Unmanned aerial vehicle laboratory test method and device

Abstract

The invention provides an unmanned aerial vehicle laboratory testing method and device, and belongs to the technical field of unmanned aerial vehicle testing. The scheme comprises the following steps: determining a global reference point based on the position of a laboratory, and further determining a conversion relation between a local northeast high coordinate system and a global coordinate system and a plurality of fixed reference points; according to the fixed reference point, sequentially executing stand setting and starting of an automatic measurement tracker in a laboratory, and further obtaining real-time coordinates of the to-be-tested unmanned aerial vehicle by using the automatic measurement tracker; the GNSS signal generator generates a simulation navigation signal according to the real-time coordinates of the to-be-tested unmanned aerial vehicle and records a simulation track; the to-be-tested unmanned aerial vehicle performs indoor flight test based on the simulation navigation signal; and comparing the simulation track with a flight track recorded by a flight control system of the to-be-tested unmanned aerial vehicle so as to output a laboratory test result. The scheme does not depend on hardware modification of the unmanned aerial vehicle, the implementation difficulty is reduced, the implementation cost is saved, and controllable testing of medium and large unmanned aerial vehicles in the indoor environment is facilitated.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) testing technology, and in particular to a UAV laboratory testing method and apparatus. Background Art

[0002] Currently, mainstream professional drones used in fields such as plant protection, inspection, and surveying rely heavily on GNSS (Global Navigation Satellite System, such as China's BeiDou Navigation Satellite System and the United States' GPS) for their flight control. The main function of GNSS is to provide real-time position information to the flight control system. Drone hovering maneuvers, precision operations (such as fixed-route inspection, fertilization, and aerial imagery surveying), and wind resistance performance heavily depend on GNSS and its related satellite navigation differential positioning technology, RTK (Real-Time Kinematic). As a line-of-sight radio signal, GNSS is limited to outdoor environments with open skies. However, in outdoor environments, environmental factors such as wind speed, temperature, and humidity are uncontrollable, making it impossible to construct a controllable environment to test the drone's endurance, wind resistance, and adaptability to different temperature and humidity conditions during flight.

[0003] To address this, some existing technologies have modified drones to replace the role of providing GNSS location information for drones. For example, Chinese patent (publication number CN119246121A) provides a method for evaluating the flight control and navigation performance of indoor drones based on autonomous positioning GNSS. In indoor environments where GNSS signals are unavailable, this method uses lidar combined with SLAM mapping technology to obtain the high-precision real-time position of the drone and "injects" this position information into a GNSS / RTK signal generator, thereby providing a "simulated GNSS signal" for the drone's flight control, making it mistakenly believe that it is in a real outdoor GNSS environment.

[0004] However, the aforementioned technologies require different modifications to the hardware of different drone models. On the one hand, drone hardware modification presents challenges such as high time and personnel costs and significant implementation difficulties. On the other hand, for some special testing scenarios, such as specific drone models (e.g., medium to large-sized drones), directly applying the above solutions still cannot meet the needs of indoor testing. Therefore, there is a need for a universally applicable and high-precision improved technical solution for indoor drone testing. Summary of the Invention

[0005] The purpose of this application is to provide a laboratory testing method and apparatus for unmanned aerial vehicles (UAVs), aiming to solve the problems in the prior art that make it impossible to conduct a comprehensive and realistic test of the UAV flight control system due to the unavailability of GNSS signals indoors, the large time delay and high time delay uncertainty of traditional VIO / SLAM systems, and the reliance on hardware modifications, or that the UAV cannot complete indoor flight normally due to the aforementioned time delay problems.

[0006] To achieve the above objectives, this application provides the following technical solution: This application provides a laboratory testing method for unmanned aerial vehicles (UAVs), including: S1. Determine the global reference point based on the location of the laboratory, and then determine the transformation relationship between the local northeast high coordinate system and the global coordinate system, as well as multiple fixed reference points; S2. Based on the fixed reference point, the automatic measurement and tracking instrument is set up and started in the laboratory in sequence, and then the automatic measurement and tracking instrument is used to obtain the real-time coordinates of the UAV under test. S3, the GNSS signal generator generates simulated navigation signals and records simulated trajectories based on the real-time coordinates of the UAV under test; S4. The UAV under test conducts indoor flight tests based on the simulated navigation signals, and records its own flight trajectory during the test through the flight control system of the UAV under test. S5. Using the simulated trajectory recorded by the GNSS signal generator as the true trajectory, compare the true trajectory with the flight trajectory recorded by the flight control system of the UAV under test to output the laboratory test results.

[0007] The aforementioned technical solution, on the one hand, clearly establishes the transformation relationship between the local northeastern high coordinate system (ENU) and the global coordinate system, and sets multiple fixed reference points for rapid equipment reset and calibration, ensuring data consistency across multiple rounds of testing and improving the spatial matching accuracy of trajectory acquisition and signal generation. On the other hand, it acquires the UAV's real-time coordinates (frequency can be set from 10H to 1000Hz) through an automatic measurement tracker, and then dynamically generates GNSS signals using a GNSS signal generator. This eliminates the need to add sensors to the UAV itself or modify the existing flight control system to complete indoor testing of the UAV without GNSS signals, significantly reducing the workload of test preparation, minimizing errors or safety hazards that may be introduced by modifications, and improving the versatility of the test system. Furthermore, the GNSS signal generator dynamically generates GNSS signals based on the UAV's real-time coordinates. The UAV performs flight tests based on these signals, comparing the "true trajectory" with the "flight control trajectory," thus constructing a complete closed-loop system of GNSS signal—flight control response—trajectory feedback, which can effectively evaluate the positioning performance of the flight control system in a simulated GNSS environment.

[0008] In some embodiments, the automatic measurement and tracking device and the GNSS signal generator use a first electrical signal as a unified trigger source to start synchronously; The first signal is a high-level signal emitted by the real-time data processing module, and the first signal is synchronously transmitted to the automatic measurement and tracking instrument and the GNSS signal generator via a coaxial cable.

