An unmanned aerial vehicle jamming system and device based on phased array radar direction finding

Abstract

The application relates to the field of radio navigation and countermeasure technology, and particularly discloses an unmanned aerial vehicle interference system and device based on phased array radar direction finding. The system comprises a phased array radar direction finding module, an environmental electromagnetic propagation characteristic modeling module, an interference beam dynamic programming module and a digital beam forming interference module. By constructing a three-dimensional electromagnetic propagation model and based on the model predicted propagation loss and multipath effect, the beam forming weight for pre-compensating the environmental influence is optimized and calculated, and finally the interference antenna array radiates the environment pre-compensated 'intelligent beam', so that high-intensity interference is formed at the actual physical position of the target unmanned aerial vehicle, the direction finding error is overcome, and the interference efficiency and electromagnetic compatibility are improved.

Description

Technical Field

[0001] This invention belongs to the field of radio navigation and countermeasures technology, specifically relating to a UAV jamming system and device based on phased array radar direction finding. Background Technology

[0002] In the fields of radio spectrum management and electronic warfare, the detection, identification, and countermeasures against non-cooperative drones are key technologies for ensuring low-altitude security. Each directional jamming technique, due to its advantages such as concentrated energy and minimal collateral damage, has become an important means of countering drones. Its core lies in rapidly and accurately determining the target drone's location to guide the jamming beam for precise suppression.

[0003] Direction finding technology based on phased array radar is widely used for acquiring target location information due to its beam agility and strong multi-target tracking capabilities. This technology achieves rapid beam scanning and pointing in space by controlling the phase of each radiating element in the array antenna, thereby determining the direction of arrival of the signal source.

[0004] Existing technologies typically use the direction finding results of phased array radar directly to guide the beam of jamming antennas. However, in real-world application environments such as complex urban canyons or mountainous terrain, electromagnetic wave propagation is severely affected by multipath effects, terrain obstruction, and reflection, resulting in inherent azimuth errors in radar direction finding results.

[0005] This error causes the actual direction-finding beam to deviate from the target, making it difficult to accurately predict the actual coverage and energy distribution of the jamming signal in complex propagation environments. The direct consequence is the creation of jamming blind spots, making it impossible to effectively suppress target drones, or generating excessive interference that affects legitimate radio services in the surrounding area. Therefore, overcoming the impact of direction-finding errors on jamming effectiveness in complex electromagnetic environments and achieving reliable and accurate directional jamming has become an urgent technical challenge. Summary of the Invention

[0006] The purpose of this invention is to provide a UAV jamming system and device based on phased array radar direction finding, so as to solve the technical problem that the direction finding error of phased array radar leads to inaccurate jamming beam pointing and reduced jamming effectiveness in complex electromagnetic environments.

[0007] This invention provides a UAV jamming system based on phased array radar direction finding, comprising:

[0008] The phased array radar direction finding module is used to detect the remote control signals or image transmission signals transmitted by the target UAV, and form a beam that scans rapidly in space by controlling the feed phase of each radiating element in its antenna array. Based on the spatial spectrum distribution of the received signal strength during the beam scanning process, the module calculates the initial azimuth angle estimate of the target UAV and outputs a real-time target trajectory data stream containing the initial azimuth angle estimate and its corresponding timestamp.

[0009] The phased array radar direction finding module also includes a track filtering engine, which is used to smooth and predict the initial azimuth angle estimate to generate the real-time target track data stream.

[0010] The environmental electromagnetic propagation characteristics modeling module is used to build and maintain a digital model that characterizes the three-dimensional spatial electromagnetic propagation characteristics of the system deployment area.

[0011] The jamming beam dynamic planning module is used to receive the initial azimuth angle estimate from the phased array radar direction finding module and the three-dimensional spatial propagation loss field and multipath structure database from the environmental electromagnetic propagation characteristics modeling module.

[0012] The digital beamforming jamming module includes a jamming antenna array that is co-located with or has a known relative position to the phased array radar direction finding module, multiple radio frequency channels, and a digital signal processor.

[0013] Preferably, the environmental electromagnetic propagation characteristic modeling module is connected to a digital map database containing terrain elevation data, building 3D outline data, and land cover type data;

[0014] The environmental electromagnetic propagation characteristic modeling module has a built-in ray tracing calculation engine. The ray tracing calculation engine takes the array center of the phased array radar direction finding module as the emission source point and the initial azimuth angle estimate as the center. Within a preset angle sector, it emits virtual test rays into space. Based on the principles of geometric optics and the theory of uniform diffraction, it calculates the propagation path of each virtual test ray in the environment defined by the digital map, records the path loss and propagation delay of each ray from the emission source point to each sampling point in space, and generates a three-dimensional spatial propagation loss field and multipath structure database covering the angle sector.

