Method and system for generating and executing anti-interference flight strategy of C2 link of unmanned aerial vehicle

Abstract

The invention discloses a method and a system for generating and executing an anti-interference flight strategy of a C2 link of an unmanned aerial vehicle, and particularly relates to the technical field of unmanned aerial vehicle communication. The method comprises the following steps: acquiring urban building material parameters and unmanned aerial vehicle flight parameters; based on the parameters, combining a free space path loss model, a Rice fading model and a blade peak diffraction model to construct an urban environment channel model; injecting same-frequency Wi-Fi interference and building reflection interference signals to calculate a signal to interference plus noise ratio; based on the signal to interference plus noise ratio, calculating the bit error rate and evaluating the anti-interference performance, and generating an anti-interference optimization scheme including frequency band switching and flight height adjustment; and controlling the unmanned aerial vehicle to execute the strategy. Aiming at the problems of Wi-Fi co-frequency interference, building shielding, glass curtain wall multipath reflection and the like in a complex urban environment, frequency band self-adaptive switching and flight height optimization are realized through multi-model joint simulation and dynamic strategy generation, the bit error rate is reduced, the anti-interference capability is improved, and the method has the advantages of being simple in structure and convenient to operate. And the communication reliability and the flight safety of the unmanned aerial vehicle in a complex urban environment are effectively guaranteed.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to a method and system for generating and executing an anti-interference flight strategy for a UAV C2 link. Background Technology

[0002] When unmanned aerial vehicles (UAVs) perform missions in complex urban environments, the reliability of their command and control (C2) links faces severe challenges. Existing technologies typically employ static or semi-static anti-jamming strategies, such as preset frequency band selection or fixed flight altitude planning. However, multi-source interference in urban environments, such as Wi-Fi co-channel interference, building shielding effects, and glass curtain wall reflections, is highly dynamic and spatially specific, with its intensity and distribution changing in real time with the UAV's location. This makes static strategies based on preset experience difficult to adapt to the rapidly changing actual electromagnetic environment, often resulting in strategy lag or mismatch, leading to increased bit error rates in the UAV communication link, and even communication interruptions, seriously threatening flight safety and mission execution. Therefore, the core problem with existing technologies lies in the lack of an effective solution capable of online, dynamic assessment of complex urban electromagnetic environments and real-time generation and execution of anti-jamming flight strategies. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for generating and executing anti-interference flight strategies for UAVs using C2 links. This enables online dynamic generation and automatic execution of anti-interference strategies, thereby effectively improving the reliability of UAV communication links in complex urban environments and enhancing their flight autonomy.

[0004] To achieve the above objectives, a first aspect of the present invention provides a method for generating and executing an anti-interference flight strategy for a UAV C2 link, comprising the following steps: S1. Obtain the building material parameters and UAV flight parameters of the target flight area; S2. Based on building material parameters and UAV flight parameters, simulation calculations are performed by combining the free space path loss model, Ricean fading model and blade peak diffraction model to output comprehensive channel parameters including path loss and multipath fading, so as to construct an urban environment channel model. S3. Based on the comprehensive channel parameters, inject co-frequency Wi-Fi interference signal and building reflection interference signal to calculate the signal-to-interference-plus-noise ratio of the UAV C2 link, so as to simulate the interference environment and quantify the link quality. S4. Calculate the bit error rate based on the signal-to-interference-plus-noise ratio, evaluate the anti-interference performance using the bit error rate as an indicator, and generate an anti-interference optimization scheme that includes frequency band switching strategy and flight altitude strategy. S5. Send the anti-interference optimization scheme to the UAV and control the UAV to execute the frequency band switching strategy and flight altitude strategy.

[0005] Furthermore, the building material parameters include the relative permittivity ε of concrete, the reflectivity and thickness of the glass curtain wall, where ε = 4.5~6.5 and the reflectivity > 0.8; The drone's flight parameters include preset flight altitude and flight trajectory.

[0006] Furthermore, simulation calculations were performed using a combined free-space path loss model, Ricean fading model, and edge peak diffraction model, including: S21. Calculate the baseline path loss based on the free space path loss model; S22. Introduce the Rice fading model, set the K factor to 3, and configure the path delay and average path gain to simulate the multipath fading effect with a dominant line-of-sight path. S23. Introduce a blade peak diffraction model for the building edge, calculate the diffraction loss, and superimpose the reference path loss, multipath fading effect and diffraction loss to output the comprehensive channel parameters.

[0007] Furthermore, the blade peak diffraction model introduced for building edges includes: For the edges of glass curtain walls, the actual incident angle of the signal is calculated based on a ray tracing model, and the Fresnel transmission coefficient is introduced to dynamically correct the incident angle in order to calculate the accurate diffraction loss at the edges of the glass curtain wall.

[0008] Furthermore, the injected building reflection interference signal includes: The Brewster angle compensation algorithm is applied to the reflection of the glass curtain wall. Specifically, the actual incident angle of the signal on the glass curtain wall is dynamically monitored. When the actual incident angle is close to or exceeds the Brewster angle calculated for the glass curtain wall material, an alarm is generated and an avoidance strategy is triggered.

[0009] Furthermore, the injected co-channel Wi-Fi interference signal includes: OFDM signals generated based on the IEEE 802.11n standard are used as interference sources, and their power is dynamically adjusted within the range of -20dBm to 20dBm before being superimposed onto the main channel.

[0010] Furthermore, an anti-interference optimization scheme is generated, which includes frequency band switching strategies and flight altitude strategies. Specifically, the following steps are included: S41. Set a bit error rate threshold. When the evaluated bit error rate is higher than the threshold, generate an instruction to switch from the 2.4GHz band to the 5.8GHz band. S42. Based on comprehensive channel parameters, identify communication blind spots caused by building obstruction and generate instructions to increase the flight altitude to a preset percentage higher than the surrounding buildings.

[0011] Furthermore, the frequency band switching strategy further includes: Based on dynamic spectrum sensing, the channel occupancy status of the 2.4GHz and 5.8GHz frequency bands is monitored in real time, and the frequency band switching is completed before the interference power exceeds the UAV signal power by 10dB.

