aerial vehicle through linkage detection of multi-target unmanned aerial vehicle, then performs model preliminary screening based on the radar detection data, performs model pre-selection based on the radio frequency monitoring data and acoustic detection data, performs final matching based on the spectral detection data to determine the model of the target unmanned aerial vehicle, and then adaptively matches the corresponding unmanned aerial vehicle countermeasure strategy for signal interference, so that efficient and accurate multi-target unmanned aerial vehicle detection and signal interference countermeasures can be realized. The present application can accurately distinguish different types of unmanned aerial vehicles by fusing multi-dimensional linkage detection data and performing multi-level information matching, and can implement optimized countermeasures in a targeted manner to improve the countermeasure efficiency of the unmanned aerial vehicle, eliminate unmanned aerial vehicle black flight and intrusion events, and effectively protect low-altitude safety. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0041] Figure 1 It is a flowchart of the method in the embodiment 1 of the present application.

[0042] Figure 2 It is a schematic diagram of the structure of the device in the embodiment 2 of the present application.

[0043] Figure 3 It is a schematic diagram of the structure of the device in the embodiment 3 of the present application. DETAILED DESCRIPTION

[0044] It should be noted that the description of these embodiment modes is used to help understand the present application, but does not constitute a limitation on the present application. The specific structure and functional details disclosed herein are only used to describe the example embodiments of the present application. However, the present application can be embodied in many alternative forms, and should not be understood as being limited in the embodiments set forth herein.

[0045] It should be understood that, unless otherwise explicitly specified and limited, the corresponding terms should be broadly understood, for example, "connection" can be fixed connection, or detachable connection, or integrally connected; can be directly connected, or indirectly connected through an intermediate medium, or internal communication of two elements. For those skilled in the art, the specific meaning of the above-mentioned terms in the embodiments can be understood according to the specific circumstances.

[0046] In the following description, specific details are provided to facilitate a full understanding of the example embodiments. However, a person of ordinary skill in the art will understand that the example embodiments can be practiced without these specific details. For example, devices can be shown in block diagram form to avoid obscuring the examples. In other instances, well-known processes, structures, and techniques have not been shown in detail to avoid obscuring the embodiments.

[0047] Embodiment 1

[0048] The embodiment provides a UAV countermeasure method based on multi-target detection, and can be applied to a corresponding UAV countermeasure system. The UAV countermeasure system is provided with a radar detection subsystem, an RF monitoring subsystem, a spectrum detection subsystem, an acoustic detection subsystem, a signal interference subsystem, and a controller. As shown in the figure, the method comprises the following steps: Figure 1

[0049] S1. Obtain a multi-target UAV radar detection result, wherein the multi-target UAV radar detection result comprises azimuth parameters, distance parameters, speed parameters, and radar cross section (RCS) parameters of each target UAV.

[0050] In a specific implementation, the radar detection subsystem can be used to perform real-time radar scanning on a target low-altitude area where flight is prohibited, to detect whether there is a UAV flying in the target low-altitude area. When a target UAV is detected to be flying, the radar detection results of each target UAV can be summarized as a multi-target UAV radar detection result, and the multi-target UAV radar detection result can be sent to the controller for subsequent processing. The multi-target UAV radar detection result comprises azimuth parameters, distance parameters, speed parameters, and radar cross section (RCS) parameters of each target UAV.

[0051] S2. Determine a corresponding linkage detection range according to the azimuth parameters and the distance parameters of the target UAV, and perform RF monitoring, spectrum detection, and acoustic detection on the corresponding linkage detection range, to obtain RF monitoring data, spectrum detection data, and acoustic detection data of the corresponding target UAV.