[0009] In the above technical solution, the same physical signal source (first electrical signal) is used to trigger both devices, which are transmitted via coaxial cable. This avoids the delay uncertainties introduced by wireless communication, thereby achieving hardware-level synchronization between the automatic measurement and tracking device and the GNSS signal generator. It eliminates delay differences caused by software triggering and network synchronization, ensuring strict alignment of the two systems on the timeline. Furthermore, trajectory acquisition (automatic measurement and tracking device) and signal generation (GNSS signal generator) start at the same moment, and all subsequent data can be compared and analyzed based on the same timestamp. This improves the accuracy of comparing the "true trajectory" and the "flight control trajectory," supports precise evaluation of key parameters such as flight control response delay and the time point of deception signal injection, and provides fundamental support for conducting advanced security tests involving deception / interference. This setup supports complex scenario tests such as multipath effects and signal interruption recovery, and can be used to verify the RTK fixed solution establishment process.

[0010] In some embodiments, before the GNSS signal generator generates simulated navigation signals based on the real-time coordinates of the UAV under test, the method further includes: initializing the GNSS signal generator, as follows: The GNSS signal generator is synchronized with the time and ephemeris using real-world GNSS signals. The time of the test scenario is set, the signal types are set to BeiDou and GPS signals, and the vehicle trajectory is set to external trajectory input mode.

[0011] The aforementioned technical solution utilizes real-world GNSS signals for time and ephemeris synchronization, ensuring that the simulated signal remains consistent with the current operational status of the global satellite navigation system. This avoids misjudgments by the flight control system due to time or ephemeris deviations, making it more suitable for high-precision applications such as medium-to-large UAVs and RTK fixed-solution verification. Simultaneously, it supports BeiDou B1I and GPS L1 C / A signals, meeting the GNSS system selection needs of different brands and models of UAVs. More importantly, by setting the vehicle trajectory to "external trajectory input mode," it indicates that the GNSS signal is not preset but dynamically generated based on real-time acquired UAV coordinates. This achieves a GNSS signal generation mechanism driven by dynamic trajectory, laying the foundation for advanced tests such as deception signal injection and interference response assessment.

[0012] In some embodiments, the S3 GNSS signal generator generates simulated navigation signals and records simulated trajectories based on the real-time coordinates of the UAV under test, specifically: The real-time data processing module receives the real-time coordinates of the UAV under test and performs coordinate transformation according to the transformation relationship between the local northeast elevation coordinate system and the global coordinate system to obtain the transformed real-time coordinates. The GNSS signal generator generates simulated navigation signals and records simulated trajectories based on the converted real-time coordinates.

[0013] In the above technical solution, the automatic measurement and tracking device collects the local coordinates of the UAV in real time (such as based on the ENU coordinate system). According to the transformation relationship between the local northeast elevation coordinate system and the global coordinate system determined in step S1, the local coordinates are converted into standard geographic coordinates (such as WGS-84 or ECEF). The converted coordinates are used to dynamically generate GNSS signals that conform to the standard format (such as Beidou B1I+GPS L1 C / A). The trajectory output by the GNSS signal generator is recorded simultaneously as the "true value trajectory". In this way, the coordinate data of multiple devices are consistent in space, which improves the universality and repeatability of the test system and facilitates subsequent trajectory comparison and flight control behavior evaluation.

[0014] In some embodiments, in S4, before the UAV under test performs an indoor flight test based on the simulated navigation signal, the method further includes: The GNSS signal generator adjusts the power of the analog navigation signal output by the radiating antenna based on a preset signal strength threshold and a free space path loss model.

[0015] In the aforementioned technical solution, modern GNSS modules typically have a signal strength detection mechanism. If the signal is too strong, it is judged as interference / spoofing; if the signal is too weak, it enters satellite search mode or loses positioning; if the signal fluctuates drastically, it leads to unstable positioning or switching positioning modes. Here, the required signal strength at different distances can be calculated using a free-space path loss model, and the transmission power can be adjusted accordingly to avoid misjudgment by the flight control system or positioning failure due to signal strength fluctuations. In some embodiments, in S4, before the UAV under test performs an indoor flight test based on the simulated navigation signal, the method further includes: The update frequency of the GNSS signal generator or the automatic measurement and tracking instrument is compared with a preset frequency range. If the update frequency of the GNSS signal generator or the automatic measurement and tracking instrument is lower than the preset frequency range, a carrier phase smoothing algorithm is used to smooth the analog navigation signal. Based on the correlation between the trajectory points recorded by the automatic measurement and tracking device and the trajectory recorded by the GNSS signal generator, the time delay between the GNSS signal generator and the automatic measurement and tracking device is calculated, and the time delay is compensated in the UAV flight control module.

[0016] In the above scheme, when the update frequency of the automatic measurement and tracking device or GNSS signal generator is lower than the preset frequency range, the trajectory update is discontinuous. The GNSS signal generator uses a carrier phase smoothing algorithm to interpolate the trajectory, which can alleviate the cycle slip problem caused by the unsmooth trajectory injection. The smoothed GNSS signal trajectory is closer to the actual flight path, reducing RTK differential calculation failures caused by trajectory jumps. This is particularly suitable for professional-grade UAV testing that is strictly dependent on fixed RTK solutions. Secondly, by using the correlation analysis between the real trajectory recorded by the automatic measurement and tracking device and the simulated trajectory recorded by the GNSS signal generator, the time delay between the two systems can be accurately calculated, realizing the quantitative evaluation of system time delay and flight control compensation, optimizing the flight control system's adaptability to delay, and providing a precise data foundation for conducting deception signal response tests, wind resistance control tests, etc.

[0017] In some embodiments, the GNSS signal generator includes an RTK base station simulation module, the parameter settings of which are matched with the RTK module of the UAV under test; Correspondingly, the simulated navigation signal is an RTCM differential signal, which is sent by the GNSS signal generator to a server supporting the NTRIP protocol, and then broadcast by the server to the UAV under test.

[0018] In the above technical solution, the RTK base station simulation module can simulate a virtual RTK reference station in an indoor environment to provide differential correction data to the UAV flight control system, supporting RTK fixed positioning and achieving centimeter-level high-precision positioning. Simultaneously, it avoids reliance on external real RTK base stations, enhancing the independence and controllability of the test system. The parameters of the RTK base station simulation module (such as base station coordinates, RTCM version, baud rate, etc.) can be flexibly configured according to the needs of different UAV models, ensuring that the UAV can correctly parse differential data and improving the versatility of the test system. RTCM differential data is transmitted using the standard NTRIP protocol, and the differential data is broadcast via Wi-Fi network, eliminating the need for additional radio equipment. This allows multiple UAVs to simultaneously access the same NTRIP source, facilitating concurrent multi-UAV testing and log recording.

