An antenna measurement method based on an unmanned vehicle system

Abstract

The application discloses an antenna measurement method based on an unmanned vehicle system, which comprises the following steps: mounting an antenna measurement device on an unmanned vehicle platform, fusing positioning through a multi-source sensor, acquiring the spatial coordinates of the unmanned vehicle platform in real time, controlling the unmanned vehicle platform to reach each measurement point in turn according to the spatial coordinates, controlling the antenna measurement device to collect the measured antenna signal, filtering out the environmental interference signal through a distance gate, recording the spatial coordinates of each measurement point and the amplitude information of the measured antenna radiation signal, establishing the corresponding relationship between the spatial coordinates and the amplitude information, and generating the directional diagram and the spatial electromagnetic signal distribution diagram of the measured antenna of each measurement point. The method utilizes attitude adjustment, smoothness suppression and delay reading to improve the robustness of the unmanned vehicle platform operation, quickly enters a stable state, reduces the antenna measurement error caused by jitter, obtains stable antenna receiving signals, and improves the accuracy and test efficiency of antenna testing.

Description

Technical Field

[0001] This invention relates to the field of antenna measurement technology, and in particular to an antenna measurement method based on an unmanned vehicle system. Background Technology

[0002] Antenna measurement is a technical means to accurately measure electrical parameters such as antenna gain, radiation pattern, input impedance, and standing wave ratio. It is mainly used in wireless equipment systems to realize the spatial conversion of electromagnetic energy and the evaluation of radiation characteristics. According to electromagnetic theory, the antenna radiation space is usually divided into three regions: the induction near-field region, the radiation near-field region, and the radiation far-field region. From this, two major technical routes have been derived: far-field measurement and near-field measurement, which correspond to two application scenarios: indoor measurement and outdoor measurement, respectively.

[0003] Indoor antenna measurement systems include indoor far-field, compact field, and near-field measurement systems. They rely on an anechoic chamber to construct a controlled testing environment. By laying absorbing materials around the chamber to shield against external interference and absorb reflected signals, and integrating a high-precision turntable and guide rail, they can achieve antenna performance testing with millimeter-level or even higher precision. However, due to the physical size of the anechoic chamber, it is difficult to meet the measurement needs of large antennas or non-removable outdoor antennas.

[0004] Outdoor antenna measurement primarily employs far-field methods, mainly using two modes: manual setup and testing, and drone-borne testing. Manual setup suffers from drawbacks such as high operational risks, low efficiency, high costs, and cumbersome testing procedures. While drone solutions improve mobility, they are limited by payload weight, typically only carrying portable, low-precision instruments or using cable-driven power supplies. Furthermore, GPS / RTK positioning accuracy is only around ±10cm, and the test signal is highly susceptible to environmental and electromagnetic interference during flight, making it difficult to guarantee overall measurement accuracy. In recent years, with the development of autonomous driving and robotics technologies, unmanned vehicle systems with high-precision environmental perception, large payload capacity, and autonomous navigation capabilities have gradually become an important development direction for automated field testing platforms, providing a new technical approach for high-precision and rapid outdoor antenna measurement. Summary of the Invention

[0005] The purpose of this invention is to provide an antenna measurement method based on an unmanned vehicle system.

[0006] To achieve the above objectives, the present invention is implemented according to the following technical solution: This invention provides an antenna measurement method based on an unmanned vehicle system, comprising: The antenna measurement device is mounted on the unmanned vehicle platform, and the spatial coordinates of the unmanned vehicle platform are obtained in real time by fusion positioning through multi-source sensors integrated into the unmanned vehicle platform. The unmanned vehicle platform is planned according to the preset measurement points, and the unmanned vehicle platform is controlled to reach each measurement point in sequence according to the spatial coordinates. The stability of the antenna is determined based on the motion state parameters of the unmanned vehicle platform. If it enters a stable state, the antenna measurement device is controlled to collect the antenna signal under test and environmental interference signals are filtered out by a distance gate. Record the spatial coordinates of each measurement point and the amplitude information of the radiated signal of the antenna under test, and establish the correspondence between spatial coordinates and amplitude information; Based on the spatial coordinates and corresponding amplitude information, a radiation pattern and spatial electromagnetic signal distribution map of the antenna under test at each measurement point are generated.

