Low-altitude unmanned aerial vehicle countermeasure method based on 5G-A sensing integration technology

Abstract

The application discloses a low-altitude unmanned aerial vehicle countermeasure method based on 5G-A sensing integration technology, and relates to the technical field of low-altitude safety protection.The method comprises the following steps: using trajectory prediction and tracking confidence to perform type identification and risk level determination on each target, generating a dynamic risk level, performing space-time correlation analysis on the dynamic risk level, and generating a disposal level and a countermeasure trigger condition; when the countermeasure trigger condition is met, generating countermeasure instruction parameters corresponding to the disposal level, implementing directional repulsion on a target unmanned aerial vehicle by emitting a navigation deception signal, generating a countermeasure execution state and a target deviation trajectory; performing closed-loop verification and parameter adaptive adjustment on the countermeasure execution state and the target deviation trajectory, writing whole-process data into a digital twin model for simulation evaluation and strategy updating, and generating an updated base station working parameter set and a protection strategy.The application realizes intelligent fusion of distributed sensing data and high-precision trajectory prediction capability.

Description

Technical Field

[0001] This invention relates to the field of low-altitude safety protection technology, and in particular to a method for countering low-altitude drones based on 5G-A integrated sensing technology. Background Technology

[0002] As a key component of low-altitude security, low-altitude drone countermeasures technology has undergone continuous evolution in recent years due to the rapid development of drone technology and the expanding application scenarios. Current technical solutions mainly employ a combination of technologies such as radar detection, radio spectrum detection, electro-optical tracking, and physical interception. Radar detection technology, with its long detection range and all-weather capability, has unique advantages in target detection; however, its detection accuracy and false alarm suppression capabilities still have room for improvement in low-altitude, slow-moving, and small target detection scenarios. Radio spectrum detection technology achieves target localization by analyzing drone communication signals; however, its reliability faces challenges when encountering non-cooperative targets employing complex anti-jamming technologies. Electro-optical tracking technology is significantly affected by ambient light and weather conditions, and its stability in specific application scenarios needs further improvement.

[0003] Existing technologies suffer from the following prominent problems: First, the independent deployment of multiple technologies, such as radar detection, radio spectrum detection, electro-optical tracking, and physical interception, leads to severe information silos. The target information output by each technology has heterogeneous formats and inconsistent spatiotemporal references, making it difficult to achieve efficient data fusion and collaborative decision-making, thus reducing the overall reliability and response efficiency of countermeasures. Second, traditional countermeasure methods lack the ability to intelligently identify the intentions of drones. They can only trigger countermeasures based on simple rules (such as electronic fence intrusion) and cannot adaptively adjust the response strategy according to the dynamic risk level of the target, which can easily lead to false alarms or untimely countermeasures. For example, when a drone hovers and conducts reconnaissance outside a sensitive area, it may be missed because the electronic fence is not triggered; while when a friendly drone intrudes, it may be mistakenly attacked because its identity cannot be accurately identified. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a low-altitude UAV countermeasure method based on 5G-A integrated sensing technology to solve the information silo problem caused by the independent deployment of multiple technologies in traditional methods and the lack of dynamic adaptability of static rule-based countermeasure strategies.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides a method for countering low-altitude unmanned aerial vehicles (UAVs) based on 5G-A integrated sensing technology. The method includes: collecting three-dimensional geographic information data and historical electromagnetic environment monitoring data to generate a base station operating parameter set for low-altitude sensing; using the base station operating parameter set to collect echo signals and channel state information of low-altitude targets, and performing clutter suppression and time-frequency feature extraction to obtain candidate initial detection confidence levels and a set of low-altitude targets; estimating ranging, velocity, and angle parameters for the candidate low-altitude target set, and using the initial detection confidence level as a weight to generate a tracking confidence level and trajectory prediction for each target; and utilizing trajectory prediction and tracking... The confidence level is used to identify the execution type and risk level of each target, generate a dynamic risk level, and perform spatiotemporal correlation analysis on the dynamic risk level to generate a response level and countermeasure triggering conditions. When the countermeasure triggering conditions are met, countermeasure command parameters corresponding to the response level are generated, and the target UAV is driven away by transmitting navigation deception signals, generating the countermeasure execution status and the target deviation trajectory. The countermeasure execution status and the target deviation trajectory are checked in a closed loop and the parameters are adaptively adjusted. The entire process data is written into a digital twin model for simulation evaluation and strategy update, generating an updated base station working parameter set and protection strategy.

[0007] As a preferred embodiment of the low-altitude drone countermeasure method based on 5G-A sensing integration technology described in this invention, the specific steps for generating the base station operating parameter set for low-altitude sensing are as follows: Acquire three-dimensional geographic information data and historical electromagnetic environment monitoring data of the area to be protected, and generate an airspace electromagnetic environment profile; Based on the spatial electromagnetic environment profile, the base station coverage blind spots are obtained through line-of-sight propagation characteristics, and the deployment location, antenna height and initial beam direction of the 5G-A integrated sensing base station are obtained by combining the characteristics of interference sources, thus forming a base station deployment scheme. Based on the geographical coordinates and coverage of the base stations in the base station deployment scheme, configure the time slot ratio and cyclic prefix length of the communication subframe and sensing subframe in the 5G-A integrated sensing frame structure, and set the transmit power spectral density of the sensing subframe. Based on the time slot ratio, cyclic prefix length, and transmit power spectral density, the time-frequency resource blocks of communication subframes and sensing subframes are dynamically allocated, and a base station operating parameter set is generated.

[0008] As a preferred embodiment of the low-altitude UAV countermeasure method based on 5G-A integrated sensing technology described in this invention, the specific steps for obtaining the candidate initial detection confidence level and the low-altitude target set are as follows: Based on the base station operating parameter set, control the receiving link configuration and collect the raw data stream of echo signals and channel state information of low-altitude targets; The original data stream is subjected to clutter suppression processing, and the time-domain envelope and frequency-domain Doppler features are extracted from the original data stream. Based on the temporal envelope and frequency domain Doppler characteristics, potential target traces are identified using a constant false alarm rate detector, and the signal-to-noise ratio of each target trace is calculated as the initial detection confidence. Potential target points and initial detection confidence are clustered and associated to form a candidate low-altitude target set.

[0009] As a preferred embodiment of the low-altitude UAV countermeasure method based on 5G-A sensing integration technology described in this invention, the specific steps for generating the tracking confidence and trajectory prediction for each target are as follows: The time delay features, Doppler frequency shift features, and angle response features of the corresponding echo signals are extracted from the candidate low-altitude target set, and the features are matched and corrected in combination with the corresponding channel state information to obtain the target range parameters, target radial velocity parameters, and target spatial angle parameters, respectively. The target range parameters, target radial velocity parameters, and target spatial angle parameters are correlated and combined to obtain the preliminary motion parameter set of each target. Using the initial detection confidence as the fusion weight, the initial motion parameter set corresponding to each target is used to identify the same source target and remove duplicate targets. After the initial motion parameter set corresponding to each target is retained after the same source target identification and duplicate target removal, multi-base station joint weighted fusion positioning is performed to obtain the position component and velocity component of each target in a unified spatial coordinate system, and generate the three-dimensional position coordinates and velocity vector of each target. Based on three-dimensional position coordinates and velocity vectors, the current observation data is input into the trajectory filtering algorithm, and the motion state of the corresponding target at the historical moment is continuously updated and corrected. The trajectory is continuously tracked and the motion state of each target is estimated, generating the real-time trajectory state and tracking confidence of each target. The current position, current velocity, and current direction of motion of the target are extracted from the real-time trajectory status, and the extrapolation algorithm is used to recursively predict the changes in the target's position in subsequent moments to generate a trajectory prediction for each target.