[0012] Furthermore, the anti-interference optimization scheme also includes an antenna polarization adjustment strategy, specifically: Based on the main polarization direction of the interference signal reflected from buildings, the polarization mode of the UAV communication antenna is dynamically adjusted to reduce signal attenuation caused by polarization mismatch.

[0013] A second aspect of the present invention provides a system for generating and executing anti-jamming flight strategies for UAVs via C2 links, comprising: The data acquisition module is used to acquire the building material parameters of the target flight area and the flight parameters of the UAV; The model building module, connected to the data acquisition module, is used to perform simulation calculations based on the acquired building material parameters and UAV flight parameters by combining the free space path loss model, Ricean fading model and the blade peak diffraction model, and output comprehensive channel parameters including path loss and multipath fading to construct an urban environment channel model. The interference simulation module, connected to the model building module, is used to calculate the signal-to-interference-plus-noise ratio (SIR) of the UAV C2 link by injecting co-frequency Wi-Fi interference signals and building reflection interference signals based on comprehensive channel parameters, so as to simulate the interference environment and quantify the link quality. The strategy generation module, connected to the interference simulation module, is used to calculate the bit error rate based on the signal-to-interference-plus-noise ratio, evaluate the anti-interference performance using the bit error rate as an indicator, and generate an anti-interference optimization scheme that includes frequency band switching strategy and flight altitude strategy. The strategy execution module, connected to the strategy generation module, is used to send anti-interference optimization schemes to the UAV and control the UAV to execute frequency band switching strategies and flight altitude strategies.

[0014] Compared with the prior art, the method and system for generating and executing anti-interference flight strategies for UAVs via C2 links provided by the present invention have the following technical effects: This invention constructs a dynamic simulation closed loop encompassing "parameter acquisition – channel modeling – interference simulation – performance evaluation – strategy generation." Based on real-time environmental parameters and flight status, it accurately simulates the actual performance of the link under the current electromagnetic environment (signal-to-interference-plus-noise ratio and bit error rate), and generates the most targeted frequency band switching and flight altitude strategies accordingly, thus completely overcoming the blindness of traditional static strategies. Furthermore, by directly sending the generated optimized strategy and controlling the UAV to execute it automatically, complete automation from "perception – decision-making – execution" is achieved. This enables the UAV to autonomously and rapidly take evasive actions when facing communication interference, as if possessing a "conditioned reflex," greatly improving the real-time response of the system and the level of flight intelligence. With its forward-looking simulation evaluation and automated strategy execution capabilities, this method can proactively avoid communication blind spots and areas of strong interference, fundamentally reducing the risk of link interruption and ensuring the stable and smooth transmission of control commands and data, providing technical assurance for the safe and reliable flight of UAVs in densely populated urban areas. Attached Figure Description

[0015] Figure 1 A flowchart illustrating a method for generating and executing an anti-interference flight strategy for a UAV using a C2 link, provided in an embodiment of the present invention; Figure 2 A flowchart illustrating another method for generating and executing an anti-interference flight strategy for a UAV C2 link, provided in an embodiment of the present invention; Figure 3 A path loss curve as a function of distance is provided in an embodiment of the present invention; Figure 4 A system framework diagram for multi-scenario joint modeling provided in an embodiment of the present invention; Figure 5 This is a screenshot of the code for a complete simulation process of realizing the penetration of UAV link signals through a glass curtain wall, provided by an embodiment of the present invention. Figure 6 A trend curve showing the deterioration of bit error rate as the tilt angle of a glass curtain wall changes, provided for an embodiment of the present invention; Figure 7 Screenshot of program code for channel model and interference signal generation for simulation provided in an embodiment of the present invention; Figure 8 A graph showing the relationship between the bit error rate of an unmanned aerial vehicle (UAV) communication link and the distance between buildings is provided for an embodiment of the present invention. Figure 9 This is a schematic diagram of the structure of a UAV C2 link anti-interference flight strategy generation and execution system provided in an embodiment of the present invention; Figure label: Data acquisition module-101, model building module-102, interference simulation module-103, strategy generation module-104, strategy execution module-105. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0017] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0018] Example 1 See Figure 1 , Figure 1 This is a flowchart illustrating a method for generating and executing an anti-jamming flight strategy for a UAV C2 link, as provided in an embodiment of this application. The method includes: S100: Obtain the building material parameters of the target flight area and the flight parameters of the UAV; S200. Based on the building material parameters and UAV flight parameters, simulation calculations are performed using a combination of free-space path loss model, Ricean fading model and blade peak diffraction model to output comprehensive channel parameters including path loss and multipath fading. S300. Based on the comprehensive channel parameters, inject co-frequency Wi-Fi interference signal and building reflection interference signal to calculate the signal-to-interference-plus-noise ratio of the UAV C2 link; S400. Calculate the bit error rate based on the signal-to-interference-plus-noise ratio, evaluate the anti-interference performance using the bit error rate as an indicator, and generate an anti-interference optimization scheme that includes frequency band switching strategy and flight altitude strategy. S500: Send the anti-interference optimization scheme to the UAV and control the UAV to execute the frequency band switching strategy and the flight altitude strategy.

[0019] In this embodiment, building material parameters refer to physical quantities characterizing the electromagnetic properties of building materials, including the relative permittivity of concrete (with a value range of 4.5). -6.5) Parameters such as the reflection coefficient (typical value >0.8) and thickness of the glass curtain wall; UAV flight parameters include preset flight altitude (typical range 50-150 meters), flight speed (typical range 8-15 meters / second), and navigation data such as track coordinates; the free space path loss model is a mathematical model describing the power attenuation of electromagnetic waves propagating in an ideal unobstructed space; the Ricean fading model is a simulation model applicable to multipath channels with direct propagation paths, and its K-factor characterizes the power ratio of the direct path to the scattered path; the blade peak diffraction model is a physical model for calculating the diffraction loss generated when electromagnetic waves bypass the edge of a building; co-frequency Wi-Fi interference signal refers to the IEEE 802.11 standard wireless signal operating in a frequency band overlapping with the UAV communication band; building reflection interference signal refers to the multipath interference signal generated after reflection from the building surface; the signal-to-interference-plus-noise ratio (SINR) is the ratio of effective signal power to interference noise power, characterizing the quality of the communication link; the bit error rate (BER) is the proportion of bits that are erroneous during data transmission, used to quantify communication reliability; anti-interference performance refers to the ability of the communication system to maintain normal communication in an interference environment.

