Method and system for evaluating GNSS (Global Navigation Satellite System) anti-deception jamming performance of unmanned aerial vehicle in darkroom

Abstract

The invention relates to the technical field of unmanned aerial vehicle performance testing and electromagnetic compatibility, and discloses a method and system for evaluating GNSS anti-deception jamming performance of an unmanned aerial vehicle in a darkroom, and the method comprises the steps: adjusting the support rod diameter of a hoisting system, adjusting bearing parameters, and fixing the unmanned aerial vehicle in a central region of the darkroom according to the model of the tested unmanned aerial vehicle; the method comprises the following steps: starting system self-inspection, after ensuring that each module works normally, starting a first GNSS signal simulator to emit a real navigation signal, controlling the unmanned aerial vehicle to lift off and stably hover, and recording initial positioning precision P0 and flight attitude parameters including a pitch angle theta 0, a roll angle phi 0 and a yaw angle psi 0; synchronously starting a magnetic field interference source and a second GNSS signal simulator, and applying mixed interference according to a set time sequence; coupling noise is eliminated by using a mixed interference data processing algorithm; and judging whether the anti-deception jamming performance of the unmanned aerial vehicle reaches the standard or not according to a quantitative evaluation standard corresponding to the model, and recording an anti-interference critical value, thereby realizing accurate evaluation of the anti-deception jamming performance of the unmanned aerial vehicles of different models in an environment close to an actual complex environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicle performance testing and electromagnetic compatibility, and particularly relates to a method and system for evaluating GNSS anti-deception interference performance of unmanned aerial vehicles in a darkroom. BACKGROUND

[0002] With the promotion of low-altitude economy policy, the types of unmanned aerial vehicles have expanded from traditional consumer-grade aerial photography drones to passenger-carrying pilotless aircrafts, flying cars and other diversified models. The importance of flight safety and airspace management is increasingly highlighted. In the anti-unmanned aerial vehicle technology, interference blocking and deception control are the core non-violent means. Interference blocking blocks communication and navigation links through electromagnetic interference, and deception control deceives the navigation module through false GNSS signals. Therefore, it is necessary to verify the anti-deception sensitivity of consumer-grade unmanned aerial vehicles to ensure that they can be effectively counteracted, and to protect the anti-deception interference of passenger-carrying aircrafts to avoid safety accidents caused by countermeasures.

[0003] The existing related technologies have obvious limitations: the patent document CN121078438A only realizes single GNSS position deception test and does not involve the coupling effect of magnetic field interference and GNSS signal, and lacks a standardized multi-model evaluation system; the patent document CN110907741A focuses on single electromagnetic radiation interference test of unmanned aerial vehicle flight control module and does not cover GNSS deception interference, and the test object is only aimed at the module rather than the whole machine, which cannot reflect the overall anti-interference performance of the unmanned aerial vehicle; the patent document CN212620384U mentions the combination of electromagnetic and GPS induction, but it is only used for unmanned aerial vehicle countermeasures and not performance evaluation, and does not consider the timing coordination and quantitative control of interference signals.

[0004] Specifically, the core problems of the existing technology include: First, the test scene is single, mostly single interference type, without simulating the mixed scene of magnetic field interference and GNSS signal interference in the actual environment and the coupling effect, which is quite different from the real use environment; Second, the evaluation standard is ambiguous, lacking quantitative indicators classified by machine type, which cannot accurately determine the anti-interference performance of different types of unmanned aerial vehicles; Third, the multi-source interference control is insufficient, and the existing system cannot realize the precise timing coordination of magnetic field interference and GNSS signal interference, with a synchronization error usually not less than 20ms; Fourth, the hoisting system has poor adaptability, traditional metal supports either have insufficient bearing capacity or are too expensive, and it is difficult to simulate the real flight attitude of unmanned aerial vehicles; Fifth, the data processing algorithm has insufficient adaptability, and the single interference processing algorithm cannot eliminate the cross-coupling noise of mixed interference, resulting in low precision of test data.

[0005] These problems make it extremely difficult to evaluate the GNSS anti-spoofing interference performance of UAVs, and the results have low credibility, which cannot meet the safety testing needs of diversified UAVs, so it is urgent to develop a darkroom evaluation method and system that can simulate a complex mixed interference environment, adapt to multiple models, and have clear quantitative standards. SUMMARY

[0006] The present application aims to provide a darkroom UAV GNSS anti-spoofing interference performance evaluation method and system to solve the problems of ambiguous evaluation standards, poor multi-model adaptability and insufficient multi-source interference synchronization control in the prior art.

[0007] To solve the above problems, the present application adopts the following technical solutions: Scheme one: a darkroom UAV GNSS anti-spoofing interference performance evaluation method, comprising the following steps: S1: Construct a darkroom UAV GNSS anti-spoofing interference performance evaluation system, the system includes a darkroom main body, a mixed interference source module, a hoisting system, a data acquisition module and a data processing module, the electromagnetic shielding effectiveness of the darkroom main body is greater than or equal to 80dB@1GHz-18GHz, and the mixed interference source module supports the cooperative output of magnetic field interference and GNSS signal interference; S2: According to the types of the measured UAVs divided by consumer level, passenger carrying level and flying car, adjust the support rod diameter of the hoisting system in the range of 20mm-80mm, adjust the bearing parameter in the range of 5kg-500kg, and fix the UAV in the central area of the darkroom; S3: Start system self-checking, after ensuring that each module is working properly, start the first GNSS signal simulator to emit real navigation signals, control the UAV to take off and hover stably, and record the initial positioning accuracy P0, the flight attitude parameters pitch angle θ0, roll angle φ0 and yaw angle ψ0; S4: Synchronously start the magnetic field interference source and the second GNSS signal simulator, apply mixed interference according to the set time sequence, gradually increase the magnetic field interference strength by 0.01mT / step, and gradually increase the GNSS signal interference power by 0.5dBm / step; S5: Real-time acquisition of UAV positioning data, attitude data and navigation log is realized through the data acquisition module, and coupling noise is eliminated by using mixed interference data processing algorithm; S6: According to the corresponding quantitative evaluation standard of the model, determine whether the anti-spoofing interference performance of the UAV meets the standard, and record the anti-interference critical value.

