An automated method and system for passive detection and interference based on unmanned aerial vehicles

Abstract

The present invention relates to the field of passive detection technologies, and discloses a passive detection and interference automation method based on an unmanned aerial vehicle (UAV), which includes: connecting the system to a common processing module, a sub-device plug-in module, and a main control situation display module; acquiring radar original track information and UAV spectrum information; performing data fusion processing to obtain the actual coordinates of the current UAV; performing curve fitting processing of nth-order linear fitting on the fusion data, processing the curve-fitted data in the curve fitting module of the common processing module, and calculating the virtual scene coordinates of the UAV from the curve-fitted data; realizing the automation of passive detection and interference in a no-takeoff area and a no-flight-path area according to the actual coordinates and virtual scene coordinates of the UAV. The present invention also provides a passive detection and interference automation system based on an UAV. Through data fusion and data linear fitting preprocessing, the present invention provides highly accurate source data, and can also achieve unattended operation and automatic control.

Description

Technical Field

[0001] The present invention relates to the technical field of passive detection, and specifically, to a passive detection and interference automation method and system based on an unmanned aerial vehicle (UAV). Through data fusion and data linear fitting preprocessing, high-accuracy source data is provided, and the effects of unattended operation and automatic control are achieved. Background Art

[0002] Existing single-device control software can control a single device for manual operation. For example, there is radar control single-device control software, electronic detection single-device control software, and interference and navigation deception single-device control software. Under the condition that manual judgment and operation are possible, the single-device control software can achieve manual strikes. However, usually, due to the excessive number of devices, the efficiency of manual operation is too low. In the defense system against low, slow, and small UAVs, the devices need high-precision UAV target guidance information for real-time combat strikes. At the same time, this poses high requirements for the processing power of the computer and also for the architecture design. Under the existing technical conditions, such systems have defects such as low data processing efficiency, small processing capacity, and high coupling, making it difficult to meet the usage requirements.

[0003] Therefore, in order to solve the above problems, a technical solution is urgently needed that can improve data processing efficiency, while providing high-accuracy source data, and achieving the effects of unattended operation and automatic control. Summary of the Invention

[0004] The purpose of the present invention is to provide a passive detection and interference automation method based on an unmanned aerial vehicle, which can improve data processing efficiency, while providing high-accuracy source data, and achieving the effects of unattended operation and automatic control.

[0005] The present invention is achieved through the following technical solutions: A passive detection and interference automation method based on an unmanned aerial vehicle includes the following steps:

[0006] Step S1. Connect the passive detection and interference automation system to a common processing module, a sub-device plug-in module, and a main control situation display module;

[0007] Step S2. Obtain radar original track information according to the radar device in the sub-device plug-in module, and use the UAV spectrum information measured by the electronic detection device as the original data. Perform standardization processing and filtering processing on the original data to obtain the signal characteristics of interest;

[0008] Step S3. Receive the radar original track data and signal data according to the data interaction module in the common processing module, and perform data fusion processing on the radar original track data and signal data according to the data processing module in the common processing module to obtain the actual coordinates of the current UAV;

[0009] Step S4. The data interaction module of the public processing module performs curve fitting processing of nth-order linear fitting on the fusion data, and the curve fitting module of the public processing module processes the data after curve fitting, and calculates the data after curve fitting to obtain the virtual scene coordinates of the UAV.

[0010] Step S5. Connect the data processing module and the special area processing module, connect the data interaction module and the special target processing module, and realize the automation of passive detection interference in the no-takeoff area and the track no-fly area according to the actual coordinates and virtual scene coordinates of the UAV, combined with the special area processing module and the special target processing module.

[0011] To better implement the present invention, further, the method for obtaining the signal features of concern in step S2 includes:

[0012] First, perform unpacking processing and preprocessing on the original data;

[0013] Then integrate the original data, and use the traveling ant clustering algorithm and the matching classification algorithm to perform the first classification on the original data to obtain the first classification result;

[0014] Finally, evaluate the first classification result according to the information in the UAV feature library of the passive detection interference automation system by the intelligent matching algorithm, and at the same time perform secondary classification on the first classification result to obtain the processing result of the signal features of concern and send it to the data processing module;

[0015] The signal features of concern include target model information, ID information, average energy information, and angle information.