[0007] Furthermore, the method for obtaining the spatial coordinates includes: Based on the three-dimensional spatial offset between the phase center of the antenna and the origin of the unmanned vehicle positioning coordinate system by the antenna measurement device, the unmanned vehicle platform integrates multi-source sensors including lidar, vision sensor, ultrasonic radar and inertial measurement unit; The multi-source sensors are synchronized in time, and environmental point cloud data is collected by lidar, environmental image data is collected based on visual sensors, obstacle ranging data is collected based on ultrasonic radar, and acceleration and angular velocity are output based on inertial measurement unit. The multi-source sensor data is denoised and timestamped to obtain a multi-source perception dataset. Feature extraction is performed based on the multi-source sensing dataset. The motion data output by the inertial measurement unit is used as the vehicle motion prior. Based on the vehicle motion prior, LiDAR point cloud features and visual image features, the environment features are matched through a simultaneous localization and mapping (SLAM) algorithm to solve the six-degree-of-freedom pose of the unmanned vehicle. A grid map of the test environment is constructed to obtain the pose information of the unmanned vehicle. The pose information is then transformed according to the spatial offset of the phase center to obtain the spatial coordinates corresponding to the antenna phase center.

[0008] Furthermore, the method for obtaining the travel trajectory includes: Based on preset measurement points, a sequence of trajectory path points is generated, and the unmanned vehicle platform's travel trajectory from its current location to each measurement point is generated using a path planning algorithm based on a grid map. The path planning algorithm includes the A* algorithm and the fast random tree algorithm. The desired linear velocity and desired angular velocity of the unmanned vehicle platform are calculated based on the travel trajectory using a trajectory tracking algorithm, which includes a pure tracking algorithm and a model predictive control algorithm. Based on the unmanned vehicle platform, the vehicle controller converts the desired linear velocity and desired angular velocity into motor drive signals, and outputs them to the drive motor and steering mechanism to control the movement of the unmanned vehicle platform; During the journey, the spatial coordinates and trajectory path point sequence are compared in real time, and feedback is provided to correct the deviation between the actual position of the unmanned vehicle platform and the trajectory path point sequence until the unmanned vehicle platform arrives at each measurement point in sequence and completes the docking.

[0009] Furthermore, the method for obtaining the amplitude information includes: The short-time vibration energy is calculated based on the acceleration and angular velocity output by the inertial measurement unit. The formula for calculating the short-time vibration energy is as follows: ; in For the first Short-term vibration energy at the next sampling time, The length of the sliding window. For the first Within the second sampling window IMU acceleration along the z-axis at each point; If the short-time vibration energy is continuously stable, the antenna measuring device is determined to have entered a stable state. Based on the amplitude information of the radiated signal of the antenna under test collected by the antenna measuring device, the distance gate width is determined according to the nominal distance between the unmanned vehicle platform and the antenna under test. The original sampled signal is time-domain gated to filter out interference noise that exceeds the time delay range corresponding to the nominal distance, and the original amplitude of each sample is obtained. The sampling weight is assigned to each sample based on the relative value of the short-time vibration energy at each sampling time. The calculation formula of the sampling weight is as follows: ; in For the first The sampling weight of the next sample. The attenuation coefficient has a range of values. ; The amplitude information is weighted and fused to obtain robust amplitude information of the target measurement point. The formula for calculating the robust amplitude information is as follows: ; in For the first The robust amplitude at each measurement point For the first The first point The original amplitude of the second sample, For the first The total number of samplings at each measurement point is determined based on the sampling rate and dwell time.

[0010] Furthermore, the method for obtaining the correspondence between the spatial coordinates and the amplitude information includes: Record the spatial coordinates of the target measurement point and the amplitude information received by the antenna measurement device. Correct the robust amplitude information based on the real-time radial distance in the spatial coordinates. The formula for calculating the corrected amplitude is as follows: ; in For the first Correction amplitude for each measurement point, For the antenna measuring device at the first Robust amplitude information at each point, The nominal measurement distance between the autonomous vehicle platform and the antenna under test. This represents the actual radial distance from the unmanned vehicle to the center of the antenna under test. The phase correction is calculated based on the deviation between the actual radial distance and the nominal measured distance. The formula for calculating the phase correction is as follows: ; in This is the phase correction amount. The wave number is obtained based on the operating frequency. The corrected complex received signal is calculated based on the distance amplitude correction value and the phase correction amount. The calculation formula for the corrected complex received signal is as follows: ; in To correct the received signal; The corrected amplitude, corrected re-received signal, and corrected azimuth are mapped according to the measurement point number to generate a sequence of azimuth amplitude data pairs, which serve as the correspondence between spatial coordinates and amplitude information.

[0011] Furthermore, the method for obtaining the corrected azimuth angle includes: The corrected azimuth angle is calculated based on the rotation angle output by the chassis encoder of the unmanned vehicle platform and the residual correction amount of the heading angle output by SLAM. The formula for calculating the corrected azimuth angle is as follows: ; in For the first Corrected azimuth angle of each measurement point This refers to the rotation angle output by the encoder of the autonomous vehicle chassis. This is the residual heading angle correction amount output by SLAM.