[0020] It should be noted that traditional UAV anti-jamming methods mainly employ preset static strategies, which cannot adapt to the dynamically changing interference characteristics in complex urban environments. This embodiment achieves adaptive response to dynamic interference environments by constructing a complete simulation-decision-execution closed-loop system. Specifically, a propagation environment model conforming to actual electromagnetic characteristics is established through accurate acquisition of building material parameters; a multi-model joint simulation method overcomes the accuracy limitations of a single model in complex scenarios; a dual evaluation mechanism based on signal-to-interference-plus-noise ratio and bit error rate ensures the accuracy of anti-jamming strategy generation; and a coordinated strategy of dynamic frequency band switching and flight altitude adjustment achieves multi-dimensional anti-jamming optimization from communication parameters to spatial position.

[0021] In one possible implementation, in step S100, building material parameters are obtained from the city building information model database, and real-time flight parameters are obtained from the flight control system. In step S200, multi-model co-simulation is implemented using the MATLAB communication toolbox: first, the baseline path loss is calculated using the free space path loss model; then, the Ricean fading model is introduced to simulate multipath effects, for example, setting the K-factor to 3 and the path delay to [0, 1e-6] microseconds; finally, the edge peak diffraction model is used to calculate the building edge diffraction loss. The outputs of the three models are used to generate comprehensive channel parameters through a weighted fusion algorithm, for example, setting the weight coefficients to 0.4:0.3:0.3. In step S300, an OFDM (Orthogonal Frequency Division Multiplexing) Wi-Fi interference signal is generated based on the IEEE 802.11n standard, with the power adjustable in the range of -20dBm to 20dBm; simultaneously, the building reflection path is calculated using a ray tracing algorithm, and Fresnel coefficient correction is introduced for glass curtain wall reflection. In step S400, a dual-threshold mechanism for the bit error rate is set, for example, a warning threshold of 1e-4 and a switching threshold of 1e-3. When the bit error rate exceeds the switching threshold, a frequency band switching command (such as switching between 2.4GHz and 5.8GHz) and a flight altitude adjustment command (such as increasing to 1.2 times the building height) are generated. In step S500, the optimized scheme is sent to the UAV flight control system via the LoRa data transmission module to control the UAV to perform the corresponding frequency band switching and altitude adjustment actions.

[0022] This invention establishes a simulation foundation for electromagnetic propagation in a realistic urban environment through multi-dimensional parameter acquisition and precise channel modeling. Dynamic interference injection and link quality assessment mechanisms enable accurate perception of complex interference environments. An intelligent decision-making system based on bit error rate thresholds ensures the timeliness and effectiveness of anti-interference strategies. Coordinated optimization of frequency band switching and flight altitude adjustment enhances anti-interference capabilities from both communication parameter and spatial location dimensions. A closed-loop control mechanism guarantees reliable strategy execution, forming a complete perception-decision-execution technology chain. Compared to existing technologies, this solution significantly improves the communication reliability and flight safety of UAVs in complex urban environments, effectively reducing the risk of link interruption due to signal interference.

[0023] In a preferred embodiment, the method further includes step S410: when the generated anti-interference optimization scheme includes a flight altitude strategy, the strategy is modified by combining real-time meteorological data.

[0024] Preferably, the flight altitude strategy correction is achieved through the following method: First, real-time wind speed, wind direction, and turbulence intensity data of the target area are obtained from the meteorological service interface to establish an atmospheric disturbance model. Based on the UAV's dynamic characteristics, wind resistance performance indicators for each altitude layer are calculated, and combined with a preset safety margin, the theoretical flight altitude generated in step S400 is dynamically adjusted. For example, when strong wind shear is detected at a specific altitude layer, the system will prioritize selecting a neighboring altitude layer as the actual execution strategy and mark wind speed limits in the strategy instructions. Simultaneously, the system continuously monitors meteorological changes during execution. When environmental conditions exceed the safety threshold, a strategy re-evaluation mechanism is automatically triggered, and step S400 is re-executed to generate an optimized scheme adapted to the latest environmental conditions.

[0025] The meteorological data correction mechanism enhances flight safety through multi-source data fusion: real-time wind speed data is used to assess the UAV's attitude stability, ensuring operational reliability during altitude adjustments; wind direction information is used to optimize flight path planning, avoiding positioning deviations caused by crosswinds; and turbulence intensity data is used to predict the impact of signal jitter on communication quality. This design effectively overcomes the shortcomings of traditional methods that solely consider communication quality while neglecting flight safety through an environmental adaptive strategy, achieving a unified optimization of communication reliability and flight safety. Experiments show that the meteorological correction flight altitude strategy can reduce the communication interruption rate of UAVs under severe weather conditions by more than 40%, while maintaining excellent flight stability.

[0026] This embodiment significantly improves the environmental adaptability of the anti-interference strategy by introducing a real-time meteorological data correction mechanism. The combination of atmospheric disturbance modeling and UAV dynamics ensures the feasibility and safety of the altitude adjustment strategy. The dynamic reassessment mechanism provides an effective response to sudden weather changes, enhancing the system's robustness. This design overcomes the limitations of traditional anti-interference methods that only consider the electromagnetic environment, achieving multi-objective coordination between communication optimization and flight safety, and providing comprehensive protection for the reliable operation of UAVs in complex urban environments.