[0052] ​In implementation, after obtaining the radar detection results of each target UAV, the controller can determine the reference point of the target UAV in the set spatial coordinate system according to the azimuth parameter and the distance parameter of the target UAV, take the line connecting the reference point and the origin of the set spatial coordinate system as the reference line, and demarcate a conical space region of a set size around the reference line as the linkage detection range. Then the controller controls the radio frequency monitoring subsystem, the spectral detection subsystem and the acoustic detection subsystem to perform radio frequency monitoring, spectral detection and acoustic detection on the linkage detection range of each target UAV, respectively, to obtain the radio frequency monitoring data, the spectral detection data and the acoustic detection data of the corresponding target UAV in each linkage detection range. The radio frequency monitoring data contains the radio frequency fingerprint feature (i.e. the electromagnetic spectrum fingerprint, such as the carrier frequency, bandwidth, modulation mode and typical power of the target UAV transmission / control signal, which can be obtained by radio frequency scanning code and signal modulation analysis of the radio frequency monitoring subsystem), the spectral detection data can contain the contour feature, texture feature and infrared feature (which can be obtained by visible light and long-wave infrared imaging of the target UAV by the spectral detection subsystem, and corresponding image feature extraction of the visible light image and the infrared image), and the acoustic detection data can contain the voiceprint feature (which can be obtained by sound array collection of the target UAV by the acoustic detection subsystem, and frequency spectrum feature processing of the collected sound signal).

[0053] S3. Extracting the radio frequency fingerprint feature from the radio frequency monitoring data of the target UAV, the contour feature, the texture feature and the infrared feature from the spectral detection data of the target UAV, and the voiceprint feature from the acoustic detection data of the target UAV.

[0054] In implementation, the controller can extract the radio frequency fingerprint feature from the radio frequency monitoring data of the target UAV, the contour feature, the texture feature and the infrared feature from the spectral detection data of the target UAV, and the voiceprint feature from the acoustic detection data of the target UAV, to perform subsequent UAV type identification and matching based on the radio frequency fingerprint feature, the voiceprint feature, the contour feature, the texture feature and the infrared feature.

[0055] S4. Screening the initial screening model set of the target UAV from the model library according to the speed parameter and the radar cross section area parameter of the target UAV, wherein the initial screening model set contains several initial screening models.

[0056] In implementation, in the first-level UAV type matching process, the controller can screen the initial screening model set of the target UAV from the model library according to the speed parameter and the radar cross section area parameter of the target UAV. The model library contains several UAV models, and each UAV model is associated with a corresponding attribute feature set, which includes the speed parameter interval, the radar cross section area parameter interval, the template radio frequency fingerprint feature, the template voiceprint feature, the template contour feature, the template texture feature and the template infrared feature.

[0057] In the screening of the initial screening model set of the target UAV from the UAV model library, the controller first compares the speed parameter and the radar scattering cross-section parameter of the target UAV with the speed parameter interval and the radar scattering cross-section parameter interval associated with each UAV model in the UAV model library; if the speed parameter of the target UAV is within the speed parameter interval of the corresponding UAV model, and the radar scattering cross-section parameter of the target UAV is within the radar scattering cross-section parameter interval of the corresponding UAV model, the corresponding UAV model is taken as the initial screening model of the target UAV; after the initial screening models of the target UAV are screened from the UAV model library, the initial screening model set of the target UAV is obtained.

[0058] S5. Screening the pre-selected model set of the target UAV from the initial screening model set according to the radio frequency fingerprint feature and the voiceprint feature of the target UAV, the pre-selected model set containing several pre-selected models.

[0059] S6. Determining the optical feature similarity between the target UAV and each pre-selected model in the pre-selected model set based on the contour feature, the texture feature and the infrared feature of the target UAV, and taking the pre-selected model satisfying the set condition of the optical feature similarity with the target UAV as the target model of the target UAV.

[0060] S7. Matching the corresponding countermeasures according to the target model of the target UAV, and performing signal interference on the target UAV by the countermeasures.