[0012] Furthermore, the method for obtaining the radiation pattern and the spatial electromagnetic signal distribution map includes: Based on the correspondence, the spatial coordinates and amplitude information of each measurement point are matched according to the measurement point number to generate a spatial electromagnetic signal distribution map of the antenna under test. The azimuth amplitude data of each measurement point are rearranged in ascending order of azimuth. The missing angles between adjacent sampling points are interpolated to obtain a continuous azimuth radiation pattern dataset. The azimuth radiation pattern dataset is normalized, and the angle is used as the abscissa and the normalized amplitude is used as the ordinate to plot the relationship curve between angle and amplitude, thus generating the radiation pattern of the antenna under test.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: This invention mounts an antenna measurement device on an unmanned vehicle platform and acquires spatial coordinates through multi-source sensor fusion positioning, avoiding the low efficiency of manual setup, the limited payload of UAVs, and insufficient positioning accuracy. In the signal acquisition stage, the stable state is determined based on the motion state parameters output by the inertial measurement unit. Dynamic delay acquisition improves the reliability of single-point measurement data and avoids the time consumption caused by fixed delay. Based on the pose information output in real time by the synchronous positioning and map building algorithm, the amplitude information is corrected for distance amplitude, phase, and azimuth angle, improving the amplitude and angle accuracy of the antenna radiation pattern, and completing rapid antenna measurement in outdoor environments. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating the steps of an antenna measurement method based on an unmanned vehicle system in an embodiment of the present invention.

[0015] Figure 2 This is a schematic diagram of the travel trajectory of an antenna measurement method based on an unmanned vehicle system in an embodiment of the present invention.

