A full-band UAV countermeasure method and system based on acoustic, optical and electrical composite detection

Abstract

The present application provides a full-band countermeasure method and system for drones based on acoustic, optical and electrical composite detection. The present application generates a composite detection signal through synchronous collaborative detection of acoustic wave sensing, optical imaging and electromagnetic spectrum analysis; extracts drone operating mode characteristics and generates dynamic interference parameters based on the correlation between acoustic characteristic frequency bands and electromagnetic control frequency bands; generates synchronous interference instructions based on the time domain modulation characteristics of the dynamic interference parameters; combines the abnormal noise distribution characteristics captured by the acoustic module and the flight trajectory offset identified by the optical module to correct the power distribution and frequency band coverage of the interference instructions in real time and generate optimized interference parameters; finally, through the overlapping spectrum fusion of dynamic interference parameters and optimized interference parameters, a dynamic interference map is generated to achieve precise collaborative suppression, and achieve real-time and precise suppression of the full-band navigation, communication and power control signals of the target drone, significantly improving the countermeasure efficiency in complex scenarios.

Description

Technical Field

[0001] The present application relates to the field of drone detection and countermeasure technology, and in particular to a full-band drone countermeasure method and system based on acoustic, optical and electrical composite detection. Background Art

[0002] With the popularization of drone applications, extremely high requirements have been placed on the real-time, accuracy and spectrum resource utilization efficiency of drone jamming strategies.

[0003] The current mainstream technical solution to address this need is a dynamic jamming system based on electromagnetic spectrum analysis. This system scans the target drone's electromagnetic radiation signals in real time, extracting its navigation signals (such as GPS bands), communication signals (such as WiFi / image transmission bands), and control signal bands. Using machine learning algorithms to predict the drone's frequency hopping patterns, it dynamically generates a broadband jamming signal and employs a power-grading control mechanism to reduce interference energy in non-target frequency bands. This solution attempts to balance jamming efficiency and regulatory compliance through frequency band matching and power regulation.

[0004] However, existing solutions have significant flaws. First, they rely on a preset interference strategy library and cannot correlate the drone's acoustic characteristics (such as propeller noise) with the dynamic changes of electromagnetic signals in real time, resulting in insufficient suppression efficiency for adaptive frequency hopping or encrypted communication signals. Second, they rely on electromagnetic signal analysis in a single dimension and lack the fusion of acoustic positioning and optical trajectory data, making it difficult to simultaneously destroy the drone's navigation stability and power control link in complex terrain. Third, the power-grading control of broadband interference makes it difficult to accurately match the real-time frequency band offset of the target drone, which can easily cause false interference in legitimate frequency bands (such as civil aviation communication bands) and waste spectrum resources. Summary of the Invention

[0005] The present application provides a full-band countermeasure method and system for drones based on acoustic, optical and electrical composite detection, which is used to solve the problems in the existing technology of mismatch between static interference strategies and dynamic targets, lack of coordination of one-dimensional electromagnetic interference, and waste of spectrum resources and compliance risks.

[0006] In the first aspect, the present application provides a full-band countermeasure method for drones based on acoustic, optical and electrical composite detection, comprising:

[0007] Through the synchronous and coordinated detection of acoustic wave sensing, optical imaging and electromagnetic spectrum analysis, a composite detection signal covering the entire frequency band of the target UAV is generated;

[0008] Based on the correlation between the acoustic characteristic frequency band and the electromagnetic control frequency band in the composite detection signal, the operating mode characteristics of the target UAV are extracted to generate dynamic interference parameters for matching the interference requirements;

[0009] generating, based on the time-domain modulation characteristics of the dynamic interference parameters, synchronous interference instructions for target UAV signals, wherein the target UAV signals include navigation signals, communication signals, and power control signals;

[0010] Based on the abnormal noise distribution characteristics captured in advance by the acoustic detection module and the flight trajectory offset identified by the optical imaging module, the power allocation and frequency band coverage of the synchronous interference instruction are corrected in real time to generate optimized interference parameters;

[0011] The dynamic interference parameters and the optimized interference parameters are overlapped and spectrally fused within a preset cooperative suppression period to generate a dynamic interference map covering the entire working frequency band of the target UAV.

[0012] Optionally, based on the abnormal noise distribution characteristics captured in advance by the acoustic detection module and the flight trajectory offset identified by the optical imaging module, the power allocation and frequency band coverage of the synchronous interference instruction are corrected in real time to generate optimized interference parameters, including:

[0013] Extracting the gradient distribution of noise energy as it changes with spatial position from the abnormal noise distribution characteristics, locating the center area of ​​the power noise source of the target UAV according to the extreme value points of the gradient distribution, and generating a noise energy gradient map;

[0014] Based on the horizontal offset angle in the flight trajectory offset, calculating the lateral deviation ratio of the target UAV relative to the preset protection boundary as a frequency band offset coefficient;

[0015] Based on the areas in the noise energy gradient map where the noise energy gradient is greater than a preset threshold, dividing the space where the target UAV is located into high-interference-sensitive sub-areas, and generating sensitive area boundary coordinates corresponding to the high-interference-sensitive sub-areas;

[0016] Performing spatial mapping of the frequency band offset coefficient and the boundary coordinates of the sensitive area, and calculating a power gradient value of the navigation signal suppression frequency band and a coverage extension threshold of the communication signal suppression frequency band in the synchronization interference instruction;

[0017] The power distribution of the synchronous interference instruction is weighted in layers according to the power gradient value, and the frequency band coverage range of the synchronous interference instruction is dynamically expanded based on the coverage extension threshold to obtain optimized interference parameters.

[0018] Optionally, hierarchically weighting the power allocation of the synchronization interference instruction according to the power gradient value, and dynamically expanding the frequency band coverage range of the synchronization interference instruction based on the coverage extension threshold to obtain optimized interference parameters, including:

[0019] Dividing the power gradient value into multiple power levels according to a preset interference intensity level, and generating a weight distribution ratio for each power level based on the overlapping area between the spatial position corresponding to each power level and the boundary coordinates of the sensitive area;

[0020] Determine, based on the coverage extension threshold, the extension step lengths of the upper and lower frequency limits of the communication signal suppression frequency band, and generate a dynamic boundary of the frequency band coverage associated with the real-time horizontal offset direction of the target UAV;

[0021] Accumulating the power values ​​of the navigation signal suppression frequency band in the synchronization interference instruction layer by layer according to the weight distribution ratio to generate a hierarchical weighted navigation power distribution sequence;

[0022] Bidirectionally expanding the coverage of the communication signal suppression frequency band according to the dynamic boundary of the frequency band coverage to generate an expanded communication signal suppression interval;

[0023] The navigation power distribution sequence is superimposed on the communication frequency band suppression interval in the frequency domain to generate optimized interference parameters including a power-frequency band joint mapping relationship, wherein the power-frequency band joint mapping relationship matches the real-time spatial position and motion state of the target UAV.

[0024] Optionally, overlapping spectrum fusion of the dynamic interference parameters and the optimized interference parameters is performed within a preset collaborative suppression period to generate a dynamic interference spectrum covering the entire operating frequency band of the target UAV, including:

[0025] Dividing a plurality of interference time windows within the cooperative suppression period according to the time domain characteristics of the navigation signal suppression frequency band in the dynamic interference parameter, and generating a time-sharing weight coefficient corresponding to each time window;

[0026] Calculating the frequency band extension priority of the communication signal suppression frequency band within the interference time window based on the communication signal suppression frequency band coverage extension threshold in the optimized interference parameter, and generating a frequency band priority sequence;

[0027] Performing time-sequential frequency band association mapping on the time-sharing weight coefficient and the frequency band priority sequence to generate a power allocation ratio of the navigation signal suppression frequency band and a frequency band switching step size of the communication signal suppression frequency band within the interference time window;

[0028] Performing time window segment weighting on the navigation signal suppression power in the dynamic interference parameter according to the power allocation ratio to obtain a navigation interference power distribution after time domain modulation;

[0029] Performing periodic frequency shift expansion on the communication signal suppression frequency band in the optimized interference parameters according to the frequency band switching step size of the communication signal suppression frequency band, to generate a communication interference frequency band distribution after dynamic frequency domain expansion;

[0030] Performing spectrum energy superposition on the navigation interference power distribution after time domain modulation and the communication interference frequency band distribution after dynamic expansion in the frequency domain to generate an initial interference spectrum;

[0031] Based on the spatial position correlation between the acoustic characteristic frequency band in the dynamic interference parameters and the sensitive area boundary coordinates in the optimized interference parameters, the interference energy density in the initial interference map is regionally redistributed to generate a dynamic interference map covering the entire working frequency band of the target UAV.

[0032] Optionally, based on the spatial position correlation between the acoustic characteristic frequency band in the dynamic interference parameter and the boundary coordinates of the sensitive area in the optimized interference parameter, the interference energy density in the initial interference map is regionally redistributed to generate a dynamic interference map covering the entire operating frequency band of the target UAV, including:

[0033] Divide the airspace where the target UAV is located into a plurality of equally spaced spatial grid units according to the spatial distribution of the boundary coordinates of the sensitive area, and generate an acoustic energy density distribution matrix corresponding to the acoustic characteristic frequency band;

[0034] Based on the time domain characteristics of the navigation signal suppression frequency band in the dynamic interference parameters, calculating the electromagnetic energy superposition coefficient of each spatial grid unit within the cooperative suppression period, and generating a grid time domain weight sequence corresponding to the interference time window;

[0035] Performing space-time correlation mapping on the acoustic energy density distribution matrix and the grid time domain weight sequence to generate a composite energy density factor for each spatial grid unit;

[0036] Performing weighted correction on the interference energy of the corresponding spatial grid unit in the initial interference map according to the composite energy density factor to obtain a corrected interference energy distribution that matches the central area of ​​the target UAV power noise source;

[0037] Calculating the phase offset compensation amount of the communication signal suppression frequency band in the spatial grid unit based on the communication signal suppression frequency band coverage extension threshold in the optimized interference parameter, and generating a grid phase compensation sequence corresponding to the frequency band switching step size;

[0038] Performing phase synchronization adjustment on the communication signal suppression frequency band in the corrected interference energy distribution according to the grid phase compensation sequence to generate a phase-aligned interference energy distribution spectrum;

[0039] Based on the electromagnetic control frequency band range in the dynamic interference parameters, the interference energy distribution spectrum after phase alignment is subjected to frequency band truncation filtering to filter out the interference energy frequency band that exceeds the preset electromagnetic compliance constraint to generate a dynamic interference spectrum covering the full working frequency band of the target UAV.