[0010] As a preferred embodiment of the low-altitude drone countermeasure method based on 5G-A sensing integration technology described in this invention, the specific steps for generating dynamic risk levels are as follows: The motion feature parameters of the target are extracted from the trajectory prediction, and multimodal feature comparison is performed by combining the feature matching rules in the list of key elements for low-altitude target identification to generate a target type identification label. The target type identification mark and tracking confidence are input into the risk assessment algorithm, and the comprehensive risk quantification value is calculated by combining the dynamic judgment rules in the perception parameter threshold list; The basic risk level is determined based on the threshold range of the comprehensive risk quantification value, and the basic risk level is dynamically adjusted based on the target movement trend to generate a dynamic risk level.

[0011] As a preferred embodiment of the low-altitude UAV countermeasure method based on 5G-A integrated sensing technology described in this invention, the specific steps for generating the disposal level and countermeasure triggering conditions are as follows: Using the spatial constraints of electronic fences, a spatiotemporal correlation analysis is performed on the dynamic risk level. When both the risk threshold and the spatial intrusion condition are met, a response level is generated. The parameters of the handling level are parsed and the instructions are mapped to generate the corresponding countermeasure trigger conditions.

[0012] As a preferred embodiment of the low-altitude UAV countermeasure method based on 5G-A integrated sensing technology described in this invention, the step of generating countermeasure command parameters corresponding to the handling level according to a preset protection strategy when the countermeasure triggering conditions are met is as follows: When the countermeasure triggering conditions are met, the system reads the response priority, scope of action, and response intensity information corresponding to the response level, and retrieves the set of countermeasure rules corresponding to the response level from the preset protection strategy. The system uses the target type identification identifier to extract the navigation system features, flight behavior features, and control link features corresponding to the target UAV, and matches the navigation system features, flight behavior features, and control link features with the adaptation conditions in the set of countermeasure rules. Countermeasure rules that do not match the current target UAV are eliminated, and countermeasure rules that meet the response level requirements and are adapted to the target type identification identifier are retained to obtain the applicable strategy. The corresponding signal system configuration, direction of action configuration, power adjustment configuration, and duration configuration are extracted from the applicable strategy. Combined with the risk intensity, approach trend, and space intrusion status corresponding to the dynamic risk level, the parameters of the signal system configuration, direction of action configuration, power adjustment configuration, and duration configuration are adjusted in a coordinated manner. Based on the parameters after the coordinated adjustment, the carrier configuration, modulation configuration, code pattern configuration, power configuration, and beam pointing configuration of the navigation decoy signal are determined. Timing start information, continuous control information, and termination control information corresponding to the carrier configuration, modulation configuration, code pattern configuration, power configuration, and beam pointing configuration are generated to obtain the waveform parameters and transmission control commands of the navigation decoy signal. The navigation decoy signal waveform parameters and transmission control commands are subjected to field integrity verification, parameter conflict verification, and timing logic verification. Based on the current base station sensing resource occupancy status, transmitter load status, and target tracking timeliness requirements, a resource occupancy pre-assessment is performed. When the logic verification passes and the resource occupancy pre-assessment meets the execution constraints, the navigation decoy signal waveform parameters and transmission control commands are encapsulated and mapped according to the device interface protocol to generate corresponding countermeasure command parameters.

[0013] As a preferred embodiment of the low-altitude UAV countermeasure method based on 5G-A sensor integration technology described in this invention, the specific steps for generating the countermeasure execution state and target deviation trajectory are as follows: The countermeasure command parameters are parsed to generate waveform control parameters and transmission timing commands that can be executed by the navigation decoy signal transmitter; Based on waveform control parameters and transmission timing commands, the navigation deception signal transmitter is controlled to transmit navigation deception signals in a directional manner toward the target UAV. The trajectory data of the target drone is collected in real time by the 5G-A integrated sensing base station and compared with the expected driving away trajectory to generate trajectory deviation error data. Statistical analysis and threshold comparison are performed on the trajectory deviation error data to generate countermeasure execution status evaluation parameters, and the deviation trajectory data is recorded simultaneously.

[0014] As a preferred embodiment of the low-altitude UAV countermeasure method based on 5G-A integrated sensing technology described in this invention, the specific steps for generating the updated base station operating parameter set and protection strategy are as follows: Data fusion processing is performed on the countermeasure execution status and the target deviation trajectory to generate countermeasure effect evaluation indicators and trajectory deviation feature vectors; Based on the countermeasure effect evaluation index and trajectory deviation feature vector, a set of strategy optimization suggestion parameters is obtained by conducting multi-scenario simulation tests through a digital twin model. Based on the strategy optimization suggestion parameter set, the base station working parameter set and protection strategy are adaptively adjusted to generate candidate parameter combinations; The candidate parameter combinations are validated and conflict detected to generate an updated set of base station operating parameters and protection strategies.

[0015] As a preferred embodiment of the low-altitude UAV countermeasure method based on 5G-A integrated sensing technology described in this invention, the full-process data refers to the initial detection confidence level, tracking confidence level, dynamic risk level, countermeasure command parameters, countermeasure execution status, and target deviation trajectory.

[0016] The beneficial effects of this invention are as follows: by estimating the range, velocity and angle parameters of the candidate low-altitude target set, it realizes the intelligent fusion of distributed sensing data and high-precision trajectory prediction capabilities, and reduces the sensing blind zone and the false alarm and missed detection rate; through closed-loop optimization and strategy iteration driven by digital twin, it realizes the real-time quantitative evaluation of the countermeasure effect and the autonomous optimization and evolution of the protection strategy. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a low-altitude drone countermeasure method based on 5G-A integrated sensing technology.

[0019] Figure 2 A flowchart for generating the base station operating parameter set.

[0020] Figure 3 A flowchart for forming a set of candidate low-altitude targets.

[0021] Figure 4 A flowchart for generating trajectory predictions. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for countering low-altitude unmanned aerial vehicles based on 5G-A sensing integration technology, including the following steps: S1. Collect three-dimensional geographic information data and historical electromagnetic environment monitoring data to generate a set of base station operating parameters for low-altitude sensing.

[0026] Acquire three-dimensional geographic information data and historical electromagnetic environment monitoring data of the area to be protected, and generate an airspace electromagnetic environment profile.

[0027] The specific process includes acquiring three-dimensional geographic information data and historical electromagnetic environment monitoring data of the area to be protected. Based on the terrain undulations, building distribution, and ground object occupancy relationships in the three-dimensional geographic information data, combined with the spectrum occupancy, signal strength variation patterns, and frequency of interference sources recorded in the historical electromagnetic environment monitoring data, the electromagnetic signal characteristics at different locations and altitudes are integrated through time-series statistical analysis and spatial interpolation methods to generate an airspace electromagnetic environment profile that reflects the distribution status of electromagnetic signals in the airspace, interference hotspots, and propagation-restricted areas.

[0028] It should be noted that 3D geographic information data can be obtained through remote sensing methods such as lidar scanning, stereo photogrammetry, or synthetic aperture radar interferometry. It can also be extracted from existing geographic information system databases, including digital elevation models, digital surface models, and 3D building models containing elevation, ground feature outlines, and spatial locations. Historical electromagnetic environment monitoring data is obtained by continuously collecting and recording airborne and ground electromagnetic signals at different times using fixed or mobile electromagnetic spectrum monitoring equipment deployed in the protected area. This data covers parameters such as frequency, bandwidth, signal strength, modulation method, and occurrence time, and is accumulated over a long period to form a traceable historical electromagnetic environment dataset. Airspace electromagnetic environment profiling is a comprehensive modeling and visualization of the type, intensity, spectrum distribution, and spatiotemporal variation characteristics of radio signals in use within a specific low-altitude area. Its purpose is to provide real-time and accurate electromagnetic situational awareness support for configuring transmission parameters for navigation decoy signals, interference avoidance, and effectiveness assessment of countermeasures.