[0008] Beneficial effects: First, build a standardized evaluation system and then carry out testing to ensure the stability of the testing environment and equipment, and the step logic is more rigorous, which can adapt to the testing needs of multiple models such as consumer level, passenger carrying level and flying car, and solve the problem of poor adaptability of the prior art.

[0009] Preferably, the sequence of applying the magnetic field interference and GNSS signal interference in step S4 is: first synchronously applying the basic interference, the magnetic field strength is 0.01mT-1mT, the GNSS interference power is-130dBm to-50dBm, and after 5 minutes, the coupling formula B=0.8×P_GNSS+0.05mT is used to strengthen the interference, wherein P_GNSS is the GNSS interference power, B is the magnetic field strength, and the coupling interference lasts for 10 minutes.

[0010] Beneficial effects: the sequence of applying the mixed interference and the coupling relationship are clear, the progressive and coupling effects of the interference signals in the actual environment are accurately simulated, the problem that the existing single interference test is disconnected with the real scene is solved, and the realness of the measurement and evaluation is greatly improved.

[0011] Preferably, the mixed interference data processing algorithm in step S5 includes positioning deviation calculation and coupling noise filtering, and the positioning deviation formula is:

[0012]

[0013]

[0014] The filtering formula is: y(n)=α×x(n)+(1-α)×y(n-1) Wherein: a is the difference between the latitude of the GNSS signal simulator output position and the latitude of the unmanned aerial vehicle output position, in radians (rad); b is the difference between the longitude of the GNSS signal simulator output position and the longitude of the unmanned aerial vehicle output position, in radians (rad); Lat1 is the latitude of the GNSS signal simulator output position, in radians (rad); Lat2 is the latitude of the unmanned aerial vehicle output position, in radians (rad); Lng1 is the longitude of the GNSS signal simulator output position, in radians (rad); Lng2 is the longitude of the unmanned aerial vehicle output position, in radians (rad); is the difference between the GNSS signal simulator output position and the unmanned aerial vehicle output position, in meters (m); x(n) is the original collected data, y(n) is the filtered data, and α=0.3+0.6×(P_GNSS / P_max), P_max is the maximum GNSS interference power.

[0015] Beneficial effects: It achieves accurate data processing under mixed interference through a dedicated formula, effectively suppresses cross-coupling noise between magnetic field and GNSS signal, and achieves positioning deviation calculation accuracy of ±0.01m. Compared with existing single interference algorithms, it is more suitable for complex mixed interference scenarios.

[0016] Preferably, the quantitative evaluation criteria for different models in step S6 are as follows: Consumer-grade drones: Positioning deviation ΔP≤5m, attitude deviation Δθ, Δφ, Δψ≤5°, navigation and return-to-home functions are normal, and are judged to meet the sensitivity standards; Passenger-carrying drones: Positioning deviation ΔP≤1m, attitude deviation Δθ, Δφ, Δψ≤2°, horizontal movement and turning function are normal, and the disturbance resistance is judged to meet the standard. Flying car: Positioning deviation ΔP≤0.5m, attitude deviation Δθ, Δφ, Δψ≤1°, positioning return and navigation functions are normal, and the anti-interference level is deemed to be up to standard; Where attitude deviations Δθ=|θ_meas-θ0|, Δφ=|φ_meas-φ0|, and Δψ=|ψ_meas-ψ0|, θ_meas, φ_meas, and ψ_meas are attitude measurements under disturbance conditions.

[0017] Beneficial effects: Establishing clear quantitative evaluation standards, clarifying the performance thresholds of different models, solving the problem of ambiguity in existing technical evaluation standards, and making the evaluation results more comparable and authoritative.

[0018] Preferably, in step S4, the power increase frequency of the second GNSS signal simulator is set according to the model: once every 2 seconds for consumer-grade drones, once every 5 seconds for passenger-carrying drones, and once every 10 seconds for flying cars, and the maximum power value satisfies: ≤-90dBm for consumer-grade, ≤-70dBm for passenger-carrying drones, and ≤-50dBm for flying cars, with a power adjustment accuracy of ±0.1dBm.

[0019] Beneficial effects: Based on the differences in anti-interference capabilities of different models, the frequency and upper limit of GNSS interference power can be precisely controlled to avoid excessive damage to the drone during testing, while ensuring the relevance and effectiveness of the evaluation. The power adjustment accuracy is better than ±0.5dBm of existing technologies.

[0020] Preferably, the method further includes step S7: if the UAV positioning deviation ΔP exceeds the threshold of the corresponding model for 3 seconds, or the attitude deviation exceeds the threshold for 5 seconds, then the emergency handling mechanism is activated, the interference source is immediately stopped, and the UAV is controlled to slowly descend at a speed of 0.5 m / s by the attitude adjustment motor of the hoisting system. The magnetic field interference intensity B_failure and the GNSS interference power P_failure at this time are recorded, and the anti-interference critical value K=P_failure / B_failure is calculated. The larger the K value, the stronger the anti-interference performance.

[0021] Beneficial effects: The dual threshold judgment and emergency landing mechanism not only ensure test safety but also accurately capture the anti-interference limit of the drone, providing quantitative data support for drone performance optimization and avoiding equipment damage.

[0022] Option 2: A darkroom-based UAV GNSS anti-deception interference performance evaluation system, comprising a darkroom main body, a hybrid interference source module, a hoisting system, a data acquisition module, and a data processing module; the inner wall of the darkroom main body is covered with absorbing material, with an electromagnetic shielding effectiveness ≥80dB@1GHz-18GHz, and an effective internal test space ≥5m×5m×5m; The hybrid interference source module includes a magnetic field interference generator, a first GNSS signal simulator, and a second GNSS signal simulator; the hoisting system includes a carbon fiber modular support, a tension sensor, and an attitude adjustment motor; the output range of the magnetic field interference generator is 0.01mT-10mT, with an adjustment accuracy of 0.001mT; the first GNSS signal simulator supports all frequency points of GPS, BeiDou, and GLONASS, with a signal power of -150dBm to -100dBm; the second GNSS signal simulator supports repeater-type and regenerative spoofing, with a signal power of -130dBm to -50dBm.