[0015] Preferably, the interference beam dynamic planning module includes a beamforming weight solver based on interference effectiveness optimization;

[0016] The beamforming weight solver substitutes the propagation loss field as the channel matrix into the receiving equation of the antenna array, and calculates a set of optimal complex beamforming weights by solving a constrained convex optimization problem.

[0017] The objective function of the convex optimization problem is defined as maximizing the power flux density of the interference signal in the spatial region where the target UAV is most likely to appear, under the constraint of the total transmit power of the digital beamforming interference module, while ensuring that the power flux density of the interference signal in the sensitive area to be protected is less than a preset safety threshold.

[0018] The spatial region where the target UAV is most likely to appear is defined by a conical space with the initial azimuth angle estimate as the central axis and the subtended angle being twice the preset direction finding error angle.

[0019] Preferably, the digital beamforming jamming module receives the optimal beamforming weights from the jamming beam dynamic planning module; the digital signal processor performs digital domain weighting and phase shifting processing on the jamming baseband signal to be transmitted according to the weights, generating multiple baseband signal streams with specific amplitude and phase relationships. These baseband signal streams are fed to the corresponding radio frequency channels after data conversion and up-conversion, and are finally radiated by the jamming antenna array.

[0020] Preferably, the objective function in the interference beam dynamic planning module is: to satisfy the condition that the total transmit power is less than or equal to the preset maximum power. And for all sampling points defined as sensitive regions The received power is less than or equal to the preset protection threshold. Under the constraints, maximize the sampling points within the target area. The weighted sum of the received power; each sampling point within each target area. The weighting coefficient is inversely proportional to the angular distance from the sampling point to the axis of the initial azimuth estimation direction.

[0021] Preferably, the ray tracing calculation engine in the environmental electromagnetic propagation characteristics modeling module adopts a multi-level subdivision method for the emission strategy of virtual test rays;

[0022] The first stage emits a coarse-grained cluster of rays aimed at covering the entire preset angle sector;

[0023] After the first-level ray tracing identifies key regions with reflective surfaces or diffraction edges, the second-level ray tracing is initiated, firing denser, finer-grained ray clusters at these key regions.

[0024] Preferably, the system further includes an online correction and learning module;

[0025] This module monitors the changes in the quality indicators of the target UAV's remote control signal or image transmission signal in real time after the interference is implemented, and compares the actual changes in the signal quality indicators with the interference intensity that should be reached at that position as predicted by the interference beam dynamic planning module, and generates a correction error.

[0026] The correction error is fed back to the environmental electromagnetic propagation characteristics modeling module, which is used to adaptively adjust the material electromagnetic parameters in the ray tracing calculation engine, or to correct specific path loss values ​​in the propagation loss field database.

[0027] The signal quality indicators include the degree of decrease in signal-to-noise ratio or the degree of increase in bit error rate.

[0028] Preferably, the phased array radar direction finding module and the digital beamforming jamming module share the same set of phased array antenna hardware in a time-division duplex manner.

[0029] Within the repetition period, the first part of the time slice is used for radar detection and direction finding, and the second part of the time slice is used for transmitting jamming signals.

[0030] The phased array radar direction finding module and the digital beamforming jamming module share the same set of phased array antenna hardware using frequency division duplex mode.

[0031] The radar operating frequency band and the jamming transmission frequency band are isolated by a duplexer to achieve simultaneous transmission and reception.

[0032] Preferably, the beamforming weight solver of the interference beam dynamic planning module also introduces spatial null generation constraints when calculating the optimal weights; the spatial null generation constraint forced optimization algorithm forms radiation pattern nulls with a depth greater than a preset value in the direction of known friendly communication equipment or satellite navigation ground augmentation station when calculating the weights.

[0033] The present invention also provides a UAV jamming device based on phased array radar direction finding, the device including the above-mentioned UAV jamming system based on phased array radar direction finding, for realizing UAV jamming.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] 1. This invention fundamentally changes the simple mode of traditional directional jamming systems that directly use direction-finding angles to guide the beam. It innovatively introduces an environmental electromagnetic propagation characteristic modeling module and a jamming beam dynamic planning module. By constructing a high-fidelity three-dimensional propagation channel model and incorporating the propagation loss and multipath effects predicted by the model as prior knowledge into the weighting optimization process of digital beamforming, the final generated jamming beam pattern is a "smart beam" that has undergone propagation environment pre-compensation. This beam can automatically bypass obstructions and focus on the actual target area rich in multipath propagation, thereby achieving precise energy delivery to the target UAV in physical space rather than a simple angular space, overcoming the interference blind zone or performance attenuation problems caused by direction-finding errors.