[0029] Based on the spatial electromagnetic environment profile, the base station coverage blind spots are obtained through line-of-sight propagation characteristics, and the deployment location, antenna height, and initial beam direction of the 5G-A integrated sensing base station are obtained by combining the characteristics of interference sources, thus forming a base station deployment scheme.

[0030] The specific process includes, based on the spatial electromagnetic environment profile, utilizing the line-of-sight propagation characteristics of electromagnetic waves in free space, analyzing signal attenuation areas caused by terrain obstruction, building blockage, and multipath effects, and identifying base station coverage blind spots that cannot be effectively covered by the 5G-A integrated sensing base station; simultaneously, combining the interference source characteristics recorded in the spatial electromagnetic environment profile, including the location, frequency band, transmission power, and duration of the interference source, avoiding areas with strong interference or including them in the collaborative sensing range, and determining the deployment location, antenna height, and initial beam direction of the 5G-A integrated sensing base station accordingly, thus forming a base station deployment plan.

[0031] It should be noted that line-of-sight propagation characteristics refer to the characteristics of electromagnetic waves propagating along a straight path under unobstructed conditions.

[0032] Based on the geographical coordinates and coverage of the base stations in the base station deployment scheme, configure the time slot ratio and cyclic prefix length of the communication subframe and sensing subframe in the 5G-A integrated sensing frame structure, and set the transmit power spectral density of the sensing subframe.

[0033] The specific process includes adjusting the time slot ratio of communication subframes and sensing subframes in the 5G-A integrated sensing frame structure based on the geographical coordinates and coverage of the base stations in the base station deployment scheme, according to the terrain obstruction, service user density, and the distance and speed resolution requirements of the sensing task at the location of each 5G-A integrated sensing base station, in order to balance communication capacity and sensing performance. The cyclic prefix length is determined based on the maximum coverage distance and multipath delay spread. At the same time, the transmit power spectral density of the sensing subframe is set in combination with the required detection sensitivity and electromagnetic compatibility limitations of the sensing subframe (for example, in densely populated urban areas, a longer cyclic prefix length is used due to severe multipath effects, and the transmit power spectral density of the sensing subframe is limited to no more than -30 dBm per megahertz due to the proximity of sensitive frequency bands, while in open suburban areas, the proportion of sensing subframes can be appropriately increased and the transmit power spectral density can be increased to enhance detection capabilities).

[0034] It should be noted that the required detection sensitivity of the sensing subframe refers to the minimum received signal power required for the sensing subframe of the 5G-A integrated sensing base station to reliably detect the echo signal when performing the target detection task; the electromagnetic compatibility limit setting of the sensing subframe refers to the electromagnetic compatibility limit set in the 5G-A integrated sensing base station to constrain its transmission parameters in order to avoid harmful interference to other communication systems or electronic equipment caused by the transmission signal of the sensing subframe.

[0035] Based on the time slot ratio, cyclic prefix length, and transmit power spectral density, and combined with the time-frequency reuse strategy, the time-frequency resource blocks of communication subframes and sensing subframes are dynamically allocated, and a base station operating parameter set is generated.

[0036] The specific process includes: dividing the time slot structure for communication and sensing in each radio frame according to the time slot ratio, cyclic prefix length, and transmit power spectral density, combined with the time-frequency reuse strategy; determining the symbol duration based on the cyclic prefix length and avoiding inter-carrier interference; constraining the radiation intensity of each sub-band using transmit power spectral density; configuring the time-frequency resource blocks occupied by communication sub-frames and sensing sub-frames in the frequency and time dimensions in an alternating manner according to the time-frequency reuse strategy to ensure that the two do not conflict with each other under the shared spectrum; and integrating the finally determined sub-frame configuration, resource block mapping relationship, power allocation, and frame structure parameters to generate the base station operating parameter set.

[0037] It should be noted that time-frequency reuse strategy refers to arranging the signals of communication and sensing functions orthogonally or non-orthogonally in the time and frequency dimensions to achieve coexistence and coordination between the two under shared spectrum; sensing resources refer to the collective term for schedulable physical resources such as time slots, subcarriers, power and beams used in 5G-A integrated sensing base stations to perform communication transmission and radar sensing tasks.

[0038] S2. Use the base station operating parameter set to collect echo signals and channel state information of low-altitude targets, and perform clutter suppression and time-frequency feature extraction to obtain candidate initial detection confidence and low-altitude target set.

[0039] Based on the base station operating parameter set, the receiving link configuration is controlled to collect the raw data stream of echo signals and channel state information of low-altitude targets.

[0040] The specific process includes adjusting the radio frequency gain, sampling clock, filtering bandwidth, and synchronization mechanism of the receiving link of the 5G-A integrated sensing base station according to the subframe configuration parameters and resource allocation strategy in the base station operating parameter set; receiving electromagnetic wave signals reflected back from low-altitude targets as echo signals within the time-frequency resource block allocated in the sensing subframe; estimating the wireless propagation path characteristics through pilot symbols within the time-frequency resource block allocated in the communication subframe to obtain channel state information, and forming a continuous data stream of echo signals and channel state information.

[0041] It should be noted that channel state information is a set of parameters describing the characteristics of a signal in a wireless communication link during propagation, such as attenuation, phase shift, time delay, and multipath effects. These parameters include channel gain, phase response, time delay spread, and Doppler shift. Pilot symbols are reference signals with known amplitude and phase that are pre-inserted into the wireless communication signal and are used by the receiver for channel estimation, frequency synchronization, and phase noise compensation.

[0042] The original data stream is subjected to clutter suppression processing, and the time-domain envelope and frequency-domain Doppler features are extracted from the original data stream.

[0043] The specific process includes: performing clutter suppression processing on the data stream containing echo signals and channel state information; using space-time adaptive filtering or constant false alarm rate detection methods to eliminate strong clutter components generated by ground reflection, meteorological interference, and static objects; extracting the amplitude change profile of the echo signal in the time domain through envelope detection or matched filtering to obtain the time domain envelope; and using fast Fourier transform in the frequency domain to analyze the frequency offset characteristics of the echo signal to obtain the frequency domain Doppler characteristics.

[0044] It should be noted that frequency domain Doppler characteristics refer to the distribution characteristics of the received signal frequency shift caused by the Doppler effect during the target's motion in the frequency domain, reflecting the radial velocity of the target relative to the observation station and its variation law.

[0045] Based on the time-domain envelope and frequency-domain Doppler characteristics, potential target traces are identified using a constant false alarm rate (CFAR) detector, and the signal-to-noise ratio (SNR) of each target trace is calculated as the initial detection confidence level, expressed as: ； in, Indicates the first The signal-to-noise ratio of each target point trace. Indicates the index number of the target point. The weight vector representing the temporal envelope features. Representing the time domain, Indicates the first The weight vector of the temporal envelope features of each target point trace. The weight vector represents the frequency domain Doppler characteristics. Represents the frequency domain. Indicates the first The frequency domain Doppler eigenvectors of the target point traces Indicates the first The power estimate of the local background noise of the detection unit where each target point is located. This indicates local background noise.

[0046] It should be noted that the weight vector of the temporal envelope feature is derived from the machine learning training and calibration of historical target signal samples, and is determined based on the discriminative feature distribution of various targets in the temporal envelope; the weight vector of the frequency domain Doppler feature is derived from the machine learning training and calibration of the classification performance of historical target signal samples in the frequency domain Doppler feature space, and is determined based on the contribution of different speed modes to target recognition; the power estimate of the local background noise is derived from the average noise power obtained by sliding window statistics or idle resource block measurement of the received signal of the 5G-A integrated sensing base station during the time period or frequency band in which the target signal does not appear.