[0061] In actual implementation, after the target model of the target UAV is determined, the controller can match a corresponding countermeasure strategy according to the target model of the target UAV, the countermeasure strategy including a signal interference mode, a signal interference frequency band, and a signal interference intensity, the signal interference mode including communication interference (the communication interference aims to block the communication link between the UAV and the remote controller or the ground control station, so that the UAV cannot receive the control instruction), navigation interference (the navigation interference is mainly directed to a satellite navigation system relied on by the UAV, such as GPS, Beidou, etc., and by emitting an interference signal, the UAV cannot obtain accurate position information, thereby losing control and deviating from the predetermined flight path or executing an incorrect flight instruction), or electromagnetic shielding (the electromagnetic shielding emits a powerful electromagnetic signal to form an electromagnetic shielding space in a specific area, so that the communication signal, the navigation signal, etc. of the UAV cannot be normally transmitted, thereby achieving the countermeasure purpose). When the corresponding countermeasure strategy is matched according to the target model of the target UAV, the controller first determines the model number corresponding to the target model of the target UAV; then substitutes the model number into the preset countermeasure strategy table to match the signal interference mode, the signal interference frequency band, and the signal interference intensity corresponding to the model number, the countermeasure strategy table containing a plurality of model numbers and the signal interference mode, the signal interference frequency band, and the signal interference intensity associated with each model number; and then uses the signal interference mode, the signal interference frequency band, and the signal interference intensity matched by the model number to form the corresponding countermeasure strategy.

[0062] After being adapted to the corresponding countermeasure strategy of the target UAV, the controller can control the signal interference subsystem to perform tracking signal interference on the target UAV, so as to block the communication link or the navigation link of the target UAV, so that the target UAV loses the control signal or cannot accurately obtain the position information, thereby achieving the countermeasure purpose.

[0063] The method can accurately distinguish different types of UAVs by fusing multi-dimensional linkage detection data and performing multi-level information matching, and can implement an optimized countermeasure strategy in a targeted manner, so as to improve the countermeasure efficiency on the UAV, eliminate the UAV black flight and intrusion event, and effectively ensure the low-altitude safety.

[0064] Embodiment 2

[0065] The embodiment provides a UAV countermeasure device based on multi-target detection, as shown in Figure 2 The UAV countermeasure device includes a radar detection unit, a linkage detection unit, a feature extraction unit, a model preliminary screening unit, a model preselection unit, a model determination unit, and a target countermeasure unit, wherein:

[0066] The radar detection unit is configured to obtain multi-target UAV radar detection results, the multi-target UAV radar detection results including the azimuth parameter, the distance parameter, the speed parameter, and the radar scattering cross-section parameter of each target UAV.

[0067] The linkage detection unit is configured to determine a corresponding linkage detection range according to the azimuth parameter and the distance parameter of the target UAV, and perform radio frequency monitoring, spectrum detection, and acoustic detection on the corresponding linkage detection range to obtain radio frequency monitoring data, spectrum detection data, and acoustic detection data of the target UAV.

[0068] The feature extraction unit is configured to extract a radio frequency fingerprint feature from the radio frequency monitoring data of the target UAV, extract an outline contour feature, a texture feature, and an infrared feature from the spectrum detection data of the target UAV, and extract a voiceprint feature from the acoustic detection data of the target UAV.

[0069] The model preliminary screening unit is configured to screen a preliminary screening model set of the target UAV from the model library according to the speed parameter and the radar scattering cross-section parameter of the target UAV, the preliminary screening model set including a plurality of preliminary screening models.

[0070] The model preselection unit is configured to screen a preselection model set of the target UAV from the preliminary screening model set according to the radio frequency fingerprint feature and the voiceprint feature of the target UAV, the preselection model set including a plurality of preselection models.

[0071] The model determination unit is configured to determine an optical feature similarity between the target UAV and each preselection model in the preselection model set based on the outline contour feature, the texture feature, and the infrared feature of the target UAV, and determine a target model of the target UAV as a preselection model that satisfies a set condition in the optical feature similarity.

[0072] The target countermeasure unit is configured to match a corresponding countermeasure strategy according to the target model of the target UAV, and perform signal interference on the target UAV according to the countermeasure strategy.