[0036] 2. This invention transforms the interference task into a mathematical problem of finding the optimal solution under multiple constraints through rigorous convex optimization problem modeling in the dynamic programming module of the interference beam. The optimization objective simultaneously maximizes interference effectiveness and minimizes electromagnetic safety. By maximizing the power flux density in the target area and strictly limiting interference leakage in sensitive areas, a balance between interference effectiveness and electromagnetic compatibility is achieved. Compared with beam pointing based on fixed patterns, this beam design method based on optimization theory has higher flexibility and environmental adaptability, and can generate customized optimal interference schemes for each detection and each unique geographical environment.

[0037] 3. The online correction and learning module designed in this invention endows the system with the ability to continuously evolve from practical feedback. By comparing the error generated by the predicted interference intensity with the actual interference effect, the system can reverse-correct its core environmental channel model parameters, enabling the model to increasingly accurately reflect the electromagnetic propagation characteristics of the real world over time. This closed-loop learning mechanism improves the system's robustness to slow environmental changes or initial model errors during long-term deployment, ensures the long-term stability and reliability of interference effectiveness, and reduces the system's over-reliance on the accuracy of initial modeling. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the overall technical architecture of the UAV jamming system based on phased array radar direction finding proposed in this invention;

[0039] Figure 2 This is a schematic diagram of the core principle framework of the interference beam dynamic planning module in this invention, which optimizes beams based on environmental modeling.

[0040] Figure 3 This is a flowchart illustrating the logical process of constructing a three-dimensional spatial propagation model using the environmental electromagnetic propagation characteristics modeling module in this invention.

[0041] Figure 4 This is a schematic diagram of the closed-loop interaction and data flow between the phased array radar direction finding module, the interference beamforming module, and the online correction module in this invention. Detailed Implementation

[0042] This invention provides a UAV jamming system and device based on phased array radar direction finding. Please refer to the appendix. Figures 1 to 4 This system is a comprehensive electronic countermeasures system integrating high-precision spatial perception, electromagnetic modeling of complex environments, intelligent beam planning, and adaptive jamming transmission. Its core design concept lies in upgrading the traditional jamming mode, which is based on direct geometric guidance, to an intelligent energy delivery mode based on pre-compensation of physical space channel characteristics.

[0043] This system dynamically generates an optimal interference beam that overcomes direction-finding errors and environmental obstruction by deeply fusing real-time detection data with a high-fidelity environmental electromagnetic model. This beam is pre-distorted by the propagation channel and can maximize the concentration of interference energy at the actual physical location of the target UAV, while minimizing interference leakage in sensitive areas that need protection.

[0044] The hardware deployment of the entire system typically uses a fixed ground station or vehicle-mounted platform. This platform requires a stable power supply, the ability to communicate with the back-end command center, and the necessary mechanical structure to install the various antenna arrays. After system startup, each module works collaboratively according to strict timing and data interface protocols, forming a complete closed loop from target detection, environmental analysis, decision planning, to jamming execution and effect evaluation. The following section will provide a detailed explanation of the working mechanisms, data processing flows, and parameter interaction details of each core module and its internal sub-components, following the natural order of the system's data flow.

[0045] First, the phased array radar direction-finding module is responsible for continuous electromagnetic surveillance of the airspace. Its core task is to detect, identify, and continuously track target UAVs. This module includes a phased array antenna array, a corresponding multi-channel RF receiving front-end, a high-speed digital down-conversion and acquisition unit, and a real-time spatial spectrum estimation and trajectory filtering engine integrated into a digital signal processor. The phased array antenna array consists of dozens to hundreds of antenna radiating elements arranged in a specific geometry, such as a rectangular grid or a circular array.

[0046] Each radiating element is connected to an independent radio frequency receiving channel, which includes a low-noise amplifier, a bandpass filter, and a mixer to downconvert the received radio frequency signal to an intermediate frequency.

[0047] The intermediate frequency signals of all channels are acquired synchronously and converted into digital baseband signal streams via high-speed analog-to-digital converters before being sent to digital signal processors.

[0048] Inside the digital signal processor (DSP), the spatial spectrum estimation engine operates continuously. Its workflow begins with preprocessing the multi-channel digital baseband signals, including digital down-conversion to zero intermediate frequency (IF), IQ-channel quadrature separation, and digital filtering for the typical signal frequency bands of the target UAV. The preprocessed multi-channel complex signal data is organized into snapshot data vectors. The engine employs a scanning direction-finding method based on digital beamforming. Specifically, the DSP generates a series of digital beam steering vectors sequentially on a spatial angle grid consisting of azimuth and elevation dimensions, according to a preset scanning strategy.