[0047] The specific process includes identifying candidate points that may correspond to low-altitude targets in the echo signal using a constant false alarm rate (CFAR) detector based on the temporal envelope and frequency domain Doppler characteristics. For each candidate point, the temporal envelope characteristics are weighted and combined with preset temporal weights, and the frequency domain Doppler characteristics are weighted and combined with preset frequency domain weights. The two weighted results are combined to form a comprehensive feature energy, and the local background noise power around the detection unit where the candidate point is located is estimated. Based on this, the ratio between the comprehensive feature energy and the local background noise power is calculated. The logarithm of this ratio is taken to the base 10 and amplified by 10 to obtain the signal-to-noise ratio (SNR) in decibels. This SNR is used as the initial detection confidence level of the candidate point.

[0048] It should be noted that the preset time-domain weights are determined through offline training based on the statistical characteristics of the time-domain envelope of valid targets in historical echo signals; the preset frequency-domain weights are determined through offline training based on the statistical distribution of the frequency-domain Doppler characteristics of valid targets in historical echo signals; the constant false alarm rate detector is a target detection method that maintains a constant false alarm probability by dynamically adjusting the detection threshold in environments with varying background noise or clutter power.

[0049] Potential target points and initial detection confidence are clustered and associated to form a candidate low-altitude target set.

[0050] The specific process includes inputting potential target points and initial detection confidence into a clustering algorithm based on distance and Doppler similarity, grouping and associating potential target points according to the spatial proximity and consistency of initial detection confidence, merging multiple points belonging to the same physical target into a single target representation, and retaining comprehensive confidence information to form a candidate low-altitude target set.

[0051] S3. Estimate the range, velocity and angle parameters of the candidate low-altitude target set, and use the initial detection confidence as weight to generate the tracking confidence and trajectory prediction for each target.

[0052] The time delay features, Doppler frequency shift features, and angle response features of the corresponding echo signals are extracted from the candidate low-altitude target set, and the features are matched and corrected in combination with the corresponding channel state information to obtain the target range parameters, target radial velocity parameters, and target spatial angle parameters, respectively. The target range parameters, target radial velocity parameters, and target spatial angle parameters are correlated and combined to obtain the preliminary motion parameter set of each target.

[0053] The specific process includes: retrieving the echo signal and channel state information corresponding to each target from the candidate low-altitude target set one by one; extracting the time delay feature that can characterize the difference in reflection and return time from the echo signal using matched filtering, and calculating the target range parameter based on the time delay feature; extracting the Doppler frequency shift feature that can characterize the frequency shift state from the echo signal using fast Fourier transform, and calculating the target radial velocity parameter based on the Doppler frequency shift feature; extracting the phase response and angle response features corresponding to the multi-antenna array from the channel state information, and determining the target spatial angle parameter using a direction-of-arrival estimation algorithm, thereby forming the target range parameter, target radial velocity parameter, and target spatial angle parameter corresponding to each target.

[0054] The target range parameters, target radial velocity parameters, and target spatial angle parameters are organized according to the target identifiers in the candidate low-altitude target set. The target range parameters, target radial velocity parameters, and target spatial angle parameters are matched and corrected by combining the channel gain, phase response, time delay spread, and Doppler frequency shift in the channel state information. After eliminating abnormal parameters that are inconsistent with the motion state of the same target, the target range parameters, target radial velocity parameters, and target spatial angle parameters corresponding to the same target are associated and combined to form a preliminary set of motion parameters that can characterize the target's spatial position, radial velocity, and directional state.

[0055] It should be noted that the direction-of-arrival estimation algorithm is a signal processing method that uses the phase difference, time difference of arrival, or spatial spectrum characteristics between the received signals of a multi-antenna array to estimate the incident angle of an electromagnetic wave from the target direction.

[0056] Using the initial detection confidence as the fusion weight, the initial motion parameter set corresponding to each target is used to identify the same source target and remove duplicate targets. After the initial motion parameter set corresponding to each target is retained after the same source target identification and duplicate target removal, multi-base station joint weighted fusion positioning is performed to obtain the position component and velocity component of each target in a unified spatial coordinate system, and generate the three-dimensional position coordinates and velocity vector of each target.

[0057] The specific process includes: organizing the preliminary motion parameter sets corresponding to each target and the initial detection confidence scores according to the target identifiers in the candidate low-altitude target set; identifying targets from the same source based on the correspondence between target distance parameters, target radial velocity parameters, and target spatial angle parameters in terms of temporal continuity, spatial proximity, and consistency of motion direction; retaining the preliminary motion parameter sets with higher confidence and more stable parameter relationships by using the initial detection confidence scores as the fusion weights for multiple preliminary motion parameter sets that are determined to be the same physical target, and eliminating redundant preliminary motion parameter sets formed by repeated observations from multiple base stations or repeated associations of adjacent points; then performing multi-base station joint weighted fusion positioning on the retained preliminary motion parameter sets according to the base station geographic coordinates, target distance parameters, target radial velocity parameters, and target spatial angle parameters; transforming the spatial position and motion velocity relationships corresponding to each target to a unified spatial coordinate system; obtaining the position and velocity components; and generating the three-dimensional position coordinates and velocity vector of each target.

[0058] Based on three-dimensional position coordinates and velocity vectors, the current observation data is input into the trajectory filtering algorithm, and the motion state of the corresponding target at the historical time is continuously updated and corrected. The trajectory is continuously tracked and the motion state of each target is estimated, generating the real-time trajectory state and tracking confidence of each target.

[0059] The specific process includes: organizing the three-dimensional position coordinates and velocity vectors of each target in chronological order according to the target identifiers in the candidate low-altitude target set to form the observation data at the current moment; associating the observation data at the current moment with the motion state of the corresponding target at a historical moment; continuously updating the spatial position, velocity, and direction of motion of the target at the current moment through a trajectory filtering algorithm; and using the motion state of the corresponding target at a historical moment to smooth and correct the random deviations in the observation data at the current moment to obtain the updated motion state corresponding to each target.

[0060] The updated motion state is continuously compared with the motion state of the target at the corresponding historical time to determine whether the changes in spatial position, velocity, and direction of motion of the target are consistent between adjacent time points. When the continuity is strong, the tracking confidence of the corresponding target is increased; when the continuity is weak, the tracking confidence of the corresponding target is decreased. The spatial position, velocity, direction of motion, and tracking confidence after continuous updates and state correction by the trajectory filtering algorithm are correlated and organized to generate the real-time trajectory state and tracking confidence of each target.

[0061] It should be noted that the trajectory filtering algorithm is a recursive signal processing method that combines the target's historical motion state with current observation data to continuously estimate, smooth, and suppress noise in the target's position, velocity, and direction of motion.

[0062] The current position, current velocity, and current direction of motion of the target are extracted from the real-time trajectory status, and the extrapolation algorithm is used to recursively predict the changes in the target's position in subsequent moments to generate a trajectory prediction for each target.

[0063] The specific process includes: based on the spatial position, velocity, and direction of motion continuously updated and corrected by the trajectory filtering algorithm in the real-time trajectory state, the current position, velocity, and direction of motion of the target are extracted respectively, and then organized according to the target identifier in the candidate low-altitude target set; the current position and velocity of the target are associated to determine the spatial position and velocity of each target at the current moment, and the current direction of motion is associated with the current velocity to determine the direction of motion extension of each target after the current moment; then, the current position, velocity, and direction of motion of the target are recursively processed using an extrapolation algorithm, and the changes in the target's position at subsequent moments are continuously extended according to the current velocity and direction of motion to obtain the spatial position that each target may reach at subsequent moments, and the spatial positions that may reach at subsequent moments are arranged in chronological order, forming a continuous motion path together with the current position of the target, thus generating the trajectory prediction of each target.