[0027] Example 2 This embodiment, based on Embodiment 1, further optimizes parameter configuration and supplements implementation paths to meet the full-scenario requirements of UAV C2 link anti-interference in complex urban environments. It also improves the practicality and operability of the solution by incorporating simulation data and accompanying diagrams from the technical disclosure document. The method in this embodiment still follows the core process of "parameter acquisition—channel modeling—interference injection—policy generation—execution control," see [link to documentation]. Figure 2 The specific implementation is as follows: In a preferred embodiment, the overall methodology still revolves around five core steps: The first step is to obtain the building material parameters and UAV flight parameters of the target flight area; the second step, based on these two types of parameters, performs simulation calculations using a combined free-space path loss model, Ricean fading model, and blade peak diffraction model to output comprehensive channel parameters including path loss and multipath fading, thereby constructing an urban environment channel model; the third step, based on the comprehensive channel parameters, injects co-frequency Wi-Fi interference signals and building reflection interference signals to calculate the signal-to-interference-plus-noise ratio (SIRR) of the UAV C2 link, achieving interference environment simulation and link quality quantification; the fourth step, calculates the bit error rate (BER) based on the SIRR, uses the BER as an indicator to evaluate anti-interference performance, and generates an anti-interference optimization scheme including frequency band switching strategies, flight altitude strategies, and antenna polarization adjustment strategies; the fifth step, sends the anti-interference optimization scheme to the UAV, controls the UAV to execute the strategies, and ensures link stability during flight.

[0028] During the parameter acquisition phase, the definitions and values ​​of building material parameters and UAV flight parameters need to be precisely set based on the actual scenario. Building material parameters include the relative permittivity ε of concrete, the reflectivity and thickness of glass curtain walls, with ε ranging from 4.5 to 6.5. This range covers common urban concrete types, such as ordinary concrete (ε = 4.5) and reinforced concrete (ε = 6.5). The reflectivity of glass curtain walls is greater than 0.8, specifically referring to Low-E coated glass (see Table 5 in the technical disclosure document). - 4. The reflection coefficient used in the simulation is 0.85, and the thickness is uniformly taken as 1cm, which is the typical thickness of a building curtain wall. The UAV flight parameters include preset flight altitude and flight trajectory. The preset flight altitude refers to the range of 50-150 meters in Example 1, and can be adjusted according to mission requirements. For example, 80 meters is used for aerial photography missions, and 120 meters is used for logistics delivery. The flight trajectory is pre-planned by the ground station and adopts a broken line trajectory. For example, from point A (30°N, 120°E) to point B (30.01°N, 120.01°E), passing through 3 densely built areas, the trajectory data format is a latitude-longitude sequence in the WGS84 coordinate system. Regarding the methods for acquiring parameters, data can be obtained through Building Information Modeling (BIM) databases. This method is efficient and has a wide coverage, but it suffers from the problem of missing data for older buildings, making it more suitable for newly built urban areas and planned industrial zones. Alternatively, on-site instrument measurements can be used. This method offers high data accuracy, with errors controlled within 0.1, but it is time-consuming and costly, making it more suitable for parameter acquisition of key buildings such as glass curtain wall complexes. Accurate material parameters can effectively reduce channel modeling errors. If the concrete ε value deviates from the actual value by 1 (e.g., actual ε = 5.5, but mistakenly taken as 4.5), it will lead to an 8dB deviation in path loss calculation. Figure 3 A path loss curve as a function of distance is provided for an embodiment of the present invention, such as... Figure 3As shown, the loss difference between n=3.5 and n=4.5 at 500 meters is 25dB. However, this solution uses BIM combined with on-site calibration to control the parameter error within 0.2, improving the modeling accuracy by 60%.

[0029] After acquiring the parameters, the channel modeling stage begins. This stage involves simulation calculations using a combination of the free-space path loss model, the Ricean fading model, and the edge peak diffraction model. First, the baseline path loss is calculated based on the free-space path loss model, using the formula... Where d is the communication distance (100–500 meters), f is the carrier frequency (2.4 GHz / 5.8 GHz), and c is the speed of light (3 × 10⁻⁶). 8 (m / s), for example, when d=200 meters and f=2.4 GHz, PL is calculated to be 84 dB. Figure 4 A system framework diagram for multi-scenario joint modeling provided in an embodiment of the present invention is shown below. Figure 4 As shown, the loss at 200 meters is approximately 116 dB when n=3.5. This is the baseline value; subsequent loss values ​​need to be superimposed. Next, a Ricean fading model is introduced to simulate the multipath fading effect where a dominant line-of-sight path exists. This model is implemented using the MATLAB communication toolbox, with the following parameter settings: K-factor is 3 (the technical disclosure states that K=3 is suitable for urban line-of-sight scenarios, where the direct path power is 3 times that of the scattered path), path delay is [0, 1e-6] microseconds (0 microseconds for the direct path, 1 microsecond for the reflected path, corresponding to multipath delay spread), and average path gain is [0, - [3]dB (direct path gain 0dB, reflected path attenuation 3dB). Simulation results show that after introducing the Rice model, the signal amplitude fluctuation range expands from ±2dB (free space) to ±5dB, which is more consistent with the actual urban multipath environment. Finally, for building edges (such as concrete corners and glass curtain wall corners), a blade peak diffraction model is introduced to calculate the diffraction loss and superimpose it with the reference path loss and multipath fading effect to output the comprehensive channel parameters. The blade peak diffraction model adopts the formula... Where v is the Fresnel parameter ( h is the edge height difference, and λ is the wavelength. (where λ is the diffraction path length), for example, the height difference at the edge of a glass curtain wall is h = 10 meters, and λ = 0.125 meters (2.4 GHz). When the distance is 200 meters, v = 1.26. =12dB, and the final synthesized channel parameters are output through a weighted fusion algorithm (weight coefficients 0.4:0.3:0.3). The implementation process of this stage can be found in [reference needed]. Figure 4The illustrated "Multi-Scenario Joint Modeling" process shows the following steps: "Parameter Initialization" corresponds to the material and flight parameter inputs mentioned earlier; "Line-of-Sight Path Calculation" corresponds to the baseline loss calculation of the free-space model; "Multipath Interference Analysis" corresponds to the multipath effect simulation of the Rice model; and "Blade Peak Diffraction Compensation" corresponds to the loss calculation of the blade peak diffraction model. Finally, "Communication Quality Assessment" outputs comprehensive channel parameters, validating the rationality of this stage of the process. Regarding the implementation of channel modeling, the multi-model joint approach offers higher accuracy, with errors controlled within 5dB. However, it has a longer computation time, approximately 200ms per calculation, making it suitable for densely built-up urban areas. In contrast, while the single free-space model has a computation time of only 50ms per calculation, its error exceeds 15dB, making it only suitable for open suburban areas.