[0049] Each steering vector corresponds to a set of complex weights applied to the digital signals of each receiving channel. The phase difference of these weights is precisely calculated so that the array's synthesized beam "points" in the digital domain to the spatial direction represented by that grid point. The system correlates the current snapshot data vector with the steering vector of each scanning direction to calculate the synthesized signal power in that direction. By traversing the entire scanning spatial domain, the engine obtains a two-dimensional or three-dimensional spatial spectrum of the incoming signal intensity in each spatial direction at the current moment.

[0050] When a peak value exceeding a preset detection threshold appears in the spatial spectrum, the engine determines that a potential target has been detected. The engine uses a peak search algorithm, such as finding local maxima, to calculate the estimated azimuth and pitch angles corresponding to that peak value; this is the initial azimuth estimate of the target UAV. This calculation process involves interpolation of the discrete spatial spectrum to improve angular resolution. To connect the discrete detection points into a continuous, smooth track, a track filtering engine is activated. This engine typically implements a Kalman filter or an Alpha-Beta filter.

[0051] The filter takes the initial azimuth estimate and its corresponding high-precision timestamp as initial observation inputs. The filter's state vector includes at least the target's angular position and angular velocity, and can be extended to angular acceleration. The filter predicts the state based on the target's motion model and fuses the new angular observations with the predicted values, outputting a smoothed and predicted real-time target trajectory data stream. This data stream is a continuous time series, where each data frame contains the filtered azimuth, pitch, corresponding estimation error covariance matrix, and a timestamp accurate to the microsecond level. This trajectory data stream is pushed in real-time to the environmental electromagnetic propagation characteristics modeling module and the interference beam dynamic planning module via a high-speed data bus.

[0052] Next, the environmental electromagnetic propagation characteristics modeling module begins operation. This module is the knowledge foundation and core prerequisite for the intelligent beam planning of this invention. Please refer to the appendix. Figure 3 This module is essentially a complex electromagnetic simulation software system running on a high-performance computing server. Its task is to build a high-fidelity, digital database of electromagnetic propagation characteristics for the local three-dimensional space where the system is deployed. The input sources for this module include multiple aspects.

[0053] The primary input is geographic information data from a digital map database. This data includes at least high-precision digital elevation model data covering the deployment area, with a grid resolution typically better than 5 meters; three-dimensional outline model data of major buildings, towers, and other man-made structures, provided in the form of polygonal grids or point clouds, which must include the height and outline dimensions of the structures; and data on land cover types, such as water bodies, forests, grasslands, concrete, and asphalt, with each type corresponding to a set of preset electromagnetic parameters, such as dielectric constant, conductivity, and roughness.

[0054] After the environmental electromagnetic propagation characteristics modeling module is started, it first performs scene initialization and data fusion. It unifies the coordinate system of digital map data from different sources, typically transforming it to a local Cartesian coordinate system with the phase center of the phase array antenna array of the phased array radar direction-finding module as the origin. Once the coordinate system is established, the module's built-in ray tracing calculation engine begins executing the core propagation prediction task. Ray tracing is a high-frequency electromagnetic field approximation calculation method based on geometric optics and uniform diffraction theory. Its basic principle is to treat the electromagnetic wavefront as a series of discrete ray tubes and calculate the field strength by tracing the propagation, reflection, diffraction, and transmission processes of these rays in a complex environment.

[0055] The ray tracing computation engine employs the multi-level subdivision method described in the invention to balance computational accuracy and real-time requirements. In the first-level coarse-grained tracing stage, the engine uses the phase center of the phased array antenna array as the virtual ray emission source.

[0056] The launch direction is not unidirectional, but rather uses the initial azimuth estimate of the current target provided by the phased array radar direction-finding module as the central axis, uniformly launching a large number of virtual test rays within preset azimuth and elevation sectors. The angle of this sector needs to be comprehensively determined based on the system's maximum expected direction-finding error, the target's maneuverability, and the spatial range required for beam optimization; for example, ±15 degrees each in the azimuth and elevation directions. Each ray carries unit power and includes metadata such as the launch azimuth angle and launch time.

[0057] Based on a 3D geometric scene constructed from a digital map, the engine calculates the propagation paths of these rays one by one. The calculation process follows strict physical rules. For each ray, the engine first determines whether there is an unobstructed direct path from the emission point to the surface of the first object in the scene. If so, it records the geometric length of the direct path and calculates its path loss according to the free-space propagation model. Subsequently, the engine recursively calculates the possible reflections of the ray on the object surface.