[0016] To better implement the present invention, further, step S3 includes:

[0017] Compare the signal data according to the spectrum feature recognition technology and the pre-established spectrum feature library to obtain the UAV model information;

[0018] Detect the remote control and video transmission signals of the UAV according to the UAV model information, and effectively identify the remote control and video transmission signals of the UAV in the time domain and frequency domain.

[0019] To better implement the present invention, further, the method for curve fitting processing in step S4 includes:

[0020] Express the curve fitting p as p = polyfit(x, y, n); where the curve fitting p is the coefficient of the polynomial p(x) of order n returned, and the order n is the best fitting order of the data in y in the least squares method; y is the value for fitting longitude or latitude respectively, and x is a natural number that increases naturally;

[0021] Arrange the coefficients in the curve fitting polynomial \(p\) in descending order of powers, and take the length of \(p\) as \(n + 1\);

[0022] Select the \(n\)-th order fitting according to the curve characteristics and represent it as;

[0023] In the curve fitting module, transform the data into an augmented matrix by elementary row transformation, use elementary transformation to transform the augmented matrix into a row echelon form, and substitute back to find the coefficient solution of the equation polynomial \(p(x)\);

[0024] After data processing on the curve-fitted data, take the last term value, and finally the longitude and latitude coordinate information of each target point after fitting can be obtained respectively.

[0025] To better implement the present invention, further, step S5 includes:

[0026] Implement closed-loop operation according to the pre-set no-fly zone and no-fly area settings, strike method, strike time, determination success condition, and maximum strike time

[0027] time;

[0028] Transmit the jammer device D to be struck, target information, and strike actions to the plug-in manager of the common processing module. The plug-in manager matches the device plug-in according to the strike device ID, and transmits the strike actions to the device through the UDP network socket to achieve the strike.

[0029] To better implement the present invention, further, the method for realizing the automation of passive detection and interference in step S5 further includes:

[0030] Obtain the communication interference equation represented by the input interference-to-signal ratio of the target communication receiver according to the transmission power model of the communication and interference link;

[0031] Output the power model that can suppress the UAV target control signal according to the communication interference equation;

[0032] Judge whether the target device enters the warning area according to the fitting data prediction. If so, give an alarm. If not, make a judgment in time.

[0033] To better implement the present invention, the present invention also provides a passive detection and interference automation system based on UAVs, including a common processing module, a sub-device plug-in module, and a main control situation display module, where:

[0034] Interact between the common processing module and the main control situation display module, and interact between the common processing module and the sub-device plug-in module;

[0035] The sub-device plugin module includes a communication module for acquiring radar raw point track information; it is used to measure the UAV spectrum information as raw data, perform normalization processing and filtering processing on the raw data to obtain the concerned signal features;

[0036] The common processing module includes a data interaction module, a data processing module and a curve fitting module, which is used to perform curve fitting processing of nth-order linear fitting on the fusion data, process the data after curve fitting, and calculate the virtual scene coordinates of the UAV from the data after curve fitting;

[0037] Connect the data processing module in the common processing module with the special area processing module, and connect the data interaction module in the common processing module with the special target processing module, and realize the automation of passive detection and interference in the no-takeoff area and the track prohibited area according to the actual coordinates and virtual scene coordinates of the UAV in combination with the special area processing module and the special target processing module;

[0038] The main control situation display module is used to provide a human-computer interaction operation interface.

[0039] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0040] (1) The present invention provides highly accurate source data through data fusion and data linear fitting preprocessing;

[0041] (2) The plug-in development framework of the present invention can achieve code isolation;

[0042] (3) The processing of the task mode in the present invention can achieve the unattended automation function;

[0043] (4) The system of the present invention has the characteristics of low coupling, high efficiency, fast response speed, clear structure, flexibility and scalability, and fully considers the safety characteristics of control devices, etc.;

[0044] (5) The system in the present invention achieves code isolation in the form of replacing device plugins, and has high scalability and low coupling;

[0045] (6) The system in the present invention can effectively solve problems such as target loss, error, and virtual scene in the application scenario of real-time tracking;

[0046] (7) The system in the present invention can achieve unattended and automatic control. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The present invention will be further described in conjunction with the following drawings and embodiments. All the innovative concepts of the present invention should be regarded as the disclosed content and the protection scope of the present invention.