[0023] Beneficial effects: The system configuration is standardized and the parameters are quantifiable. The anechoic chamber has high shielding effectiveness. The mixed interference sources have a wide coverage and high accuracy. The hoisting system takes into account both load-bearing capacity and attitude simulation accuracy, ensuring the authenticity and stability of the test environment and solving the problems of single test scenarios and insufficient accuracy of existing systems.

[0024] Preferably, the hybrid interference source module is equipped with a multi-source signal synchronization controller, which uses a PID algorithm to achieve timing coordination between magnetic field interference and GNSS signal interference, with a synchronization error ≤10ms. The PID algorithm formula is: u(t)=Kp×e(t)+Ki +Kd×de(t) / dt, where e(t) is the deviation between the actual value and the set value of the interference signal, and Kp=0.8, Ki=0.2, Kd=0.1 are the proportional, integral, and derivative coefficients.

[0025] Beneficial effects: This algorithm improves the coordination accuracy of magnetic field interference and GNSS signal interference by 40%. By employing a PID synchronization control algorithm, it overcomes the technical bottleneck of asynchronous multi-source interference in existing systems, achieving precise timing coordination of mixed interference signals. The synchronization error is better than the existing technology's ≤20ms, significantly improving the accuracy of mixed interference simulation.

[0026] Preferably, the carbon fiber modular support of the hoisting system consists of 3 splicable rods with a rod diameter that can be replaced within the range of 20mm-80mm. The support weighs 5kg-30kg, has a maximum load capacity of 500kg, a load density of ≥16.7kg / kg, and a disassembly and assembly time of ≤10 minutes.

[0027] Beneficial effects: Compared with traditional metal brackets, the cost is reduced by 40%. The modular design adapts to the installation requirements of different models. It has high load-bearing density, low cost, and convenient assembly and disassembly. It solves the problems of poor adaptability, high cost, and cumbersome assembly and disassembly of existing hoisting systems, and significantly improves testing efficiency.

[0028] Preferably, the data acquisition module includes a three-dimensional positioning sensor, an attitude sensor, and a GNSS signal receiver; the data processing module has a built-in hybrid interference data processing algorithm and evaluation standard database, which can automatically generate an evaluation report containing positioning deviation, attitude deviation, and anti-interference threshold, with a report generation time of ≤5 minutes; the three-dimensional positioning sensor has a measurement accuracy of ±0.01m and a sampling rate of 10Hz; the attitude sensor has a measurement accuracy of ±0.1° and a sampling rate of 20Hz; the GNSS signal receiver has a sampling rate of 10Hz and a signal acquisition sensitivity of ≤-160dBm.

[0029] Beneficial effects: High data acquisition accuracy and high sampling rate; data processing algorithm adaptable to mixed interference scenarios; standardized evaluation criteria; automation of the evaluation process and quantification of results; and high report generation efficiency, which is significantly improved compared to the ≤15 minutes of existing technologies.

[0030] Working principle and advantages (I) Working Principle The core of this invention is to construct a hybrid interference scenario of "magnetic field interference + GNSS signal interference," and in a highly shielded, dark room, to accurately evaluate the GNSS anti-spoofing interference performance of UAVs through standardized procedures and quantitative algorithms. The specific process is as follows: First, based on the model under test, the shielding effectiveness (≥80dB@1GHz-18GHz) and interference source parameters of the anechoic chamber were set, and the UAV was fixed by a modular hoisting system to simulate real flight attitude. Secondly, the first GNSS signal simulator is activated to transmit real navigation signals, enabling the UAV to hover stably and record baseline data such as initial positioning accuracy and attitude parameters. Then, the hybrid interference sources are synchronously started by the multi-source signal synchronization controller (PID algorithm), and magnetic field interference and GNSS signal interference are applied according to the timing sequence of "basic interference + coupled enhanced interference" with the set gradient. Next, positioning and attitude data are collected using high-precision sensors, and cross noise is eliminated using a hybrid interference-specific processing algorithm (positioning deviation formula + coupled noise filtering formula) to calculate key performance deviations. Finally, based on the quantitative evaluation standards for different models, it is determined whether the anti-interference performance of the drone meets the standards, and the anti-interference threshold value K=P_failure / B_failure is recorded.

[0031] Throughout the process, the hoisting system simulates the drone's horizontal movement and turning through attitude adjustment motors, ensuring that the test scenario closely resembles the actual usage environment.

[0032] (II) Advantages 1. Realistic Mixed Interference Scenarios: For the first time, magnetic field interference and GNSS signal interference are combined. Through coupling formulas and timing control, the progressive and coupling effects of the two are restored. Compared with existing single interference tests, this is closer to the actual scenario, and the evaluation results are more valuable for reference. 2. Strong compatibility with multiple models: The modular design of the hoisting system covers a load capacity of 5kg-500kg. The evaluation standards are classified and quantified according to the model, meeting the testing needs of different types of drones such as consumer-grade, passenger-grade, and flying car, and solving the compatibility limitations of existing systems. 3. Precise and efficient data processing: A dedicated filtering algorithm and deviation calculation model are designed for mixed interference, which improves the coupling noise suppression ratio by 30% and the positioning deviation accuracy reaches ±0.01m. Compared with the existing single interference processing algorithm, it is more suitable for complex scenarios. 4. Excellent system controllability: The system achieves time-series coordination of multi-source interference signals through PID synchronous control algorithm, with a synchronization error of ≤10ms and power regulation accuracy of ±0.1dBm, solving the problem of poor synchronization of multi-source interference in existing systems. 5. Safe and reliable testing: A dual threshold judgment and emergency handling mechanism is set up. When the performance deviation of the UAV exceeds the standard, the interference will be stopped automatically and the UAV will be guided to land to avoid equipment damage and ensure the safety of the testing process. 6. Excellent cost-effectiveness: The hoisting system adopts carbon fiber composite materials and modular design, which reduces the cost by 40% compared with traditional metal brackets, and the disassembly and assembly time is ≤10 minutes, which greatly improves the testing efficiency and economy. Attached Figure Description

[0033] Figure 1 This is a flowchart of the evaluation method of the present invention.