[0019] In some embodiments, the GNSS signal generator further includes: a GNSS spoofing signal generator and an interference signal generator. The GNSS spoofing signal generator and the GNSS jamming signal generator are used to generate GNSS spoofing signals and GNSS jamming signals, respectively, to rate the satellite navigation jamming spoofing capability of the UAV under test.

[0020] In the aforementioned technical solution, the GNSS spoofing signal generator can simulate false GNSS satellite signals, constructing deception scenarios such as "false coordinates," "no-fly zone guidance," and "reverse guidance" to test whether the UAV can identify and respond to abnormal signals. The GNSS interference signal generator can simulate high-power noise interference signals or interfere with specific frequency bands to test the UAV's behavior under signal loss or severe interference conditions, verifying mechanisms such as RTK interruption recovery, positioning mode switching, and emergency landing. Thus, it supports the verification of UAV spoofing detection mechanisms and the evaluation of anti-interference capabilities, achieving comprehensive testing of UAV security protection capabilities.

[0021] In some embodiments, initializing the GNSS signal generator further includes setting the GNSS signal generator to a test scenario where only BeiDou signals exist or BeiDou signals are prioritized.

[0022] Among these, "BeiDou signal priority" means that in the GNSS signals simulated by the GNSS signal generator, BeiDou satellite signals are given priority for use by the flight control system; "BeiDou signal only" means that the GNSS signal generator only simulates BeiDou satellite signals and does not simulate signals from other systems such as GPS and GLONASS. Testing UAVs under either the single BeiDou or BeiDou priority conditions allows for targeted GNSS reception strategies for BeiDou signals, meeting the testing requirements for BeiDou priority and expanding the influence of BeiDou satellites.

[0023] This application provides a laboratory testing apparatus for unmanned aerial vehicles (UAVs), which is used to perform the method provided in any of the above embodiments, including: The coordinate transformation module is configured to determine a global reference point based on the location of the laboratory, and then determine the transformation relationship between the local northeast high coordinate system and the global coordinate system, as well as multiple fixed reference points; The real-time coordinate acquisition module is configured to sequentially set up and start the automatic measurement and tracking instrument in the laboratory based on the fixed reference point, and then use the automatic measurement and tracking instrument to acquire the real-time coordinates of the UAV under test. The analog signal generation module is configured as a GNSS signal generator to generate analog navigation signals and record analog trajectories based on the real-time coordinates of the UAV under test. The testing and recording module is configured to allow the UAV under test to conduct indoor flight tests based on the simulated navigation signals, and to record its own flight trajectory during the test through the flight control system of the UAV under test. The output module is configured to use the simulated trajectory recorded by the GNSS signal generator as the true trajectory, compare the true trajectory with the flight trajectory recorded by the flight control system of the UAV under test, and output the laboratory test results.

[0024] The technical solution provided in this application can construct a high-precision, low-latency, and highly synchronous GNSS signal simulation system in an indoor environment, thereby achieving a comprehensive performance evaluation of the UAV flight control system. This solution has the following beneficial effects: It acquires the real-time three-dimensional coordinates of the UAV through an automatic measurement and tracking device, without relying on hardware modifications to the UAV, significantly reducing implementation difficulty and saving costs. Furthermore, since the automatic measurement and tracking device can track and measure the UAV's position coordinates in real time, there is no complex processing delay associated with SLAM mapping, enabling the GNSS signal generator to generate stable, low-latency simulated navigation signals. This facilitates controlled testing of medium and large UAVs in indoor environments, promotes the standardization and streamlining of the UAV testing industry, and ultimately improves the efficiency of airworthiness reviews and advanced technology research for UAVs and even the low-altitude economy. Attached Figure Description

[0025] Figure 1 This is a technical logic block diagram of a drone laboratory testing method provided according to some embodiments of this application.

[0026] Figure 2 This is a flowchart illustrating a laboratory testing method for unmanned aerial vehicles (UAVs) provided according to some embodiments of this application.

[0027] Figure 3 The real-time coordinates of the UAV under test, expressed in local northeastern elevation coordinates, are obtained by an automatic measurement and tracking instrument.

[0028] Figure 4 A flowchart illustrating the execution of a carrier phase smoothing algorithm by a GNSS signal generator provided in some embodiments. Detailed Implementation

[0029] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0030] 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 separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0031] "Multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0032] The embodiments of this application will now be described with reference to the accompanying drawings.

[0033] To facilitate understanding of the technical solution of this application, the relevant terms are explained below.

[0034] LiDAR positioning system: LiDAR obtains the three-dimensional coordinates (X,Y,Z) of a target object by emitting laser pulses and measuring the time of reflection (ToF) or phase difference (triangulation), forming point cloud data.

[0035] Simultaneous Localization and Mapping (SLAM) technology: using data acquired by a lidar positioning system to build an environmental map in real time and estimate the position of the moving vehicle itself.

[0036] Visual-Inertial Odometry (VIO) is a technology that fuses data from visual sensors (such as cameras) with data from inertial measurement units (IMUs) to estimate the position, attitude, and velocity of a moving vehicle in real time.

[0037] Satellite navigation jamming technology (hereinafter referred to as jamming technology) involves transmitting high-power satellite navigation jamming signals to a drone, interfering with the drone's navigation system, making it unable to navigate and locate itself correctly, and thus preventing the drone from flying normally along its navigation route.

[0038] Satellite navigation spoofing technology (also known as deception or trickery): This technique involves continuously transmitting false satellite navigation signals (such as fake GPS coordinates) from the ground to a target drone, causing the drone to receive incorrect navigation information and deviate from its intended flight path. If the false satellite navigation signals mislead the drone into believing it is in a no-fly zone, the drone will automatically make an emergency landing. If the false satellite navigation signals mislead the drone into believing it is flying in the opposite direction, the drone will be guided to fly in the opposite direction, thus changing its flight path.