[0048] Figure 1Flowchart of a passive detection and interference automation method based on an unmanned aerial vehicle provided by the present invention.

[0049] Figure 2 Schematic structural diagram of a passive detection and interference automation system based on an unmanned aerial vehicle provided by the present invention.

[0050] Figure 3 Schematic diagram of the data processing design architecture in a passive detection and interference automation method based on an unmanned aerial vehicle provided by the present invention.

[0051] Figure 4 Schematic diagram of the identification of the telemetry and remote control spectrum signals of an unmanned aerial vehicle in a passive detection and interference automation method based on an unmanned aerial vehicle provided by the present invention.

[0052] Figure 5 Schematic diagram of the process of the automatic mode in a passive detection and interference automation method based on an unmanned aerial vehicle provided by the present invention.

[0053] Figure 6 Schematic diagram of the power transmission model of the communication and interference link in a passive detection and interference automation method based on an unmanned aerial vehicle provided by the present invention.

[0054] Figure 7 Schematic structural diagram of the composition of the interference device in a passive detection and interference automation method based on an unmanned aerial vehicle provided by the present invention. Detailed implementation manners

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It should be understood that the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments, and therefore should not be regarded as a limitation on the protection scope. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0056] In the description of the present invention, it should be noted that unless otherwise clearly defined and limited, the terms "set", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can also be directly connected, or indirectly connected through an intermediate medium, and can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0057] Embodiment 1:

[0058] A passive detection and interference automation method based on an unmanned aerial vehicle in this embodiment, asFigure 1 As shown in Figure 1 , in this embodiment, the data interaction module of the common processing module performs curve fitting processing of n-order linear fitting on the fusion data. The curve fitting module of the common processing module processes the data after curve fitting, calculates the virtual scene coordinates of the UAV from the data after curve fitting, and provides highly accurate source data through data fusion and data linear fitting preprocessing. In this embodiment, the passive detection and interference automation system is connected to the sub-device plug-in module to achieve code isolation in the form of a plug-in development framework. The processing of the task mode provided by the present invention can achieve unattended automation functions. The passive detection and interference automation system in this embodiment is connected to the main control situation display module, and through data fusion and data linear fitting preprocessing, it can effectively solve problems such as target loss, errors, and virtual scenes in real-time tracking application scenarios, and can achieve unattended and automatic control.

[0059] Embodiment 2:

[0060] On the basis of Embodiment 1, this embodiment is further optimized. In this embodiment, when the passive detection and interference automation system is working, multiple antennas are used to simultaneously receive radio signals from various directions, and then submit them to the RF receiver at the same time. The RF receiver processes the radio and converts the radio signal into a digital signal, and sends the obtained data to the FPGA module. The FPGA module (the internal digital signal processing module of the radio detection device) is a further development product based on programmable devices such as PAL and GAL. As a common module, it will not be elaborated here. The FPGA module preprocesses and packages the data and, as an antenna data sending module, sends the data to the processing computer. After receiving the data from the antenna system, the processing computer first unpacks and preprocesses the data, then integrates the data, and uses the traveling ant clustering algorithm and the matching classification algorithm to perform the first classification on the signal. The traveling ant clustering algorithm randomly scatters the pulse signal sets that are already at the same order of magnitude on a two-dimensional plane, and generates some virtual ants on this plane.

[0061] The traveling ants randomly scatter the pulse signal sets that are already at the same order of magnitude on a two-dimensional plane, and generate some virtual ants on this plane. Their purpose is to judge whether the things they carry are similar to the surrounding objects. This method randomly distributes the data in an nxn grid. The ants can observe the objects in a fixed area around the initial location. For an object o, the similarity with the surrounding objects at the low point r is calculated according to the following formula:

[0062]

[0063] Where is the object The similarity between the low point r and surrounding objects, a is a parameter measuring the dissimilarity, and S is a fixed - area region. is the distance between two objects in a two - dimensional space, generally the Euclidean distance, and Neigh(r) is a fixed - area region centered on r. Here, it is necessary to judge the probability of each ant picking up an object and putting down an object. It can be carried out according to the following two formulas:

[0064] ;

[0065] Among them, is the probability that the ant picks up the object O i and is the probability of putting down the object. k1 and k2 are constants. Compare the randomly generated number with the calculated probabilities of picking up and putting down. If the random number is less than, perform the picking - up or putting - down operation to achieve the purpose of data convergence.