[0040] Optionally, based on the correlation between the acoustic characteristic frequency band and the electromagnetic control frequency band in the composite detection signal, the operating mode characteristics of the target UAV are extracted, and dynamic interference parameters for matching the interference requirements are generated, including:

[0041] Extracting an acoustic pulse interval sequence associated with the propeller power noise of the target UAV from the acoustic characteristic frequency band, and generating an acoustic feature vector including an acoustic pulse interval mean and an acoustic pulse interval variance;

[0042] Separating the modulation period of the target UAV navigation signal and the frequency hopping step of the communication signal from the electromagnetic control frequency band, and generating an electromagnetic modulation feature vector including a modulation period set and a frequency hopping step set;

[0043] Calculate the time domain correlation coefficient between the mean value of the acoustic pulse interval and the modulation period of the navigation signal to generate the acoustic-electrical time domain correlation coefficient;

[0044] Determine the operating mode of the target UAV based on the acoustic-electrical time domain correlation and a preset correlation threshold interval, and generate a mode determination identifier, wherein the operating mode of the target UAV includes a navigation-dominated mode and a communication-dominated mode;

[0045] Based on the mode determination identifier, calling the corresponding navigation signal suppression frequency band range, communication signal suppression frequency band step size and power control signal interference weight from a preset interference strategy library to generate an initial interference parameter set;

[0046] Dynamically modifying the power control signal interference weight in the initial interference parameter set according to the acoustic pulse interval variance to generate a modified power control interference weight;

[0047] Calculating the adaptive adjustment amplitude of the communication signal suppression frequency band step size based on the statistical distribution characteristics of the frequency hopping step size set to generate a frequency domain control sequence;

[0048] The navigation signal suppression frequency band range, the corrected power control interference weight and the frequency domain control sequence are parameter-bound to generate dynamic interference parameters for matching interference requirements.

[0049] Optionally, generating a synchronous jamming instruction for a target UAV signal according to the time domain modulation characteristics of the dynamic jamming parameter includes:

[0050] According to the navigation signal suppression frequency band range in the dynamic interference parameters, the preset cooperative suppression period is divided into multiple interference time windows, and a time-sharing weight coefficient corresponding to each time window is generated;

[0051] Based on the adaptive adjustment amplitude of the communication signal suppression frequency band in the dynamic interference parameters, the frequency shift step length of the communication signal suppression frequency band in each interference time window is calculated to generate a frequency shift compensation sequence that matches the frequency hopping behavior of the target UAV;

[0052] Determining the pulse duty cycle of the power control signal suppression in each interference time window according to the power control signal interference weight in the dynamic interference parameter, and generating a pulse duty cycle sequence;

[0053] Based on the time-sharing weight coefficient, frequency shift compensation sequence and pulse duty cycle sequence, the time domain modulation parameters of the navigation signal suppression frequency band, the communication signal suppression frequency band and the power control signal suppression frequency band are respectively configured in a time-sharing manner to generate synchronous interference instructions.

[0054] Secondly, this application provides a full-band drone countermeasure system based on acoustic, optical and electrical composite detection, including:

[0055] The detection module is used to generate a composite detection signal covering the entire frequency band of the target UAV through the simultaneous coordinated detection of acoustic wave sensing, optical imaging and electromagnetic spectrum analysis;

[0056] An extraction module is used to extract the operating mode characteristics of the target UAV based on the correlation between the acoustic characteristic frequency band and the electromagnetic control frequency band in the composite detection signal, and generate dynamic interference parameters for matching the interference requirements;

[0057] a generating module, configured to generate a synchronous jamming instruction for a target UAV signal according to the time-domain modulation characteristics of the dynamic jamming parameter, wherein the target UAV signal includes a navigation signal, a communication signal, and a power control signal;

[0058] a correction module, configured to perform real-time correction of the power allocation and frequency band coverage of the synchronous jamming instruction based on the abnormal noise distribution characteristics previously captured by the acoustic detection module and the flight trajectory offset identified by the optical imaging module, thereby generating optimized jamming parameters;

[0059] The fusion module is used to perform overlapping spectrum fusion of the dynamic interference parameters and the optimized interference parameters within a preset collaborative suppression period to generate a dynamic interference map covering the entire operating frequency band of the target UAV.

[0060] In a third aspect, an embodiment of the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a full-band countermeasure method for drones based on acoustic, optical and electrical composite detection as described in the first aspect above.

[0061] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a full-band countermeasure method for drones based on acoustic, optical and electrical composite detection as described in the first aspect.

[0062] The embodiment of the present application generates a composite detection signal covering the entire frequency band of the target UAV through the synchronous collaborative detection of acoustic wave sensing, optical imaging and electromagnetic spectrum analysis, breaking through the frequency band coverage limitation of a single detection method; extracts the UAV working mode characteristics based on the cross-domain correlation between the acoustic characteristic frequency band and the electromagnetic control frequency band, dynamically generates interference parameters that match the interference requirements, and solves the mismatch problem between traditional static strategies and dynamic targets; further combines the acoustic abnormal noise distribution and optical trajectory offset data to correct the power distribution and frequency band coverage of the interference command in real time, and improves the interference accuracy under complex terrain; finally, through the overlapping spectrum fusion of dynamic interference parameters and optimized interference parameters, a dynamic interference map with time-frequency-space multi-dimensional coordination is generated, which realizes the full-band synchronous suppression of the UAV navigation, communication and power control signals, and significantly improves the countermeasure efficiency and electromagnetic compliance.

[0063] Furthermore, based on the acoustic noise energy gradient map, the center area of ​​the UAV's power noise source is located, and the frequency band offset coefficient is calculated in combination with the flight trajectory offset. The high-interference-sensitive sub-areas are divided and the boundary coordinates of the sensitive areas are generated. The navigation signal suppression power gradient value and the communication frequency band coverage expansion threshold are calculated through spatial mapping, realizing hierarchical weighting of interference power and dynamic expansion of frequency bands. This solution transforms the spatial correlation between acoustic noise energy distribution and optical trajectory offset into a basis for optimizing interference parameters, focusing interference energy on highly sensitive areas. At the same time, it dynamically adapts to the UAV's frequency band offset behavior, solving the problems of interference energy dispersion and rigid frequency band coverage in traditional solutions. It achieves precise delivery of interference power and on-demand expansion of frequency band resources in complex electromagnetic environments, effectively improving the synchronous suppression effect of power control links and communication signals.

[0064] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0066] Figure 1 A flowchart of a full-band UAV countermeasure method based on acoustic, optical, and electrical composite detection provided by the present application is shown;

[0067] Figure 2 The present invention provides a schematic structural diagram of a UAV full-band countermeasure system based on acoustic, optical and electrical composite detection;

[0068] Figure 3 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0069] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0070] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0071] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0072] Figure 1 The present invention provides a flowchart of a full-band countermeasure method for drones based on acoustic, optical and electrical composite detection, as shown in FIG. Figure 1 As shown, the method includes:

[0073] Step 101: Generate a composite detection signal covering the entire frequency band of the target UAV through simultaneous collaborative detection using acoustic wave sensing, optical imaging, and electromagnetic spectrum analysis.

[0074] In this step, acoustic wave sensing refers to the collection of acoustic signals generated by the target UAV through a microphone array, such as the periodic pulse intervals and fundamental frequency harmonic energy distribution generated by the rotation of the propeller; optical imaging refers to the use of a high-frame-rate camera to capture the flight trajectory, shape outline and attitude angle of the UAV, and generate optical data containing features such as pixel displacement and contour edge gradient; electromagnetic spectrum analysis refers to the use of a wideband receiver to scan the navigation signals, communication signals and control signals emitted by the UAV, and analyze their frequency band energy distribution, modulation type and frequency hopping rules; the composite detection signal is a multi-dimensional data set formed by the fusion of acoustic eigenvectors, optical trajectory matrices and electromagnetic spectrum feature tensors, covering the full-band signal characteristics of the UAV.

[0075] In an embodiment of the present application, the acoustic signal of the target drone is first collected by the microphone array of the acoustic wave sensing module, and the acoustic pulse interval sequence and fundamental harmonic energy distribution are extracted by short-time Fourier transform technology to generate an acoustic feature vector; secondly, the optical imaging module continuously captures drone images through a high-frame rate camera, calculates the pixel displacement between adjacent frames based on the optical flow method, and extracts the gradient change of the drone outline in combination with the edge detection algorithm to generate an optical trajectory matrix containing the horizontal offset angle and attitude angle; at the same time, the electromagnetic spectrum analysis module uses a wideband receiver covering multiple frequency bands to scan the target signal in real time, generates a frequency band energy distribution map through fast Fourier transform, and uses a modulation recognition algorithm to analyze the modulation parameters of the navigation signal and the frequency hopping step size of the communication signal, and outputs an electromagnetic spectrum feature tensor; finally, the acoustic feature vector, optical trajectory matrix and electromagnetic spectrum feature tensor are aligned according to the timestamp, and a composite detection signal is generated through a multimodal data fusion algorithm, whose data dimensions cover the time domain, spatial domain and frequency domain characteristics of the entire working frequency band of the drone.

[0076] For example, in the low-altitude protection scenario of an airport, the acoustic wave sensing module is deployed around the runway, and the acoustic signal of the target drone is collected through a microphone array. After analysis, the periodic pulse interval and the acoustic feature vector of the specific fundamental frequency are extracted; the optical imaging module captures the drone approaching the runway with a horizontal offset angle through a high-frame rate camera, and the optical flow method calculates its movement speed and generates an optical trajectory matrix; the electromagnetic spectrum analysis module detects that the drone transmits a modulated signal in the navigation frequency band, and at the same time hops the frequency with a specific step size in the communication frequency band, and outputs the corresponding electromagnetic spectrum feature tensor; finally, the central processing unit aligns the acoustic features, optical trajectories and electromagnetic spectrum features according to the timestamps, and generates a composite detection signal through data fusion, providing full-band data support for the subsequent interference parameter generation.

[0077] Step 102: extracting the operating mode characteristics of the target UAV based on the correlation between the acoustic characteristic frequency band and the electromagnetic control frequency band in the composite detection signal, and generating dynamic interference parameters for matching the interference requirements;

[0078] In this step, the working mode characteristics refer to the UAV operating status identified by analyzing the correlation between the acoustic pulse interval and the electromagnetic signal modulation period, such as hovering, straight flight or evasive maneuver; the dynamic interference parameters refer to the navigation signal suppression frequency band range, communication signal frequency hopping tracking step and power control signal interference weight generated according to the working mode; the acoustic-electric correlation matching matrix refers to the numerical matrix generated by calculating the correlation between the mean value of the acoustic pulse interval and the electromagnetic modulation period, which is used to quantify the synergy between the acoustic characteristics and the electromagnetic signal.

[0079] In this embodiment, first, the pulse interval mean of the acoustic characteristic frequency band and the modulation period of the electromagnetic control frequency band are extracted from the composite detection signal, and the time domain correlation between the two is calculated by the Pearson correlation coefficient algorithm to generate an acoustic-electrical correlation matching matrix; secondly, according to a preset matching threshold, the UAV working mode is determined to be a navigation-dominated mode or a communication-dominated mode, and a mode determination identifier is generated; then, based on the mode determination identifier, the corresponding navigation signal suppression frequency band, communication signal frequency hopping tracking step and power control signal interference weight are called from the preset interference strategy library; finally, the power control signal interference weight is dynamically corrected according to the pulse interval variance of the acoustic characteristic frequency band, and dynamic interference parameters including the frequency band range, frequency hopping step and interference weight are generated.