[0034] Figure 2 This is a schematic diagram of the layout structure of the evaluation system of the present invention. Detailed Implementation

[0035] The following detailed description illustrates the specific implementation method: The specific steps of the method for evaluating the anti-spoofing interference performance of UAVs in a darkened indoor environment according to the present invention are as follows: First, a complete evaluation system is built, which includes an anechoic chamber main body, a hybrid interference source module, a hoisting system, a data acquisition module, and a data processing module. The electromagnetic shielding effectiveness of the anechoic chamber main body is no less than 80dB@1GHz-18GHz, and the hybrid interference source module can achieve coordinated output of magnetic field interference and GNSS signal interference.

[0036] Next, the drone is placed on the hoisting system. Depending on the specific model of the drone being tested, including consumer drones, passenger drones, and flying cars, the support rod diameter and load-bearing parameters of the hoisting system are adjusted. The support rod diameter can be selected between 20mm and 80mm, and the load-bearing parameters cover the range of 5kg to 500kg. After adjustment, the drone is fixed in the center area of ​​the darkroom.

[0037] Subsequently, two sets of GPS signal simulators and amplifiers were placed in the anechoic chamber. The first set of GPS signal simulators was activated to provide location information. After the system self-test program was started and all modules were confirmed to be working properly, the first GNSS signal simulator was activated to transmit real navigation signals. The UAV was then controlled to take off and reach a stable hovering state. At the same time, the initial positioning accuracy P0 and flight attitude parameters of the UAV, including pitch angle θ0, roll angle φ0, and yaw angle ψ0, were recorded.

[0038] Subsequently, the magnetic field interference source and the second GNSS signal simulator were simultaneously activated, and hybrid interference was applied according to a preset timing sequence. The magnetic field interference intensity was gradually increased in steps of 0.01 mT, and the GNSS signal interference power was gradually increased in steps of 0.5 dBm. During the application of interference, the UAV's positioning data, attitude data, and navigation logs were collected in real time through the data acquisition module. A hybrid interference data processing algorithm was used to eliminate the coupling noise between the magnetic field interference and the GNSS signal interference.

[0039] Finally, based on the quantitative evaluation standards corresponding to the model, it is determined whether the anti-spoofing and interference performance of the drone meets the standards, and the anti-interference threshold is recorded.

[0040] The advantages of this method are that by first building a standardized evaluation system and then conducting tests, the stability of the testing environment and equipment can be ensured, the steps and logic are more rigorous, and it is compatible with the testing needs of multiple aircraft types such as consumer-grade, passenger-grade, and flying cars, effectively solving the problem of poor compatibility of existing technologies.

[0041] In the above evaluation method, the application of magnetic field interference and GNSS signal interference follows a specific order and coupling relationship. First, basic interference is applied simultaneously, with a magnetic field strength ranging from 0.01 mT to 1 mT and a GNSS interference power ranging from -130 dBm to -50 dBm. The duration of basic interference is 5 minutes. After the basic interference ends, enhanced interference is applied according to the coupling formula B = 0.8 × P_GNSS + 0.05 mT, where P_GNSS represents the GNSS interference power (in dBm) and B represents the magnetic field strength (in mT). The duration of coupled interference is 10 minutes.

[0042] The beneficial effect of this rule is that it clarifies the order of application and coupling relationship of mixed interference, can accurately simulate the progressive and coupling effects of interference signals in the actual environment, solves the problem of the disconnect between the single interference test and the real scenario in the existing technology, and greatly improves the authenticity of the evaluation.

[0043] In the above evaluation method, the hybrid interference data processing algorithm includes two parts: positioning deviation calculation and coupled noise filtering. The positioning deviation calculation formula is as follows: Where Lat1 is the latitude of the GNSS signal simulator output position in radians (rad); Lat2 is the latitude of the UAV output position in radians (rad); Lng1 is the longitude of the GNSS signal simulator output position in radians (rad); Lng2 is the longitude of the UAV output position in radians (rad); the coupling noise filtering calculation formula is y(n)=α×x(n)+(1-α)×y(n-1), where x(n) is the original acquired data, y(n) is the filtered data, α=0.3+0.6×(P_GNSS / P_max), and P_max is the maximum power of GNSS interference. Compared with the single interference processing algorithm, this algorithm improves the coupling noise suppression ratio by 30%.

[0044] The beneficial effect of this algorithm is that it achieves accurate data processing under mixed interference through a dedicated formula, effectively suppresses cross-coupling noise between magnetic fields and GNSS signals, and achieves a positioning deviation calculation accuracy of ±0.01m. Compared with existing single interference algorithms, it is more suitable for complex mixed interference scenarios.

[0045] 4. Quantitative Evaluation Standards for Multiple Aircraft Models The quantitative evaluation standards differ for different drone models in the above evaluation methods. For consumer-grade drones, the requirements are: positioning deviation ΔP ≤ 5m, attitude deviation Δθ, Δφ, Δψ ≤ 5°, and normal navigation and return-to-home functions. Meeting these conditions is considered acceptable for sensitivity testing. For passenger-grade drones, the requirements are: positioning deviation ΔP ≤ 1m, attitude deviation Δθ, Δφ, Δψ ≤ 2°, and normal horizontal movement and steering functions. Meeting these conditions is considered acceptable for interference immunity. For flying cars, the requirements are: positioning deviation ΔP ≤ 0.5m, attitude deviation Δθ, Δφ, Δψ ≤ 1°, and normal positioning, return-to-home, and navigation functions. Meeting these conditions is considered acceptable for interference immunity. The attitude deviations are defined as Δθ = |θ_meas - θ0|, Δφ = |φ_meas - φ0|, and Δψ = |ψ_meas - ψ0|, where θ_meas, φ_meas, and ψ_meas are attitude measurements under interference conditions.

[0046] The beneficial effects of this evaluation standard are that it establishes a clear quantitative evaluation system, clarifies the performance thresholds of different models, solves the problem of ambiguity in existing technical evaluation standards, and makes the evaluation results more comparable and authoritative.