[0066] Send the classification result to the intelligent matching algorithm. The intelligent matching algorithm uses the information in the UAV feature library to evaluate the classification result, and at the same time performs secondary classification on the result, giving the processing result of the effective target, that is, the signal features of concern, and submitting it to the data processing module in the public processing module, including the target model, ID, average energy, angle, etc.

[0067] Other parts of this embodiment are the same as those of Embodiment 1, so they will not be elaborated here.

[0068] Embodiment 3:

[0069] This embodiment further optimizes on the basis of the above - mentioned Embodiment 1. In this embodiment, data processing is performed on the point - track information obtained by the radar system. The data interaction module can receive the original radar point - track data, the data processing module can perform data processing according to the existing original point - track data, and the observer module is used to clear the point - track data that has not been updated for a long time.

[0070] The electronic reconnaissance equipment, that is, the principle of the radio detection system is as Figure 3 shown. During the flight of the UAV, it needs to receive the remote - control signal from the remote controller. At the same time, the remote - control operator receives the video - transmission signal from the UAV. All these wireless communication links will occupy spectrum resources. The radio detection equipment can extract the signal features of concern through signal detection technology, and use the spectrum feature recognition technology to compare with the established spectrum feature library, and then obtain the model information. As Figure 4 shown. According to the above analysis, by detecting the remote - control and video - transmission signals of the UAV, the signal can be effectively identified in the time domain and frequency domain.

[0071] The data processing module in the common processing module performs data fusion processing on the original radar track data and signal data. After fusion, the actual coordinates of the current UAV are obtained and stored in the storage module in the sub-device plugin module. It is judged whether to enter the automatic mode. If not, the collaborative mode is processed. If so, countermeasure processing is required. It is judged whether the countermeasure strategy is completed. If so, the actual coordinates are transmitted to the communication module in the sub-device plugin module. If not, the countermeasure processing continues.

[0072] The data processing module in the common processing module evaluates the positioning result, completes the work of eliminating outliers, etc., and submits the data to the display interface of the main control situation display module. The display interface is responsible for displaying the positioning result, ending a detection cycle. In order to obtain effective center frequency, bandwidth, signal strength and other information, direction angle, distance, fit the UAV position information and then match the radar real track, and perform data matching and fusion into complete UAV target information.

[0073] Other parts of this embodiment are the same as those of the above embodiment, so they will not be described in detail.

[0074] Embodiment 4:

[0075] This embodiment is further optimized on the basis of the above Embodiment 1. The fitting is performed according to the effective actual coordinate points within a fixed time, and the purpose of the fitting is to obtain the predicted future virtual scene coordinates of the UAV. In this embodiment, for the fused data, there is a certain probability of errors such as missing points, jumping points, and virtual scenes. For this phenomenon. The system will perform nth-order linear fitting on the known track points to ensure data validity and continuity. The implementation method of this fitting is as follows:

[0076] Store the original data, linear fitting data, and target virtual scene in the data interaction module, and regularly clear invalid and timeout data.

[0077] Curve fitting p = polyfit(x, y, n) returns the coefficients of the polynomial p(x) of order n, which is the best fit (in the least squares sense) of the data in y. The coefficients in p are arranged in descending powers, and the length of p is n + 1. Here, due to the characteristics of the curve, using 3rd-order fitting is sufficient to meet its formula as:

[0078] + ;

[0079] The curve fitting module transforms the data into an augmented matrix AX = B by elementary row transformation, uses elementary transformation to transform the augmented matrix into a row echelon matrix, and back-substitutes to find the solution of the polynomial coefficients of the equation. For the fitted data obtained from data processing, take the last term value and calculate the virtual scene value (virtual scene coordinates) at the preset time point according to the target speed, time, and speed for storage for use by other modules.