[0058] Reflection calculations require looking up the frequency-dependent complex reflection coefficients based on the material properties of the object's surface, in order to determine the intensity and phase changes of the reflected rays.

[0059] When a ray propagates to a sharp edge of an object, the engine initiates diffraction calculations, generating a series of diffracted ray cones emanating from the edge point based on uniform diffraction theory. For certain non-metallic thin-layer structures, transmitted rays may also be calculated.

[0060] After the first stage of ray tracing is completed, the engine analyzes the endpoint distribution and path history of all rays. It identifies regions with multiple reflections, significant diffraction edges, or unusually sparse ray density; these regions are marked as "critical areas for electromagnetic propagation." The engine then initiates the second stage of fine-grained ray tracing. In this stage, the engine directionally fires virtual test ray clusters with a density far exceeding that of the first stage towards these critical areas.

[0061] For example, within a "canyon" passage formed by two buildings identified by Level 1, Level 2 tracking would fire denser rays to precisely capture the complex multipath interference patterns within the passage.

[0062] This multi-level strategy ensures that computational resources are concentrated in areas with complex electromagnetic environments within a limited total computational load, thereby efficiently generating high-precision propagation models.

[0063] After all ray tracing calculations are completed, the engine needs to discretize and sample the space. It establishes a regular 3D voxel mesh within the physical space corresponding to the 3D angular sector of interest. For each voxel mesh's center sampling point, the engine collects all ray paths that pass through that point or fall within a small volume near that point. Each such path contributes a "sub-channel" from the emission source point to that sampling point.

[0064] The engine aggregates the parameters of all these sub-channels to form a multipath channel impulse response summary for that sampling point. This summary includes at least the relative delay, relative path loss, and relative phase of each path. Finally, the module generates and outputs a structured three-dimensional spatial propagation loss field and multipath structure database. This database is essentially a lookup table indexed by the coordinates of the spatial sampling points.

[0065] For each entry in the table, i.e., each spatial sampling point, a set of channel parameters is explicitly recorded, including but not limited to:

[0066] The total path loss from the array center to that point, the arrival direction angle of the strongest path, the number of multipaths, and the time delay spread and angle spread statistics of each multipath component.

[0067] This database contains complete prior knowledge about "how the environment distorts electromagnetic waves," upon which subsequent beam optimization algorithms rely.

[0068] Subsequently, the jamming beam dynamic planning module begins operation. This module serves as the system's "intelligent decision-making center," transforming target information and the environmental model into executable, optimal jamming commands. Please refer to the appendix. Figure 2 The module receives two core inputs: one is the real-time target trajectory data from the phased array radar direction finding module, especially the current azimuth angle estimate and its error covariance; the other is the three-dimensional spatial propagation loss field and multipath structure database covering the current target direction sector from the environmental electromagnetic propagation characteristics modeling module.

[0069] The module first defines the spatial regions of the target area and the sensitive area. The target area, i.e., the spatial range in which the target UAV is most likely to appear, is not a single point, but a spatial distribution of probability. The module constructs a spatial confidence region based on the current azimuth estimate and its error covariance. A specific and efficient implementation is to define a conical space with the initial azimuth estimate direction as its axis and its half-cone angle being twice the preset direction-finding error angle. All sampling points within this conical space are initially included in the target area set.

[0070] Sensitive areas are pre-marked on digital maps by operators or automatically identified by the system from a map database. Examples of sensitive areas include the coordinates of hospitals, airport runways, friendly communication base stations, and observatories. Each sensitive area is also mapped as a set of three-dimensional volumes in space, such as a sphere centered on the sensitive point and bounded by a certain radius.

[0071] After defining the spatial region, the core algorithm unit of the module—the beamforming weight solver based on interference effectiveness optimization—begins to operate. This solver needs to solve a rigorous mathematical optimization problem. Assume the transmitting antenna array of the digital beamforming jamming module consists of… It consists of several array elements. What needs to be solved is the force applied to this array. Complex excitation weight vectors on each array element. The optimization problem is constructed following these principles.

[0072] The optimization objective is to maximize interference effectiveness within the target area. The direct measure of interference effectiveness is the sum of the power flux density of the interference signals within the target area. Considering that the probability of a target appearing within the area is not uniform, with a higher probability closer to the center of the direction-finding axis, it is necessary to weight the contributions of different locations within the area. Let the target area contain... For the nth discrete sampling point, for the nth For each sampling point, its weight coefficient is inversely proportional to the angular distance from that point to the axis of the initial azimuth estimation direction. A specific weighting function can be a Gaussian weighting function, centered on the axis direction, with the weights decaying exponentially towards the edges.