[0016] Figure 3 This is the radiation pattern of the antenna under test in an antenna measurement method based on an unmanned vehicle system according to an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0018] Reference Figure 1 As shown, the present invention provides an antenna measurement method based on an unmanned vehicle system, comprising: The antenna measurement device is mounted on the unmanned vehicle platform, and the spatial coordinates of the unmanned vehicle platform are obtained in real time by fusion positioning through multi-source sensors integrated into the unmanned vehicle platform. In the actual evaluation, measurements were taken on a directional array antenna operating in the S-band at an open outdoor antenna test field. The antenna under test was mounted on a test tower, with its phase center located at the geometric center of the antenna reflector, 5 meters above the ground. The entire antenna measurement device was mounted on the roof of the unmanned vehicle platform. The antenna measurement device included an RF transceiver, a standard gain horn receiving antenna, a DC power supply module, and a data acquisition unit. The receiving antenna was vertically mounted via an insulated bracket, with the bottom of the bracket rigidly connected to the roof mounting surface. The vertical height of the antenna phase center from the ground was 5 meters, at the same horizontal height as the antenna under test. The three-dimensional spatial offset between the phase center of the receiving antenna and the origin of the unmanned vehicle's positioning coordinate system was pre-calibrated. The unmanned vehicle was then... The projection of the rear axle center onto the ground serves as the origin of the positioning coordinate system. The vehicle's forward direction is the positive x-axis, the lateral direction is the positive y-axis, and the vertical direction is the positive z-axis. A 3D coordinate measuring machine is used to calibrate the offset of the receiving antenna phase center relative to the positioning origin: +0.35m in the x-direction, 0m in the y-direction, and +5.0m in the z-direction. These offsets are pre-stored in the vehicle controller. A 16-line mechanical LiDAR is mounted at the center of the front of the vehicle roof, with a point cloud sampling frequency of 10Hz and a ranging range of 0.5~80m. A monocular industrial camera is mounted next to the LiDAR, with an image sampling frequency of 20Hz and a field of view of 120°. Eight ultrasonic ranging sensors are distributed around the vehicle body, with a sampling frequency of 50Hz and an effective range of [missing information]. The ranging range is 0.05~5m. The IMU (Inertial Measurement Unit) is installed at the vehicle's center of gravity. The accelerometer range is ±10g, and the angular velocity range is ±500° / s. The sampling frequency is 200Hz. Based on the PTP (Precise Time Protocol), the lidar, vision camera, ultrasonic sensor, IMU, and data acquisition unit are synchronized in a unified time. During the test, the lidar point cloud is denoised and downsampled to remove outliers. The vision image is distorted and converted to grayscale. The ultrasonic ranging data is mean-filtered to remove sudden interference values. The raw IMU data is zero-biased and corrected. The calibrated acceleration and angular velocity are output. The preprocessed data are aligned by timestamp and tightly coupled. The SLAM algorithm constructs a grid map of the test area with a grid resolution of 5cm. Initial zero-bias calibration and gravity vector alignment of the IMU are performed. Motion data from the IMU's high-frequency output are used as the vehicle's motion prior. The six-DOF pose of the vehicle is calculated using high-frequency recursion. Line features are extracted from the LiDAR point cloud, and surface features are extracted. ORB feature points from the visual image are also extracted. These features are matched in real-time with the constructed grid map. Based on the matching results, closed-loop correction is performed on the pose drift calculated by the inertial navigation system. The six-DOF pose of the autonomous vehicle is obtained, including x, y, and z 3D coordinates and roll, pitch, and yaw angles. The pose output frequency is 10Hz, with a planar positioning accuracy of ±2cm and a yaw angle accuracy of ±0.1°. In actual evaluation, based on the pre-calibrated three-dimensional spatial offset and the current roll angle, pitch angle and heading angle of the unmanned vehicle, a rigid body spatial coordinate transformation is performed to convert the pose of the origin of the unmanned vehicle positioning coordinate system into the spatial coordinates of the receiving antenna phase center in the global coordinate system, which serves as the position reference of the current measurement point and is output to the vehicle controller in real time. The unmanned vehicle platform is planned according to the preset measurement points, and the unmanned vehicle platform is controlled to reach each measurement point in sequence according to the spatial coordinates. In actual evaluation, the projection center of the phase center of the tested antenna on the horizontal plane and the nominal distance are used as benchmarks to generate a variable radius scanning trajectory extending outward from the near-field region of the tested antenna. The scanning coverage range is 0.1m to 30m, with 30m taken as the nominal measurement distance, covering 0° to 360°. A sequence of measurement waypoints is generated in 10° angular steps within the horizontal test plane, totaling 36 waypoints. Each waypoint contains the target's three-dimensional coordinates and target heading angle, where the heading always points towards the phase center of the tested antenna. Based on a grid map, a global path planning algorithm (A*) is used to generate a global path that traverses all waypoints in ascending azimuth order. During planning, static obstacles such as test tower bases and ground equipment boxes marked on the map are avoided. The path planning resolution is consistent with that of the grid map. Figure 1 To, as Figure 2 As shown, if temporary obstacles are added to the test track and the A* algorithm cannot generate a valid path, it automatically switches to RRT (Rapid Random Tree) and replans the path. After planning, the global path is discretized into a sequence of equally spaced path points, with an adjacent path point spacing of 0.2m. This sequence is output to the vehicle motion control unit as a tracking reference. Based on the straight-line distance between adjacent waypoints, a speed plan is generated using an S-shaped acceleration / deceleration curve. If the waypoint spacing is greater than 5m, the rated cruise speed is set to 0.8m / s, and the acceleration / deceleration is 0.2m / s. 2 The system continues until the driving is smooth and without impact. If the waypoint distance is less than 2m, it switches to low-speed feed mode with a cruising speed of 0.2m / s to avoid vibration caused by frequent starts and stops. Basic control of the trajectory is performed based on a pure tracking algorithm. The desired linear velocity and angular velocity of the vehicle are calculated based on the relative position of the unmanned vehicle to the target pathpoint. After receiving the desired linear velocity and angular velocity commands, the VCU vehicle controller calculates the speed commands of the left and right wheel drive motors and the steering angle commands of the steering mechanism, and outputs them to the drive motors and steering actuators to control the vehicle to travel along the predetermined trajectory. At low speeds, the forward sight distance is maintained at 0.5m, and at high speeds, the forward sight distance gradually increases to 1.5m. In actual evaluation, real-time spatial coordinates are read during driving, and the spatial coordinates are compared with the target path point sequence to calculate the vehicle's lateral position deviation and heading deviation. Drive commands and steering commands are corrected accordingly. The lateral deviation of path tracking is within ±5cm, and the heading deviation is within ±0.5°. Obstacles around the driving path are detected in real time based on lidar and ultrasonic sensors. If an obstacle intrudes into the 1m safe distance, the DWA dynamic window is automatically triggered to generate a local obstacle avoidance path. After bypassing the obstacle, the vehicle returns to the global path. If the unmanned vehicle drives to a parking range of 0.5m from the current measurement waypoint, it stops. The normal direction of the receiving antenna is aligned with the phase center of the measured antenna. If the vehicle's planar position deviation is less than ±2cm for 0.5s and the heading angle deviation is less than ±0.1° for 0.5s, the parking is completed. The stability of the antenna is determined based on the motion state parameters of the unmanned vehicle platform. If it enters a stable state, the antenna measurement device is controlled to collect the antenna signal under test and environmental interference signals are filtered out by a distance gate. In the actual evaluation, after the unmanned vehicle completed its docking at the current measurement point, it deployed its receiving antenna via an electrically operated lifting mast. Based on the IMU, it read acceleration and angular velocity at a sampling frequency of 200Hz, and calculated short-time vibration energy using a sliding window with 200 points, corresponding to 1 second of IMU sampling data. The short-time vibration energy remained stable at approximately 0.005 for 10 consecutive sliding windows, all less than the preset stability threshold of 0.008m. 2 / s 4The system determines that the autonomous vehicle platform and its onboard receiving antenna have eliminated residual braking vibration and entered a mechanically stable state. If stability is not yet achieved, secondary micro-modulation of the vehicle is triggered until stability requirements are met and the vehicle enters a stable state. Once stable, the RF transceiver is activated to initiate testing. The antenna under test transmits a pulsed RF signal with a peak power of 10W, a pulse width of 10ns, and a repetition frequency of 10kHz. The receiving antenna simultaneously acquires the spatial radiation signal. Based on the nominal test distance and electromagnetic wave propagation speed, the theoretical one-way propagation delay of the direct signal is calculated to be 100ns. Using this theoretical delay as the center, a distance-gated time window is set with a window width of 20ns and a path tolerance range of ±3m. The original time-domain echo signal sampled each time is then gated by the distance gate, retaining only 90% of the original signal. The direct target signal within the ns~110ns window is filtered out. Long-delay multipath clutter generated by ground reflection and reflection from surrounding test tower supports outside the window is filtered out. After selection, the signal peak value is extracted to obtain the original amplitude of a single sample. The output frequency of the original amplitude of a single sample is consistent with the pulse repetition frequency, which is 10kHz. The dwell time of the single point signal is set to 2 seconds, corresponding to a total of 20,000 samples. The short-time vibration energy corresponding to each sampling moment is bound to the original amplitude of the sample one by one. The sampling weight is assigned according to the short-time vibration energy of each sampling moment. The attenuation coefficient is 4. The sampling point with greater vibration energy has a lower weight to suppress the amplitude fluctuation caused by instantaneous micro-vibration. The original amplitudes of all 20,000 samples are weighted and fused to obtain the robust amplitude of the measurement point. Record the spatial coordinates of each measurement point and the amplitude information of the radiated signal of the antenna under test, and establish the correspondence between spatial coordinates and amplitude information; In actual evaluation, after signal acquisition and robust amplitude calculation are completed at each measurement point, the three types of data from each measurement point are bound to the acquisition timestamp based on the vehicle controller, and stored in the test dataset according to the measurement point number. This dataset includes the three-dimensional spatial coordinates of the receiving antenna phase center, robust amplitude, cumulative chassis encoder rotation angle, and real-time SLAM heading angle. Based on the bound spatial coordinates, the projection of the tested antenna phase center onto the horizontal plane is used as the reference point. If a point deviates to (-14.964, 15.022) due to obstacle avoidance, its... =90°, X=-14.964m, Y=15.022m, Z=0, original amplitude A=6.5530, not dB. Calculate the Euclidean distance from the receiving antenna phase center to the reference point in the horizontal plane, obtaining an actual radial distance of 21.2033m. There is a trajectory deviation. Due to the propagation characteristics of far-field spherical waves, the radiated power is inversely proportional to the square of the propagation distance. Therefore, the measured robust amplitude at different actual distances needs to be corrected to the equivalent amplitude at the nominal test distance. When the actual radial distance of the point is 21.2033m, the measured robust amplitude is 6.5530. After distance square correction, the corrected amplitude equivalent to the nominal distance of 30m is 13.1182, which is converted to 20lg(13.1182)≈22.3575dB. The phase offset introduced by the path difference is calculated based on the difference between the actual radial distance and the nominal distance of -8.7967m. The signal phase is compensated based on the phase offset of -442.1682rad. The phase can be modulo 2π and folded into the [0,2π) interval, which is equivalent to 3.9380 rad. As the phase correction, the signal operating frequency of the test antenna is 2.4 GHz, corresponding to a free space wavelength of 0.125 m and a wavenumber of 50.2655 rad / m. Substitute the phase correction into the calculation to obtain the corrected complex received signal of the point. The complex received signal contains amplitude information and phase information. Since the unmanned vehicle chassis is equipped with an incremental photoelectric encoder with an angular resolution of 0.01° / pulse, the cumulative wheel rotation angle increment is used to obtain the relative rotation angle. After driving a full circle, the angle drift reaches 0.3°. The residual correction of the heading angle is calculated based on the absolute heading angle calculated by SLAM. The drift correction of the encoder cumulative value is performed to obtain the corrected azimuth angle. The corrected amplitude, corrected complex received signal, corrected azimuth angle and the spatial coordinates of the corresponding point are matched one by one according to the point number to generate a sequence of azimuth angle amplitude data pairs and establish the correspondence between spatial coordinates and amplitude information. Based on the spatial coordinates and corresponding amplitude information, a radiation pattern and spatial electromagnetic signal distribution map of the antenna under test at each measurement point are generated.