[0064] It should be noted that the extrapolation algorithm is a method of recursively predicting the spatial position that the target may reach in subsequent moments based on the target's current position, current velocity, current direction of motion, and existing motion change patterns.

[0065] S4. Utilize trajectory prediction and tracking confidence to identify the execution type and determine the risk level for each target, generate a dynamic risk level, and perform spatiotemporal correlation analysis on the dynamic risk level to generate the response level and countermeasure triggering conditions.

[0066] The motion feature parameters of the target are extracted from the trajectory prediction, and multimodal feature comparison is performed by combining the feature matching rules in the list of key elements for low-altitude target identification to generate a target type identification label.

[0067] The specific process includes extracting the target's motion characteristic parameters from trajectory prediction, including flight altitude change rate, horizontal speed stability, turning angular velocity, and track curvature. These motion characteristic parameters are then compared item by item with the feature matching rules in the low-altitude target key element identification list. Based on the threshold range and logical conditions defined in the feature matching rules, it is determined whether the target's behavior pattern conforms to the typical characteristics of a specific type of low-altitude target (e.g., when the flight altitude change rate is less than two meters per second, the horizontal speed stability is higher than 90%, the turning angular velocity does not exceed five degrees per second, and the track curvature is close to zero, the target is determined to conform to the flight characteristics of a consumer-grade multi-rotor UAV; while if the flight altitude change rate is large, the horizontal speed is high, and the track is straight and uniform, it is more likely to correspond to a fixed-wing UAV). The target attributes are comprehensively determined through multimodal feature comparison to generate a target type identification label.

[0068] It should be noted that the feature matching rules in the list of key elements for identifying low-altitude targets are a set of predefined logical conditions and threshold ranges used to compare the motion characteristic parameters of the target with the typical behavioral characteristics of known low-altitude target types to determine its category. The logical conditions and threshold ranges are predefined based on historically accumulated flight behavior data of various low-altitude targets and their typical motion characteristic statistical patterns, using machine learning classification methods.

[0069] The target type identification and tracking confidence level are input into the risk assessment algorithm, and the comprehensive risk quantification value is calculated by combining the dynamic judgment rules in the perception parameter threshold list. The expression is as follows: ; in, This represents the overall risk quantification value. Indicates the target type identification identifier. Indicates the tracking confidence level. This represents the weighting coefficient of the distance risk term. Indicates the distance threshold. This indicates the real-time distance between the target and the sensitive area. This represents the weighting coefficient of the speed risk item. Indicates the speed of the target. Indicates the speed threshold. The weighting coefficient for the high deviation risk term. Indicates the target's flight altitude. This indicates the height threshold.

[0070] It should be noted that the expression has been dimensionless by normalizing the physical quantities of each risk item. Specifically, the distance item is dimensionless by being compared with the distance threshold, the speed item is dimensionless by being compared with the speed threshold, and the altitude deviation item is dimensionless by being compared with the altitude threshold, thereby ensuring that the items are comparable when weighted and summed.

[0071] The dynamic judgment rules in the perception parameter threshold list are a set of judgment boundary values ​​related to the behavior of low-altitude targets, including distance threshold, speed threshold, and altitude threshold, which are used to compare and quantify the real-time motion status of targets during risk assessment.

[0072] The weighting coefficient for the distance risk item is pre-set based on the sensitivity of different areas to intrusion distance in low-altitude security scenarios, through statistical analysis of historical threat events. The weighting coefficient for the speed risk item is predetermined based on the impact of low-altitude targets on security threats in different speed ranges, through statistical analysis of historical threat events. The weighting coefficient for the altitude deviation risk item is pre-set based on the degree of impact of low-altitude target flight altitude deviation from the allowable range on safety threats, through statistical analysis of historical threat events.

[0073] The distance threshold refers to the critical distance value between a target and a sensitive area set in a low-altitude security scenario to determine whether a target poses a potential threat. The distance threshold is determined based on the security level of the protected area, the terrain environment, and the approach capability of typical threat targets, through security specifications or historical event analysis. An exemplary range is usually from one hundred meters to two thousand meters. The speed threshold refers to the critical speed value used in low-altitude security scenarios to determine whether the speed of a target is abnormal or threatening. The speed threshold is determined by statistical analysis of historical threat events based on the typical cruise speed of common legal low-altitude aircraft, the penetration speed characteristics of illegal targets, and the response capability of the protected area. The exemplary value range is usually from five meters per second to fifty meters per second. The altitude threshold refers to the critical altitude value used in low-altitude security scenarios to determine whether the flight altitude of a target exceeds the permitted airspace or poses a potential threat. The altitude threshold is determined based on local regulations for the management of low-altitude airspace, the protection needs of sensitive areas, and the flight capabilities of typical drones, through airspace control policies, measured data, or historical event analysis. An exemplary value range is usually from ten meters to one hundred and fifty meters.

[0074] The specific process includes inputting the target type identification mark and tracking confidence level into the risk assessment algorithm, combining the dynamic judgment rules in the perception parameter threshold list, performing basic classification of risk level through the target type identification mark, adjusting the reliability weight of risk calculation according to the tracking confidence level, and calculating distance risk item, speed risk item and altitude deviation risk item respectively based on the difference between the real-time distance between the target and the sensitive area and the preset distance threshold, the ratio of the target speed to the speed threshold, and the absolute deviation of the target flight altitude from the altitude threshold. The risk items are then weighted and summed with corresponding weight coefficients to generate a comprehensive risk quantification value.

[0075] The basic risk level is determined based on the threshold range of the comprehensive risk quantification value, and the basic risk level is dynamically adjusted based on the target movement trend to generate a dynamic risk level.

[0076] The specific process includes determining the basic risk level based on the threshold range of the comprehensive risk quantification value. This threshold range is jointly defined by the low-altitude target key element identification list and the perception parameter threshold list. The basic risk level is dynamically adjusted in combination with the approach rate, altitude change direction and heading stability reflected in the target's movement trend. If the target shows a trend of continuously approaching the sensitive area, rapidly descending or abnormal maneuvering, the risk level is increased. If the target moves away or tends to stabilize, the risk level is maintained or decreased, thus generating a dynamic risk level.

[0077] It should be noted that the target movement trend refers to the behavioral tendency of the target in terms of approaching, moving away, hovering, or maneuvering, as reflected in the changes in the target's position, speed, and heading over a continuous time series.

[0078] Using the spatial constraints of electronic fences, a spatiotemporal correlation analysis is performed on the dynamic risk level. When both the risk threshold and the spatial intrusion condition are met simultaneously, a response level is generated.

[0079] The specific process includes using the spatial constraints of the electronic fence to perform spatiotemporal correlation analysis on the dynamic risk level, comparing the real-time three-dimensional location coordinates of the target with the geographical boundary defined by the electronic fence, determining whether the target is in or about to enter the protected airspace, and checking whether the dynamic risk level reaches or exceeds the preset risk threshold. When the target meets both the dynamic risk level not lower than the risk threshold and the spatial location intrusion into the electronic fence area, the joint judgment logic is triggered to generate the disposal level.

[0080] Electronic fence spatial constraints refer to the boundaries of protected areas and their buffer zones defined in three-dimensional geographic space, which serve as the geometric and logical basis for determining whether low-altitude targets have entered or approached sensitive airspace.

[0081] The risk threshold is a comprehensive risk quantification value pre-set based on the definition of the acceptable level of threats in low-altitude security missions, combined with historical event statistics and protection level requirements. An exemplary value range is usually 0.3 to 0.8.

[0082] Space intrusion conditions refer to the criteria for determining whether the real-time three-dimensional position coordinates of a low-altitude target fall within the protected area or early warning buffer zone defined by the spatial constraints of an electronic fence.