[0073] At the same time, optimization problems must include strict constraints.

[0074] The first constraint is the total transmit power constraint, which means that the power of the beamforming weight vector itself must be less than or equal to the preset maximum power allowed by the system. This ensures that the transmitter operates safely within the linear region.

[0075] The second constraint is the electromagnetic safety constraint, which applies to all areas defined as sensitive. At each sampling point, the received power of the interference signal generated by the weight vector must be less than a preset protection threshold. This threshold is usually set based on the interference immunity threshold of the sensitive equipment itself or electromagnetic environment management regulations.

[0076] Most importantly, when calculating the received power at any sampling point in the target area and sensitive area, a simple free space propagation model cannot be used; instead, the complex channel model provided by the environmental electromagnetic propagation characteristics modeling module must be substituted.

[0077] For the first space from array The channel with 1 sampling point can be modeled as a single row provided by the propagation model database. The complex channel row vector represents the amplitude attenuation and phase change from each array element to the sampling point, including the superposition effects of all direct, reflected, and diffracted paths. Therefore, the received signal power at this sampling point is proportional to the squared modulus of the scalar obtained by left-multiplying the beamforming weight vector by the channel matrix.

[0078] Based on the above definitions, the optimization problem solved by the beamforming weight solver can be mathematically expressed as a constrained convex optimization problem: maximizing... satisfy:

[0079] ;

[0080] ;

[0081] This indicates that a solution needs to be found. Complex beamforming weight column vector. Indicates the distance from the array to the target region. One row of each sampling point The conjugate transpose of the column complex channel row vector. It is the first The non-negative weighting coefficients for each sampling point. Indicates the distance from the array to the sensitive region. One row of each sampling point The conjugate transpose of the column complex channel row vector. It is a preset maximum total transmit power constraint. It is a preset protection power threshold for sensitive areas.

[0082] The objective function of this optimization problem is a convex function, and the constraints are also convex, thus falling under the category of convex optimization. Efficient and reliable numerical solutions exist, such as the interior-point method or the gradient projection method based on Lagrange duality. The solver iteratively calculates and ultimately outputs a set of optimal complex beamforming weight vectors. The physical meaning of these weights is profound:

[0083] It not only contains the conventional array steering vector phase required to point the main lobe of the beam to the target angle, but more importantly, it contains amplitude and phase adjustments for “pre-distortion” or “pre-compensation” of the complex channel effects experienced by the electromagnetic wave as it propagates from the array to the target area.

[0084] In other words, this set of weighted command transmission arrays radiates a special wavefront. After being distorted by reflection, diffraction and other distortions in the real environment, this wavefront can coherently superimpose at the actual physical location of the target UAV to form an intensity peak. In sensitive areas, the wavefronts cancel each other out due to their careful design, forming a deep null.

[0085] Furthermore, the beamforming weight solver of the interference beam dynamic programming module can introduce additional design constraints to enhance system functionality when constructing the optimization problem. An important extension is the spatial null generation constraint. In addition to protecting predefined, fixed sensitive areas, the system may also need to generate depth nulls in known, dynamically friendly device directions.

[0086] For example, when the system detects signals from friendly emergency communication vehicles or satellite navigation ground augmentation stations nearby, the precise orientation of these devices can be added as an additional constraint point to the optimization problem.

[0087] When calculating weights, the solver is forced to ensure that the effective radiated power of the array is below a very low level in these specific directions, thereby achieving active avoidance of friendly equipment and minimizing collateral interference.

[0088] After the optimal beamforming weight vector is calculated, it is sent to the digital beamforming jamming module in real time. The digital beamforming jamming module is the final executor of the system. This module includes a jamming transmitting antenna array, a corresponding multi-channel RF transmitting link, and a high-speed digital beamformer. The jamming transmitting antenna array is usually co-located with the receiving array of the phased array radar direction finding module, and their relative positions are precisely measured and input into the system as known parameters. In some highly integrated designs, the two can share the same phased array antenna hardware using time-division duplex or frequency-division duplex methods.

[0089] In time-division duplex mode with shared hardware, the system operates in repetitive cycles. Within a cycle, in the previous time slice, for example, lasting several milliseconds, the antenna array and all radio frequency channels switch to receive mode to perform the detection and direction finding tasks of the phased array radar direction finding module. After completing data acquisition and processing, the system quickly switches to the next time slice, at which point the antenna array and radio frequency channels switch to transmit mode.

[0090] The digital beamformer processes the jamming baseband signal to be transmitted in real time based on the received weight vector. The jamming baseband signal varies depending on the jamming pattern, and can be targeted continuous wave noise, frequency sweep jamming signal, or digitally coded deception signal.