[0080] For example, in a low-altitude airport defense scenario, the composite detection signal includes an acoustic eigenvector (pulse interval mean 0.12 seconds, fundamental frequency 200 Hz), an optical trajectory matrix (horizontal offset angle 15°, velocity 12 m / s), and an electromagnetic spectrum characteristic tensor (navigation signal 1559-1606 MHz BPSK modulation, communication signal 2400-2483 MHz with a 20 MHz frequency hopping step). The system first calculates the correlation coefficient between the 0.12-second acoustic pulse interval mean and the 1-ms navigation signal modulation period, which is 0.85 (greater than the threshold of 0.8), indicating a navigation-dominated mode. The system then uses a preset strategy library to determine the navigation suppression frequency band of 1559-1606 MHz, a communication frequency hopping step of 20 MHz, and a power control interference weight of 0.6. The system then adjusts the power control interference weight to 0.7 based on the 0.02-second squared acoustic pulse interval variance (reflecting power system fluctuations), ultimately generating dynamic interference parameters.

[0081] Step 103: generating a synchronous jamming instruction for a target UAV signal based on the time-domain modulation characteristics of the dynamic jamming parameter, wherein the target UAV signal includes a navigation signal, a communication signal, and a power control signal;

[0082] In this step, the time-sharing weight coefficient refers to the interference intensity distribution ratio of each time period within the interference time window divided according to the navigation signal suppression frequency band range; the frequency shift compensation sequence refers to the frequency band switching step sequence dynamically generated to match the frequency hopping behavior of the communication signal; the pulse duty cycle sequence refers to the proportion sequence of the high-level duration in the periodic interference pulse suppressed by the power control signal.

[0083] In this embodiment, first, according to the navigation signal suppression frequency band range in the dynamic interference parameters, the preset collaborative suppression period is divided into multiple interference time windows, and a time-sharing weight coefficient is assigned to each window to control the interference intensity in different time periods; secondly, based on the adaptive adjustment amplitude of the communication signal suppression frequency band, the frequency shift step of the communication signal suppression frequency band in each time window is calculated, and a frequency shift compensation sequence that matches the target UAV frequency hopping behavior in real time is generated; then, according to the power control signal interference weight, the pulse duty cycle of the power control signal suppression in each time window is determined, and a pulse sequence whose duty cycle is dynamically adjusted with the power system fluctuation is generated; finally, the time-sharing weight coefficient, the frequency shift compensation sequence and the pulse duty cycle sequence are configured with time domain modulation parameters to generate a synchronous interference instruction including navigation signal time-sharing suppression, communication signal frequency hopping tracking and power control pulse interference.

[0084] For example, in the low-altitude protection scenario at an airport, dynamic interference parameters include the navigation signal suppression frequency band, the communication signal frequency hopping step, and the power control interference weight. The system first divides the cooperative suppression period into five interference time windows. The first two windows are assigned high time-sharing weight coefficients to focus on suppressing the navigation signal. Then, a frequency shift compensation sequence is generated based on the communication frequency hopping step to ensure that the interference signal closely follows the UAV communication channel switching. At the same time, a pulse duty cycle sequence is generated based on the power control interference weight to dynamically destroy the motor control signal. Finally, a synchronous interference command is generated:

[0085] Step 104: Based on the abnormal noise distribution characteristics previously captured by the acoustic detection module and the flight trajectory offset identified by the optical imaging module, the power allocation and frequency band coverage of the synchronous jamming instruction are modified in real time to generate optimized jamming parameters.

[0086] In this step, the noise energy gradient map refers to the gradient distribution map reflecting the change of noise energy with spatial position generated by analyzing the abnormal noise distribution characteristics captured by the acoustic module, which is used to locate the highly interference-sensitive areas of the UAV power noise source; the frequency band offset coefficient refers to the lateral deviation ratio of the UAV relative to the preset protection boundary calculated based on the flight trajectory offset (such as the horizontal offset angle) identified by the optical module, which is used to dynamically adjust the interference frequency band coverage range; the sensitive area boundary coordinates refer to the boundary coordinates of the spatial area where the noise energy exceeds the preset threshold, which is divided from the noise energy gradient map, and is used to guide the focused allocation of interference power; the power gradient value refers to the interference power allocation weight generated according to the spatial mapping relationship between the noise energy gradient and the frequency band offset coefficient; the coverage extension threshold refers to the communication signal suppression band extension boundary dynamically calculated based on the frequency band offset coefficient.

[0087] In this embodiment, first, the abnormal noise distribution characteristics are obtained through the acoustic detection module, and the gradient descent algorithm is used to extract the gradient distribution of noise energy with spatial position, generate a noise energy gradient map, and locate the high interference sensitive area of ​​the UAV power noise source; secondly, based on the horizontal offset angle of the flight trajectory identified by the optical imaging module, the lateral deviation ratio of the UAV relative to the protection boundary is calculated by geometric projection, and the frequency band offset coefficient is generated; then, according to the area where the gradient value exceeds the preset threshold in the noise energy gradient map, the high interference sensitive sub-area is divided, and its boundary coordinates are extracted; then, the frequency band offset coefficient is spatially mapped with the boundary coordinates of the sensitive area, and the power gradient value of the navigation signal suppression band and the coverage extension threshold of the communication signal suppression band are generated by the linear weighted algorithm; finally, the power allocation of the synchronous interference instruction is hierarchically weighted according to the power gradient value, and the range of the communication signal suppression band is expanded based on the coverage extension threshold, so as to generate optimized interference parameters matching the real-time position and motion state of the UAV.

[0088] For example, in the low-altitude protection scenario of an airport, the acoustic detection module detects that the noise energy gradient in the right front area of ​​the drone is significantly higher than that in other areas, generates a noise energy gradient map and locates the boundary coordinates of the highly sensitive area; the optical imaging module identifies that the drone's flight trajectory is offset to the left, and calculates its lateral deviation ratio relative to the airport protection boundary as the frequency band offset coefficient; the system spatially maps the frequency band offset coefficient with the boundary coordinates of the sensitive area to generate a power gradient value (high weight) for the right front area and a coverage extension threshold (extended to the left) for the communication suppression frequency band; based on this, the power distribution of the synchronous interference command is corrected: the navigation signal suppression power in the right front area is increased, and the communication suppression frequency band is expanded to the left; and finally, the optimized interference parameters are generated to ensure that the interference energy is focused on the current position and movement direction of the drone.

[0089] Step 105: performing overlapping spectrum fusion on the dynamic interference parameters and the optimized interference parameters within a preset cooperative suppression period to generate a dynamic interference spectrum covering the entire operating frequency band of the target UAV;

[0090] In this step, the time-sharing weight coefficient refers to the interference intensity weight assigned to each window after the collaborative suppression period is divided into multiple interference time windows according to the navigation signal suppression frequency band characteristics in the dynamic interference parameters; the frequency band priority sequence refers to the priority ranking of the communication signal suppression frequency band extension in each time window calculated based on the communication signal suppression frequency band coverage extension threshold in the optimized interference parameters; the grid time domain weight sequence refers to the time domain interference energy superposition weight generated for different spatial grids based on the spatial correlation between the acoustic characteristic frequency band and the boundary coordinates of the sensitive area.

[0091] In this embodiment, first, based on the time domain characteristics of the navigation signal suppression frequency band in the dynamic interference parameters (such as the pulse interval), the cooperative suppression period is divided into multiple interference time windows, and a time-sharing weight coefficient is assigned to each window to control the navigation signal suppression intensity in different time periods. Secondly, based on the communication signal suppression frequency band coverage extension threshold in the optimized interference parameters, the extension priority of the communication signal suppression frequency band in each time window is calculated to generate a frequency band priority sequence. Then, the time-sharing weight coefficient and the frequency band priority sequence are mapped in a time-band correlation manner to generate the navigation signal suppression power allocation ratio and the communication signal suppression frequency band switching step size in each time window. Subsequently, the navigation signal suppression power is weighted in time domain according to the power allocation ratio to generate a time domain modulated navigation interference power distribution; at the same time, the communication signal suppression frequency band is periodically frequency shifted and expanded according to the frequency band switching step to generate a frequency domain dynamically expanded communication interference frequency band distribution; the navigation interference power distribution and the communication interference frequency band distribution are spectrally superimposed to generate an initial interference map; finally, based on the spatial correlation between the acoustic characteristic frequency band in the dynamic interference parameters and the boundary coordinates of the sensitive area in the optimized interference parameters, the interference energy density in the initial interference map is spatially gridded and redistributed to generate a dynamic interference map covering the entire working frequency band of the UAV.

[0092] For example, in the low-altitude protection scenario at an airport, dynamic interference parameters include the navigation signal suppression frequency band and the communication signal frequency hopping step size. The optimized interference parameters include the boundary coordinates of the sensitive area (the high-interference sensitive area in the right front) and the communication frequency band coverage expansion threshold (extending to the left). The system first divides the coordinated suppression period into multiple interference time windows, assigning high time-sharing weight coefficients to the first two windows to focus on suppressing the navigation signal. A frequency band priority sequence is generated based on the communication frequency band coverage expansion threshold (the front window has high priority). A time-series-frequency band mapping is used to generate the navigation signal suppression power allocation ratio and the communication signal frequency band switching step size. The navigation signal suppression power is then weighted in time domain segments, while the communication suppression frequency band is periodically shifted to the left. The navigation power distribution is superimposed on the communication frequency band expansion to generate an initial interference map. Finally, the initial map is spatially gridded based on the boundary coordinates of the sensitive area (in the right front), increasing the interference energy density in the right front area and generating a dynamic interference map covering the entire frequency band of the drone's navigation, communication, and power control.

[0093] In order to solve the problem in the prior art that interference parameters cannot dynamically adapt to the target position and movement trend, based on this, in some embodiments, according to step 104, based on the abnormal noise distribution characteristics pre-captured by the acoustic detection module and the flight trajectory offset identified by the optical imaging module, the power allocation and frequency band coverage of the synchronous interference instruction are corrected in real time to generate optimized interference parameters, including:

[0094] Step 201: extracting the gradient distribution of noise energy varying with spatial position from the abnormal noise distribution characteristics, locating the center area of ​​the power noise source of the target UAV according to the extreme value points of the gradient distribution, and generating a noise energy gradient map;

[0095] In this step, the noise energy gradient distribution refers to the spatial gradient field formed by analyzing the abnormal noise signals captured by the acoustic module and calculating the intensity change rate of the noise energy at different spatial positions; the central area of ​​the dynamic noise source refers to the spatial area corresponding to the extreme point of the noise energy gradient distribution (that is, the maximum or minimum gradient point), which represents the position of the main noise radiation source of the UAV power system (such as propellers and motors).

[0096] In this embodiment, first, the noise signal of the target drone is collected through the distributed microphone array of the acoustic detection module, and the noise energy value of each microphone node is calculated using the short-time energy integration algorithm; secondly, based on the spatial coordinates and noise energy value of each node, the gradient distribution of the noise energy in three-dimensional space is calculated using the gradient descent algorithm to generate a noise energy gradient field; then, the local extreme points of the noise energy gradient field (such as the gradient maximum point) are extracted, and the central area of ​​the dynamic noise source is located according to the spatial coordinates of the extreme points; finally, the gradient distribution and the extreme point coordinates are mapped to a spatial grid model to generate a visually expressed noise energy gradient map.