[0030] In a preferred embodiment, the diffraction calculation for the edge of the glass curtain wall can be further optimized by calculating the actual incident angle of the signal based on the ray tracing model and introducing the Fresnel transmission coefficient to dynamically correct the incident angle, so as to obtain a more accurate diffraction loss. Figure 5 This is a screenshot of the code for a complete simulation process of realizing the penetration of UAV link signals through a glass curtain wall, as provided in an embodiment of the present invention. Figure 5 As shown, specifically, the actual incident angle is first calculated using a ray tracing model, combined with the real-time position of the UAV ( , , ) and the coordinates of the glass curtain wall edge ( , , ), using formula Calculate the angle between the incident ray and the curtain wall normal. Where n is the curtain wall normal vector, and d is the straight-line distance from the UAV to the edge, for example, when the UAV coordinates are (100, 50, 80), the edge coordinates are (150, 50, 50), and the normal vector is (1, 0, 0), =37°. Then, the Fresnel transmission coefficient is introduced for correction, and the relative permittivity of the glass curtain wall is... =6.5 (parameter in technical disclosure), the transmission coefficient of the TM mode (magnetic field perpendicular to the incident surface) is calculated according to Fresnel's formula. ,in The angle of refraction (according to Snell's law) Calculation), when At 37°, =14°, =0.72, the corrected diffraction loss = original loss × (1 - ² (including losses in the reflected portion), i.e., the original When the value is 12dB, the corrected value is 12+3=15dB. Without correction, the calculation error of diffraction loss at the edge of the glass curtain wall can reach 10dB (mistakenly treating the glass as concrete and ignoring the transmission effect); after introducing Fresnel coefficient correction, the error is reduced to within 3dB, which improves the accuracy of subsequent signal-to-interference-plus-noise ratio calculation by 40%. Figure 6 This invention provides a trend curve showing the deterioration of bit error rate as a function of the tilt angle of a glass curtain wall. Figure 6 It is known that the incident angle affects the bit error rate, and accurate loss calculation can avoid misjudging the link quality.

[0031] After channel modeling is completed, the interference injection phase begins. This phase primarily injects co-channel Wi-Fi interference signals and building reflection interference signals. Regarding building reflection interference signal injection, a Brewster angle compensation algorithm can be applied to glass curtain wall reflections. First, the Brewster angle of the glass curtain wall material is calculated. satisfy Substitute =6.5 ≈68° (in) Figure 6 In the diagram, a tilt angle of 122° corresponds to an incident angle of 68° (the Brewster angle inflection point). Next, the actual incident angle of the glass curtain wall is obtained by dynamically monitoring the signal and using the ray tracing model mentioned earlier. ,when When the angle is ≥65° (close to Brewster's angle, with a 3° redundancy), the system generates an alarm "incident angle close to Brewster's angle"; subsequently, an avoidance strategy is triggered. <68°, adjust the drone's heading (e.g., deflect by 5°) to make Drop below 60°C; if ≥68° triggers an increase in flight altitude (e.g., from 80 meters to 100 meters), reducing the angle of incidence by changing the altitude difference. Figure 6 The relationship between bit error rate and glass curtain wall tilt angle verifies the necessity of this algorithm: when the tilt angle increases from 90° (0° incident angle) to 122° (68° incident angle), the bit error rate slowly increases from 1e-6 to 1e-4; after exceeding 122°, the bit error rate deteriorates sharply to 1e-2. This compensation algorithm can trigger avoidance before the tilt angle reaches 122°, keeping the bit error rate below 1e-4 and avoiding link interruption. Without the compensation algorithm, glass curtain wall reflection causes the bit error rate to deteriorate by two orders of magnitude (1e-4 → 1e-2); after implementation, the bit error rate is maintained in the range of 1e-5 to 1e-4, and the anti-reflection interference capability is improved by 10 times.

[0032] For co-channel Wi-Fi interference injection, an OFDM signal was generated based on the IEEE 802.11n standard as the interference source, and its power was dynamically adjusted within the range of -20dBm to 20dBm before being superimposed onto the main channel. The interference signal was generated using the wlanWaveformGenerator function in MATLAB. Figure 7 Screenshots of program code for channel model and interference signal generation for simulation provided in this embodiment of the invention, such as... Figure 7 As shown, the parameter configuration is as follows: standard IEEE 802.11n, bandwidth 20MHz, modulation method QPSK (the basic scheme in the technical disclosure document; the alternative is OFDM-64QAM, which improves spectral efficiency by 3 times but reduces anti-interference capability by 1dB), power range from -20dBm (weak interference, such as distant Wi-Fi hotspots) to 20dBm (strong interference, such as rooftop Wi-Fi base stations), dynamically adjusted in 5dBm steps. During interference superposition, the generated Wi-Fi interference signal is linearly superimposed with the UAV's main channel signal (2.4GHz, power 20dBm). The total signal power after superposition = main signal power + interference signal power (considering phase randomness, the sum of powers is taken). For example, when the main signal is 20dBm + the interference signal is -5dBm, the total power ≈ 20dBm (small interference impact); when the main signal is 20dBm + the interference signal is 20dBm, the total power ≈ 23dBm (severe interference, signal-to-interference-plus-noise ratio decreases by 10dB). (Technical Disclosure Document) Figure 5 - 3. The "Wi-Fi Interference Modeling" code forms the basis for this interference injection implementation. The parameters in the code—fc=2.4e9 (carrier frequency), wifiPowerRange=-20:5:20 (power range), and modulation='QPSK' (modulation method)—are completely consistent with the parameters used in this stage. The interference signal generated by this code can accurately simulate a Wi-Fi hotspot density of 200 / km² in a city, ensuring the realism of the interference environment. Regarding the interference generation method, the IEEE 802.11nOFDM method boasts a spectrum matching accuracy of up to 95%, consistent with actual Wi-Fi signals, and supports dynamic power adjustment from -20 to 20 dBm, making it suitable for densely populated urban Wi-Fi environments. In contrast, the sine wave interference has a spectrum matching accuracy of only 60%, a single spectrum, and only supports fixed power, making it suitable only for simple interference testing scenarios.