[0083] The parameters of the handling level are parsed and the instructions are mapped to generate the corresponding countermeasure trigger conditions.

[0084] The specific process includes parsing parameters and mapping instructions for the handling level, extracting parameters such as countermeasure response type, scope of action and activation timing based on the preset rule set corresponding to the handling level, matching these parameters with available countermeasures, including operation modes such as electromagnetic interference, navigation deception or communication blocking, and generating countermeasure triggering conditions that strictly correspond to the handling level.

[0085] It should be noted that the preset rule set is pre-set based on the handling strategies for low-altitude security tasks and emergency response plans under different threat scenarios, by summarizing historical handling cases.

[0086] S5. When the countermeasure triggering conditions are met, generate countermeasure command parameters corresponding to the handling level, and conduct directional drive-away of the target UAV by transmitting navigation deception signals, generating countermeasure execution status and target deviation trajectory.

[0087] When the countermeasure triggering conditions are met, the system reads the response priority, scope of action, and response intensity information corresponding to the response level, and retrieves the set of countermeasure rules corresponding to the response level from the preset protection strategy. The system uses the target type identification identifier to extract the navigation system features, flight behavior features, and control link features corresponding to the target UAV, and matches the navigation system features, flight behavior features, and control link features with the adaptation conditions in the set of countermeasure rules. Countermeasure rules that do not match the current target UAV are eliminated, and countermeasure rules that meet the response level requirements and are adapted to the target type identification identifier are retained to obtain the applicable strategy.

[0088] The specific steps are as follows: When the countermeasure triggering condition is met, based on the handling level associated with the countermeasure triggering condition, read the handling priority, scope of action, and response strength information corresponding to the handling level, determine the order of countermeasure processing based on the handling priority, limit the area of ​​action corresponding to the navigation deception signal based on the scope of action, and determine the strength requirements of countermeasure processing based on the response strength information; search according to the handling level in the protection strategy, extract the set of countermeasure rules corresponding to the handling level, and form candidate countermeasure rules that meet the requirements of the handling level.

[0089] The target type identification identifier is used to extract the navigation system characteristics, flight behavior characteristics, and control link characteristics corresponding to the target UAV. The navigation system characteristics, flight behavior characteristics, and control link characteristics are then matched with the adaptation conditions in the countermeasure rule set item by item. Countermeasure rules that are inconsistent with the navigation system characteristics, flight behavior characteristics, or control link characteristics are removed. Countermeasure rules that meet the handling level requirements and are compatible with the target type identification identifier are retained. The retained countermeasure rules are then organized into applicable strategies.

[0090] It should be noted that the protection strategy is pre-configured based on the protection level of the low-altitude security mission, the spatial constraints of the electronic fence, historical handling cases, and the set of countermeasure rules corresponding to different handling levels; the handling level requirements refer to the handling priority, scope of action, response intensity, activation timing, and the conditions that the countermeasure rules must meet to be adopted, which correspond to the handling level.

[0091] Extract the corresponding signal system configuration, direction of action configuration, power adjustment configuration, and duration configuration from the applicable strategy. Combine the risk intensity, approach trend, and space intrusion status corresponding to the dynamic risk level to perform parameter linkage adjustment on the signal system configuration, direction of action configuration, power adjustment configuration, and duration configuration. Based on the parameters after linkage adjustment, determine the carrier configuration, modulation configuration, code pattern configuration, power configuration, and beam pointing configuration of the navigation decoy signal. Generate timing start information, continuous control information, and termination control information corresponding to the carrier configuration, modulation configuration, code pattern configuration, power configuration, and beam pointing configuration to obtain the navigation decoy signal waveform parameters and transmission control commands.

[0092] The specific process includes: extracting signal system configuration, direction of action configuration, power adjustment configuration, and duration configuration from the applicable strategy; and organizing these configurations in conjunction with the navigation system characteristics of the target UAV, the target's current position, current direction of motion, space intrusion status, and the risk intensity and approach trend corresponding to the dynamic risk level; determining the carrier configuration, modulation configuration, and code pattern configuration required for the navigation decoy signal based on the correspondence between the signal system configuration and the navigation system characteristics; determining the beam pointing configuration of the navigation decoy signal based on the correspondence between the direction of action configuration and the target's current position, current direction of motion, and space intrusion status; determining the power configuration of the navigation decoy signal based on the correspondence between the power adjustment configuration and the risk intensity; and determining the timing start information, continuous control information, and termination control information based on the correspondence between the duration configuration and the approach trend; and finally, associating and organizing the carrier configuration, modulation configuration, code pattern configuration, power configuration, beam pointing configuration, and corresponding timing control content to obtain the waveform parameters and transmission control commands of the navigation decoy signal.

[0093] The navigation decoy signal waveform parameters and transmission control commands are subjected to field integrity verification, parameter conflict verification, and timing logic verification. Based on the current base station sensing resource occupancy status, transmitter load status, and target tracking timeliness requirements, a resource occupancy pre-assessment is performed. When the logic verification passes and the resource occupancy pre-assessment meets the execution constraints, the navigation decoy signal waveform parameters and transmission control commands are encapsulated and mapped according to the device interface protocol to generate corresponding countermeasure command parameters.

[0094] The specific process includes: performing field integrity checks on the navigation decoy signal waveform parameters and transmission control commands, checking whether the carrier configuration, modulation configuration, code pattern configuration, power configuration, beam pointing configuration, timing start information, continuous control information, and termination control information are complete; then performing parameter conflict checks to determine whether the power configuration matches the transmitter load status, whether the beam pointing configuration matches the target's current position, and whether the signal system configuration is compatible with the navigation system characteristics; subsequently performing timing logic checks to confirm that the sequence of timing start information, continuous control information, and termination control information meets the target tracking timeliness requirements; after completing the checks, performing a resource occupancy pre-assessment based on the current base station sensing resource occupancy status, transmitter load status, and target tracking timeliness requirements to determine whether the navigation decoy signal transmission process meets the execution constraints; when the field integrity checks, parameter conflict checks, and timing logic checks all pass, and the resource occupancy pre-assessment meets the execution constraints, the navigation decoy signal waveform parameters and transmission control commands are encapsulated and mapped according to the device interface protocol to generate corresponding countermeasure command parameters.

[0095] The countermeasure command parameters are parsed to generate waveform control parameters and transmission timing commands that can be executed by the navigation decoy signal transmitter.

[0096] The specific process includes parsing the countermeasure command parameters, extracting information such as modulation type, pseudorange offset, carrier phase disturbance mode, signal power level, transmission pointing angle, signal duration and transmission start time, and converting them into waveform control parameters and transmission timing commands that can be directly identified and executed by the navigation decoy signal transmitter, ensuring that the signal generation and radiation process strictly match the requirements of the countermeasure mission.

[0097] It should be noted that waveform control parameters refer to the control quantities used to configure the navigation decoy signal transmitter to generate specific radio frequency signals, including modulation type, pseudorange offset, carrier phase perturbation mode, and signal power level.

[0098] Based on waveform control parameters and transmission timing commands, the navigation deception signal transmitter is controlled to transmit navigation deception signals in a directional manner toward the target UAV.

[0099] The specific process includes driving the navigation decoy signal transmitter to generate a navigation deception signal at the specified launch start time according to the waveform control parameters and launch timing instructions, based on the modulation type, pseudorange offset, carrier phase disturbance mode and signal power level. The signal is then radiated directionally along the spatial direction determined by the launch pointing angle through the antenna array, so that the target UAV can receive and solve the controlled false navigation information, thereby interfering with its positioning and heading.

[0100] The trajectory data of the target drone is collected in real time by a 5G-A integrated sensing base station and compared with the expected driving trajectory to generate trajectory deviation error data.