[0080] Here, we only use the valid data of the last point, calculate the virtual scene coordinates of the UAV at the preset time point according to the target airspeed, time and speed, and store them in the data interaction module for use by other modules.

[0081] Other parts of this embodiment are the same as those of the above-mentioned Embodiment 1, so they will not be elaborated here.

[0082] Embodiment 5:

[0083] This embodiment further optimizes on the basis of the above-mentioned Embodiment 1. For example, Figure 5 As shown, in this embodiment, the data processing module and the special area processing module are connected, and the data interaction module and the special target processing module are connected to perform preset business processing according to the UAV's situational information. Judge the target trend according to the virtual scene coordinates of the UAV after fitting processing at a future fixed time. If the target will appear within the warning area range, implement warning processing. If the UAV has currently appeared within the warning range, implement the warning business processing. Both the warning and warning business processing are custom-made by humans, and the UAV navigation signal, control signal, and video transmission signal can be suppressed and interfered with simultaneously. Their interference patterns are all frequency-swept signals, only the interference frequency band and bandwidth are different.

[0084] In this embodiment, on the basis of the target information provided in the above-mentioned embodiment. According to the preset no-fly zone and restricted flight area settings, strike methods, strike times, judgment success conditions, and the preset maximum strike time, a closed-loop operation is realized. Transmit the strike device ID, target information, and strike actions to the plugin manager. The plugin manager matches the device plugin according to the ID and transmits the strike actions to the device through the UDP network socket to achieve the strike. The strike target in this embodiment refers to the UAV target.

[0085] The module group further includes a special area processing module, which can be connected to the data processing module. The module group further includes a special target processing module, which can be connected to the data synchronization and interaction center. The special area processing module mainly provides special areas such as no-takeoff areas and no-flight track areas, and realizes automatic defense within the area according to the specified strike strategy processing criteria.

[0086] In this embodiment, the data processing module performs automatic confrontation processing and has valid position information, sorts the priorities according to the target danger level and configures the servo turret to track. There is valid frequency information, configures the interference frequency using the interference instruction in the interference plugin, emits interference signals, and judges whether the time has timed out according to whether the target has disappeared. If so, exit; if not, return to the automatic confrontation processing.

[0087] Other parts of the embodiment are the same as those of the above-mentioned embodiment, so they will not be elaborated here.

[0088] Example 6

[0089] This example further optimizes on the basis of the above Example 1. In this example, as Figure 6 shown, based on the detection of communication signals, the jammer adopts digital full-band fast frequency conversion technology, uses multi-octave antenna technology and broadband power amplifier technology, and quickly responds to the main control countermeasure strategy, and can effectively deal with UAV targets in the frequency range of 600 MHz - 6 GHz. The device adopts a multi-band broadband scheme to realize the communication link for UAVs of multiple types of communication bands, and adopts a high-precision pan-tilt plus directional antenna scheme to realize 180° steering coverage interference and time-sharing multi-target interference. The jammer integrates various interference waveforms, including white noise, swept frequency signal, comb spectrum, editable arbitrary wave, etc., and can select targeted waveforms according to the characteristics of the communication signals of the UAV's uplink and downlink to block communication.

[0090] From the transmission power model of the communication and interference link, according to Figure 2 it can be easily deduced the communication interference equation expressed by the input interference-to-signal ratio of the target communication receiver:

[0091] ; ;

[0092] In the formula:

[0093] P ji and P si are the interference and signal input powers respectively;

[0094] P Tj and P Ts are the transmission powers of the interference and the signal respectively;

[0095] G Tj and G Ts are the interference and signal transmitting antenna gains respectively;

[0096] G Rj and G Rs are the interference and signal receiving antenna gains respectively;

[0097] L j and L s are the transmission path losses of the interference and the signal respectively;

[0098] L f is the frequency domain coincidence loss (filtering loss) of the interference and the signal;

[0099] L t is the time domain coincidence loss of the interference and the signal;

[0100] L p is the polarization loss.

[0101] Let the suppression coefficient be K, then from the interference equation, we get:

[0102]

[0103] The communication is suppressed, so the communication interference equation ensures that the above equation holds. This equation is the general form of the communication interference equation.