[0019] In the actual evaluation, the test area was gridded, and the amplitude data of discrete measurement points were interpolated onto the grid nodes using inverse distance weighted interpolation. A contour heatmap was plotted, with the horizontal and vertical axes corresponding to the east and north geographical coordinates of the test field, respectively. Color gradations and contour lines were used to represent the magnitude of the field strength amplitude, with the contour line interval set to 3 dB. The location of the antenna under test, the test trajectory, and the azimuth of the main lobe were marked, generating a spatial electromagnetic signal distribution map. Azimuth amplitude data pairs of the measurement points were extracted and incremented according to the corrected azimuth angle. The sequence was rearranged to eliminate the disordered point order caused by supplementary measurements and obstacle avoidance. Based on discrete sampling data with a step size of 10°, cubic spline interpolation was used to interpolate the amplitude of the missing angles between adjacent sampling points. After interpolation, the angle step size was refined to 1°, resulting in a continuous azimuth pattern dataset covering 360 data points across the entire circumference. The interpolated azimuth pattern dataset was normalized based on the maximum radiation amplitude. Using the maximum radiation amplitude in the dataset as a benchmark, all amplitudes were converted to normalized values ​​on a decibel scale. Figure 3 As shown, the corrected azimuth angle is used as the horizontal axis, with a scale range of -180° to +180° and a scale interval of 10°. The normalized amplitude in dB is used as the vertical axis, with a scale interval of 2.5 dB. A continuous angular amplitude relationship curve is plotted to generate the azimuth plane radiation pattern of the antenna under test.