[0097] Step 202: Calculate the lateral deviation ratio of the target UAV relative to the preset protection boundary based on the horizontal deviation angle in the flight trajectory deviation as a frequency band deviation coefficient;

[0098] In this step, the lateral deviation ratio refers to the degree to which the UAV deviates from the preset protection boundary in the horizontal direction, expressed as a percentage or normalized value, reflecting its lateral displacement relative to the boundary; the frequency band offset coefficient refers to the parameter for dynamically adjusting the communication suppression frequency band range according to the lateral deviation ratio, which is used to expand or offset the interference frequency band to adapt to the change in the UAV position.

[0099] In this embodiment, the optical imaging module first acquires the horizontal deviation angle of the drone's flight trajectory. Based on the geometric coordinates of a pre-set protection boundary (e.g., the boundary of an airport runway), trigonometric functions are used to calculate the shortest lateral distance between the drone's current position and the boundary. This distance is then compared to the total effective width of the protection boundary to generate a lateral deviation ratio. Secondly, based on the sign (left or right) and magnitude of the lateral deviation ratio, the coverage direction and expansion range of the communication suppression frequency band are dynamically adjusted to generate a frequency band deviation coefficient. For example, if the drone deviates to the right from the boundary, the frequency band deviation coefficient will indicate that the interference frequency band will expand to the right. A larger deviation ratio indicates a greater frequency band expansion.

[0100] Step 203: based on the areas in the noise energy gradient map where the noise energy gradient is greater than a preset threshold, the space where the target UAV is located is divided into high-interference-sensitive sub-areas, and sensitive area boundary coordinates corresponding to the high-interference-sensitive sub-areas are generated;

[0101] In this step, the high-interference-sensitive sub-area refers to the spatial area where the noise energy gradient is significantly higher than the preset threshold, which represents the core radiation range of the UAV power noise source; the sensitive area boundary coordinates refer to the vertex coordinate set that describes the outer contour of the high-interference-sensitive sub-area generated by spatial geometric calculation, which is used to define the focusing boundary of the interference energy distribution.

[0102] In this embodiment, first, based on the gradient values ​​of each spatial position in the noise energy gradient map, areas with gradients exceeding a preset threshold (such as gradient values ​​higher than twice the mean) are screened out; secondly, based on a spatial neighborhood connectivity analysis algorithm (such as the region growing method), adjacent qualified gradient areas are merged into continuous high-interference-sensitive sub-areas; then, a contour extraction algorithm (such as Marching Cubes) is used to model the three-dimensional boundaries of the high-interference-sensitive sub-areas to generate an outer contour point set; finally, the contour point set is converted into a vertex coordinate sequence in a geographic coordinate system to form the boundary coordinates of the sensitive area.

[0103] Step 204: spatially map the frequency band offset coefficient to the boundary coordinates of the sensitive area, and calculate the power gradient value of the navigation signal suppression frequency band and the coverage extension threshold of the communication signal suppression frequency band in the synchronization interference instruction;

[0104] In this step, spatial mapping refers to the process of associating the frequency band offset coefficient (characterizing the horizontal deviation ratio of the drone) with the boundary coordinates of the sensitive area (characterizing the spatial range of the high-interference sensitive sub-area) through geometric projection or interpolation algorithm; the power gradient value refers to the navigation signal suppression power weight value generated according to the spatial distribution of the boundary coordinates of the sensitive area and the frequency band offset coefficient, which is used to distribute differentiated interference energy in different spatial sub-areas; the coverage extension threshold refers to the expansion amplitude of the communication signal suppression frequency band range dynamically adjusted based on the frequency band offset coefficient, which is used to adapt to the frequency band drift caused by the drone position offset.

[0105] In this embodiment, first, the frequency band offset coefficient (such as right deviation +20%) is mapped to the geographic space corresponding to the boundary coordinates of the sensitive area through a spatial projection algorithm, and the relative position relationship between the deviation direction of the drone and the sensitive area is calculated; secondly, based on the mapping result, a linear weighted algorithm is used to generate power gradient values ​​for different sub-areas in the sensitive area (such as a higher gradient value is assigned to the right front sensitive area); at the same time, based on the deviation direction (left / right) and amplitude of the frequency band offset coefficient, the expansion direction (left shift / right shift) and expansion amplitude (such as expansion of 20MHz) of the communication signal suppression band are determined to generate a coverage extension threshold; finally, the power gradient value and the coverage extension threshold are integrated into the synchronous interference instruction to form a space-frequency band joint optimization parameter.

[0106] Step 205: performing hierarchical weighting on the power allocation of the synchronization interference instruction according to the power gradient value, and dynamically expanding the frequency band coverage of the synchronization interference instruction based on the coverage extension threshold to obtain optimized interference parameters;

[0107] In this step, hierarchical weighting refers to dividing the interference power into multiple intensity levels according to spatial sensitivity based on the power gradient value, and assigning a weight ratio to each level; dynamic expansion refers to directional adjustment of the upper and lower limit frequencies of the communication suppression band based on the coverage extension threshold to adapt to the frequency band drift caused by the drone position offset; optimized interference parameters refer to the interference strategy parameters containing the joint mapping relationship of power and frequency bands generated by power hierarchical weighting and dynamic band expansion, which match the real-time position and motion status of the drone.

[0108] In this embodiment, power gradient values ​​are first divided into multiple levels according to preset interference intensity levels (e.g., high, medium, and low). A weight distribution ratio is calculated based on the overlap between the spatial region corresponding to each level and the boundary coordinates of the sensitive area. For example, the larger the coverage area of ​​a high-gradient sensitive area, the higher the weight of its corresponding power level. Secondly, based on the coverage extension threshold, the extension direction (left / right extension) and step size of the upper and lower frequency limits of the communication signal suppression band are determined to generate a dynamic frequency band coverage boundary associated with the horizontal offset direction of the drone. Next, the power values ​​of the navigation signal suppression band are accumulated layer by layer according to the weight distribution ratio to generate a hierarchically weighted navigation power distribution sequence. Simultaneously, the communication suppression band is bidirectionally extended according to the dynamic frequency band coverage boundary to generate an expanded communication band suppression interval. Finally, the navigation power distribution sequence and the communication band suppression interval are superimposed in the frequency domain to generate optimized interference parameters containing a joint power-frequency band mapping relationship.

[0109] In order to solve the problems of interference energy dispersion and rigid frequency band coverage in the prior art, based on this, in some embodiments, according to step 205, the power allocation of the synchronization interference instruction is hierarchically weighted according to the power gradient value, and the frequency band coverage range of the synchronization interference instruction is dynamically expanded based on the coverage extension threshold to obtain optimized interference parameters, including:

[0110] Step 301: Divide the power gradient value into multiple power levels according to a preset interference intensity level, and generate a weight distribution ratio for each power level based on the overlapping area between the spatial position corresponding to each power level and the boundary coordinates of the sensitive area;

[0111] In this step, the power level refers to the interference power level (such as high, medium, and low) divided according to the intensity range of the power gradient value, which is used to differentiate the distribution of interference energy; the weight distribution ratio refers to the percentage of the interference power corresponding to each power level in the total power, which is determined by the proportion of the overlapping area between the coverage area of ​​the level and the sensitive area.

[0112] In this embodiment, first, according to the preset interference intensity level (such as high, medium and low levels), the power gradient value is divided into corresponding power level intervals, for example, the high gradient value interval corresponds to the high power level; secondly, through the spatial superposition analysis algorithm, the overlapping area of ​​the spatial area corresponding to each power level and the boundary coordinates of the sensitive area is calculated to obtain the area ratio of each level; then, the total interference power is normalized and distributed according to the area ratio to generate a weight distribution ratio; for example, if the coverage area of ​​the high power level accounts for 60% of the sensitive area, then its weight distribution ratio is 60%.

[0113] Step 302: Determine the extension step lengths of the upper and lower frequency limits of the communication signal suppression frequency band based on the coverage extension threshold, and generate a dynamic frequency band coverage boundary associated with the real-time horizontal offset direction of the target UAV;

[0114] In this step, the coverage extension threshold refers to the communication signal suppression frequency band extension amplitude parameter set according to the drone's horizontal offset ratio, which is used to dynamically adjust the frequency band coverage range; the extension step refers to the single adjustment amount of the frequency band upper or lower limit frequency calculated based on the coverage extension threshold, which determines the granularity of the frequency band extension; the frequency band coverage dynamic boundary refers to the communication signal suppression frequency band range boundary updated in real time according to the extension step and the drone's horizontal offset direction (left / right), to ensure that the interference range adapts to changes in the target position.

[0115] In this embodiment, first, according to the coverage extension threshold and the real-time horizontal offset direction of the UAV (such as right offset), the horizontal offset ratio is converted into the frequency domain expansion direction through the spatial projection algorithm (right offset corresponds to right expansion of the upper limit frequency, and left offset corresponds to left expansion of the lower limit frequency); secondly, the expansion step size is calculated based on the coverage extension threshold (such as step size = threshold value / number of expansions), and the increment of each frequency band adjustment is determined; then, according to the expansion step size, the upper and lower limit frequencies of the communication signal suppression band are dynamically adjusted (such as the upper limit frequency increases the step size successively when it is right offset, and the lower limit frequency decreases the step size successively when it is left offset), and the dynamic boundary of the frequency band coverage that matches the real-time position of the UAV is generated.

[0116] Step 303: Accumulate the power values ​​of the navigation signal suppression frequency band in the synchronization interference instruction layer by layer according to the weight distribution ratio to generate a hierarchical weighted navigation power distribution sequence;

[0117] In this step, layer-by-layer accumulation refers to the process of superimposing the power values ​​corresponding to the weight distribution ratios in order from high to low according to the power level to form a cumulative sum; the navigation power distribution sequence refers to a set of interference power distribution parameters sorted by power level, which characterizes the distribution of navigation signal suppression intensity in different spatial regions.

[0118] In this embodiment, first, according to the weight distribution ratio (such as 60%, 30% and 10% for high, medium and low levels respectively), the total power value of the navigation signal suppression frequency band in the synchronization interference instruction is proportionally divided into the initial power values ​​of the corresponding levels; secondly, starting from the high power level, the power value of the current level is superimposed with the cumulative sum of the previous level through the accumulation algorithm to generate a power distribution sequence that increases layer by layer; for example, the power of the high level is 60% of the total power, the 30% of the middle level is superimposed to 90% after accumulation, and the 10% of the low level is superimposed to 100% after accumulation; finally, a navigation power distribution sequence sorted by level is generated to ensure that higher power density is allocated to highly sensitive areas.

[0119] Step 304: bidirectionally expand the coverage of the communication signal suppression frequency band according to the frequency band coverage dynamic boundary to generate an expanded communication signal suppression interval;

[0120] In this step, bidirectional expansion refers to the expansion or contraction of the upper and lower frequency limits of the communication signal suppression band based on the directional parameters of the frequency band coverage dynamic boundary (such as left / right offset indication) to adapt to the frequency band drift caused by the horizontal offset of the drone; the communication band suppression interval refers to the frequency band range formed after expansion that contains the updated upper and lower frequency limits, which is used to dynamically cover the actual working frequency band of the target drone communication signal.