[0039] As described in the background section, existing indoor drone testing solutions require hardware modifications, such as installing a LiDAR positioning system as an indoor / outdoor positioning device, combined with visual SLAM mapping technology for positioning. However, since the LiDAR positioning system itself lacks the ability to perceive drone attitude and acceleration information, this solution requires external sensors of different types, such as accelerometers and gyroscopes, to acquire drone attitude, acceleration, and other status information. These hardware modifications not only require consideration of the technicians' capabilities but also the availability of suitable installation locations for the added hardware, whether the added equipment will affect flight performance due to increased drone weight, and power supply issues for the new hardware (e.g., whether additional power is needed, whether the original drone system can support the new hardware, and battery life). Clearly, such modifications are costly and complex, hindering efficient indoor drone testing by testing organizations (such as third-party quality inspection agencies). More importantly, even when applying the modified solution to special aircraft types (such as medium-to-large drones) or special testing scenarios (such as deception and interference security tests), the following shortcomings still exist: Latency Issues: Medium and large UAVs are highly sensitive to GNSS latency. When converting indoor positioning solutions such as Visual-Inertial Odometry (VIO), LiDAR positioning system + SLAM, etc., into GNSS signals, there is a delay of hundreds of milliseconds. Moreover, this delay depends on the update frequency of the LiDAR system and the computing resources on which SLAM mapping depends, and is therefore uncertain (nondeterministic). This makes it very easy for UAVs to lose control in practical applications, making it impossible to conduct effective indoor testing.

[0040] In view of this, this application provides a laboratory testing method and apparatus for unmanned aerial vehicles (UAVs). This solution enables laboratory and testing institutions to conduct tests in various special scenarios without modifying the hardware of the UAVs, improving the applicability of indoor UAV testing solutions, reducing implementation complexity, saving testing costs, facilitating the process and standardization of UAV testing, and improving testing efficiency.

[0041] Example 1 This application provides a laboratory testing method for unmanned aerial vehicles (UAVs), such as... Figure 1 , Figure 2 As shown, the method includes the following steps: S1. Determine the global reference point based on the location of the laboratory, and then determine the transformation relationship between the local northeast high coordinate system and the global coordinate system, as well as multiple fixed reference points.

[0042] In step S1, before conducting indoor UAV testing, a global reference point is first established based on the specific location of the laboratory. This global reference point serves as the benchmark for establishing a global geographic coordinate system. Based on this global reference point, a local northeast-northeast elevation coordinate system (ENU coordinate system) is constructed, and the coordinate transformation relationship between this local coordinate system and the global geographic coordinate system is determined. The local northeast-northeast elevation coordinate system uses the laboratory environment as a reference, typically with East, North, and Up as the coordinate axis directions. Furthermore, multiple fixed reference points are arranged within the laboratory to assist the automatic measurement and tracking instrument in quickly completing station setup operations in different test cycles and to ensure the uniformity and consistency of the coordinate system during each test. Step S1 provides a unified spatial benchmark for subsequent UAV trajectory acquisition, GNSS signal simulation, and trajectory comparison, forming the foundation for constructing a closed-loop testing system.

[0043] S2. Based on a fixed reference point, the automatic measurement and tracking instrument is set up and started in the laboratory in sequence, and then the automatic measurement and tracking instrument is used to obtain the real-time coordinates of the UAV under test.

[0044] After establishing the coordinate system in step S1, step S2 involves sequentially setting up the automatic measurement and tracking instrument using multiple fixed reference points arranged within the laboratory. The automatic measurement and tracking instrument is a measuring device with high-precision ranging and angle measurement functions, including but not limited to a total station or laser tracker. Using the established fixed reference points, the automatic measurement and tracking instrument's positioning calibration and coordinate system initialization are completed, ensuring that the data it collects is consistent with the local northeast-high coordinate system and global coordinate system established in S1. After setting up the instrument, the automatic measurement and tracking instrument is activated and locked onto a reflective object, such as a prism or laser reflector sphere, mounted on the UAV under test. The automatic measurement and tracking instrument continuously tracks the spatial position of the reflective object and outputs real-time three-dimensional coordinate data of the UAV during flight. This step achieves high-precision dynamic acquisition of the UAV's flight trajectory, providing a reliable position input basis for the subsequent GNSS signal generator to simulate navigation signals.

[0045] The S3 GNSS signal generator generates simulated navigation signals and records simulated trajectories based on the real-time coordinates of the UAV under test.

[0046] In this embodiment, after the automatic measurement and tracking device acquires the real-time three-dimensional coordinates of the UAV under test, in step S3, the coordinate data is input to the GNSS signal generator. Based on the received coordinate information, the GNSS signal generator generates a GNSS simulated navigation signal conforming to a standard format. The simulated navigation signal includes, but is not limited to, BeiDou B1I and GPS L1C / A signals, and can be set to a mode with only BeiDou signals or with BeiDou signals prioritized, depending on the test requirements. Simultaneously, the GNSS signal generator records the flight trajectory corresponding to the generated simulated navigation signal, forming a simulated trajectory. This simulated trajectory serves as the true trajectory in subsequent test result analysis, used for comparison and analysis with the actual flight trajectory recorded by the UAV flight control system. Step S3 achieves dynamic generation of GNSS signals based on the actual motion state of the UAV, providing a traceable data foundation for flight control performance evaluation.

[0047] S4. The UAV under test conducts indoor flight tests based on simulated navigation signals, and records its own flight trajectory during the test through the flight control system of the UAV under test.

[0048] In this embodiment, after the GNSS signal generator generates and continuously outputs a simulated navigation signal, the UAV under test receives the signal and executes an indoor flight test task based on the navigation logic of its flight control system. The flight test task includes, but is not limited to, verification of basic flight control and navigation performance such as hovering, path flight, wind resistance response, and environmental adaptability flight. During the flight test, the UAV's flight control system analyzes the received simulated navigation signal in real time and controls the UAV's flight state accordingly. Simultaneously, the flight control system records its actual flight trajectory during the test, forming trajectory data that can be used for subsequent analysis. This step achieves a complete testing process for the flight control and navigation performance of a UAV based on simulated GNSS signals in an indoor environment without real GNSS signal coverage.

[0049] S5. Using the simulated trajectory recorded by the GNSS signal generator as the true trajectory, compare the true trajectory with the flight trajectory recorded by the flight control system of the UAV under test to output the laboratory test results.

[0050] In step S5, after completing the indoor flight test, two key trajectory data points are acquired: First, the simulated trajectory recorded by the GNSS signal generator is used as the true trajectory in this test process; Second, the actual flight trajectory recorded by the flight control system of the drone under test.

[0051] The two sets of trajectory data were aligned based on timestamps and compared and analyzed in the same coordinate system to evaluate the flight control performance of the UAV after receiving simulated navigation signals. Based on indicators such as trajectory deviation, response delay, and path-keeping capability, corresponding laboratory test results were output to evaluate the positioning accuracy, RTK fixed-solution performance, environmental adaptability, and response behavior to spoofing / jamming signals of the UAV flight control system.