[0104] The radio interference equipment adopts a multi-autonomous interference source architecture to achieve full coverage and effective countermeasure against frequencies in the range of 600 MHz - 6000 MHz. The system working process is divided into 5 segments, namely 4 parts such as 600 MHz - 1000 MHz, 1000 MHz - 2000 MHz, 2000 MHz - 4000 MHz, 4000 MHz - 6000 MHz and the navigation frequency band of 1.15 GHz - 1.65 GHz. It can work in a time-sharing manner and also has the ability to work simultaneously. Its maximum interference bandwidth is 175 MHz and the bandwidth is adjustable. The interference signal separation and bandwidth extension are achieved through a fast frequency hopping method of not less than 40000 hops / s. The jammer equipment is as Figure 7 shown.

[0105] If the target is to predict entering the warning area based on the fitting data, then relevant warning processing is implemented. The warning processing is artificially specified: for example, the suppression method of navigation interference. If the target actually appears in the warning area, warning processing can be used: for example, implementing the suppression transmission of the video transmission signal and the control signal. The principle of the suppression signal is generated by the communication interference model of the jammer. Only the services are different and the generated waveform frequencies are different. The principle is the same, and they are all sweep signals.

[0106] Other parts of this embodiment are the same as those of the above Embodiment 1, so they will not be elaborated here.

[0107] Embodiment 7

[0108] This embodiment provides a passive detection and interference automation system based on an unmanned aerial vehicle, as Figure 2 shown, which includes a common processing module, a sub-device plug-in module, and a main control situation display module, where:

[0109] Interactions occur between the common processing module and the main control situation display module, and between the common processing module and the sub-device plug-in module;

[0110] The common processing module includes a task module, a curve fitting module, a data processing module, a data interaction module, a plug-in manager, a no-fly zone setting module, a no-fly zone warning module, a setting module, and a countermeasure processing module;

[0111] The sub-device plug-in module includes a main control plug-in unit, an interference plug-in unit, an electronic detection plug-in unit, a radar plug-in unit, and an optical plug-in unit; the radar original track information is obtained according to the radar device in the radar plug-in unit of the sub-device plug-in module, and the UAV spectrum information measured by the electronic detection device in the electronic detection plug-in unit of the sub-device plug-in module is used as the original data, and the original data is subjected to standardization processing and filtering processing to obtain the signal features of concern.

[0112] The main control situation display module includes a two-dimensional GIS engine unit, a point track processing unit, and an information display bar unit.

[0113] The passive detection and interference automation system is connected to the common processing module, the sub-device plug-in, and the main control situation display module. The common processing module includes a module group containing multiple functional modules and a data synchronization and interaction center capable of connecting to each functional module and realizing information exchange. The data synchronization and interaction center can receive the original tracks generated by the radar system scanning and the frequency band information generated by electronic detection. The data processing module performs fusion processing according to the corresponding algorithm. The countermeasure processing module inputs the fusion data according to the preset countermeasure strategy and accurately controls the device to perform automatic strikes through network communication.

[0114] The above is only a preferred embodiment of the present invention, and does not impose any form of limitation on the present invention. Any simple modification or equivalent change made to the above embodiments based on the technical essence of the present invention falls within the protection scope of the present invention.

Claims

1. An automated method for passive detection and interference based on drones, characterized in that, It includes the following steps: Step S1. Connect the passive detection and interference automation system to the common processing module, sub-device plug-in module, and main control situation display module; Step S2. Obtain the radar original track information according to the radar device in the sub-device plug-in module, use the UAV spectrum information measured by the electronic detection device as the original data, and perform standardization processing and filtering processing on the original data to obtain the signal features of interest; Step S3. Receive the radar original track data and signal data according to the data interaction module in the common processing module, and perform data fusion processing on the radar original track data and signal data according to the data processing module in the common processing module to obtain the actual coordinates of the current UAV; Step S4. Perform curve fitting processing of n-order linear fitting on the fusion data in the data interaction module of the common processing module, process the data after curve fitting in the curve fitting module of the common processing module, and calculate the virtual scene coordinates of the UAV from the data after curve fitting; Step S5. Connect the data processing module and the special area processing module, connect the data interaction module and the special target processing module, and realize the automation of passive detection and interference in the no-takeoff area and the no-flight track area according to the actual coordinates and virtual scene coordinates of the UAV, combined with the special area processing module and the special target processing module.