[0091] The digital beamformer maintains a digital complex weight for each transmit channel, which is the component of the corresponding channel in the globally optimal weight vector.

[0092] The interfering baseband signal stream is first copied as Each path undergoes a complex multiplication operation with the complex weights of its corresponding channel. This operation simultaneously performs amplitude weighting and phase rotation.

[0093] After weighted phase shift The roadbed signal stream is sent separately to There are 16 parallel transmission channels. Each transmission channel includes a digital-to-analog converter, an up-converter, a power amplifier, and other components to convert the digital baseband signal into a high-power radio frequency signal, which is then fed to the corresponding antenna radiating element.

[0094] Since the signals of all channels are coherent and their amplitude and phase relationships are precisely controlled by optimal weights, the spatial radiation superposition forms the "intelligent interference beam" that has been pre-compensated by the environment.

[0095] In frequency division duplex mode, the receiving link and the transmitting link operate in different frequency bands and are isolated by a circulator or duplexer. This allows for near-simultaneous detection and interference, improving the system's real-time response capability, but it also places higher demands on the front-end and back-end isolation of the hardware.

[0096] The system's operation does not end after the jamming beam is emitted. To evaluate the jamming effect and achieve system self-optimization, the online calibration and learning module begins operation. Please refer to the appendix. Figure 4 This module forms a feedback loop from interference execution to model correction. It monitors two key signal sources in real time.

[0097] The first signal source is the remote control or image transmission signal from the target UAV that the phased array radar direction finding module continues to receive during the jamming process. Despite the jamming, this signal may not have completely disappeared. The module extracts certain quality indicators of this signal from the digital signal processor, such as the signal-to-noise ratio of the received signal, the bit error rate of the signal, or the depth of the signal envelope fluctuation.

[0098] The second signal source is the interference beam dynamic planning module. When planning the beam, the solver not only outputs the optimal weights, but also predicts the theoretically achievable interference signal strength at the target's current location, such as the predicted interference-to-signal ratio, based on the final determined weights and the channel model.

[0099] The online calibration and learning module includes a built-in comparator. It compares the measured signal quality metrics, such as the measured signal-to-noise ratio (SNR) drop, with the theoretical SNR drop corresponding to the predicted interference intensity at that location from the planning module. The difference between the two constitutes the calibration error. This error reveals the discrepancy between the predictions of the environmental electromagnetic propagation characteristics modeling module and the actual propagation situation. Possible causes include: inaccurate setting of the material electromagnetic parameters of certain objects in the digital map, the presence of unmodeled temporary objects in the environment, and changes in the dielectric properties of vegetation due to seasonal variations.

[0100] The correction error is used as a feedback signal and fed into the environmental electromagnetic propagation characteristics modeling module. This module maintains a set of adjustable parameters, such as the equivalent reflection coefficients for different land cover types and the loss factors of building walls. The module employs adaptive filtering or machine learning algorithms, such as recursive least squares, driven by the correction error, to fine-tune these adjustable parameters. The adjustment aims to make the predicted target point interference intensity more consistent with the measured results after ray tracing calculations using the new parameters. Alternatively, the module can directly utilize this error information to locally correct the path loss values ​​in the propagation loss field database, from the array to the vicinity of the current target direction, in a non-parametric manner.

[0101] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0102] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A UAV jamming system based on phased array radar direction finding, characterized in that, include: The phased array radar direction finding module is used to detect the remote control signals or image transmission signals transmitted by the target UAV, and form a beam that scans rapidly in space by controlling the feed phase of each radiating element in its antenna array. Based on the spatial spectrum distribution of the received signal strength during the beam scanning process, the module calculates the initial azimuth angle estimate of the target UAV and outputs a real-time target trajectory data stream containing the initial azimuth angle estimate and its corresponding timestamp. The phased array radar direction finding module also includes a track filtering engine, which is used to smooth and predict the initial azimuth angle estimate to generate the real-time target track data stream. The environmental electromagnetic propagation characteristics modeling module is used to build and maintain a digital model that characterizes the three-dimensional spatial electromagnetic propagation characteristics of the system deployment area. The jamming beam dynamic planning module is used to receive the initial azimuth angle estimate from the phased array radar direction finding module and the three-dimensional spatial propagation loss field and multipath structure database from the environmental electromagnetic propagation characteristics modeling module. The digital beamforming jamming module includes a jamming antenna array that is co-located with or has a known relative positional relationship with the phased array radar direction finding module, multiple radio frequency channels, and a digital signal processor. The interference beam dynamic programming module includes a beamforming weight solver based on interference effectiveness optimization. The beamforming weight solver substitutes the propagation loss field as the channel matrix into the receiving equation of the antenna array, and calculates a set of optimal complex beamforming weights by solving a constrained convex optimization problem. The objective function of the convex optimization problem is defined as maximizing the power flux density of the interference signal in the spatial region where the target UAV is most likely to appear, under the constraint of the total transmit power of the digital beamforming interference module, while ensuring that the power flux density of the interference signal in the sensitive area to be protected is less than a preset safety threshold. The spatial region where the target UAV is most likely to appear is defined by a conical space with the initial azimuth angle estimate as the central axis and the subtended angle being twice the preset direction finding error angle; The digital beamforming interference module receives the optimal beamforming weights from the interference beam dynamic planning module. The digital signal processor performs digital domain weighting and phase shifting on the interference baseband signal to be transmitted according to the weight, generating multiple baseband signal streams with specific amplitude and phase relationships. These baseband signal streams are fed to the corresponding radio frequency channels after data conversion and up-conversion, and are finally radiated out by the interference antenna array.