[0052] As a further improvement to the above scheme, in some embodiments, the automatic measurement tracker and the GNSS signal generator use a first electrical signal as a unified trigger source to start synchronously; wherein, the first signal is a high-level signal issued by the real-time data processing module, and the first signal is synchronously transmitted to the automatic measurement tracker and the GNSS signal generator through a coaxial cable.

[0053] As a further improvement to the above scheme, in some embodiments, before the GNSS signal generator generates simulated navigation signals based on the real-time coordinates of the UAV under test, the method further includes: initializing the GNSS signal generator configuration, as follows: The GNSS signal generator was synchronized with the time and ephemeris using real-world GNSS signals. The time of the test scenario was set, the signal types were set to BeiDou and GPS signals, and the vehicle trajectory was set to external trajectory input mode.

[0054] Among them, real-world GNSS signals refer to Global Navigation Satellite System (GNSS) signals directly received from satellite systems in a real environment, as opposed to simulated environments or analog signals.

[0055] As a further improvement to the above scheme, in some embodiments, the S3 GNSS signal generator generates simulated navigation signals and records simulated trajectories based on the real-time coordinates of the UAV under test, specifically: The real-time data processing module receives the real-time coordinates of the UAV under test and performs coordinate transformation according to the transformation relationship between the local northeast high coordinate system and the global coordinate system to obtain the transformed real-time coordinates. The GNSS signal generator generates simulated navigation signals and records simulated trajectories based on the converted real-time coordinates.

[0056] As a further improvement to the above scheme, in some embodiments, in S4, before the UAV under test conducts indoor flight testing based on simulated navigation signals, the method further includes: The GNSS signal generator adjusts the power of the analog navigation signal output by the radiating antenna based on a preset signal strength threshold (such as -130dBm) and a free space path loss model.

[0057] As a further improvement to the above scheme, in some embodiments, in S4, before the UAV under test conducts indoor flight testing based on simulated navigation signals, the method further includes: The update frequency of the GNSS signal generator or automatic measurement and tracking instrument is compared with the preset frequency range. If the update frequency of the GNSS signal generator or automatic measurement and tracking instrument is lower than the preset frequency range (e.g., 10~100Hz), the carrier phase smoothing algorithm is used to smooth the analog navigation signal. Based on the correlation between the trajectory points recorded by the automatic measurement and tracking device and the trajectory recorded by the GNSS signal generator, the time delay between the GNSS signal generator and the automatic measurement and tracking device is calculated, and the time delay is compensated in the UAV flight control module.

[0058] As a further improvement to the above scheme, in some embodiments, the GNSS signal generator includes an RTT base station simulation module, the parameter settings of which match the RTK module of the UAV under test; correspondingly, the simulated navigation signal is an RTCM differential signal, which is sent by the GNSS signal generator to a server that supports the NTRIP protocol, and then the server sends it to the UAV under test via broadcast.

[0059] Traditional technologies are insufficiently applicable to security testing scenarios (such as deception and interference tests): existing technologies can typically only simulate real GNSS signals. However, signal interference and deception against drones are becoming increasingly common, and the difficulty and cost of implementing such actions are decreasing significantly. Drone countermeasures have become a common law enforcement action against illegal drone activities. A key technical means of drone countermeasures is the interference and deception of satellite navigation signals. The implementation of interference and deception often causes drones to lose control, easily leading to property damage and even endangering public safety. Currently, tests targeting interference and deception are divided into two types: module-level testing or whole-machine outdoor testing. Module-level testing is characterized by only the GNSS module being in the loop, making it impossible to assess the actual performance of the entire drone. Whole-machine testing involves strict radio management and control, resulting in low controllability, non-repeatability, and high implementation costs. Therefore, developing an indoor drone evaluation scheme for such scenarios is beneficial for conducting such evaluations and for research on low-altitude flight and countermeasure technologies. Some drones' GNSS modules use the network to obtain ephemeris or almanacs to speed up positioning, and there is logic to determine whether the current GNSS signal is a spoofing signal. From the perspective of the universality of the test scheme, the generated indoor GNSS must be synchronized with the real network GNSS time.

[0060] Therefore, as a further improvement to the above scheme, in some embodiments, the GNSS signal generator further includes: a GNSS spoofing signal generator and a GNSS interference signal generator, which are used to generate GNSS spoofing signals and GNSS interference signals, respectively, to rate the satellite navigation interference spoofing capability of the UAV under test.

[0061] As a further improvement to the above scheme, some embodiments include initializing the GNSS signal generator by setting the GNSS signal generator to a test scenario where only BeiDou signals exist or BeiDou signals are prioritized.

[0062] Example 2 This application provides a drone laboratory testing apparatus for performing any of the drone laboratory testing methods provided in the above embodiments, including: The coordinate transformation module is configured to determine a global reference point based on the location of the laboratory, and then determine the transformation relationship between the local northeast high coordinate system and the global coordinate system, as well as multiple fixed reference points; The real-time coordinate acquisition module is configured to sequentially set up and start the automatic measurement and tracking instrument in the laboratory based on a fixed reference point, and then use the automatic measurement and tracking instrument to acquire the real-time coordinates of the UAV under test. The analog signal generation module is configured as a GNSS signal generator to generate analog navigation signals and record analog trajectories based on the real-time coordinates of the UAV under test. The test and recording module is configured to enable the UAV under test to conduct indoor flight tests based on simulated navigation signals, and to record its own flight trajectory during the test through the flight control system of the UAV under test. The output module is configured to use the simulated trajectory recorded by the GNSS signal generator as the true trajectory, compare the true trajectory with the flight trajectory recorded by the flight control system of the UAV under test, and output the laboratory test results.

[0063] The UAV laboratory testing device provided in this embodiment can execute the process and steps of the UAV laboratory testing method provided in any of the above embodiments and achieve the same technical effect, which will not be described in detail here.

[0064] The above technical solution will be described in detail below with specific examples.

[0065] As an example, a drone laboratory testing facility may include the following equipment / modules: 1. Automatic tracking measuring instrument: The automatic measuring tracking instrument can be a total station with prism locking tracking function or a laser tracker. Preferably, the automatic measuring tracking instrument is a laser tracker.