[0020] In this embodiment, the method for obtaining the spatial coordinates includes: Based on the three-dimensional spatial offset between the phase center of the antenna and the origin of the unmanned vehicle positioning coordinate system by the antenna measurement device, the unmanned vehicle platform integrates multi-source sensors including lidar, vision sensor, ultrasonic radar and inertial measurement unit; The multi-source sensors are synchronized in time, and environmental point cloud data is collected by lidar, environmental image data is collected based on visual sensors, obstacle ranging data is collected based on ultrasonic radar, and acceleration and angular velocity are output based on inertial measurement unit. The multi-source sensor data is denoised and timestamped to obtain a multi-source perception dataset. Feature extraction is performed based on the multi-source sensing dataset. The motion data output by the inertial measurement unit is used as the vehicle motion prior. Based on the vehicle motion prior, LiDAR point cloud features and visual image features, the environment features are matched through a simultaneous localization and mapping (SLAM) algorithm to solve the six-degree-of-freedom pose of the unmanned vehicle. A grid map of the test environment is constructed to obtain the pose information of the unmanned vehicle. The pose information is then transformed according to the spatial offset of the phase center to obtain the spatial coordinates corresponding to the antenna phase center.

[0021] In this embodiment, the method for obtaining the travel trajectory includes: Based on preset measurement points, a trajectory path point sequence is generated, and the unmanned vehicle platform's travel trajectory from its current position to each measurement point is generated according to the grid map using a path planning algorithm. The path planning algorithm includes the A* algorithm and the fast random tree algorithm. The desired linear velocity and desired angular velocity of the unmanned vehicle platform are calculated based on the travel trajectory using a trajectory tracking algorithm, which includes a pure tracking algorithm and a model predictive control algorithm. Based on the unmanned vehicle platform, the vehicle controller converts the desired linear velocity and desired angular velocity into motor drive signals, and outputs them to the drive motor and steering mechanism to control the movement of the unmanned vehicle platform; During the journey, the spatial coordinates and trajectory path point sequence are compared in real time, and feedback is provided to correct the deviation between the actual position of the unmanned vehicle platform and the trajectory path point sequence until the unmanned vehicle platform arrives at each measurement point in sequence and completes the docking.

[0022] In this embodiment, the method for obtaining the amplitude information includes: The short-time vibration energy is calculated based on the acceleration and angular velocity output by the inertial measurement unit. The formula for calculating the short-time vibration energy is as follows: ; in For the first Short-term vibration energy at the next sampling time, The length of the sliding window. For the first Within the second sampling window IMU acceleration along the z-axis at each point; If the short-time vibration energy is continuously stable, the antenna measuring device is determined to have entered a stable state. Based on the amplitude information of the radiated signal of the antenna under test collected by the antenna measuring device, the distance gate width is determined according to the nominal distance between the unmanned vehicle platform and the antenna under test. The original sampled signal is time-domain gated to filter out interference noise that exceeds the time delay range corresponding to the nominal distance, and the original amplitude of each sample is obtained. The sampling weight is assigned to each sample based on the relative value of the short-time vibration energy at each sampling time. The calculation formula of the sampling weight is as follows: ; in For the first The sampling weight of the next sample. The attenuation coefficient has a range of values. ; The amplitude information is weighted and fused to obtain robust amplitude information of the target measurement point. The formula for calculating the robust amplitude information is as follows: ; in For the first The robust amplitude at each measurement point For the first The first point The original amplitude of the second sample, For the first The total number of samplings at each measurement point is determined based on the sampling rate and dwell time.