[0121] In this embodiment, first, the expansion direction recorded in the dynamic boundary of the frequency band coverage is analyzed (such as right deviation indicates that the upper limit frequency is expanded to the right, and left deviation indicates that the lower limit frequency is expanded to the left) and the expansion step (such as 5MHz expansion each time); secondly, the upper and lower limit frequencies of the communication signal suppression band are adjusted in both directions according to the expansion direction: if it is right-deflected, the upper limit frequency is increased by steps, and the lower limit frequency is maintained or fine-tuned to maintain the bandwidth; if it is left-deflected, the lower limit frequency is decreased by steps, and the upper limit frequency is maintained or fine-tuned; if two-way expansion is required, the upper and lower limits are adjusted synchronously in proportion. Then, a new communication band suppression interval is generated based on the expanded upper and lower limit frequencies to ensure that it completely covers the real-time communication band of the drone.

[0122] Step 305: Superimpose the navigation power distribution sequence and the communication frequency band suppression interval in the frequency domain to generate optimized interference parameters including a power-frequency band joint mapping relationship, wherein the power-frequency band joint mapping relationship matches the real-time spatial position and motion state of the target UAV;

[0123] In this step, frequency domain superposition refers to integrating the power distribution sequence of the navigation signal suppression frequency band with the coverage interval of the communication signal suppression frequency band to form a unified interference energy allocation strategy; the power-band joint mapping relationship refers to the correspondence between the interference power allocation and frequency band coverage parameters dynamically associated with the real-time spatial position and motion state of the UAV, such as the collaborative strategy of high-power suppression in the right front corresponding to the right extension of the communication frequency band.

[0124] In this embodiment, first, the navigation power distribution sequence is mapped to a spatial grid model to generate the power density distribution of the navigation signal suppression band; second, the communication band suppression interval is divided into multiple sub-bands according to the frequency band expansion direction, and the interference energy is allocated according to the frequency band priority; then, through the frequency domain energy superposition algorithm, the navigation power density distribution and the communication band interference energy distribution are jointly mapped to generate optimized interference parameters that include space-power relationship, frequency band-coverage relationship and dynamic matching rules.

[0125] In order to solve the problem of separation between time domain and frequency domain interference strategies in the prior art, in some embodiments, according to step 105, the dynamic interference parameters and the optimized interference parameters are overlapped and fused within a preset cooperative suppression period to generate a dynamic interference map covering the entire operating frequency band of the target UAV, including:

[0126] Step 401: Divide a plurality of interference time windows within a cooperative suppression period according to the time domain characteristics of the navigation signal suppression frequency band in the dynamic interference parameter, and generate a time-sharing weight coefficient corresponding to each time window;

[0127] In this step, the time domain characteristics of the navigation signal suppression frequency band refer to the time modulation characteristics of the navigation signal suppression strategy, such as pulse interval, duration and duty cycle; the interference time window refers to the continuous time period into which the cooperative suppression period is divided according to the time modulation characteristics, which is used to implement the differentiated interference strategy in stages; the time-sharing weight coefficient refers to the interference intensity weight ratio assigned to each interference time window, which is used to dynamically adjust the suppression intensity in different time periods.

[0128] In this embodiment, first, the time-domain modulation characteristics of navigation signal suppression in the dynamic interference parameters (such as pulse interval and duty cycle) are analyzed, and the cooperative suppression period is divided into multiple continuous time windows based on the pulse interval period; second, according to the target intensity requirement of navigation signal suppression in each time window (such as high-intensity suppression in the initial stage and gradual attenuation thereafter), a linear weighting algorithm is used to assign a time-sharing weight coefficient to each window.

[0129] Step 402: Calculate the frequency band extension priority of the communication signal suppression frequency band within the interference time window based on the communication signal suppression frequency band coverage extension threshold in the optimized interference parameter, and generate a frequency band priority sequence;

[0130] In this step, the frequency band extension priority refers to the priority level assigned to the frequency band extension behavior in different interference time windows based on the coverage extension threshold of the communication signal suppression frequency band and the real-time motion status of the UAV; the frequency band priority sequence refers to the set of frequency band extension priority parameters sorted by time window, which represents the urgency of the frequency band extension in each time period.

[0131] In this embodiment, first, based on the communication signal suppression frequency band coverage extension threshold (such as the maximum expansion amplitude) in the optimized interference parameters, combined with the real-time movement speed and offset direction of the UAV, the frequency band extension priority within each interference time window is calculated through a dynamic priority algorithm; secondly, a frequency band priority sequence is generated in the order of the time windows according to the priority calculation results.

[0132] Step 403: Perform time-series frequency band association mapping on the time-sharing weight coefficient and the frequency band priority sequence to generate a power allocation ratio for the navigation signal suppression frequency band and a frequency band switching step for the communication signal suppression frequency band within the interference time window;

[0133] In this step, time-series frequency band association mapping refers to the process of associating and calculating the time-sharing weight coefficient (characterizing the interference intensity weight of different time windows) with the frequency band priority sequence (characterizing the urgency of frequency band expansion in different time windows) through a cross-domain parameter fusion algorithm; the power allocation ratio refers to the power allocation weight of the navigation signal suppression frequency band in each time window generated by the mapping relationship; the frequency band switching step refers to the step size of the communication signal suppression frequency band expansion or switching in each time window generated by the mapping relationship.

[0134] In this embodiment, first, the time-sharing weight coefficient and the frequency band priority sequence are arranged in the order of the time window into a two-dimensional parameter matrix, and the associated mapping weight of the two is calculated by matrix multiplication; secondly, the power allocation ratio of the navigation signal suppression frequency band is extracted based on the associated mapping weight, wherein the time-sharing weight coefficient dominates the power allocation intensity of the time dimension, and the frequency band priority sequence constrains the expansion demand of the frequency domain dimension; at the same time, the frequency band switching step of each time window is calculated based on the dynamic change amplitude of the frequency band priority sequence, and the higher the priority, the larger the step

[0135] Step 404: performing time window segment weighting on the navigation signal suppression power in the dynamic interference parameter according to the power allocation ratio to obtain a navigation interference power distribution after time domain modulation;

[0136] In this step, time window segment weighting refers to the process of weighting the navigation signal suppression power in each interference time window according to the power allocation ratio; the navigation interference power distribution after time domain modulation refers to the set of interference power allocation parameters arranged in the order of time windows, which characterizes the dynamic power intensity of navigation signal suppression in different time periods.

[0137] In this embodiment, first, the total power of the navigation signal suppression in the dynamic interference parameters is split into initial power values ​​of each time window according to a preset power allocation ratio; second, the initial power values ​​of each time window are weighted and superimposed according to the time-sharing weight coefficient to generate a weighted power value of each time window; then, the weighted power values ​​are accumulated in the order of the time windows to generate a time-domain continuous power distribution curve; finally, the power distribution curve is time-domain modulated by a smoothing filtering algorithm to eliminate power mutation points and generate a navigation interference power distribution after time-domain modulation.

[0138] Step 405: performing periodic frequency shift expansion on the communication signal suppression frequency band in the optimized interference parameters according to the frequency band switching step size of the communication signal suppression frequency band, and generating a communication interference frequency band distribution after dynamic frequency domain expansion;

[0139] In this step, periodic frequency shift extension refers to the process of dynamically offsetting and extending the communication signal suppression frequency band at fixed time intervals according to the preset frequency band switching step; the communication interference frequency band distribution after dynamic frequency domain expansion refers to the set of interference frequency bands generated by periodic frequency shift that covers the target UAV communication frequency band and its potential frequency hopping range.

[0140] In this embodiment, first, a periodic frequency shift sequence is generated based on the frequency band switching step size, and the frequency shift step size within each period is dynamically adjusted according to the coverage extension threshold in the optimized interference parameter; secondly, the initial center frequency point of the communication signal suppression band is shifted in sequence according to the frequency shift sequence to generate multiple extended interference sub-bands; then, the extended sub-bands are merged with the original suppression band through the frequency domain superposition algorithm to form a continuously covered communication interference frequency band distribution; finally, the extension direction is dynamically adapted according to the real-time movement direction of the UAV (such as left or right deviation) to generate a frequency domain dynamic extension result that matches the frequency hopping behavior of the target UAV

[0141] Step 406: Superimpose spectrum energy of the navigation interference power distribution after time domain modulation and the communication interference frequency band distribution after dynamic expansion in the frequency domain to generate an initial interference spectrum.

[0142] In this step, spectrum energy superposition refers to the process of cross-domain energy fusion of the navigation interference power distribution after time domain modulation (dynamic power intensity in the time dimension) and the communication interference frequency band distribution after dynamic expansion in the frequency domain (coverage range in the frequency dimension) through a multi-dimensional data fusion algorithm; the initial interference spectrum refers to the time-frequency two-dimensional matrix generated by superposition, which represents the joint interference energy distribution of the navigation signal suppression power and the communication signal suppression frequency band in different time windows.

[0143] In this embodiment, first, the navigation interference power distribution after time domain modulation is arranged in the order of time windows as a time axis vector, and at the same time, the communication interference frequency band distribution after dynamic expansion in the frequency domain is arranged as a frequency axis vector according to the frequency band expansion range; secondly, the time axis vector and the frequency axis vector are tensor-expanded by the Kronecker product algorithm to generate a time-frequency two-dimensional energy matrix; then, the energy matrix is ​​spatially weighted based on the real-time motion trajectory of the UAV (such as the offset direction and speed) to enhance the interference energy density in the target direction; finally, energy overflow is eliminated through normalization processing to generate an initial interference map covering the entire working frequency band of the target UAV.

[0144] Step 407: Based on the spatial position correlation between the acoustic characteristic frequency band in the dynamic interference parameters and the boundary coordinates of the sensitive area in the optimized interference parameters, the interference energy density in the initial interference map is regionally redistributed to generate a dynamic interference map covering the entire operating frequency band of the target UAV.

[0145] In this step, regional reallocation refers to the process of dynamically adjusting the interference energy density of different airspace grids in the initial interference map based on the spatial correlation between the acoustic characteristic frequency band and the boundary of the sensitive area; the dynamic interference map refers to the time-frequency-space three-dimensional interference energy distribution matrix generated by regional reallocation, covering the entire frequency band of UAV navigation, communication and power control, and adapting to its real-time position and movement trend.

[0146] In this example, first, the acoustic characteristic frequency band in the dynamic interference parameters (such as the propeller fundamental frequency 200Hz) and the boundary coordinates of the sensitive area in the optimized interference parameters (such as the high-sensitive area in the right front) are extracted, and the energy density correlation between the two in three-dimensional space is calculated through the spatial interpolation algorithm to generate a spatial energy density gradient field; secondly, based on the gradient distribution of the energy density gradient field, the interference energy density in the initial interference map is divided into high-sensitive areas, medium-sensitive areas and low-sensitive areas according to the spatial grid; then, the interference energy density of each grid is weighted and adjusted through the regional weight matrix; finally, the adjusted interference energy density is normalized to generate a dynamic interference map covering the entire frequency band of the drone and focusing the energy on the target area.