[0033] After interference injection is completed and the signal-to-interference-plus-noise ratio (SINR) is obtained, the anti-interference optimization scheme generation stage begins. This stage calculates the bit error rate (BER) based on the SINR and uses the BER as an indicator to generate an optimized scheme incorporating multiple strategies. For frequency band switching and flight altitude strategy generation, BER thresholds are first set, with a warning threshold of 1e-4 (indicating increased interference) and a switching threshold of 1e-3 (triggering frequency band switching). The SINR calculated earlier is then substituted into the QPSK BER formula. When BER ≥ 1e-3, a command is generated to switch from the 2.4GHz band to the 5.8GHz band; simultaneously, communication blind spots are identified based on comprehensive channel parameters. When the path loss exponent n in the comprehensive channel parameters > 4.5 (see... Figure 3 When n=4.5 corresponds to a densely built-up area, and SINR<5dB, it is determined to be a communication blind zone. At this time, an instruction is generated to increase the flight altitude to a preset percentage higher than the surrounding buildings. The preset percentage is 20% (i.e., flight altitude = the highest height of the surrounding buildings × 1.2). For example, if the highest surrounding buildings are 50 meters, an instruction to "increase to 60 meters" is generated. Table 1 can verify the effect of this strategy. The data in the table shows that the average BER of the 2.4GHz band is 1.2e-3 (higher than the handover threshold), and the BER of the 5.8GHz band is 3.5e-5 (lower than the warning threshold). The anti-interference capability is improved by 34 times after handover.

[0034] In a preferred embodiment, the frequency band switching strategy can also be implemented in conjunction with dynamic spectrum sensing, i.e., real-time monitoring of the channel occupancy status of the 2.4GHz and 5.8GHz bands, and completing the frequency band switching before the interference power exceeds the UAV signal power by 10dB. Specifically, using the MATLAB spectrum analysis toolbox, the channel occupancy rate (%) and interference power (dBm) of the two bands are monitored in real time at a frequency of 100ms / time. When the interference power of the 2.4GHz band is ≥ the UAV signal power + 10dB (i.e., 20dBm + 10dB = 30dBm, the actual interference power in urban areas is at most 20dBm, so it is triggered 5dB in advance), the frequency band switching is initiated; the switching command is transmitted through the LoRa data transmission module, taking only 20ms (Table 1 shows that the 5.8GHz interference avoidance response time is 20ms, which is 6 times better than the 120ms of 2.4GHz). Table 1, "Simulation Results," verifies the advantages of this strategy in detail, with specific data as follows: Table 1 Comparison of simulation results for different frequency bands

[0035] As shown in the table, the 5.8GHz handover strategy combined with dynamic spectrum awareness is significantly better than the 2.4GHz static strategy in terms of bit error rate, response speed, and concurrency capability.

[0036] Furthermore, the anti-interference optimization scheme can also include an antenna polarization adjustment strategy. This involves dynamically adjusting the polarization of the UAV communication antenna based on the main polarization direction of the building-reflected interference signal to reduce signal attenuation caused by polarization mismatch. For polarization direction detection, the UAV's onboard polarization analyzer collects the vertical polarization component (V) and horizontal polarization component (H) of the building-reflected interference signal, calculating the polarization ratio ρ = V / H. If ρ > 2, the main polarization is determined to be vertical; if ρ < 0.5, the main polarization is determined to be horizontal; if 0.5 ≤ ρ ≤ 2, it is determined to be mixed polarization. Regarding antenna adjustment, the UAV antenna supports vertical / horizontal polarization switching (mechanical rotation, switching time < 50ms). If the interference is vertically polarized, the antenna is switched to horizontal polarization (mismatch attenuation decreases from 15dB to 3dB); if it is horizontally polarized, it is switched to vertical polarization; in the case of mixed polarization, circular polarization is used (attenuation stabilizes within 5dB). Without polarization adjustment, building reflection interference causes signal attenuation of 10~15dB; after adjustment, the attenuation is reduced to 3~5dB, the received signal strength is improved by 7~10dB, and the signal-to-interference-plus-noise ratio is improved by 5~8dB.

[0037] After generating the anti-interference optimization scheme, the strategy execution control stage begins, focusing on optimizing the execution logic of the flight altitude strategy. In a preferred embodiment, when simulation predicts that the bit error rate (BER) will deteriorate sharply when the UAV flies below the height of buildings, or when an instruction is received to increase the flight altitude to a preset percentage above the surrounding buildings, the UAV is controlled to climb, ensuring its flight altitude remains above the height of all buildings along its flight path. In the pre-simulation judgment, the BER at each point along the flight path is predicted using the comprehensive channel parameters described above. If the height of a point is lower than the building height (e.g., 50 meters for the building, 30 meters for the UAV), the predicted BER is ≥ 1e-2. Figure 8 A graph showing the relationship between the bit error rate of a UAV communication link and the distance between buildings is provided for an embodiment of the present invention. Figure 8 As shown, at a height of 30 meters, the bit error rate (BER) is 1e-2, so a climb command is generated in advance. In real-time execution, after receiving the command to "climb to 60 meters," the UAV climbs at a climb acceleration of 0.5 m / s², continuously monitoring its altitude via GPS positioning (accuracy ±0.5 meters) until the target altitude is reached and maintained. In terms of execution effectiveness, when the UAV flies below the building height (30 meters), the BER is 1e-2 and the link interruption probability is 25%; while when flying above the building height (60 meters), the BER drops to 1e-5 and the link interruption probability is <1%. After implementing the altitude strategy, the link interruption probability is reduced by 24 percentage points, significantly improving flight safety.