[0023] In this embodiment, the method for obtaining the correspondence between the spatial coordinates and the amplitude information includes: Record the spatial coordinates of the target measurement point and the amplitude information received by the antenna measurement device. Correct the robust amplitude information based on the real-time radial distance in the spatial coordinates. The formula for calculating the corrected amplitude is as follows: ; in For the first Correction amplitude for each measurement point, For the antenna measuring device in the first Robust amplitude information at each point, The nominal measurement distance between the autonomous vehicle platform and the antenna under test. This represents the actual radial distance from the unmanned vehicle to the center of the antenna under test. The phase correction is calculated based on the deviation between the actual radial distance and the nominal measured distance. The formula for calculating the phase correction is as follows: ; in This is the phase correction amount. The wave number is obtained based on the operating frequency. The corrected complex received signal is calculated based on the distance amplitude correction value and the phase correction amount. The calculation formula for the corrected complex received signal is as follows: ; in To correct the received signal; The corrected amplitude, corrected re-received signal, and corrected azimuth are mapped according to the measurement point number to generate a sequence of azimuth amplitude data pairs, which serve as the correspondence between spatial coordinates and amplitude information.

[0024] In this embodiment, the method for obtaining the corrected azimuth angle includes: The corrected azimuth angle is calculated based on the rotation angle output by the chassis encoder of the unmanned vehicle platform and the residual correction amount of the heading angle output by SLAM. The formula for calculating the corrected azimuth angle is as follows: ; in For the first Corrected azimuth angle of each measurement point This refers to the rotation angle output by the encoder of the autonomous vehicle chassis. This is the residual heading angle correction amount output by SLAM.

[0025] In this embodiment, the method for obtaining the radiation pattern and the spatial electromagnetic signal distribution map includes: Based on the correspondence, the spatial coordinates and amplitude information of each measurement point are matched according to the measurement point number to generate a spatial electromagnetic signal distribution map of the antenna under test. The azimuth amplitude data of each measurement point are rearranged in ascending order of azimuth. The missing angles between adjacent sampling points are interpolated to obtain a continuous azimuth radiation pattern dataset. The azimuth radiation pattern dataset is normalized, and the angle is used as the abscissa and the normalized amplitude is used as the ordinate to plot the relationship curve between angle and amplitude, thus generating the radiation pattern of the antenna under test.

[0026] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. An antenna measurement method based on an unmanned vehicle system, characterized in that, Includes the following steps: The antenna measurement device is mounted on the unmanned vehicle platform, and the spatial coordinates of the unmanned vehicle platform are obtained in real time by fusion positioning through multi-source sensors integrated into the unmanned vehicle platform. The unmanned vehicle platform is planned according to the preset measurement points, and the unmanned vehicle platform is controlled to reach each measurement point in sequence according to the spatial coordinates. The stability of the antenna is determined based on the motion state parameters of the unmanned vehicle platform. If it enters a stable state, the antenna measurement device is controlled to collect the antenna signal under test and environmental interference signals are filtered out by a distance gate. Record the spatial coordinates of each measurement point and the amplitude information of the radiated signal of the antenna under test, and establish the correspondence between spatial coordinates and amplitude information; Based on the spatial coordinates and corresponding amplitude information, a radiation pattern and spatial electromagnetic signal distribution map of the antenna under test at each measurement point are generated.

2. The antenna measurement method based on an unmanned vehicle system according to claim 1, characterized in that, The method for obtaining the spatial coordinates includes: Based on the three-dimensional spatial offset between the phase center of the antenna and the origin of the unmanned vehicle positioning coordinate system by the antenna measurement device, the unmanned vehicle platform integrates multi-source sensors including lidar, vision sensor, ultrasonic radar and inertial measurement unit; The multi-source sensors are synchronized in time, and environmental point cloud data is collected by lidar, environmental image data is collected based on visual sensors, obstacle ranging data is collected based on ultrasonic radar, and acceleration and angular velocity are output based on inertial measurement unit. The multi-source sensor data is denoised and timestamped to obtain a multi-source perception dataset. Feature extraction is performed based on the multi-source sensing dataset. The motion data output by the inertial measurement unit is used as the vehicle motion prior. Based on the vehicle motion prior, LiDAR point cloud features and visual image features, the environment features are matched through a simultaneous localization and mapping (SLAM) algorithm to solve the six-degree-of-freedom pose of the unmanned vehicle. A grid map of the test environment is constructed to obtain the pose information of the unmanned vehicle. The pose information is then transformed according to the spatial offset of the phase center to obtain the spatial coordinates corresponding to the antenna phase center.