[0047] 5. GNSS Interference Power Control Rules In the above evaluation method, the power increase frequency of the second GNSS signal simulator is set according to the aircraft model. The power increase frequency is 1 time / 2s for consumer-grade drones, 1 time / 5s for passenger-carrying drones, and 1 time / 10s for flying cars. At the same time, there are clear limits on the maximum power: no more than -90dBm for consumer-grade drones, no more than -70dBm for passenger-carrying drones, and no more than -50dBm for flying cars. The power adjustment accuracy is ±0.1dBm.

[0048] The beneficial effect of this regulation rule is that it can accurately control the frequency and upper limit of GNSS interference power increase based on the differences in anti-interference capabilities of different models. This can avoid excessive damage to the UAV during testing, while ensuring the relevance and effectiveness of the evaluation. The power adjustment accuracy is better than ±0.5dBm of the existing technology.

[0049] 6. Emergency Response Mechanism The above evaluation method also includes emergency handling steps: if the UAV positioning deviation ΔP exceeds the threshold of the corresponding model for 3 seconds, or the attitude deviation exceeds the threshold for 5 seconds, the emergency handling mechanism is immediately activated to stop the operation of the interference source. The attitude adjustment motor of the hoisting system controls the UAV to slowly descend at a speed of 0.5 m / s. The magnetic field interference intensity B_failure and GNSS interference power P_failure at this time are recorded, and the anti-interference critical value K=P_failure / B_failure is calculated. The larger the K value, the stronger the anti-interference performance of the UAV.

[0050] The beneficial effects of this emergency response mechanism are that it sets up a dual threshold judgment and emergency landing procedure, which not only ensures the safety of the testing process, but also accurately captures the anti-interference limit of the UAV, providing quantitative data support for UAV performance optimization and avoiding equipment damage.

[0051] The indoor GNSS anti-spoofing interference performance evaluation system for unmanned aerial vehicles (UAVs) in this invention has the following features: 1. Overall System Composition The anechoic chamber GNSS anti-spoofing and jamming performance evaluation system for unmanned aerial vehicles (UAVs) consists of an anechoic chamber main body, a hybrid interference source module, a hoisting system, a data acquisition module, and a data processing module. The inner walls of the anechoic chamber are lined with absorbing material, providing electromagnetic shielding effectiveness of no less than 80dB@1GHz-18GHz, and the effective internal test space is no less than 5m×5m×5m. The hybrid interference source module includes a magnetic field interference generator, a first GNSS signal simulator, and a second GNSS signal simulator. The magnetic field interference generator has an output range of 0.01mT-10mT and an adjustment accuracy of 0.001mT. The first GNSS signal simulator supports all frequency points of GPS, BeiDou, and GLONASS, with a signal power range of -150dBm to -100dBm. The second GNSS signal simulator supports repeater-type and regenerative spoofing, with a signal power range of -130dBm to -50dBm. The hoisting system includes a carbon fiber modular support frame, a tension sensor, and an attitude adjustment motor. The tension sensor has an accuracy of 0.1N, and the attitude adjustment motor's response speed does not exceed 50ms.

[0052] The system's advantages include standardized configuration, quantifiable parameters, high shielding efficiency in the anechoic chamber, wide coverage and high accuracy of mixed interference sources, and a hoisting system that balances load-bearing capacity and attitude simulation accuracy. It ensures the authenticity and stability of the test environment and solves the problems of limited test scenarios and insufficient accuracy in existing systems.

[0053] 2. Multi-source signal synchronization control device In the aforementioned system, the hybrid interference source module is equipped with a multi-source signal synchronization controller. This controller employs a PID algorithm to achieve timing coordination between magnetic field interference and GNSS signal interference, with a synchronization error not exceeding 10ms. The PID algorithm formula is u(t)=Kp×e(t)+Ki∫e(t)dt+Kd×de(t) / dt, where e(t) is the deviation between the actual value and the set value of the interference signal, and Kp=0.8, Ki=0.2, and Kd=0.1 are the proportional-integral-derivative coefficients. This algorithm can improve the coordination accuracy of magnetic field interference and GNSS signal interference by 40%.

[0054] The beneficial effect of this synchronization control device is that it solves the technical bottleneck of asynchronous multi-source interference in existing systems through the PID synchronization control algorithm, realizes precise timing coordination of mixed interference signals, and achieves a synchronization error better than the existing technology of ≤20ms, which greatly improves the accuracy of mixed interference simulation.

[0055] 3. Modular hoisting structure In the above system, the carbon fiber modular support of the hoisting system consists of 3 splicable rods. The rod diameter can be replaced within the range of 20mm-80mm. The weight of the support itself is 5kg-30kg, the maximum load capacity can reach 500kg, and the load density is not less than 16.7kg / kg. Compared with traditional metal supports, the cost is reduced by 40%, and the assembly and disassembly time is no more than 10 minutes.

[0056] The advantages of this modular hoisting structure are that its modular design can adapt to the installation requirements of different models, and it features high load-bearing density, low cost, and convenient assembly and disassembly. It solves the problems of poor adaptability, high cost, and cumbersome assembly and disassembly of existing hoisting systems, and significantly improves testing efficiency.

[0057] 4. Data Acquisition and Processing Module In the above system, the data acquisition module includes a 3D positioning sensor, an attitude sensor, and a GNSS signal receiver. The 3D positioning sensor has a measurement accuracy of ±0.01m and a sampling rate of 10Hz. The attitude sensor has a measurement accuracy of ±0.1° and a sampling rate of 20Hz. The GNSS signal receiver has a sampling rate of 10Hz and a signal acquisition sensitivity of no higher than -160dBm. The data processing module has built-in the above-mentioned hybrid interference data processing algorithm and a multi-model quantitative evaluation standard database. It can automatically generate an evaluation report containing positioning deviation, attitude deviation, and anti-interference threshold, and the report generation time is no more than 5 minutes.

[0058] The beneficial effects of this data acquisition and processing module are high data acquisition accuracy and high sampling rate, data processing algorithm adaptability to mixed interference scenarios, standardized evaluation standards, automation of the evaluation process and quantification of results, and high report generation efficiency, which is a significant improvement compared to the ≤15 minutes of existing technologies.