[0101] The specific process includes collecting trajectory data of the target drone in real time through a 5G-A integrated sensing base station, obtaining its position and speed information in three-dimensional space, and comparing the trajectory data with the pre-planned expected driving trajectory at the same time point to obtain the differences between the two in spatial distance and heading angle, forming trajectory deviation error data that reflects the target drone's deviation from the expected driving trajectory.

[0102] It should be noted that the expected departure trajectory refers to the anticipated flight path that guides the target drone away from sensitive areas in a low-altitude security mission, based on the electronic fence boundary, safety buffer zone, and the target's initial position and motion state.

[0103] Statistical analysis and threshold comparison are performed on the trajectory deviation error data to generate countermeasure execution status evaluation parameters, and the deviation trajectory data is recorded simultaneously.

[0104] The specific process includes statistical analysis of trajectory deviation error data to obtain the mean, variance, and maximum deviation over time. These statistics are then compared with preset deviation assessment thresholds to determine whether the target UAV has effectively responded to the navigation deception signal (e.g., when the mean of trajectory deviation error consistently exceeds 30 meters and the maximum deviation reaches 50 meters or more, it indicates that the target has significantly deviated from the expected drive-away trajectory, and is judged as an effective response). Based on the comparison results, countermeasure execution status assessment parameters are generated. At the same time, the complete trajectory deviation error data is recorded synchronously in chronological order for subsequent effect review and strategy optimization.

[0105] It should be noted that the deviation assessment threshold is determined in advance through statistical analysis based on the maximum allowable trajectory deviation of the target UAV under effective countermeasures, combined with typical flight performance, electronic fence safety margin, and historical countermeasures effect data. The exemplary value range is usually from ten meters to fifty meters.

[0106] S6. Perform closed-loop verification and adaptive parameter adjustment on the countermeasure execution status and target deviation trajectory, and write the entire process data into the digital twin model for simulation evaluation and strategy update, generating an updated base station working parameter set and protection strategy.

[0107] Data fusion processing is performed on the countermeasure execution status and the target deviation trajectory to generate countermeasure effect evaluation indicators and trajectory deviation feature vectors.

[0108] The specific process includes data fusion processing of the countermeasure execution status and the target deviation trajectory; correlation analysis between the response effectiveness, duration and stability indicators in the countermeasure execution status evaluation parameters and the spatial offset, direction change rate and time cumulative deviation in the target deviation trajectory; generating countermeasure effect evaluation indicators that reflect the overall intervention effectiveness through weighted integration and feature extraction; and simultaneously constructing a trajectory deviation feature vector that includes offset amplitude, offset direction and offset temporal characteristics.

[0109] It should be noted that the trajectory deviation feature vector is a vector composed of multi-dimensional features extracted from the trajectory deviation error data of the target UAV during the countermeasure process. It includes elements such as offset magnitude, offset direction, offset temporal characteristics, and spatial cumulative deviation, and is used to quantitatively describe the shape and evolution of the target's deviation from the expected drive-away trajectory.

[0110] Based on the countermeasure effect evaluation index and trajectory deviation feature vector, a set of strategy optimization suggestion parameters is obtained by conducting multi-scenario simulation tests through a digital twin model.

[0111] The specific process includes inputting the state characteristics of the current countermeasure scenario into a digital twin model based on the countermeasure effect evaluation index and trajectory deviation feature vector. The digital twin model then reproduces the flight behavior of the target UAV, the mechanism of navigation deception signals, and environmental constraints. Simulation tests are run in various typical low-altitude security scenarios to compare the countermeasure response effects under different parameter configurations, identify key factors affecting countermeasure effectiveness, and output a set of strategy optimization suggestions to improve the accuracy and efficiency of subsequent countermeasures.

[0112] Based on the strategy optimization suggestion parameter set, the base station operating parameter set and protection strategy are adaptively adjusted to generate candidate parameter combinations.

[0113] The specific process includes adjusting the signal power level, transmission pointing angle, waveform modulation type and transmission timing configuration in the base station operating parameter set according to the strategy optimization suggestion parameter set, and simultaneously updating the set of countermeasure rules corresponding to the handling level and the matching logic of the target type identification mark in the protection strategy. By combining different values, multiple sets of feasible configurations that meet the constraints are formed, and candidate parameter combinations are generated.

[0114] It should be noted that the adaptive adjustment is based on the countermeasure effect evaluation index and trajectory deviation feature vector, combined with the strategy optimization suggestion parameter set output by the digital twin model. Under the premise of meeting the hardware capability boundaries and electromagnetic compatibility requirements, the time slot ratio, cyclic prefix length, transmit power spectral density and time-frequency resource block allocation in the base station operating parameter set are automatically adjusted, and the handling level mapping relationship and target type matching conditions in the protection strategy are updated simultaneously.

[0115] The candidate parameter combinations are validated and conflict detected to generate an updated set of base station operating parameters and protection strategies.

[0116] The specific process includes validating the candidate parameter combinations, checking whether each parameter meets the hardware capability boundaries, electromagnetic compatibility requirements and regulatory restrictions of the 5G-A integrated sensing base station, and performing conflict detection to identify the mutual exclusion relationships between different parameters in terms of time and frequency resources, spatial orientation or countermeasure logic. After eliminating invalid or conflicting combinations, the verified parameter combinations are applied to the base station operating parameter set and protection strategy to generate an updated base station operating parameter set and protection strategy.

[0117] It should be noted that the hardware capability boundary refers to the physical performance limits that the RF front-end, antenna array, and signal processing unit of the 5G-A integrated sensing base station can support in terms of transmission power, operating frequency, beam pointing accuracy, and timing control; electromagnetic compatibility requirements refer to the normative restrictions that the 5G-A integrated sensing base station must meet when transmitting navigation decoy signals, ensuring that it does not cause harmful interference to other wireless equipment and that it has the ability to resist external electromagnetic interference; regulatory restrictions refer to the mandatory constraints on the operating parameters and countermeasures of the 5G-A integrated sensing base station regarding radio transmission, low-altitude airspace use, and deployment of countermeasure equipment.

[0118] In summary, this invention achieves intelligent fusion of distributed sensing data and high-precision trajectory prediction capabilities by estimating the ranging, velocity, and angle parameters of a candidate low-altitude target set, thereby reducing sensing blind spots and lowering false alarm and missed detection rates. Furthermore, through closed-loop optimization and strategy iteration driven by digital twins, it enables real-time quantitative evaluation of countermeasure effects and autonomous optimization and evolution of protection strategies.