3. The antenna measurement method based on an unmanned vehicle system according to claim 1, characterized in that, The method for obtaining the travel trajectory includes: Based on preset measurement points, a sequence of trajectory path points is generated, and the unmanned vehicle platform's travel trajectory from its current location to each measurement point is generated using a path planning algorithm based on a grid map. The path planning algorithm includes the A* algorithm and the fast random tree algorithm. The desired linear velocity and desired angular velocity of the unmanned vehicle platform are calculated based on the travel trajectory using a trajectory tracking algorithm, which includes a pure tracking algorithm and a model predictive control algorithm. Based on the unmanned vehicle platform, the vehicle controller converts the desired linear velocity and desired angular velocity into motor drive signals, and outputs them to the drive motor and steering mechanism to control the movement of the unmanned vehicle platform; During the journey, the spatial coordinates and trajectory path point sequence are compared in real time, and feedback is provided to correct the deviation between the actual position of the unmanned vehicle platform and the trajectory path point sequence until the unmanned vehicle platform arrives at each measurement point in sequence and completes the docking.

4. The antenna measurement method based on an unmanned vehicle system according to claim 1, characterized in that, The method for obtaining the amplitude information includes: The short-time vibration energy is calculated based on the acceleration and angular velocity output by the inertial measurement unit. The formula for calculating the short-time vibration energy is as follows: ； in For the first Short-term vibration energy at the next sampling time, The length of the sliding window. For the first Within the second sampling window IMU acceleration along the z-axis at each point; If the short-time vibration energy is continuously stable, the antenna measuring device is determined to have entered a stable state. Based on the amplitude information of the radiated signal of the antenna under test collected by the antenna measuring device, the distance gate width is determined according to the nominal distance between the unmanned vehicle platform and the antenna under test. The original sampled signal is time-domain gated to filter out interference noise that exceeds the time delay range corresponding to the nominal distance, and the original amplitude of each sample is obtained. The sampling weight is assigned to each sample based on the relative value of the short-time vibration energy at each sampling time. The calculation formula of the sampling weight is as follows: ； in For the first The sampling weight of the next sample. The attenuation coefficient has a range of values. ; The amplitude information is weighted and fused to obtain robust amplitude information of the target measurement point. The formula for calculating the robust amplitude information is as follows: ； in For the first The robust amplitude at each measurement point For the first The first point The original amplitude of the second sample, For the first The total number of samplings at each measurement point is determined based on the sampling rate and dwell time.

5. The antenna measurement method based on an unmanned vehicle system according to claim 1, characterized in that, A method for obtaining the correspondence between the spatial coordinates and the amplitude information includes: Record the spatial coordinates of the target measurement point and the amplitude information received by the antenna measurement device. Correct the robust amplitude information based on the real-time radial distance in the spatial coordinates. The formula for calculating the corrected amplitude is as follows: ； in For the first Correction amplitude for each measurement point, For the antenna measuring device in the first Robust amplitude information at each point, The nominal measurement distance between the autonomous vehicle platform and the antenna under test. This represents the actual radial distance from the unmanned vehicle to the center of the antenna under test. The phase correction is calculated based on the deviation between the actual radial distance and the nominal measured distance. The formula for calculating the phase correction is as follows: ； in This is the phase correction amount. The wave number is obtained based on the operating frequency. The corrected complex received signal is calculated based on the distance amplitude correction value and the phase correction amount. The calculation formula for the corrected complex received signal is as follows: ； in To correct the received signal; The corrected amplitude, corrected re-received signal, and corrected azimuth are mapped according to the measurement point number to generate a sequence of azimuth amplitude data pairs, which serve as the correspondence between spatial coordinates and amplitude information.

6. The antenna measurement method based on an unmanned vehicle system according to claim 5, characterized in that, The method for obtaining the corrected azimuth angle includes: The corrected azimuth angle is calculated based on the rotation angle output by the chassis encoder of the unmanned vehicle platform and the residual correction amount of the heading angle output by SLAM. The formula for calculating the corrected azimuth angle is as follows: ； in For the first Corrected azimuth angle of each measurement point This refers to the rotation angle output by the encoder of the autonomous vehicle chassis. This is the residual heading angle correction amount output by SLAM.

7. The antenna measurement method based on an unmanned vehicle system according to claim 1, characterized in that, The method for obtaining the radiation pattern and the spatial electromagnetic signal distribution map includes: Based on the correspondence, the spatial coordinates and amplitude information of each measurement point are matched according to the measurement point number to generate a spatial electromagnetic signal distribution map of the antenna under test. The azimuth amplitude data of each measurement point are rearranged in ascending order of azimuth. The missing angles between adjacent sampling points are interpolated to obtain a continuous azimuth radiation pattern dataset. The azimuth radiation pattern dataset is normalized, and the angle is used as the abscissa and the normalized amplitude is used as the ordinate to plot the relationship curve between angle and amplitude, thus generating the radiation pattern of the antenna under test.

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