[0066] 2. Tracking measuring instrument reflector (can be a prism used in a total station, or a reflector ball / target of a laser tracker and its fixing fixture to the UAV) 3. The transmission cable and driver from the automatic tracking measuring instrument to the computer. 4. GNSS signal generator 5. GNSS signal radiating antenna and its coaxial cable 6. Data software interface of automatic tracking measuring instrument and GNSS signal generator and the computer on which it is installed. 7. GNSS signal generator signal smoothing algorithm and signal power adjustment algorithm 8. Network cable 9. Test drone 10. TNC interface coaxial cable 11. Low-noise amplifier 12. Router with Wi-Fi capability 13. GNSS spoofing signal generator and jamming signal generator 14. Real-world GNSS signal time and ephemeris synchronization device 15. Synchronous triggering device for automatic tracking measuring instrument and GNSS signal generator The GNSS signal generator can be any commercially available device, as long as it possesses the following capabilities: support for publicly available GNSS ICD signals, support for 1000Hz real-time trajectory data input via Ethernet interface, and support for simultaneous simulation of satellite navigation signals, interference signals, and spoofing signals. As an example, the Spirent GSS7000 GNSS signal generator could be specifically chosen; however, other models of GNSS signal generators can also be used. This application does not impose any restrictions on the specific selection of the GNSS signal generator.

[0067] As an example, a drone laboratory testing method can be performed as follows: 1. Constructing the transformation relationship between the local northeast-northeast coordinate system and the global coordinate system: Accurately measure the latitude, longitude, and geodetic height of a point near the laboratory as the reference point for the global coordinate system, corresponding to the origin of the local northeast-northeast coordinate system. Calculate the relationship between latitude / longitude increments and distance increments using the distances at corresponding latitudes to obtain the transformation relationship between the local coordinate system's planar coordinates and the global coordinate system's latitude / longitude. Because the spatial scope is extremely small, the global coordinate system elevation and geodetic height can be set to be equal. Establish three to four fixed reference points evenly distributed within the laboratory for later station establishment. The mathematical relationship between the two coordinate systems is determined as follows: a= 6378137.0 # semi-major axis f = 1 / 298.257223563 # flattening b = a * (1 - f) lat_rad = math.radians(ref_lat) lon_rad = math.radians(ref_lon) sin_lat = math.sin(lat_rad) cos_lat = math.cos(lat_rad) sin_lon = math.sin(lon_rad) cos_lon = math.cos(lon_rad) t = cos_lat * up - sin_lat * north z = sin_lat * up + cos_lat * north x = cos_lon * t - sin_lon * east y = sin_lon * t + cos_lon * east d_lat = x / (a ​​* math.pi / 180) d_lon = y / (a ​​* math.pi / 180 / math.cos(lat_rad)) d_h = z lat = ref_lat + d_lat lon = ref_lon + d_lon h = ref_h + d_h 2. Set up the automatic measurement and tracking system (if the position of the automatic measurement and tracking system changes, set up the system using a pre-established fixed reference point) and determine the local northeast elevation coordinate system. For example, the local northeast elevation coordinates of the UAV measured by the laser tracker are as follows: Figure 3 As shown in the figure, the laser tracker can track the drone and continuously output the trajectory coordinates of the drone under test. ).

[0068] 3. Turn on the GNSS signal generator, set the scene time through the real network GNSS signal time and ephemeris synchronization device, set the vehicle trajectory to real-time external trajectory input mode, the signal type is preferably set to 8 Beidou B1I + 8 GPS L1 C / A, and the update iteration rate is preferably 1000Hz.

[0069] 4. GNSS signals are emitted from the high-power port via a coaxial cable and a radiating antenna; a low-noise amplifier can be added if necessary. Set up an RTK base station simulation (base station coordinates, RTCM message type, transmission rate, etc., must match the settings required by the UAV's RTK module).

[0070] 5. Enable the real-time coordinate data transmission function of the automatic measurement tracker. Preferably, the real-time transmission frequency is set to 10Hz~1000Hz. When the automatic measurement tracker uses a laser tracker, the maximum frequency can be set to 1000Hz.

[0071] 6. Secure the reflector to a suitable position on the drone using a clamp, ensuring that the reflector and the automatic measurement and tracking device are not obstructed.

[0072] 7. Enable the automatic tracking function of the automatic measurement and tracking instrument to lock onto the reflective object; synchronously trigger the automatic tracking and measuring instrument (to start tracking, measuring, recording, and transmitting) and the GNSS signal generator (to start scene operation and record trajectory files) through a high-level signal via a coaxial cable to ensure nanosecond-level time synchronization of their start times.

[0073] 8. After the coordinates of the reflector are converted in real time according to the transformation relationship determined in step 1 by the computer and its running automatic tracking measurement instrument and GNSS signal generator data software, the data is transmitted to the GNSS signal generator in real time through the trajectory definition data format. The GNSS signal generator generates the GNSS signal corresponding to the location, and then emits it through the radiating antenna. This involves two signal algorithms: ① Signal power adjustment, so that the indoor GNSS signal reaching the UAV is always -130dBm (to prevent the GNSS module from having signal strength judgment logic, which would cause abnormal signal strength changes in the indoor GNSS signal generator to cause it to malfunction); ② If the update rate of the simulator or automatic tracking measurement instrument is low (10~100Hz), a carrier phase smoothing algorithm for the GNSS radio frequency signal is involved to alleviate the phenomenon of frequent cycle slips caused by the unsmooth real-time injection of the GNSS signal, so that the UAV cannot enter the RTK fixed solution or frequently exits the RTK fixed solution. The specific implementations of the two algorithms are as follows: ① Signal power adjustment Based on the Free Space Path Loss (FSPL) model, after calculating the actual position, the -130 dBm distance difference is calculated by back-calculating the distance. The GNSS signal power is then adjusted accordingly, and its logarithmic form is: , in: It is distance The received signal power (dBm); Reference distance The received power at that location is given as -130dBm; It is the path loss factor (2 in free space, and generally 2~4 indoors). It is the distance from the current coordinate to the origin; This is a reference distance, usually 1 meter.

[0074] ② Carrier phase smoothing module, processing flow is as follows Figure 4 As shown. Includes: (1) Predicted location step: Input: Current time and history Position and velocity at any given moment; Predict using dynamic models The position at that moment; Output: Predicted trajectory points (position, velocity, acceleration); (2) Trajectory smoothing , in It is a B-spline basis function, which determines the position of each control point at different times. The weights below, For the first One control point, It's time The position of the lower trajectory point.

[0075] The smoothness of the trajectory is controlled by continuity constraints: , in: and These are the maximum allowable values ​​for acceleration and jerk, respectively.