2. The automated method for passive detection and interference based on an unmanned aerial vehicle according to claim 1, wherein The method for obtaining the signal features of interest in step S2 includes: First, perform unpacking processing and preprocessing on the original data; Then, integrate the original data, and use the traveling ant clustering algorithm and the matching classification algorithm to perform the first classification on the original data to obtain the first classification result; Finally, evaluate the first classification result according to the information in the UAV feature library of the passive detection and interference automation system by using the intelligent matching algorithm, and at the same time perform secondary classification on the first classification result to obtain the processing result of the signal features of interest and send it to the data processing module; The signal features of interest include target model information, ID information, average energy information, and angle information.

3. An automated method for passive detection and interference based on an unmanned aerial vehicle according to claim 1, characterized in that, Step S3 includes: Obtain the UAV model information by comparing the signal data according to the spectrum feature recognition technology and the pre-established spectrum feature library; Detect the remote control and video transmission signals of the UAV according to the UAV model information, and effectively identify the remote control and video transmission signals of the UAV in the time domain and frequency domain.

4. The passive detection and interference automation method based on an unmanned aerial vehicle according to claim 1, wherein The method for curve fitting processing in step S4 includes: Express the curve fitting p as p = polyfit(x, y, n); where the curve fitting p is the coefficient of the polynomial p(x) of order n returned, and the order n is the best fitting order of the data in y in the least squares method; y is the value for fitting the longitude or latitude respectively, x is the natural increasing natural number, and polyfit() represents the curve fitting function; Arrange the coefficients in the curve fitting p in descending power, and take the length of p as n + 1; Select the nth-order fitting according to the curve characteristics and express it as ; In the curve fitting module, transform the data into an augmented matrix by elementary row transformation, use elementary transformation to transform the augmented matrix into a row echelon matrix, and back-substitute to find the coefficient solution of the equation polynomial p(x); After performing data processing on the data after curve fitting, take the last term value, and finally the longitude and latitude coordinate information of each target point after fitting can be obtained respectively.

5. A passive detection and interference automation method based on an unmanned aerial vehicle according to claim 1, characterized in that The said step S5 includes: Implementing closed-loop operation according to the pre-set no-fly zone and no-fly area settings, strike method, strike time, determination success condition, and maximum strike time; Transmitting the jammer device D to be struck, target information, and strike actions to the plug-in manager of the common processing module. The plug-in manager matches the device plug-in according to the strike device ID and transmits the strike actions to the device through the UDP network socket to achieve the strike.

6. The automated method for passive detection and interference based on an unmanned aerial vehicle according to claim 1, wherein The method for realizing the automation of passive detection and interference in the said step S5 further includes: Obtaining a communication interference equation represented by the input interference-to-signal ratio of the target communication receiver according to the transmission power model of the communication and interference link; outputting a power model capable of suppressing the UAV target control signal according to the communication interference equation; Judging whether the target device enters the warning area according to the fitting data prediction based on the power model. If so, give an alarm. If not, continue to make a judgment.

7. An unmanned aerial vehicle-based passive detection and interference automation system, characterized in that, It includes a common processing module, a sub-device plug-in module, and a main control situation display module, where: There is an interaction between the common processing module and the main control situation display module, and an interaction between the common processing module and the sub-device plug-in module; The sub-device plug-in module includes a communication module, which is used to obtain the original radar point track information; it is used to measure the UAV spectrum information as the original data, and perform standardization processing and filtering processing on the original data to obtain the signal characteristics of interest; The common processing module includes a data interaction module, a data processing module, and a curve fitting module, which is used to perform curve fitting processing of nth-order linear fitting on the fusion data, used to process the data after curve fitting, and calculate the data after curve fitting to Obtain the virtual scene coordinates of the UAV; Connect the data processing module and the special area processing module in the common processing module, connect the data interaction module and the special target processing module in the common processing module, and realize the automation of passive detection and interference in the no-takeoff area and the track no-fly area according to the actual coordinates and virtual scene coordinates of the UAV in combination with the special area processing module and the special target processing module; The main control situation display module is used to provide a human-computer interaction operation interface.

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