2. The UAV jamming system based on phased array radar direction finding according to claim 1, characterized in that, The environmental electromagnetic propagation characteristic modeling module accesses a digital map database containing terrain elevation data, building 3D outline data, and surface cover type data. The environmental electromagnetic propagation characteristic modeling module has a built-in ray tracing calculation engine. The ray tracing calculation engine uses the array center of the phased array radar direction finding module as the emission source point and the initial azimuth angle estimate as the center. Within a preset angle sector, it emits virtual test rays into space and calculates the propagation path of each virtual test ray in the environment defined by the digital map based on the principles of geometric optics and uniform diffraction theory. It records the path loss and propagation delay of each ray from the emission source point to each sampling point in space to generate a 3D spatial propagation loss field and multipath structure database covering the angle sector. The beamforming weight solver of the interference beam dynamic programming module, in the calculation When calculating the optimal weights, a spatial null generation constraint is also introduced; the spatial null generation constraint forced optimization algorithm forms a radiation pattern null with a depth greater than a preset value in the direction of known friendly communication equipment or satellite navigation ground augmentation station when calculating the weights. The system also includes an online correction and learning module; This module monitors the changes in the quality indicators of the target UAV's remote control signal or image transmission signal in real time after the interference is implemented, and compares the actual changes in the signal quality indicators with the interference intensity that should be reached at that position as predicted by the interference beam dynamic planning module, and generates a correction error. The correction error is fed back to the environmental electromagnetic propagation characteristics modeling module, which is used to adaptively adjust the material electromagnetic parameters in the ray tracing calculation engine, or to correct specific path loss values ​​in the propagation loss field database. The signal quality indicators include the degree of decrease in signal-to-noise ratio or the degree of increase in bit error rate.

3. The UAV jamming system based on phased array radar direction finding according to claim 2, characterized in that, The objective function in the dynamic programming module for the interference beam is specifically: when the total transmit power is less than or equal to the preset maximum power... And for all sampling points defined as sensitive regions The received power is less than or equal to the preset protection threshold. Under the constraints, maximize the sampling points within the target area. The weighted sum of the received power; each sampling point within each target area. The weighting coefficient is inversely proportional to the angular distance from the sampling point to the axis of the initial azimuth estimation direction.

4. The UAV jamming system based on phased array radar direction finding according to claim 3, characterized in that, The ray tracing calculation engine in the environmental electromagnetic propagation characteristics modeling module adopts a multi-level subdivision method for the emission strategy of virtual test rays. The first stage emits a coarse-grained cluster of rays aimed at covering the entire preset angle sector; After the first-level ray tracing identifies key regions with reflective surfaces or diffraction edges, the second-level ray tracing is initiated, firing denser, finer-grained ray clusters at these key regions.

5. A UAV jamming system based on phased array radar direction finding according to claim 1, characterized in that, The phased array radar direction finding module and the digital beamforming jamming module share the same set of phased array antenna hardware in a time-division duplex manner. Within the repetition period, the first part of the time slice is used for radar detection and direction finding, and the second part of the time slice is used for transmitting jamming signals. Alternatively, the phased array radar direction finding module and the digital beamforming jamming module share the same set of phased array antenna hardware using frequency division duplex mode. The radar operating frequency band and the jamming transmission frequency band are isolated by a duplexer to achieve simultaneous transmission and reception.

6. A UAV jamming device based on phased array radar direction finding, characterized in that, The device includes a UAV jamming system based on phased array radar direction finding as described in any one of claims 1 to 5, used to achieve UAV jamming.

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