[0059] The specific implementation process is as follows: Example 1: Consumer-grade drone (weight 2kg, model: DJI Mini 3 Pro) In actual use, according to Figure 1 The evaluation should be conducted according to the methods and steps shown. Figure 2 The system structure is arranged according to the structure shown.

[0060] (I) System Configuration Electromagnetic shielding effectiveness of the anechoic chamber: 85dB@1GHz-18GHz; Mixed interference sources: magnetic field interference generator (0.01mT-5mT, adjustment accuracy 0.001mT), first GNSS signal simulator (supports GPS / BeiDou, signal power -150dBm to -100dBm), second GNSS signal simulator (relay-type spoofing, signal power -130dBm to -50dBm); Lifting system: carbon fiber modular support (pole diameter 20mm, load capacity 5kg), tension sensor (accuracy 0.1N), attitude adjustment motor (response speed 40ms); Data acquisition module: three-dimensional positioning sensor (accuracy ±0.01m, sampling rate 10Hz), attitude sensor (accuracy ±0.1°, sampling rate 20Hz); Multi-source signal synchronization controller (PID algorithm, synchronization error ≤8ms).

[0061] Specifically, (ii) test methods 1. Construct an evaluation system, adjust the hoisting system pole diameter to 20mm, and fix the drone in the center of the darkroom; 2. After starting the system self-test, start the first GNSS signal simulator, the UAV takes off and hovers, and records the initial parameters: P0=0.3m, θ0=0°, φ0=0°, ψ0=0°; 3. Apply mixed interference: First, apply basic interference synchronously (magnetic field strength 0.01mT-1mT, GNSS interference power -130dBm to -110dBm) for 5 minutes; then, enhance the interference according to the coupling formula B=0.8×P_GNSS+0.05mT, with the magnetic field interference increasing by 0.01mT / step and the GNSS interference power increasing from -110dBm to -90dBm by 0.5dBm / step, with each step lasting 10 seconds. 4. The collected data is processed using a hybrid interference data processing algorithm to calculate the positioning deviation ΔP and attitude deviation; 5. Evaluation criteria: When the GNSS interference power reaches -100dBm and the magnetic field strength reaches 2mT, ΔP=4.8m, Δθ=3°, Δφ=2.5°, and Δψ=4°, all of which do not exceed the threshold, and the return-to-home function is normal; when the interference is further increased to GNSS power -95dBm and magnetic field strength 3mT, ΔP=5.2m, the emergency mechanism is triggered, the interference is stopped, and the UAV is guided to land at a speed of 0.5m / s. 6. The critical value for anti-interference is calculated as K=(-95dBm) / 3mT≈-31.67dBm / mT, which is considered to meet the countermeasure sensitivity standard.

[0062] (III) Explanation of Effects and Creativity Results: Successfully simulated the mixed scenario of "weak magnetic field + medium intensity GNSS deception" that consumer drones may encounter in actual use, accurately captured their anti-interference limit, and the evaluation results are highly consistent with the actual use scenario; The difference from existing technologies is that: Comparative document 1 only conducts a single GNSS deception test, does not involve magnetic field interference, and has no quantitative evaluation standard; this embodiment achieves a more comprehensive and accurate evaluation through hybrid interference coupling design, dedicated data processing algorithm and quantitative threshold determination. Non-obviousness: In Comparative Example 1 (using the single GNSS interference test scheme of Comparative Document 1), the UAV only showed excessive positioning deviation (ΔP=5.1m) when the GNSS power was -90dBm, while in this embodiment, it exceeded the standard at -95dBm under mixed interference, indicating that mixed interference has a more significant impact on UAV performance. Existing technologies cannot simulate this scenario. This invention solves the technical problem that existing technologies cannot accurately evaluate the anti-interference performance of UAVs in complex environments through mixed interference design and coupling algorithm, and has significant non-obviousness.

[0063] Example 2: Passenger-carrying drone (weight 200kg, model: EH216-S) (I) System Configuration Electromagnetic shielding effectiveness of the anechoic chamber: 90dB@1GHz-18GHz; Mixed interference sources: magnetic field interference generator (0.1mT-10mT, adjustment accuracy 0.001mT), first GNSS signal simulator (supports GPS / BeiDou / GLONASS, signal power -150dBm to -100dBm), second GNSS signal simulator (regenerative deception, signal power -130dBm to -50dBm); Lifting system: carbon fiber modular support (pole diameter 80mm, load capacity 500kg), tension sensor (accuracy 0.1N), attitude adjustment motor (response speed 30ms); Data acquisition module: three-dimensional positioning sensor (accuracy ±0.005m, sampling rate 10Hz), attitude sensor (accuracy ±0.05°, sampling rate 20Hz); Multi-source signal synchronization controller (PID algorithm, synchronization error ≤5ms).

[0064] (II) Testing Methods 1. Construct an evaluation system, adjust the hoisting system pole diameter to 80mm, and fix the drone in the center of the darkroom; 2. After starting the system self-test, start the first GNSS signal simulator, the UAV takes off and hovers, and records the initial parameters: P0=0.1m, θ0=0°, φ0=0°, ψ0=0°; 3. Apply mixed interference: First, apply basic interference synchronously (magnetic field strength 0.1mT-1mT, GNSS interference power -130dBm to -110dBm) for 5 minutes; then, enhance the interference according to the coupling formula B=0.8×P_GNSS+0.05mT, increasing the magnetic field interference by 0.1mT / step and the GNSS interference power by 0.5dBm / step from -110dBm to -70dBm, with each step lasting 30 seconds. 4. The mixed interference data processing algorithm processes the acquired data and calculates the positioning deviation ΔP and attitude deviation; 5. Evaluation criteria: When the GNSS interference power reaches -75dBm and the magnetic field strength reaches 8mT, ΔP=0.9m, Δθ=1.5°, Δφ=1.2°, and Δψ=1.8°, the horizontal movement and turning functions are normal; if the interference is further increased to GNSS power -72dBm and magnetic field strength 9mT, ΔP=1.1m, the emergency mechanism is triggered. 6. Calculate the critical value of anti-interference K=(-72dBm) / 9mT=-8dBm / mT, and determine that the anti-interference level meets the standard.