[0076] (3) Cycle slip detection and mitigation module Establish a jump scheduling queue that only allows a maximum of [number] jumps per cycle. satellites (such as) =1 or 2) cycle jumps; If the system detects that the predicted trajectory may cause cycle slips, it will use one or more of the following strategies to mitigate the problem: Prioritize keeping satellites with high signal-to-noise ratios or good viewing angles from changing direction; Stagger the jump timing by several update cycles (e.g., 1 millisecond); A trajectory disturbance buffer is introduced, and the GNSS signal generator internally performs buffering processing on higher-order changes.

[0077] 9. Configure the computer and GNSS signal generator to send RTK data from the GNSS signal generator to a computer that has deployed NTRIP Caster (Networked Transport of RTCM via Internet Port, a protocol for RTK data transmission over the Internet), and broadcast it via Wi-Fi.

[0078] 10. Because this solution adds an indoor GNSS signal generation process, it introduces a certain time delay compared to the actual GNSS signal. This increased delay is related to the performance, update rate of the GNSS signal generator, and update rate of the automatic measuring instrument, and may range from a few milliseconds to hundreds of milliseconds. For wind resistance testing, this parameter needs to be precisely adjusted so that the test results can truly reflect the actual performance of the drone in outdoor conditions. The following uses flight control parameter settings and wind speed calculations to demonstrate the impact of the time delay, proving that the time delay caused by the indoor GNSS signal generation process must be corrected before indoor wind resistance testing can be conducted: GNSS updates at a frequency of 5-10Hz in drones. The drone flight controller is configured to account for latency in the GNSS module due to update rate and data transmission link issues. If a new latency is introduced through testing, the information used by the flight controller becomes "outdated." ① Misjudgment of location and control lag: The flight controller assumes the drone is in its old position, when in fact it has drifted a considerable distance. Especially in windy conditions, the flight controller's corrective actions are delayed, leading to continued drift.

[0079] ② The aircraft exhibits noticeable hovering and drifting: During hovering, the system should quickly correct its position, but due to a delay, it responds outdatedly, much like "applying the brakes too late." This can manifest as "unstable flight" or "drifting and not returning to its original position."

[0080] ③ Oscillation or overcorrection: The flight controller may overcompensate for position errors (due to sluggishness), resulting in "back-and-forth swaying" or "shaking".

[0081] ④ Affects flight control filters (such as EKF / Fusion): Delayed data can cause state estimation drift or fusion failure. This is especially fatal for flight controllers that rely on EKF (Extended Kalman Filter) systems (such as PX4 and Ardupilot).

[0082] For medium to large-sized drones, RTK positioning is often used to perform hovering maneuvers. Without correction for the introduced time delay, the drone will continuously drift downwind. This is because the flight control system receives the position information from before the delay. In reality, the drone has already been blown away from its original position, but the flight control is unaware of this. Based on outdated coordinates, the flight control mistakenly believes it is still in place or has just begun to drift. The corrective actions given by the flight control are always delayed, insufficient, and not timely enough. As a result, the drone cannot resist the wind in time, causing it to drift slowly and continuously in the direction of the wind. Because the flight control's "perception" and "compensation" actions are both late, the drift becomes increasingly larger, and the flight control "can never catch up with the actual drift." If the wind is stable, the drift speed will gradually approach a certain equilibrium value (wind thrust = motor thrust). If the wind changes significantly, the drone will drift slowly in the air with very poor positional stability. In more serious cases, if the wind speed exceeds the flight control's ability to correct lag, such as when a gust of wind comes, the drone may even be blown away while hovering. The flight control may not be able to pull it back in time, and in extreme cases, it may trigger position loss protection, causing the drone to return to home or make an emergency landing.

[0083] The manufacturer needs to assist in adjusting the delay parameters (flight control parameters, common parameters are GPS_DELAY_MS or EKF2_GPS_DELAY). For specific delay value calculation, please refer to step 11.

[0084] 11. By calculating the correlation between the trajectory points recorded by the automatic measurement and tracking instrument and the trajectory points recorded by the GNSS signal generator, it is determined that they should belong to the same set of trajectories. However, due to time delays caused by data transmission, a cross-correlation function is used, combined with the sampling rate, to calculate the time delay between the two, as follows: set up: The trajectory points recorded by the automatic measurement and tracking instrument; : Track points of the GNSS signal generator; The cross-correlation function is defined as: , in: Lag; ,mean Move to the right (delay); The position where the maximum value occurs , indicating the most likely delay.

[0085] Furthermore, to remove the influence of amplitude magnitude, normalized cross-correlation is calculated: , In the formula, and are the trajectory points recorded by the automatic measurement and tracking instrument and the trajectory points of the GNSS signal generator, respectively. Through normalization processing, the results are always between [-1,1], which better reflects the degree of shape matching.

[0086] 12. Power on the drone and set it to connect to the NTRIP Caster via a wireless communication link using RTK data, enabling the drone to enter RTK fixed-solution mode. Maneuver the drone to a suitable hovering altitude. Depending on the test content, such as testing wind resistance, considering that airflow disturbances can significantly reduce the stability of air pressure measurements, it is preferable to adjust the altitude source priority of the flight controller during testing, with RTK being the preferred first priority.

[0087] 13. After completing the above steps, environmentally controlled tests can be conducted in the laboratory, such as hovering endurance tests, hovering wind resistance tests combined with wind wall tests, hovering performance tests under different temperature and humidity conditions, and drone last-mile delivery tests. The laboratory can further test obstacle avoidance and flight path planning capabilities, as well as GNSS multipath effect tolerance tests in urban canyons. The experimental methods are the same as the original field tests, and the true trajectory can be obtained through a GNSS signal generator. By comparing the trajectory coordinates recorded by the drone, typically GPS NMEA messages, with the true trajectory, indicators such as positioning accuracy can be obtained.

[0088] 14. For testing on UAV satellite navigation signal interference and deception, it is recommended to upgrade the indoor test site to a microwave anechoic chamber, construct UAV interference signal suppression, electronic fence area, and agile interference (high-power signal interference followed by activation of deception signal with slowly increasing power, and real network synchronous signal deception) scenarios, observe the UAV's satellite navigation anti-interference and deception capability rating in flight state, and use the smooth landing of the UAV as the standard under normal UAV countermeasure state.

[0089] 15. Similarly, in application scenarios such as single BeiDou and BeiDou priority, the GNSS signal generation can be set to only have BeiDou signals or the corresponding BeiDou priority scenario can be configured (by configuring satellite clock differences or pseudorange steps of GPS, GLONASS, and GALILEO satellite systems that are different from BeiDou systems). The tests mentioned in 13 and 14 above can be performed to test the actual dynamic performance of the UAV.