[0038] In a preferred embodiment, meteorological data can be used to correct the flight altitude strategy to improve the environmental adaptability of the anti-interference scheme. This strategy is the preferred option. Specifically, it is implemented as follows: First, real-time meteorological data of the target area is obtained from the meteorological service interface, including wind speed (0~20m / s), wind direction (0~360°), and turbulence intensity (weak / medium / strong). Then, an atmospheric disturbance model is established based on the wind speed, and the wind resistance performance index of each altitude layer is calculated according to the UAV's dynamic characteristics. Combined with a preset safety margin, the theoretical flight altitude generated above is dynamically adjusted. For example, when the wind speed is >10m / s, the target altitude is increased by 10% (e.g., 60 meters → 66 meters) to avoid altitude fluctuations caused by wind shear. Simultaneously, the system continuously monitors meteorological changes during execution. If the turbulence intensity is "strong," the anti-interference optimization scheme generation step is re-executed every 30 seconds, adjusting the altitude strategy (e.g., decreasing from 66 meters to 62 meters to avoid the turbulence zone). After adopting this weather correction strategy, the communication interruption rate of UAVs under severe weather conditions is reduced by more than 40%, while the flight attitude stability is improved by 30%. This effectively overcomes the shortcomings of traditional methods that only consider communication quality while ignoring flight safety, and achieves unified optimization of communication reliability and flight safety.

[0039] This embodiment, through its technical solution and related simulation data, achieves at least the following technical effects compared to existing technologies: In terms of modeling accuracy, relying on multi-model collaboration and Fresnel correction techniques, channel modeling errors are significantly reduced; in terms of anti-interference capability, the coordinated use of 5.8GHz band switching, antenna polarization adjustment, and Brewster angle compensation effectively optimizes the bit error rate performance and significantly enhances the anti-interference effect; in terms of execution reliability, the integration of dynamic spectrum sensing and meteorological data correction strategies reduces the risk of link interruption and further improves flight safety.

[0040] Example 3 This embodiment provides a system for generating and executing anti-interference flight strategies for UAV C2 links. Based on the method in the above embodiment, the system adopts a modular design to build an anti-interference strategy generation and execution platform, which solves the technical problems of severe co-frequency interference, prominent building shielding effect and multipath reflection interference faced by UAV C2 links in complex urban environments. Figure 9 This is a schematic diagram of the structure of a UAV C2 link anti-interference flight strategy generation and execution system provided in an embodiment of the present invention, as shown below. Figure 9 As shown, the system includes a data acquisition module 101, a model building module 102, an interference simulation module 103, a policy generation module 104, and a policy execution module 105. These modules are connected in sequence to form a closed-loop control architecture.

[0041] The data acquisition module 101 is responsible for obtaining building material parameters from the urban building information model database, including the relative permittivity ε of concrete (value 4.5~6.5), the reflectivity of glass curtain walls (greater than 0.8), and thickness parameters. Simultaneously, it acquires flight parameters such as altitude, speed, and trajectory coordinates in real time from the UAV flight control system. This module provides a reliable data foundation for subsequent modeling by accurately collecting the electromagnetic characteristics of urban buildings and the real-time status of the UAV, overcoming the shortcomings of traditional methods that rely on ideal propagation models.

[0042] The model building module 102, based on the acquired parameters, performs simulation calculations in the MATLAB simulation platform by jointly using the free-space path loss model, the Ricean fading model, and the blade peak diffraction model. The free-space path loss is calculated using the standard formula. The Ricean fading model is configured with a K-factor of 3, a path delay of [0, 1e-6] microseconds, and an average path gain of [0, -3] dB. The blade peak diffraction model calculates the actual incident angle using a ray tracing algorithm and incorporates Fresnel coefficient correction. This module constructs a high-precision urban channel model through multi-model joint simulation, effectively quantifying the path loss caused by building shielding and multipath effects, such as... Figure 3 The simulation results of path loss and shadow fading caused by obstacles fully verify the effectiveness of the model.

[0043] The interference simulation module 103 injects co-frequency Wi-Fi interference signals and building reflection interference signals into the constructed channel model. The Wi-Fi interference is based on the IEEE 802.11n standard, generating OFDM signals with adjustable power from -20dBm to 20dBm. Figure 7 The Wi-Fi interference modeling code shown accurately generates interference signals. For building reflection interference, the Brewster angle compensation algorithm is applied to address the characteristics of glass curtain walls, dynamically correcting the incident angle and calculating the reflected interference power. This module accurately simulates multi-source interference in a real urban environment, reproducing the signal-to-interference-plus-noise ratio (SNR) degradation caused by Wi-Fi co-channel interference and glass curtain wall reflection. Figure 6 The curve showing the relationship between bit error rate and glass curtain wall tilt angle clearly demonstrates the influence of the Brewster angle effect.

[0044] The strategy generation module 104 calculates the bit error rate (BER) based on the signal-to-interference-plus-noise ratio (SINR), uses the BER as an indicator to evaluate anti-interference performance, and generates an anti-interference optimization scheme that includes frequency band switching and flight altitude adjustment. This module sets a dual-threshold mechanism for the BER. When the BER exceeds the switching threshold 1e-3, it generates a frequency band switching command from 2.4 GHz to 5.8 GHz, while simultaneously monitoring the channel status in real time based on dynamic spectrum sensing. To address the building obstruction problem, it generates an adjustment command to increase the flight altitude to more than 1.2 times the height of surrounding buildings. Simulation results, as shown in Table 1, demonstrate that this strategy improves anti-interference capability by 34 times, throughput by 2.7 times, and reduces interference avoidance response time to 20 ms.

[0045] The strategy execution module 105 is responsible for sending the generated anti-interference optimization scheme to the UAV via the LoRa data transmission module, and controlling the UAV to execute frequency band switching and flight altitude strategies. This module ensures that when the simulation predicts that the bit error rate will deteriorate sharply when the UAV is flying below the height of buildings, or when it receives an altitude increase command, it controls the UAV to immediately climb to ensure that the flight altitude remains above all buildings in the path, forming a complete closed-loop control, effectively avoiding the risk of UAV loss of control due to signal interruption.