[0073] Embodiment 3:

[0074] The embodiment provides a UAV countermeasure device based on multi-target detection, as shown in Figure 3 At the hardware level, the device includes:

[0075] The data interface is configured to establish data connection between the processor and an external data terminal.

[0076] The memory is configured to store instructions.

[0077] The processor is configured to read the instructions stored in the memory, and execute the UAV countermeasure method based on multi-target detection in Embodiment 1 according to the instructions.

[0078] Optionally, the apparatus further comprises an internal bus, the processor and the memory and the data interface can be connected with each other through the internal bus, the internal bus can be a PCIe (Peripheral Component Interconnect Eexpress) bus, the bus can be divided into an address bus, a data bus, a control bus and the like. The memory can include, but is not limited to, a random access memory (Random Access Memory, RAM), a read-only memory (Read Only Memory, ROM), a flash memory (Flash Memory), a first-in first-out memory (First Input First Output, FIFO) and / or a first-in last-out memory (First In Last Out, FILO) and the like. The processor can be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP) and the like; can also be a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0079] Embodiment 4:

[0080] The embodiment provides a computer readable storage medium, and instructions are stored on the computer readable storage medium, and when the instructions run on a computer, the computer executes the anti-drone method based on multi-target detection in the embodiment 1. Wherein, the computer readable storage medium is a carrier for storing data, and can include, but is not limited to, a floppy disk, an optical disc, a hard disk, a flash memory, a USB flash disk and / or a memory stick, and the computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices.

[0081] The embodiment further provides a computer program product, and when the computer program product runs on a computer, the anti-drone method based on multi-target detection in the embodiment 1 is executed. Wherein, the computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices.

[0082] Finally, it should be noted that the above description is only the preferred embodiment of the present application, and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for anti-UAV based on multi-target detection, characterized in that, The application relates to a method for identifying a target unmanned aerial vehicle (UAV) and implementing a countermeasure strategy. The method comprises the following steps: acquiring multi-target UAV radar detection results, wherein the multi-target UAV radar detection results comprise azimuth parameters, distance parameters, speed parameters and radar scattering cross-section parameters of each target UAV; determining corresponding linkage detection ranges according to the azimuth parameters and the distance parameters of the target UAV, and performing radio frequency monitoring, spectral detection and acoustic detection on the corresponding linkage detection ranges to obtain radio frequency monitoring data, spectral detection data and acoustic detection data of the target UAV; extracting radio frequency fingerprint features from the radio frequency monitoring data of the target UAV, extracting contour features, texture features and infrared features from the spectral detection data of the target UAV, and extracting voiceprint features from the acoustic detection data of the target UAV; screening an initial screening UAV model set of the target UAV from a UAV model library according to the speed parameters and the radar scattering cross-section parameters of the target UAV, wherein the initial screening UAV model set comprises a plurality of initial screening UAV models; screening a preselected UAV model set of the target UAV from the initial screening UAV model set according to the radio frequency fingerprint features and the voiceprint features of the target UAV, wherein the preselected UAV model set comprises a plurality of preselected UAV models; determining optical feature similarities between the target UAV and each preselected UAV model in the preselected UAV model set based on the contour features, the texture features and the infrared features of the target UAV, and taking a preselected UAV model, which satisfies a set condition in the optical feature similarity with the target UAV, as a target UAV model of the target UAV; 2.The multi-target detection based UAV countermeasure method of claim 1, wherein, matching a corresponding countermeasure strategy according to the target UAV model of the target UAV, and performing signal interference on the target UAV by implementing the countermeasure strategy. The method comprises the following steps: determining reference points of the target UAV in a set space coordinate system according to the azimuth parameters and the distance parameters of the target UAV; 3.The multi-target detection based UAV countermeasure method of claim 1, wherein, taking a line connecting the reference points and an origin of the set space coordinate system as a reference line, and delimiting a conical space region with a set size around the reference line as a linkage detection range. 4.The multi-target detection based UAV countermeasure method of claim 3, wherein, The UAV model library comprises a plurality of UAV models, and each UAV model is associated with a corresponding attribute feature set, wherein the attribute feature set comprises a speed parameter interval, a radar scattering cross-section parameter interval, template radio frequency fingerprint features, template voiceprint features, template contour features, template texture features and template infrared features. The method comprises the following steps: comparing the speed parameters and the radar scattering cross-section parameters of the target UAV with the speed parameter intervals and the radar scattering cross-section parameter intervals associated with each UAV model in the UAV model library; if the speed parameters of the target UAV are within the speed parameter interval of a corresponding UAV model, and the radar scattering cross-section parameters of the target UAV are within the radar scattering cross-section parameter interval of the corresponding UAV model, then the corresponding UAV model is taken as an initial screening UAV model of the target UAV; the initial screening UAV models of the target UAV screened from the UAV model library are summarized to obtain an initial screening UAV model set of the target UAV.