[0147] In order to solve the problem of mismatch between the spatial distribution of interference energy and the target position in the prior art, based on this, in some embodiments, according to step 407, based on the spatial position correlation between the acoustic characteristic frequency band in the dynamic interference parameters and the sensitive area boundary coordinates in the optimized interference parameters, the interference energy density in the initial interference map is regionally redistributed to generate a dynamic interference map covering the entire operating frequency band of the target UAV, including:

[0148] Step 501: Divide the airspace where the target UAV is located into a plurality of equally spaced spatial grid cells according to the spatial distribution of the boundary coordinates of the sensitive area, and generate an acoustic energy density distribution matrix corresponding to the acoustic characteristic frequency band;

[0149] In this step, the equally spaced spatial grid cells refer to the uniform division of the three-dimensional airspace where the target drone is located into multiple cubic grids based on the preset grid resolution, which are used for quantitative analysis of the airspace energy density distribution; the acoustic energy density distribution matrix refers to the matrix data reflecting the spatial distribution of noise energy generated by mapping the noise energy value of the acoustic characteristic frequency band to each grid cell through the spatial interpolation algorithm.

[0150] In this embodiment, first, according to the maximum coverage range of the boundary coordinates of the sensitive area, a three-dimensional space grid unit is generated by an equally spaced division algorithm, and the side length of each grid is determined by a preset resolution; secondly, based on the noise energy data collected by the acoustic detection module (such as the sound pressure level of each microphone node), the acoustic energy density value of each grid unit is calculated by the Kriging interpolation algorithm; then, the noise energy value of the acoustic characteristic frequency band is separated from the frequency domain data to generate an acoustic energy density distribution matrix containing only the energy of the target frequency band; finally, the matrix data is bound to the grid unit coordinates to form a spatial gridded noise energy distribution database.

[0151] Step 502: Calculate the electromagnetic energy superposition coefficient of each spatial grid unit within the cooperative suppression period based on the time domain characteristics of the navigation signal suppression frequency band in the dynamic interference parameters, and generate a grid time domain weight sequence corresponding to the interference time window;

[0152] In this step, the electromagnetic energy superposition coefficient refers to the interference energy superposition weight calculated for each spatial grid unit based on the time domain modulation characteristics of the navigation signal suppression frequency band (such as pulse interval and duty cycle), which is used to quantify the interference energy density of each grid unit in different time periods; the grid time domain weight sequence refers to a set of grid unit weight parameters arranged in the order of the time windows of the collaborative suppression period, which represents the proportion of interference energy that should be allocated to each grid unit in different time windows.

[0153] In this embodiment, first, according to the time domain characteristics of the navigation signal suppression frequency band in the dynamic interference parameters (such as the pulse interval period), the cooperative suppression period is divided into multiple interference time windows, and the time-sharing weight coefficient of each time window is extracted; secondly, based on the noise energy value of each grid unit in the acoustic energy density distribution matrix, the time-sharing weight coefficient is correlated with the grid noise energy value through a weighted fusion algorithm to generate the electromagnetic energy superposition coefficient of each grid unit in each time window; then, the superposition coefficient is arranged in the order of the time windows to generate a grid time domain weight sequence corresponding to the interference time window; finally, normalization processing is performed to ensure that the sum of the grid weights in each time window does not exceed the preset threshold to avoid equipment overload.

[0154] Step 503: Perform space-time correlation mapping on the acoustic energy density distribution matrix and the grid time domain weight sequence to generate a composite energy density factor for each spatial grid unit;

[0155] In this step, space-time correlation mapping refers to the data fusion process of generating space-time joint interference energy distribution parameters by jointly analyzing the acoustic energy density distribution matrix in the spatial dimension (reflecting the noise energy of the grid unit) and the grid time domain weight sequence in the time dimension (reflecting the interference weight of the time window); the composite energy density factor refers to the control parameter generated by integrating the spatial distribution characteristics of acoustic energy and the dynamic adjustment characteristics of time domain weights, which is used to quantify the interference energy density of each grid unit during the collaborative suppression period.

[0156] In this embodiment, first, a spatiotemporal data alignment algorithm is used to dimensionally match each spatial grid cell in the acoustic energy density distribution matrix with the corresponding time window weight in the grid time domain weight sequence to generate a spatiotemporal aligned grid data set. Secondly, the Hadamard Product algorithm is used to perform an element-by-element multiplication operation on the acoustic energy density value and the time domain weight coefficient of each grid cell to obtain a spatiotemporal joint initial energy density factor. Then, based on the preset normalization rule, the initial energy density factor is calibrated in the space-time domain to ensure that the total interference energy does not exceed the equipment carrying threshold. Finally, based on the airspace sensitivity distribution of the real-time motion trajectory of the target UAV, the composite energy density factor of the highly sensitive area is dynamically weighted and improved to generate a final composite energy density factor that adapts to the target motion trend.

[0157] Step 504: performing weighted correction on the interference energy of the corresponding spatial grid unit in the initial interference map according to the composite energy density factor to obtain a corrected interference energy distribution that matches the central area of ​​the target UAV power noise source;

[0158] In this step, weighted correction refers to the process of dynamically adjusting the interference energy value of each spatial grid unit in the initial interference map through a composite energy density factor to enhance the energy density of the target area; the corrected interference energy distribution refers to the set of spatiotemporal joint interference energy parameters generated after weighted correction, and its energy density distribution is highly matched with the core area of ​​the UAV power noise source.

[0159] In this embodiment, the interference energy value (a two-dimensional time-frequency parameter) of each spatial grid cell in the initial interference map is first dimensionally aligned with the composite energy density factor (a two-dimensional space-time parameter) to generate a three-dimensional joint time-space-frequency dataset. Secondly, the initial interference energy value of each grid cell is multiplied by the corresponding composite energy density factor using element-by-element multiplication to generate a weighted interference energy value. Next, a dynamic Bayesian network is used to perform spatial energy focusing on the weighted result. Based on the real-time position of the center area of ​​the UAV's dynamic noise source (e.g., right-front offset), the interference energy in the core area is secondary enhanced. Finally, adaptive filtering technology is used to filter out redundant interference energy in low-sensitivity areas, generating a modified interference energy distribution that retains only the energy in high-sensitivity areas.

[0160] Step 505: Calculate the phase offset compensation amount of the communication signal suppression frequency band in the spatial grid unit based on the communication signal suppression frequency band coverage extension threshold in the optimized interference parameter, and generate a grid phase compensation sequence corresponding to the frequency band switching step size;

[0161] In this step, the phase offset compensation amount refers to the phase correction parameter calculated for each spatial grid unit to eliminate the phase mismatch caused by the change of frequency shift step during the dynamic expansion of the communication signal suppression band in the frequency domain; the grid phase compensation sequence refers to a set of phase offset parameters arranged according to the time window sequence of the collaborative suppression period and the frequency band switching step, which is used to ensure the phase synchronization of interference signals in different frequency bands.

[0162] In this embodiment, based on the communication signal suppression band coverage extension threshold (e.g., the maximum allowable band extension range) in the optimized interference parameters, a frequency-domain interpolation algorithm is used to calculate the signal propagation delay difference corresponding to each frequency band switching step size, generating an initial phase offset. Next, based on the relative position (e.g., distance and azimuth) between the spatial grid cell and the target drone, and in conjunction with an electromagnetic wave propagation model, a phase offset correction value is calculated for each grid cell at different frequency band switching step sizes. Next, a phase gradient matching algorithm is used to fuse the initial phase offset with the correction value to generate a phase offset compensation for each grid cell. Finally, the compensation values ​​are arranged according to the time window sequence and frequency band switching step size to generate a grid phase compensation sequence that matches the interference timing.

[0163] Step 506: performing phase synchronization adjustment on the communication signal suppression frequency band in the corrected interference energy distribution according to the grid phase compensation sequence to generate a phase-aligned interference energy distribution spectrum;

[0164] In this step, phase synchronization adjustment refers to the phase correction of the interference signal in the communication signal suppression frequency band based on the phase offset compensation amount in the grid phase compensation sequence, eliminating the phase mismatch caused by the dynamic expansion of the frequency band, and achieving the time-frequency synchronization of the interference signal; the interference energy distribution spectrum after phase alignment refers to the time-space-frequency joint interference energy parameter set generated after phase correction, which ensures the phase consistency of interference signals in different frequency bands.

[0165] In this embodiment, first, the communication signal suppression frequency band interference sequence in the corrected interference energy distribution is decomposed according to the time window and frequency band switching step size to generate an interference signal subset corresponding to the grid phase compensation sequence. Secondly, a phase gradient matching algorithm is used to superimpose the initial phase value of each subset with the compensation amount in the grid phase compensation sequence to generate a phase-corrected interference signal. Next, the phase continuity of the corrected signal is detected by a phase synchronization verification algorithm, and the signal with residual phase difference is iteratively compensated until the phase error of the interference signal in all frequency bands is lower than the preset threshold. Finally, the corrected interference signal is reorganized according to the time window and frequency band sequence to generate a phase-aligned interference energy distribution map.

[0166] Step 507: Based on the electromagnetic control frequency band range in the dynamic interference parameters, frequency band truncation filtering is performed on the interference energy distribution spectrum after phase alignment to filter out the interference energy frequency band that exceeds the preset electromagnetic compliance constraint to generate a dynamic interference spectrum covering the full operating frequency band of the target UAV;

[0167] In this step, frequency band truncation filtering refers to the process of frequency domain energy screening of the interference energy distribution map according to the electromagnetic control frequency band range, retaining the interference energy within the target frequency band and filtering out the energy of the non-certified frequency band; the dynamic interference map refers to the final interference parameter set generated after frequency band truncation and energy optimization, and its frequency band coverage strictly matches the full working frequency band of the target UAV and complies with the electromagnetic compliance constraints.

[0168] In this embodiment, a bandpass filter template for the target frequency band is first generated based on the electromagnetic control frequency band range (e.g., the 2.4-2.4835 GHz ISM band) in the dynamic interference parameters. Secondly, a dynamic threshold digital filter is used to perform a frequency-domain convolution operation on the interference energy frequency band in the phase-aligned interference energy distribution map with the filter template to filter out interference energy outside the template range. Next, based on the highly sensitive area identifiers in the acoustic energy density distribution matrix, the interference energy density within the retained frequency band is spatially weighted to ensure that the proportion of interference energy in highly sensitive areas is maximized. Finally, a spectral energy normalization algorithm is used to adjust the energy proportion of each frequency band, generating a dynamic interference map that covers the entire operating frequency band of the target drone and optimizes energy distribution.

[0169] In order to solve the problem of insufficient accuracy in identifying the working mode of drones in the prior art, in some embodiments, according to step 102, based on the correlation between the acoustic characteristic frequency band and the electromagnetic control frequency band in the composite detection signal, the working mode characteristics of the target drone are extracted, and dynamic interference parameters for matching the interference requirements are generated, including:

[0170] Step 601: extracting an acoustic pulse interval sequence associated with the target UAV propeller power noise from the acoustic characteristic frequency band, and generating an acoustic feature vector including an acoustic pulse interval mean and an acoustic pulse interval variance;

[0171] In this step, the acoustic pulse interval sequence refers to the time interval sequence of the periodic pulse signal of the propeller power noise separated from the acoustic characteristic frequency band, which is used to characterize the mechanical vibration characteristics of the UAV power system; the acoustic characteristic vector refers to a mathematical vector composed of the mean of the acoustic pulse interval (reflecting the periodicity of the power noise) and the variance of the acoustic pulse interval (reflecting the stability of the power noise), which is used to quantify the spatiotemporal characteristics of the UAV power noise.