[0046] This system, through the collaborative work of its various modules, achieves a complete technical chain from environmental perception, interference modeling, strategy generation to closed-loop execution, effectively solving the technical problems of existing technologies: It avoids co-channel interference from dense Wi-Fi networks through frequency band switching strategies and dynamic spectrum perception, significantly reducing the average bit error rate; it overcomes building shielding effects through flight altitude strategies and blade peak diffraction compensation, reducing communication interruptions caused by penetration loss from concrete buildings; and it suppresses multipath interference caused by glass curtain wall reflections through the Brewster angle compensation algorithm, maintaining a controllable bit error rate even when the incident angle exceeds 122°. Ultimately, the system provides reliable anti-interference protection for UAV C2 links in complex urban environments, significantly improving communication reliability and flight safety.

[0047] The above description is merely a preferred embodiment of this application and does not limit the patent scope of this invention. Any equivalent structural or procedural transformations made based on the description and drawings of this invention, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this invention.

Claims

1. A method for generating and executing an anti-interference flight strategy for a UAV using a C2 link, characterized in that, Includes the following steps: S1. Obtain the building material parameters and UAV flight parameters of the target flight area; S2. Based on the building material parameters and UAV flight parameters, simulation calculations are performed by combining the free space path loss model, Ricean fading model and blade peak diffraction model to output comprehensive channel parameters including path loss and multipath fading, so as to construct an urban environment channel model. S3. Based on the comprehensive channel parameters, inject co-frequency Wi-Fi interference signal and building reflection interference signal to calculate the signal-to-interference-plus-noise ratio of the UAV C2 link, so as to simulate the interference environment and quantify the link quality. S4. Calculate the bit error rate based on the signal-to-interference-plus-noise ratio, evaluate the anti-interference performance using the bit error rate as an indicator, and generate an anti-interference optimization scheme that includes frequency band switching strategy and flight altitude strategy. S5. Send the anti-interference optimization scheme to the UAV and control the UAV to execute the frequency band switching strategy and the flight altitude strategy.

2. The method according to claim 1, characterized in that: The building material parameters include the relative permittivity ε of concrete, the reflectivity and thickness of glass curtain walls, where ε = 4.5~6.5 and the reflectivity > 0.8; The drone's flight parameters include preset flight altitude and flight trajectory.

3. The method according to claim 2, characterized in that, Simulation calculations were performed using a combined free-space path loss model, Ricean fading model, and edge peak diffraction model, including: S21. Calculate the baseline path loss based on the free space path loss model; S22. Introduce the Rice fading model, set the K factor to 3, and configure the path delay and average path gain to simulate the multipath fading effect with a dominant line-of-sight path. S23. Introduce the blade peak diffraction model for the building edge, calculate the diffraction loss, and superimpose the reference path loss, the multipath fading effect, and the diffraction loss to output the integrated channel parameters.

4. The method according to claim 3, characterized in that, The blade peak diffraction model introduced for the building edge includes: For the edges of glass curtain walls, the actual incident angle of the signal is calculated based on a ray tracing model, and the Fresnel transmission coefficient is introduced to dynamically correct the incident angle in order to calculate the accurate diffraction loss of the glass curtain wall edges.

5. The method according to claim 2, characterized in that, Injected building reflection interference signals include: The Brewster angle compensation algorithm is applied to the reflection of the glass curtain wall. Specifically, the actual incident angle of the signal on the glass curtain wall is dynamically monitored. When the actual incident angle is close to or exceeds the Brewster angle calculated for the glass curtain wall material, an alarm is generated and an avoidance strategy is triggered.

6. The method according to claim 1, characterized in that, Injecting Wi-Fi interference signals at the same frequency includes: OFDM signals generated based on the IEEE 802.11n standard are used as interference sources, and their power is dynamically adjusted within the range of -20dBm to 20dBm before being superimposed onto the main channel.

7. The method according to claim 1, characterized in that, Generate an anti-interference optimization scheme that includes frequency band switching strategies and flight altitude strategies, specifically including the following steps: S41. Set a bit error rate threshold. When the evaluated bit error rate is higher than the threshold, generate an instruction to switch from the 2.4 GHz band to the 5.8 GHz band. S42. Based on the comprehensive channel parameters, identify communication blind spots caused by building obstruction, and generate an instruction to increase the flight altitude to a preset percentage higher than the surrounding buildings.

8. The method according to claim 7, characterized in that, The frequency band switching strategy also includes: Based on dynamic spectrum sensing, the channel occupancy status of the 2.4GHz and 5.8GHz frequency bands is monitored in real time, and the frequency band switching is completed before the interference power exceeds the UAV signal power by 10dB.

9. The method according to claim 1, characterized in that, The anti-interference optimization scheme also includes an antenna polarization adjustment strategy, specifically: Based on the main polarization direction of the building-reflected interference signal, the polarization mode of the UAV communication antenna is dynamically adjusted to reduce signal attenuation caused by polarization mismatch.

10. A system for generating and executing anti-interference flight strategies for UAVs via C2 links, characterized in that, include: The data acquisition module (101) is used to acquire the building material parameters of the target flight area and the flight parameters of the UAV; The model building module (102) is connected to the data acquisition module (101) and is used to perform simulation calculations based on the acquired building material parameters and UAV flight parameters by combining the free space path loss model, Rice fading model and blade peak diffraction model, and output comprehensive channel parameters including path loss and multipath fading to construct an urban environment channel model. The interference simulation module (103) is connected to the model building module (102) and is used to inject co-frequency Wi-Fi interference signal and building reflection interference signal based on the comprehensive channel parameters to calculate the signal-to-interference-plus-noise ratio of the UAV C2 link, so as to simulate the interference environment and quantify the link quality. The strategy generation module (104) is connected to the interference simulation module (103) and is used to calculate the bit error rate based on the signal-to-interference-plus-noise ratio, evaluate the anti-interference performance with the bit error rate as an indicator, and generate an anti-interference optimization scheme that includes frequency band switching strategy and flight altitude strategy. The strategy execution module (105) is connected to the strategy generation module (104) and is used to send the anti-interference optimization scheme to the UAV and control the UAV to execute the frequency band switching strategy and the flight altitude strategy.

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