[0065] (III) Explanation of Effects and Creativity Results: Successfully simulated the mixed scenario of "strong magnetic field + high-intensity GNSS deception" that passenger-carrying drones may encounter during high-altitude flight, accurately verified its anti-interference capability, and met the safety testing requirements of passenger-carrying aircraft. The difference from the prior art is that: Comparative document 2 only conducts a single electromagnetic interference test on the flight control module, which cannot reflect the overall anti-interference performance of the UAV, and there is no hoisting system adapted to heavy models; This embodiment achieves accurate evaluation of the overall anti-interference performance of passenger-grade UAVs through a modular hoisting system with a load capacity of 500kg, hybrid interference collaborative control and quantitative evaluation. Non-obviousness: In Comparative Example 2 (using the single electromagnetic interference test scheme of Comparative Document 2), the UAV only showed excessive attitude deviation (Δθ=2.1°) when the electromagnetic interference intensity was 50V / m. However, in this embodiment, under the mixed interference of "strong magnetic field + high-intensity GNSS spoofing", the critical change in positioning accuracy was accurately captured. Existing technologies cannot simultaneously take into account the synchronous control of multi-source interference and the attitude simulation of heavy aircraft. This invention solves this technical problem through modular hoisting, PID synchronous control and coupling algorithm, and has significant non-obviousness.

[0066] Comparative Example 1: Existing technology single GNSS interference test (consumer drone) Test conditions: The technical solution of patent document CN121078438A was adopted, a normal anechoic chamber (shielding effectiveness 60dB), only the second GNSS signal simulator was started (no magnetic field interference), and the hoisting system was a traditional metal bracket (load capacity 3kg). Test results: When the GNSS interference power reaches -90dBm, the positioning deviation ΔP=5.1m. The coupling effect of magnetic field interference was not considered, and the test results deviated significantly from the actual scenario. Differences from the present invention: The present invention has higher anechoic chamber shielding effectiveness (85dB), incorporates magnetic field interference and hybrid algorithms, has lower hoisting system cost and stronger adaptability, and provides more realistic test results, solving the problem of existing technologies' single interference test being disconnected from real-world scenarios.

[0067] Comparative Example 2: Existing technology single electromagnetic interference test (passenger-carrying drone) Test conditions: The technical solution of patent document CN110907741A is adopted. There is no GNSS signal interference, only electromagnetic interference is applied. The hoisting system is a fixed metal frame (the attitude cannot be adjusted). Test results: When the electromagnetic interference intensity is 50V / m, the attitude deviation Δθ=2.1°. It is impossible to test the impact of GNSS spoofing interference on positioning accuracy and it is impossible to simulate real flight attitude. Differences from the present invention: The present invention achieves "magnetic field + GNSS" hybrid interference coordination, the hoisting system has adjustable attitude, the evaluation criteria are more comprehensive, and the performance of multi-dimensional positioning, navigation and other functions can be quantified, which solves the limitation of existing technologies that can only test a single module and a single type of interference.

[0068] This invention solves the core problems of existing technologies, such as their inability to simulate complex environments, vague evaluation standards, and poor adaptability, through an innovative design of "hybrid interference scenario construction + multi-model adaptation + quantization algorithm + precise control". Its creativity is mainly reflected in: 1. For the first time, a hybrid interference evaluation scheme of "magnetic field interference + GNSS signal interference" is proposed. Through the coupling formula B=0.8×P_GNSS+0.05mT and a dedicated data processing algorithm, the coupling effect of interference in the real environment is accurately reproduced, achieving the scene realism that existing single interference tests cannot achieve. 2. The modular hoisting system adopts carbon fiber composite materials and splicing design, with a load capacity ranging from 5kg to 500kg, reducing costs by 40%. It solves the technical bottlenecks of poor adaptability and high cost of existing hoisting systems and meets the testing needs of multiple models. 3. A quantitative evaluation standard was established based on the model classification. Combined with the calculation of the anti-interference critical value K, the standardization of the evaluation process and the quantification of the results were realized, which solved the problem of the ambiguity of the existing technical evaluation standards. 4. The PID synchronous control algorithm achieves precise coordination of multi-source interference signals with a synchronization error of ≤10ms and a power regulation accuracy of ±0.1dBm, solving the technical problem of asynchronous multi-source interference in existing systems.

[0069] The comparison between the embodiments and the comparative examples shows that the evaluation results of the present invention are more realistic, more adaptable, and more controllable. It can accurately evaluate the anti-deception interference performance of different types of UAVs in complex mixed interference environments. Its technical solution is not obvious and has important practical application value.

[0070] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for evaluating the anti-spoofing interference performance of unmanned aerial vehicles (UAVs) in a dark indoor environment, characterized in that, Includes the following steps: S1: Construct an anti-deception interference performance evaluation system for UAVs in an anechoic chamber. The system includes an anechoic chamber main body, a hybrid interference source module, a hoisting system, a data acquisition module, and a data processing module. The electromagnetic shielding effectiveness of the anechoic chamber main body is ≥80dB@1GHz-18GHz. The hybrid interference source module supports the coordinated output of magnetic field interference and GNSS signal interference. S2: Based on the type of drone under test, which is classified as consumer-grade, passenger-grade, or flying car, adjust the support rod diameter of the hoisting system within the range of 20mm-80mm, and adjust the load-bearing parameters within the range of 5kg-500kg to fix the drone in the center area of ​​the darkroom. S3: Start the system self-test, and after ensuring that each module is working properly, start the first GNSS signal simulator to transmit real navigation signals, control the UAV to take off and hover stably, and record the initial positioning accuracy P0, flight attitude parameters pitch angle θ0, roll angle φ0, and yaw angle ψ0. S4: Synchronously start the magnetic field interference source and the second GNSS signal simulator, apply mixed interference according to the set timing, gradually increase the magnetic field interference intensity by 0.01mT / step, and gradually increase the GNSS signal interference power by 0.5dBm / step; S5: Real-time acquisition of UAV positioning data, attitude data and navigation logs through the data acquisition module, and elimination of coupling noise using a hybrid interference data processing algorithm; S6: Based on the quantitative evaluation standards corresponding to the model, determine whether the anti-spoofing and interference performance of the drone meets the standards and record the anti-interference threshold.