[0172] In this embodiment, an acoustic signal preprocessing algorithm is first used to bandpass filter the acoustic characteristic frequency band to isolate the fundamental frequency and harmonic components of the propeller power noise. Secondly, a peak detection algorithm is used to identify the pulse peaks in the noise signal and calculate the time intervals between adjacent peaks to generate an acoustic pulse interval sequence. Next, a statistical analysis algorithm is used to calculate the mean and variance of the pulse interval sequence, respectively characterizing the cyclic stability and disturbance intensity of the power noise. Finally, the mean and variance are fused according to preset weight coefficients to generate an acoustic feature vector containing two-dimensional features.

[0173] Step 602: Separate the modulation period of the target UAV navigation signal and the frequency hopping step of the communication signal from the electromagnetic control frequency band, and generate an electromagnetic modulation feature vector including a modulation period set and a frequency hopping step set;

[0174] In this step, the modulation period set refers to the set of periodic parameters corresponding to multiple modulation modes (such as BPSK and QPSK) extracted from the navigation signal, which is used to characterize the time domain modulation law of the navigation signal; the frequency hopping step length set refers to the set composed of multiple frequency band switching step lengths separated from the communication signal, which is used to characterize the frequency domain dynamic characteristics of the communication signal.

[0175] In this embodiment, spectrum segmentation is first used to divide the electromagnetic control frequency band into a navigation signal band and a communication signal band. Next, a time-domain autocorrelation algorithm is used to detect the modulation period in the navigation signal band, generating a set of modulation periods containing different modulation schemes. A frequency-domain peak tracking algorithm is used to detect the frequency hopping step size in the communication signal band, generating a set of frequency hopping step sizes containing different frequency shift intervals. Finally, the modulation period set and the frequency hopping step size set are combined according to a preset coding rule to generate an electromagnetic modulation feature vector containing time-frequency characteristics.

[0176] Step 603: Calculate the time domain correlation coefficient between the mean value of the acoustic pulse interval and the modulation period of the navigation signal to generate the acoustic-electrical time domain correlation coefficient;

[0177] In this step, the time domain correlation coefficient refers to the linear correlation strength parameter between the mean value of the acoustic pulse interval and the modulation period of the navigation signal calculated by the statistical analysis method; the acoustic-electric time domain correlation refers to the normalized correlation coefficient, which is used to quantify the time domain synchronization between the UAV power noise and the navigation signal.

[0178] In this embodiment, the mean pulse interval in the acoustic eigenvector is aligned with the set of modulation periods in the electromagnetic modulation eigenvector to generate a time-domain matching dataset. Next, the Pearson Correlation Coefficient algorithm is used to calculate the linear correlation between the two, generating an initial correlation coefficient. Next, the coefficient is normalized to the interval [0, 1] to generate the acoustic-electrical time-domain correlation. Finally, the correlation threshold is used to determine the coordinated working state between the UAV's power system and the navigation signal, guiding the dynamic optimization of the interference parameters.

[0179] Step 604: Determine the operating mode of the target UAV based on the acoustic-electrical time domain correlation and a preset correlation threshold interval, and generate a mode determination identifier, wherein the operating mode of the target UAV includes a navigation-dominated mode and a communication-dominated mode.

[0180] In this step, the correlation threshold interval refers to the preset acoustic-electrical time domain correlation value range, which is used to divide the determination boundary of the UAV working mode; the mode determination identifier refers to the unique coding parameter that characterizes the current working mode of the UAV, which is used to match the corresponding interference parameter combination from the interference strategy library.

[0181] In this embodiment, the acoustic-electrical time-domain correlation is first compared with a preset correlation threshold interval to determine the drone's operating mode. Next, a pattern classification algorithm is used to re-evaluate the correlation within the intermediate threshold interval. The pattern classification result is dynamically adjusted based on the frequency hopping step size set in the electromagnetic modulation feature vector. Finally, a mode determination identifier corresponding to the operating mode is generated to trigger subsequent jamming strategy invocation.

[0182] Step 605: Based on the mode determination identifier, the corresponding navigation signal suppression frequency band range, communication signal suppression frequency band step size, and power control signal interference weight are retrieved from a preset interference strategy library to generate an initial interference parameter set.

[0183] In this step, the interference strategy library refers to a pre-stored database of interference parameter combinations that match the UAV's working mode, including parameters such as the navigation signal suppression frequency band range, the communication signal suppression frequency band step, and the power control signal interference weight; the initial interference parameter set refers to the interference parameter combination extracted from the strategy library according to the mode judgment identifier, which is used to generate a dynamic interference map.

[0184] In this embodiment, the index table in the interference strategy library is first accessed based on the mode determination identifier to locate the corresponding interference parameter combination entry. Next, the navigation signal suppression frequency band range, communication signal suppression frequency band step size, and power control signal interference weight are extracted. Next, a parameter fusion algorithm dynamically optimizes the extracted interference parameters with real-time electromagnetic detection results (such as frequency band occupancy) to generate an initial interference parameter set adapted to the current scenario. Finally, a compliance check is performed on the parameter set to ensure that it meets preset electromagnetic radiation safety standards.

[0185] In order to solve the problem of single time-domain modulation of interference instructions in the prior art, based on this, in some embodiments, according to step 103, generating a synchronous interference instruction for the target drone signal according to the time-domain modulation characteristics of the dynamic interference parameter includes:

[0186] Step 701: Divide the preset cooperative suppression period into multiple interference time windows according to the navigation signal suppression frequency band range in the dynamic interference parameters, and generate a time-sharing weight coefficient corresponding to each time window;

[0187] In this embodiment, first, based on the bandwidth distribution characteristics of the navigation signal suppression frequency band, a dynamic frequency band occupancy segmentation algorithm is adopted to divide the collaborative suppression period into multiple time windows of varying durations. High-bandwidth frequency bands correspond to denser time window divisions. Secondly, a frequency-domain energy weight allocation model is used to calculate the energy weight of the navigation signal suppression within each time window. The weight value is positively correlated with the frequency band occupancy. Finally, the weights are normalized to generate a time-sharing weight coefficient sequence.

[0188] Step 702: Calculate the frequency shift step length of the communication signal suppression frequency band within each interference time window based on the adaptive adjustment amplitude of the communication signal suppression frequency band in the dynamic interference parameter, and generate a frequency shift compensation sequence that matches the frequency hopping behavior of the target UAV;

[0189] In this embodiment, the optimal frequency shift step size for the communication frequency band within each time window is calculated based on the maximum allowable adjustment range of the communication signal suppression frequency band and a prediction model for drone frequency hopping behavior (e.g., a hidden Markov chain). Secondly, a frequency shift step size compliance check is performed to ensure that the step size is adapted to the real-time electromagnetic environment constraints. Finally, the verified frequency shift step sizes are sequentially integrated across the time windows to generate a frequency shift compensation sequence.

[0190] Step 703, determining the pulse duty cycle of the power control signal suppression in each interference time window according to the power control signal interference weight in the dynamic interference parameter, and generating a pulse duty cycle sequence;

[0191] In this embodiment, a linear mapping model is first used to convert the power control signal interference weights into pulse duty cycles corresponding to the time window. Higher weights correspond to larger duty cycles. Secondly, a duty cycle safety verification algorithm is used to ensure that the duty cycle complies with pre-set electromagnetic radiation compliance constraints. Finally, the verified duty cycles are arranged in time window order to generate a pulse duty cycle sequence.

[0192] Step 704: Based on the time-sharing weight coefficient, the frequency shift compensation sequence, and the pulse duty cycle sequence, the time-domain modulation parameters of the navigation signal suppression frequency band, the communication signal suppression frequency band, and the power control signal suppression frequency band are respectively configured in a time-sharing manner to generate a synchronous interference instruction;

[0193] In this embodiment, the time-sharing weight coefficients, frequency shift compensation sequences, and pulse duty cycle sequences are first aligned according to the time window to generate a mapping table of time window-frequency band parameters. Secondly, a time-domain parameter fusion algorithm is used to jointly encode the power weight of the navigation signal, the frequency shift step size of the communication signal, and the duty cycle of the power signal to generate independent interference instructions for each time window. Finally, a timing synchronization controller is used to integrate the interference instructions of all time windows according to the cooperative suppression period to generate a global synchronized interference instruction.

[0194] Figure 2 The present invention provides a schematic diagram of a full-band UAV countermeasure system based on acoustic, optical and electrical composite detection. Figure 2 As shown, the system includes:

[0195] The detection module 21 is used to generate a composite detection signal covering the entire frequency band of the target UAV through synchronous and coordinated detection of acoustic wave sensing, optical imaging, and electromagnetic spectrum analysis;

[0196] An extraction module 22 is configured to extract the operating mode characteristics of the target UAV based on the correlation between the acoustic characteristic frequency band and the electromagnetic control frequency band in the composite detection signal, and generate dynamic interference parameters for matching the interference requirements;

[0197] A generating module 23 is configured to generate a synchronous jamming instruction for a target UAV signal according to the time-domain modulation characteristics of the dynamic jamming parameter, wherein the target UAV signal includes a navigation signal, a communication signal, and a power control signal;

[0198] A correction module 24 is configured to perform real-time correction of the power allocation and frequency band coverage of the synchronous jamming instruction based on the abnormal noise distribution characteristics previously captured by the acoustic detection module and the flight trajectory offset identified by the optical imaging module, thereby generating optimized jamming parameters;

[0199] The fusion module 25 is used to perform overlapping spectrum fusion of the dynamic interference parameters and the optimized interference parameters within a preset collaborative suppression period to generate a dynamic interference map covering the entire operating frequency band of the target UAV. Figure 2 The UAV full-band countermeasure system based on acoustic-optical composite detection can perform Figure 1 The implementation principles and technical effects of the full-band drone countermeasure method based on acoustic, optical, and optical composite detection described in the illustrated embodiment are not further elaborated. The specific manner in which each module and unit performs operations in the full-band drone countermeasure system based on acoustic, optical, and optical composite detection in the above embodiment has been described in detail in the relevant embodiments of the method and will not be elaborated on here.

[0200] In one possible design, Figure 2 The embodiment shown is a UAV full-band countermeasure system based on acoustic, optical and electrical composite detection, which can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0201] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0202] The processing component 32 is used for the above Figure 1 The embodiment provides a full-band countermeasure method for drones based on acoustic, optical and electrical composite detection.

[0203] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0204] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0205] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0206] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0207] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0208] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0209] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a full-band countermeasure method for drones based on acoustic, optical and electrical composite detection.