[0119] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for countering low-altitude unmanned aerial vehicles (UAVs) based on 5G-A sensing integration technology, characterized in that: include, Collect three-dimensional geographic information data and historical electromagnetic environment monitoring data to generate a set of base station operating parameters for low-altitude sensing; The echo signals and channel state information of low-altitude targets are collected using the base station operating parameter set, and clutter suppression and time-frequency feature extraction are performed to obtain the candidate initial detection confidence and the set of low-altitude targets. Range, velocity, and angle parameters are estimated for a candidate set of low-altitude targets, and the initial detection confidence is used as a weight to generate the tracking confidence and trajectory prediction for each target. By using trajectory prediction and tracking confidence, the execution type of each target is identified and the risk level is determined, generating a dynamic risk level. Spatiotemporal correlation analysis is then performed on the dynamic risk level to generate the response level and countermeasure triggering conditions. When the countermeasure triggering conditions are met, countermeasure command parameters corresponding to the handling level are generated, and navigation deception signals are emitted against the target UAV to carry out directional drive-away. The countermeasure execution status and target deviation trajectory are generated. The specific steps are as follows: When the countermeasure triggering conditions are met, the system reads the response priority, scope of action, and response intensity information corresponding to the response level, and retrieves the set of countermeasure rules corresponding to the response level from the preset protection strategy. The system uses the target type identification identifier to extract the navigation system features, flight behavior features, and control link features corresponding to the target UAV, and matches the navigation system features, flight behavior features, and control link features with the adaptation conditions in the set of countermeasure rules. Countermeasure rules that do not match the current target UAV are eliminated, and countermeasure rules that meet the response level requirements and are adapted to the target type identification identifier are retained to obtain the applicable strategy. The corresponding signal system configuration, direction of action configuration, power adjustment configuration, and duration configuration are extracted from the applicable strategy. Combined with the risk intensity, approach trend, and space intrusion status corresponding to the dynamic risk level, the parameters of the signal system configuration, direction of action configuration, power adjustment configuration, and duration configuration are adjusted in a coordinated manner. Based on the parameters after the coordinated adjustment, the carrier configuration, modulation configuration, code pattern configuration, power configuration, and beam pointing configuration of the navigation decoy signal are determined. Timing start information, continuous control information, and termination control information corresponding to the carrier configuration, modulation configuration, code pattern configuration, power configuration, and beam pointing configuration are generated to obtain the waveform parameters and transmission control commands of the navigation decoy signal. The waveform parameters of the navigation decoy signal and the transmission control command are checked for field integrity, parameter conflict and timing logic. Based on the current base station sensing resource occupancy status, transmitter load status and target tracking timeliness requirements, a resource occupancy pre-assessment is performed. When the logic verification passes and the resource consumption pre-assessment meets the execution constraints, the navigation decoy signal waveform parameters and the transmission control command are encapsulated and mapped according to the device interface protocol to generate the corresponding countermeasure command parameters. The countermeasure command parameters are parsed to generate waveform control parameters and transmission timing commands that can be executed by the navigation decoy signal transmitter; Based on waveform control parameters and transmission timing commands, the navigation deception signal transmitter is controlled to transmit navigation deception signals in a directional manner toward the target UAV. The trajectory data of the target drone is collected in real time by the 5G-A integrated sensing base station and compared with the expected driving away trajectory to generate trajectory deviation error data. Perform statistical analysis and threshold comparison on trajectory deviation error data to generate countermeasure execution status evaluation parameters, and simultaneously record trajectory deviation data; The countermeasure execution status and target deviation trajectory are verified in a closed loop and the parameters are adaptively adjusted. The entire process data is written into a digital twin model for simulation evaluation and policy updates, generating an updated set of base station operating parameters and protection strategies. The specific steps are as follows: Data fusion processing is performed on the countermeasure execution status and the target deviation trajectory to generate countermeasure effect evaluation indicators and trajectory deviation feature vectors; Based on the countermeasure effect evaluation index and trajectory deviation feature vector, a set of strategy optimization suggestion parameters is obtained by conducting multi-scenario simulation tests through a digital twin model. Based on the strategy optimization suggestion parameter set, the base station working parameter set and protection strategy are adaptively adjusted to generate candidate parameter combinations; The candidate parameter combinations are validated and conflict detected to generate an updated set of base station operating parameters and protection strategies.

2. The low-altitude UAV countermeasure method based on 5G-A sensing integration technology as described in claim 1, characterized in that: The specific steps for generating the base station operating parameter set for low-altitude sensing are as follows: Acquire three-dimensional geographic information data and historical electromagnetic environment monitoring data of the area to be protected, and generate an airspace electromagnetic environment profile; Based on the spatial electromagnetic environment profile, the base station coverage blind spots are obtained through line-of-sight propagation characteristics, and the deployment location, antenna height and initial beam direction of the 5G-A integrated sensing base station are obtained by combining the characteristics of interference sources, thus forming a base station deployment scheme. Based on the geographical coordinates and coverage of the base stations in the base station deployment scheme, configure the time slot ratio and cyclic prefix length of the communication subframe and sensing subframe in the 5G-A integrated sensing frame structure, and set the transmit power spectral density of the sensing subframe. Based on the time slot ratio, cyclic prefix length, and transmit power spectral density, and combined with the time-frequency reuse strategy, the time-frequency resource blocks of communication subframes and sensing subframes are dynamically allocated, and a base station operating parameter set is generated.

3. The low-altitude UAV countermeasure method based on 5G-A sensing integration technology as described in claim 2, characterized in that: The specific steps for obtaining the candidate initial detection confidence scores and the low-altitude target set are as follows: Based on the base station operating parameter set, control the receiving link configuration and collect the raw data stream of echo signals and channel state information of low-altitude targets; The original data stream is subjected to clutter suppression processing, and the time-domain envelope and frequency-domain Doppler features are extracted from the original data stream. Based on the temporal envelope and frequency domain Doppler characteristics, potential target traces are identified using a constant false alarm rate detector, and the signal-to-noise ratio of each target trace is calculated as the initial detection confidence. Potential target points and initial detection confidence are clustered and associated to form a candidate low-altitude target set.

4. The low-altitude UAV countermeasure method based on 5G-A sensing integration technology as described in claim 3, characterized in that: The specific steps for generating the tracking confidence and trajectory prediction for each target are as follows. The time delay features, Doppler frequency shift features, and angle response features of the corresponding echo signals are extracted from the candidate low-altitude target set, and the features are matched and corrected in combination with the corresponding channel state information to obtain the target range parameters, target radial velocity parameters, and target spatial angle parameters, respectively. The target range parameters, target radial velocity parameters, and target spatial angle parameters are correlated and combined to obtain the preliminary motion parameter set of each target. Using the initial detection confidence as the fusion weight, the initial motion parameter set corresponding to each target is used to identify the same source target and remove duplicate targets. After the initial motion parameter set corresponding to each target is retained after the same source target identification and duplicate target removal, multi-base station joint weighted fusion positioning is performed to obtain the position component and velocity component of each target in a unified spatial coordinate system, and generate the three-dimensional position coordinates and velocity vector of each target. Based on three-dimensional position coordinates and velocity vectors, the current observation data is input into the trajectory filtering algorithm, and the motion state of the corresponding target at the historical moment is continuously updated and corrected. The trajectory is continuously tracked and the motion state of each target is estimated, generating the real-time trajectory state and tracking confidence of each target. The current position, current velocity, and current direction of motion of the target are extracted from the real-time trajectory status, and the extrapolation algorithm is used to recursively predict the changes in the target's position in subsequent moments to generate a trajectory prediction for each target.

5. The low-altitude UAV countermeasure method based on 5G-A sensing integration technology as described in claim 4, characterized in that: The specific steps for generating dynamic risk levels are as follows: The motion feature parameters of the target are extracted from the trajectory prediction, and multimodal feature comparison is performed by combining the feature matching rules in the list of key elements for low-altitude target identification to generate a target type identification label. The target type identification and tracking confidence are input into the risk assessment algorithm, and the comprehensive risk quantification value is calculated by combining the dynamic judgment rules in the perception parameter threshold list. The basic risk level is determined based on the threshold range of the comprehensive risk quantification value, and the basic risk level is dynamically adjusted based on the target movement trend to generate a dynamic risk level.

6. The low-altitude UAV countermeasure method based on 5G-A sensing integration technology as described in claim 5, characterized in that: The specific steps for generating the response level and countermeasure triggering conditions are as follows. Using the spatial constraints of electronic fences, a spatiotemporal correlation analysis is performed on the dynamic risk level. When both the risk threshold and the spatial intrusion condition are met, a response level is generated. The parameters of the handling level are parsed and the instructions are mapped to generate the corresponding countermeasure trigger conditions.

7. The low-altitude drone countermeasure method based on 5G-A sensing integration technology as described in claim 6, characterized in that: The full-process data refers to the initial detection confidence level, tracking confidence level, dynamic risk level, countermeasure command parameters, countermeasure execution status, and target deviation trajectory.

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