5. The multi-target detection based UAV countermeasure method of claim 1, wherein, The countermeasure strategy includes a signal interference mode, a signal interference frequency band, and a signal interference intensity, the signal interference mode includes communication interference, navigation interference, or electromagnetic shielding, the corresponding countermeasure strategy is matched according to the target model of the target UAV, and the countermeasure strategy includes the following steps: Determine the model number corresponding to the target model of the target UAV; Substitute the model number into the preset countermeasure strategy table to match the signal interference mode, the signal interference frequency band, and the signal interference intensity corresponding to the model number, the countermeasure strategy table contains a plurality of model numbers and the signal interference mode, the signal interference frequency band, and the signal interference intensity associated with each model number; The signal interference mode, the signal interference frequency band, and the signal interference intensity matched by the model number are used to form the corresponding countermeasure strategy.

6. The UAV countermeasure device based on multi-target detection, characterized in that, It includes a radar detection unit, a linkage detection unit, a feature extraction unit, a model preliminary screening unit, a model preselection unit, a model determination unit, and a target countermeasure unit, wherein: The radar detection unit is used to obtain multi-target UAV radar detection results, and the multi-target UAV radar detection results contain the azimuth parameter, the distance parameter, the speed parameter, and the radar scattering cross-section parameter of each target UAV; The linkage detection unit is used to determine the corresponding linkage detection range according to the azimuth parameter and the distance parameter of the target UAV, and to perform radio frequency monitoring, spectral detection, and acoustic detection on the corresponding linkage detection range to obtain the radio frequency monitoring data, the spectral detection data, and the acoustic detection data of the corresponding target UAV; The feature extraction unit is used to extract the radio frequency fingerprint feature from the radio frequency monitoring data of the target UAV, the contour feature, the texture feature, and the infrared feature from the spectral detection data of the target UAV, and the voiceprint feature from the acoustic detection data of the target UAV; The model preliminary screening unit is used to screen the preliminary model set of the target UAV from the model library according to the speed parameter and the radar scattering cross-section parameter of the target UAV, and the preliminary model set contains a plurality of preliminary models; The model preselection unit is used to screen the preselection model set of the target UAV from the preliminary model set according to the radio frequency fingerprint feature and the voiceprint feature of the target UAV, and the preselection model set contains a plurality of preselection models; The model determination unit is used to determine the optical feature similarity between the target UAV and each preselection model in the preselection model set based on the contour feature, the texture feature, and the infrared feature of the target UAV, and to determine the preselection model that meets the set condition as the target model of the target UAV; The target countermeasure unit is used to match the corresponding countermeasure strategy according to the target model of the target UAV, and to perform signal interference on the target UAV according to the countermeasure strategy.

7. The UAV countermeasure device based on multi-target detection, characterized in that, It includes: A memory for storing instructions; A processor for reading the instructions stored in the memory and executing the multi-target detection based UAV countermeasure method according to any one of claims 1-5.

8. A computer program product, characterised in that, It includes a computer program, which, when running on a computer, executes the multi-target detection based UAV countermeasure method according to any one of claims 1-5.

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