2. The method as described in claim 1, characterized in that, In step S4, the order of applying magnetic field interference and GNSS signal interference is as follows: First, apply basic interference synchronously with a magnetic field strength of 0.01mT-1mT and a GNSS interference power of -130dBm to -50dBm for 5 minutes. Then, apply enhanced interference according to the coupling formula B=0.8×P_GNSS+0.05mT, where P_GNSS is the GNSS interference power and B is the magnetic field strength. The coupled interference lasts for 10 minutes.

3. The method as described in claim 1, characterized in that, The mixed interference data processing algorithm in step S5 includes positioning deviation calculation and coupled noise filtering. The positioning deviation formula is: The filtering formula is: y(n) = α × x(n) + (1 - α) × y(n-1) in: 'a' represents the difference in latitude between the output position of the GNSS signal simulator and the output position of the UAV, in radians (rad). b represents the difference in longitude between the output position of the GNSS signal simulator and the output position of the UAV, in radians (rad). Lat1 represents the latitude and longitude of the GNSS signal simulator output, in radians (rad). Lat2 represents the latitude and longitude of the UAV's output location, in radians (rad). Lng1 represents the longitude of the position output by the GNSS signal simulator, in radians (rad). Lng2 represents the longitude of the UAV's output position, in radians (rad). The difference between the output position of the GNSS signal simulator and the output position of the UAV is expressed in meters (m). x(n) represents the original acquired data, y(n) represents the filtered data, α = 0.3 + 0.6 × (P_GNSS / P_max), and P_max represents the maximum power of GNSS interference.

4. The method as described in claim 1, characterized in that, The quantitative evaluation criteria for different models in step S6 are as follows: Consumer-grade drones: Positioning deviation ΔP≤5m, attitude deviation Δθ, Δφ, Δψ≤5°, navigation and return-to-home functions are normal, and are judged to meet the sensitivity standards; Passenger-carrying drones: Positioning deviation ΔP≤1m, attitude deviation Δθ, Δφ, Δψ≤2°, horizontal movement and turning function are normal, and the disturbance resistance is judged to meet the standard. Flying car: Positioning deviation ΔP≤0.5m, attitude deviation Δθ, Δφ, Δψ≤1°, positioning return and navigation functions are normal, and the anti-interference level is deemed to be up to standard; Where attitude deviations Δθ=|θ_meas-θ0|, Δφ=|φ_meas-φ0|, and Δψ=|ψ_meas-ψ0|, θ_meas, φ_meas, and ψ_meas are attitude measurements under disturbance conditions.

5. The method as described in claim 1, characterized in that, In step S4, the power increase frequency of the second GNSS signal simulator is set according to the model: consumer-grade drones 1 time / 2s, passenger-carrying drones 1 time / 5s, and flying cars 1 time / 10s. The maximum power value meets the following requirements: consumer-grade ≤-90dBm, passenger-carrying drones ≤-70dBm, and flying cars ≤-50dBm. The power adjustment accuracy is ±0.1dBm.

6. The method as described in claim 1, characterized in that, It also includes step S7: If the UAV positioning deviation ΔP exceeds the threshold of the corresponding model for 3 seconds, or the attitude deviation exceeds the threshold for 5 seconds, the emergency handling mechanism is activated, the interference source is stopped immediately, and the UAV is controlled to slowly descend at a speed of 0.5 m / s by the attitude adjustment motor of the hoisting system. The magnetic field interference intensity B_failure and the GNSS interference power P_failure at this time are recorded, and the anti-interference critical value K=P_failure / B_failure is calculated. The larger the K value, the stronger the anti-interference performance.

7. A system for evaluating the anti-spoofing and interference performance of unmanned aerial vehicles (UAVs) in a dark indoor environment, characterized in that, It includes an anechoic chamber main body, a hybrid interference source module, a hoisting system, a data acquisition module, and a data processing module; the inner wall of the anechoic chamber main body is lined with wave-absorbing material, with an electromagnetic shielding effectiveness ≥80dB@1GHz-18GHz, and an effective internal test space ≥5m×5m×5m; The hybrid interference source module includes a magnetic field interference generator, a first GNSS signal simulator, and a second GNSS signal simulator; the hoisting system includes a carbon fiber modular support, a tension sensor, and an attitude adjustment motor; the output range of the magnetic field interference generator is 0.01mT-10mT, with an adjustment accuracy of 0.001mT; the first GNSS signal simulator supports all frequency points of GPS, BeiDou, and GLONASS, with a signal power of -150dBm to -100dBm; the second GNSS signal simulator supports repeater-type and regenerative spoofing, with a signal power of -130dBm to -50dBm.

8. The system as described in claim 7, characterized in that, The hybrid interference source module is equipped with a multi-source signal synchronization controller, which uses a PID algorithm to achieve timing coordination between magnetic field interference and GNSS signal interference, with a synchronization error ≤10ms. The PID algorithm formula is: u(t)=Kp×e(t)+Ki +Kd×de(t) / dt, where e(t) is the deviation between the actual value and the set value of the interference signal, and Kp=0.8, Ki=0.2, Kd=0.1 are the proportional, integral, and derivative coefficients.

9. The system as described in claim 7, characterized in that, The carbon fiber modular support of the hoisting system consists of 3 splicable rods with a rod diameter that can be replaced within the range of 20mm-80mm. The support weighs 5kg-30kg, has a maximum load capacity of 500kg, a load density of ≥16.7kg / kg, and a disassembly and assembly time of ≤10 minutes.

10. The system as described in claim 7, characterized in that, The data acquisition module includes a 3D positioning sensor, an attitude sensor, and a GNSS signal receiver; the data processing module has a built-in hybrid interference data processing algorithm and evaluation standard database, which can automatically generate an evaluation report containing positioning deviation, attitude deviation, and anti-interference threshold, with a report generation time of ≤5 minutes; the 3D positioning sensor has a measurement accuracy of ±0.01m and a sampling rate of 10Hz; the attitude sensor has a measurement accuracy of ±0.1° and a sampling rate of 20Hz; the GNSS signal receiver has a sampling rate of 10Hz and a signal acquisition sensitivity of ≤-160dBm.

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