[0210] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0211] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0212] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0213] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A full-band UAV countermeasure method based on acoustic, optical and electrical composite detection, applied to low-altitude protection scenarios at airports, characterized by: include: In the low-altitude protection scenario at airports, a composite detection signal covering the entire frequency band of the target drone is generated through the simultaneous coordinated detection of acoustic wave sensing, optical imaging, and electromagnetic spectrum analysis. Based on the correlation between the acoustic characteristic frequency band and the electromagnetic control frequency band in the composite detection signal, the operating mode characteristics of the target UAV are extracted to generate dynamic interference parameters for matching the interference requirements; generating, based on the time-domain modulation characteristics of the dynamic interference parameters, synchronous interference instructions for target UAV signals, wherein the target UAV signals include navigation signals, communication signals, and power control signals; Based on the abnormal noise distribution characteristics captured in advance by the acoustic detection module and the flight trajectory offset identified by the optical imaging module, the power allocation and frequency band coverage of the synchronous interference instruction are corrected in real time to generate optimized interference parameters; The dynamic interference parameters and the optimized interference parameters are overlapped and spectrally fused within a preset cooperative suppression period to generate a dynamic interference map covering the entire working frequency band of the target UAV.

2. The method according to claim 1, characterized in that Based on the correlation between the acoustic characteristic frequency band and the electromagnetic control frequency band in the composite detection signal, the operating mode characteristics of the target UAV are extracted, and dynamic interference parameters for matching the interference requirements are generated, including: Extracting an acoustic pulse interval sequence associated with the propeller power noise of the target UAV from the acoustic characteristic frequency band, and generating an acoustic feature vector including an acoustic pulse interval mean and an acoustic pulse interval variance; Separating the modulation period of the target UAV navigation signal and the frequency hopping step of the communication signal from the electromagnetic control frequency band, and generating an electromagnetic modulation feature vector including a modulation period set and a frequency hopping step set; Calculate the time domain correlation coefficient between the mean value of the acoustic pulse interval and the modulation period of the navigation signal to generate the acoustic-electrical time domain correlation coefficient; Determine the operating mode of the target UAV based on the acoustic-electrical time domain correlation and a preset correlation threshold interval, and generate a mode determination identifier, wherein the operating mode of the target UAV includes a navigation-dominated mode and a communication-dominated mode; Based on the mode determination identifier, calling the corresponding navigation signal suppression frequency band range, communication signal suppression frequency band step size and power control signal interference weight from a preset interference strategy library to generate an initial interference parameter set; Dynamically modifying the power control signal interference weight in the initial interference parameter set according to the acoustic pulse interval variance to generate a modified power control interference weight; Calculating the adaptive adjustment amplitude of the communication signal suppression frequency band step size based on the statistical distribution characteristics of the frequency hopping step size set to generate a frequency domain control sequence; The navigation signal suppression frequency band range, the corrected power control interference weight and the frequency domain control sequence are parameter-bound to generate dynamic interference parameters for matching interference requirements.

3. The method according to claim 2, characterized in that Generating a synchronous jamming instruction for a target UAV signal according to the time domain modulation characteristics of the dynamic jamming parameter, including: According to the navigation signal suppression frequency band range in the dynamic interference parameters, the preset cooperative suppression period is divided into multiple interference time windows, and a time-sharing weight coefficient corresponding to each time window is generated; Based on the adaptive adjustment amplitude of the communication signal suppression frequency band in the dynamic interference parameters, the frequency shift compensation of the communication signal suppression frequency band in each interference time window is calculated to generate a frequency shift compensation sequence that matches the frequency hopping behavior of the target UAV; Determining the pulse duty cycle of the power control signal suppression in each interference time window according to the power control signal interference weight in the dynamic interference parameter, and generating a pulse duty cycle sequence; Based on the time-sharing weight coefficient, frequency shift compensation sequence and pulse duty cycle sequence, the time domain modulation parameters of the navigation signal suppression frequency band, the communication signal suppression frequency band and the power control signal suppression frequency band are respectively configured in a time-sharing manner to generate synchronous interference instructions.

4. The method according to claim 1, wherein Based on the abnormal noise distribution characteristics captured in advance by the acoustic detection module and the flight trajectory offset identified by the optical imaging module, the power allocation and frequency band coverage of the synchronous interference instruction are corrected in real time to generate optimized interference parameters, including: Extracting the gradient distribution of noise energy as it changes with spatial position from the abnormal noise distribution characteristics, locating the center area of ​​the power noise source of the target UAV according to the extreme value points of the gradient distribution, and generating a noise energy gradient map; Based on the horizontal offset angle in the flight trajectory offset, calculating the lateral deviation ratio of the target UAV relative to the preset protection boundary as a frequency band offset coefficient; Based on the areas in the noise energy gradient map where the noise energy gradient is greater than a preset threshold, dividing the space where the target UAV is located into high-interference-sensitive sub-areas, and generating sensitive area boundary coordinates corresponding to the high-interference-sensitive sub-areas; Performing spatial mapping of the frequency band offset coefficient and the boundary coordinates of the sensitive area, and calculating a power gradient value of the navigation signal suppression frequency band and a coverage extension threshold of the communication signal suppression frequency band in the synchronization interference instruction; The power distribution of the synchronous interference instruction is weighted in layers according to the power gradient value, and the frequency band coverage range of the synchronous interference instruction is dynamically expanded based on the coverage extension threshold to obtain optimized interference parameters.

5. The method according to claim 4, characterized in that The power allocation of the synchronous interference instruction is hierarchically weighted according to the power gradient value, and the frequency band coverage range of the synchronous interference instruction is dynamically expanded based on the coverage extension threshold to obtain optimized interference parameters, including: Dividing the power gradient value into multiple power levels according to a preset interference intensity level, and generating a weight distribution ratio for each power level based on the overlapping area between the spatial position corresponding to each power level and the boundary coordinates of the sensitive area; Determine, based on the coverage extension threshold, the extension step lengths of the upper and lower frequency limits of the communication signal suppression frequency band, and generate a dynamic boundary of the frequency band coverage associated with the real-time horizontal offset direction of the target UAV; Accumulating the power values ​​of the navigation signal suppression frequency band in the synchronization interference instruction layer by layer according to the weight distribution ratio to generate a hierarchical weighted navigation power distribution sequence; Bidirectionally expanding the coverage of the communication signal suppression frequency band according to the dynamic boundary of the frequency band coverage to generate an expanded communication signal suppression interval; The navigation power distribution sequence is superimposed on the communication frequency band suppression interval in the frequency domain to generate optimized interference parameters including a power-frequency band joint mapping relationship, wherein the power-frequency band joint mapping relationship matches the real-time spatial position and motion state of the target UAV.

6. The method according to claim 4, characterized in that The dynamic interference parameters and the optimized interference parameters are overlapped and fused within a preset cooperative suppression period to generate a dynamic interference spectrum covering the entire operating frequency band of the target UAV, including: Dividing a plurality of interference time windows within the cooperative suppression period according to the time domain characteristics of the navigation signal suppression frequency band in the dynamic interference parameter, and generating a time-sharing weight coefficient corresponding to each time window; Calculating the frequency band extension priority of the communication signal suppression frequency band within the interference time window based on the communication signal suppression frequency band coverage extension threshold in the optimized interference parameter, and generating a frequency band priority sequence; Performing time-sequential frequency band association mapping on the time-sharing weight coefficient and the frequency band priority sequence to generate a power allocation ratio of the navigation signal suppression frequency band and a frequency band switching step size of the communication signal suppression frequency band within the interference time window; Performing time window segment weighting on the navigation signal suppression power in the dynamic interference parameter according to the power allocation ratio to obtain a navigation interference power distribution after time domain modulation; Performing periodic frequency shift expansion on the communication signal suppression frequency band in the optimized interference parameters according to the frequency band switching step size of the communication signal suppression frequency band, to generate a communication interference frequency band distribution after dynamic frequency domain expansion; Performing spectrum energy superposition on the navigation interference power distribution after time domain modulation and the communication interference frequency band distribution after dynamic expansion in the frequency domain to generate an initial interference spectrum; Based on the spatial position correlation between the acoustic characteristic frequency band in the dynamic interference parameters and the sensitive area boundary coordinates in the optimized interference parameters, the interference energy density in the initial interference map is regionally redistributed to generate a dynamic interference map covering the entire working frequency band of the target UAV.

7. The method according to claim 4, characterized in that Based on the spatial position correlation between the acoustic characteristic frequency band in the dynamic interference parameters and the boundary coordinates of the sensitive area in the optimized interference parameters, the interference energy density in the initial interference map is regionally redistributed to generate a dynamic interference map covering the entire operating frequency band of the target UAV, including: Divide the airspace where the target UAV is located into a plurality of equally spaced spatial grid units according to the spatial distribution of the boundary coordinates of the sensitive area, and generate an acoustic energy density distribution matrix corresponding to the acoustic characteristic frequency band; Based on the time domain characteristics of the navigation signal suppression frequency band in the dynamic interference parameters, calculating the electromagnetic energy superposition coefficient of each spatial grid unit within the cooperative suppression period, and generating a grid time domain weight sequence corresponding to the interference time window; Performing space-time correlation mapping on the acoustic energy density distribution matrix and the grid time domain weight sequence to generate a composite energy density factor for each spatial grid unit; Performing weighted correction on the interference energy of the corresponding spatial grid unit in the initial interference map according to the composite energy density factor to obtain a corrected interference energy distribution that matches the central area of ​​the target UAV power noise source; Calculating the phase offset compensation amount of the communication signal suppression frequency band in the spatial grid unit based on the communication signal suppression frequency band coverage extension threshold in the optimized interference parameter, and generating a grid phase compensation sequence corresponding to the frequency band switching step size; Performing phase synchronization adjustment on the communication signal suppression frequency band in the corrected interference energy distribution according to the grid phase compensation sequence to generate a phase-aligned interference energy distribution spectrum; Based on the electromagnetic control frequency band range in the dynamic interference parameters, the interference energy distribution spectrum after phase alignment is subjected to frequency band truncation filtering to filter out the interference energy frequency band that exceeds the preset electromagnetic compliance constraint to generate a dynamic interference spectrum covering the full working frequency band of the target UAV.

8. A full-band UAV countermeasure system based on acoustic, optical and electrical composite detection, applied to low-altitude protection scenarios at airports, characterized by: include: The detection module, in low-altitude airport protection scenarios, is used to generate a composite detection signal covering the entire frequency band of the target drone through the simultaneous coordinated detection of acoustic wave sensing, optical imaging, and electromagnetic spectrum analysis; An extraction module is used to extract the operating mode characteristics of the target UAV based on the correlation between the acoustic characteristic frequency band and the electromagnetic control frequency band in the composite detection signal, and generate dynamic interference parameters for matching the interference requirements; a generating module, configured to generate a synchronous jamming instruction for a target UAV signal according to the time-domain modulation characteristics of the dynamic jamming parameter, wherein the target UAV signal includes a navigation signal, a communication signal, and a power control signal; a correction module, configured to perform real-time correction of the power allocation and frequency band coverage of the synchronous jamming instruction based on the abnormal noise distribution characteristics previously captured by the acoustic detection module and the flight trajectory offset identified by the optical imaging module, thereby generating optimized jamming parameters; The fusion module is used to perform overlapping spectrum fusion of the dynamic interference parameters and the optimized interference parameters within a preset collaborative suppression period to generate a dynamic interference map covering the entire operating frequency band of the target UAV.

9. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a full-band countermeasure method for drones based on acoustic, optical and electrical composite detection as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a full-band countermeasure method for drones based on acoustic, optical and electrical composite detection as described in any one of claims 1 to 7 is implemented.

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