[0090] In summary, the technical solution provided in this embodiment fills the gap in laboratory and testing institutions' ability to conduct the following tests on drones without modification: hovering endurance testing, hovering wind resistance testing combined with wind walls, hovering performance testing under different temperature and humidity environments, drone last-mile delivery testing, verification under electronic fence flight conditions in any area, and further testing of obstacle avoidance and flight path planning capabilities in the laboratory, GNSS multipath effect tolerance testing in urban canyons, and testing of the impact of harmful factors such as satellite navigation signal interference and deception on drones under operational conditions; it can also construct environments that cannot be constructed in a real network, such as single BeiDou and BeiDou priority, to test the performance of drones in flight conditions.

[0091] This allows for controlled and repeatable testing in an indoor environment. No hardware modifications are required for the drone, and all sensors can be tested in the loop while the product is in its delivered state. It is easy to operate, can be automated, and makes testing more quantifiable, thus promoting the standardization and streamlining of the drone testing industry. This, in turn, improves the efficiency of airworthiness reviews and advanced technology research for drones and even the low-altitude economy.

[0092] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A laboratory testing method for unmanned aerial vehicles (UAVs), characterized in that, include: S1. Determine the global reference point based on the location of the laboratory, and then determine the transformation relationship between the local northeast high coordinate system and the global coordinate system, as well as multiple fixed reference points; S2. Based on the fixed reference point, the automatic measurement and tracking instrument is set up and started in the laboratory in sequence, and then the automatic measurement and tracking instrument is used to obtain the real-time coordinates of the UAV under test. S3, the GNSS signal generator generates simulated navigation signals and records simulated trajectories based on the real-time coordinates of the UAV under test; S4. The UAV under test conducts indoor flight tests based on the simulated navigation signals, and records its own flight trajectory during the test through the flight control system of the UAV under test. S5. Using the simulated trajectory recorded by the GNSS signal generator as the true trajectory, compare the true trajectory with the flight trajectory recorded by the flight control system of the UAV under test to output the laboratory test results.

2. The method according to claim 1, characterized in that, The automatic measurement and tracking instrument and the GNSS signal generator use a first electrical signal as a unified trigger source to start synchronously. The first signal is a high-level signal emitted by the real-time data processing module, and the first signal is synchronously transmitted to the automatic measurement and tracking instrument and the GNSS signal generator via a coaxial cable.

3. The method according to claim 1, characterized in that, Before the GNSS signal generator generates simulated navigation signals based on the real-time coordinates of the UAV under test, the method further includes: initializing the GNSS signal generator, as follows: The GNSS signal generator is synchronized with the time and ephemeris using real-world GNSS signals. The time of the test scenario is set, the signal types are set to BeiDou and GPS signals, and the vehicle trajectory is set to external trajectory input mode.

4. The method according to claim 1, characterized in that, Before the GNSS signal generator generates simulated navigation signals based on the real-time coordinates of the UAV under test and records the simulated trajectory, the process also includes: The real-time data processing module receives the real-time coordinates of the UAV under test and performs coordinate transformation according to the transformation relationship between the local northeast elevation coordinate system and the global coordinate system to obtain the transformed real-time coordinates. The GNSS signal generator generates a simulated navigation signal and records a simulated trajectory based on the real-time coordinates of the UAV under test. Specifically, the GNSS signal generator generates a simulated navigation signal and records a simulated trajectory based on the converted real-time coordinates.

5. The method according to claim 1, characterized in that, In S4, before the UAV under test conducts an indoor flight test based on the simulated navigation signal, the method further includes: The GNSS signal generator adjusts the power of the analog navigation signal output by the radiating antenna based on a preset signal strength threshold and a free space path loss model.

6. The method according to claim 1, characterized in that, In S4, before the UAV under test conducts an indoor flight test based on the simulated navigation signal, the method further includes: The update frequency of the GNSS signal generator or the automatic measurement and tracking instrument is compared with a preset frequency range. If the update frequency of the GNSS signal generator or the automatic measurement and tracking instrument is lower than the preset frequency range, a carrier phase smoothing algorithm is used to smooth the analog navigation signal. Based on the correlation between the trajectory points recorded by the automatic measurement and tracking device and the trajectory recorded by the GNSS signal generator, the time delay between the GNSS signal generator and the automatic measurement and tracking device is calculated, and the time delay is compensated in the UAV flight control module.

7. The method according to claim 1, characterized in that, The GNSS signal generator also includes an RTK base station simulation module, the parameter settings of which are matched with the RTK module of the UAV under test. Correspondingly, the simulated navigation signal is an RTCM differential signal, which is sent by the GNSS signal generator to a server supporting the NTRIP protocol, and then broadcast by the server to the UAV under test.

8. The method according to claim 7, characterized in that, The GNSS signal generator also includes: a GNSS spoofing signal generator and an interference signal generator. The GNSS spoofing signal generator and the GNSS jamming signal generator are used to generate GNSS spoofing signals and GNSS jamming signals, respectively, to rate the satellite navigation jamming spoofing capability of the UAV under test.

9. The method according to claim 3, characterized in that, The initial configuration of the GNSS signal generator also includes setting the GNSS signal generator to a test scenario where only BeiDou signals exist or BeiDou signals are prioritized.

10. A laboratory testing apparatus for unmanned aerial vehicles (UAVs), the apparatus being used to perform the method according to any one of claims 1 to 9, comprising: The coordinate transformation module is configured to determine a global reference point based on the location of the laboratory, and then determine the transformation relationship between the local northeast high coordinate system and the global coordinate system, as well as multiple fixed reference points; The real-time coordinate acquisition module is configured to sequentially set up and start the automatic measurement and tracking instrument in the laboratory based on the fixed reference point, and then use the automatic measurement and tracking instrument to acquire the real-time coordinates of the UAV under test. The analog signal generation module is configured as a GNSS signal generator to generate analog navigation signals and record analog trajectories based on the real-time coordinates of the UAV under test. The testing and recording module is configured to allow the UAV under test to conduct indoor flight tests based on the simulated navigation signals, and to record its own flight trajectory during the test through the flight control system of the UAV under test. The output module is configured to use the simulated trajectory recorded by the GNSS signal generator as the true trajectory, compare the true trajectory with the flight trajectory recorded by the flight control system of the UAV under test, and output the laboratory test results.

Images