Antenna pattern reconstruction method for unmanned aerial vehicle under non-ideal flight condition

Abstract

The invention relates to an antenna pattern reconstruction method for an unmanned aerial vehicle under a non-ideal flight condition, and belongs to the field of radio, and the reconstruction method comprises the steps: S1, enabling the unmanned aerial vehicle to fly on a preset space trajectory around a to-be-tested antenna, collecting position data, attitude data and received signal level data, and carrying out the time synchronization and data alignment; s2, comparing each actual sampling point with the corresponding theoretical sampling point, calculating a space offset and an antenna pointing offset angle, and constructing an error compensation model according to the space offset and the antenna pointing offset angle; and S3, replacing the antenna to be tested with an antenna of which the directional diagram actually needs to be tested, repeating the steps S1 and S2 to complete data acquisition and data synchronization, and then performing error correction and interpolation to obtain a reconstructed directional diagram. According to the invention, the dependence of the traditional measurement scheme on the platform stability is overcome, the actual measurement error is controlled within 0.5 dB, and the high-precision measurement requirements of 5G, radar, navigation and the like are met.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication, and particularly to a method for reconstructing an antenna pattern in the case of non-ideal flight of an unmanned aerial vehicle (UAV). Background Art

[0002] Currently, most UAV systems for antenna pattern measurement assume an ideal and stable flight path. However, in practical applications, UAVs often experience trajectory deviation and attitude perturbation due to environmental factors. Existing systems fail to effectively handle these errors, thus affecting the accuracy of measurement results. Therefore, traditional pattern measurement methods rely heavily on the stability of the flight trajectory. Slight deviation of the UAV will cause pattern reconstruction errors, and there is a lack of methods to compensate and correct signal sampling errors in the case of attitude perturbation and path deviation, which cannot meet the requirements of antenna measurement tasks with high precision. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art, and provides a method for reconstructing an antenna pattern in the case of non-ideal flight of an unmanned aerial vehicle, solving the deficiencies existing in the prior art.

[0004] The purpose of the present invention is achieved through the following technical solutions: A method for reconstructing an antenna pattern in the case of non-ideal flight of an unmanned aerial vehicle, the reconstruction method comprising: S1. The UAV flies on a preset spatial trajectory around the antenna to be measured, and collects position data, attitude data, and received signal level data, and performs time synchronization and data alignment; S2. Compare each actual sampling point with its corresponding theoretical sampling point, calculate the spatial offset and the antenna pointing offset angle, and construct an error compensation model based on the spatial offset and the antenna pointing offset angle; S3. Replace the antenna to be measured with the antenna whose pattern actually needs to be tested. After repeating the steps of S1 and S2 to complete data collection and data synchronization, perform error correction and interpolation to obtain the reconstructed pattern.

[0005] The time synchronization and data alignment include: Select a unified reference time axis. Taking the data sampling timestamp of the receiver module as a reference, search for the corresponding position data and attitude data in the sets of attitude data and position data. If not found, use the nearest neighbor principle or linear interpolation to form a unified sampling data format (t k , p k , R k , r k ), where t k is the timestamp of the k-th sampling data, p k is the position data of the k-th sampling data, and R kis the attitude data of the k-th sampling data, r k is the received signal level of the k-th sampling data.

[0006] The specific content of S2 is as follows: Compare each actual sampling point with its corresponding theoretical sampling point, and calculate the spatial offset ΔP k =P(t k ) - P ideal,k , calculate the antenna pointing offset angle Δ θ k = arccos(d k × V ideal,k / ||d k || × ||V ideal,k ||), where ΔP k is the spatial offset of the k-th sampling data, d k is the actually measured direction vector, V ideal,k is the ideal direction vector, Δ θ k is the antenna pointing offset angle of the k-th sampling data; Use a standard antenna with a known radiation pattern as the antenna under test, and calculate the theoretical received signal level r ideal,k at each theoretical measurement position according to the radio wave propagation model combined with the radiation pattern of the antenna under test. According to the actually measured received signal level r k measured during data acquisition, solve the error value between the actually measured received signal level and the theoretical received signal level as ε k = r k - r ideal,k , ε k is the error value between the received signal level of the k-th sampling data and the theoretical received signal level; Substitute into the multi-variable error compensation model to obtain ε = α1|ΔP| + α2Δ θ + β. According to the position data and attitude data of the theoretical flight estimate and the actual flight trajectory, solve for the spatial offset ΔP and the antenna pointing offset angle Δ θ ; Use the least squares method to fit and solve for α1, α2, and β and store them. Thus, the construction of the error compensation model is completed.

[0007] The specific content of S3 is as follows: Replace the antenna under test with the antenna whose radiation pattern actually needs to be tested, repeat the steps of S1 and S2, calculate the spatial offset and the antenna pointing offset angle according to the position information and attitude data in the measured data and the position information and attitude data of the theoretical flight trajectory, and substitute them into the error compensation model to correct the measured signal level; For each theoretical point Pideal,j Search for the nearby k sampling points in the actual point set based on the spatial Euclidean distance, where the maximum search radius is r max ; Adopt the weighted average method Estimate the signal value of the theoretical point to complete the reconstruction of the antenna pattern. Among them, r i is the data after the measured signal level is corrected by the error compensation model, is the signal strength value of the j-th point after error correction and interpolation, and the weight W ij Adopt the inverse distance squared weight for estimation.

[0008] The UAV carrying the measurement payload flies according to a predetermined trajectory to collect the wireless signals transmitted by the antenna under test. During the collection process, the measurements of the measurement payload are transmitted in real time to the ground control computer via the wireless line between the remote controller and the UAV; The measurement payload includes: an antenna, a polarization motor, a pitch motor, an azimuth motor, a receiving module and a control board; the antenna is connected to the polarization motor, the polarization motor is respectively connected to the receiver module and the control board, and the azimuth motor and the pitch motor are connected to the control board.

[0009] The position data includes: the three-dimensional data of the high-precision longitude, latitude and altitude output by the RTK module of the UAV, as well as the data sampling timestamp; The attitude data includes: the azimuth, pitch angle and roll angle of the azimuth motor, pitch motor and polarization motor, and the data sampling timestamp; The received signal level data includes: the signal level value measured by the receiver module and the data sampling timestamp.

[0010] The present invention has the following advantages: An antenna pattern reconstruction method for non-ideal flight conditions of a UAV can still effectively reconstruct an accurate antenna pattern in the case of disturbances and errors in the UAV flight path. Its error modeling and compensation mechanism significantly improves the adaptability of the test system to complex flight environments, overcomes the dependence of traditional measurement schemes on platform stability, and the measured error is controlled within 0.5 dB, meeting the high-precision measurement requirements of 5G, radar, navigation, etc. Brief Description of the Drawings

[0011] Figure 1 is a schematic flow chart of the present invention. Detailed Embodiment

[0012] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part rather than all of the embodiments of this application. Usually, the components of the embodiments of this application described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of this application provided below with reference to the accompanying drawings is not intended to limit the protection scope of the claimed application, but only represents the selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative efforts belong to the protection scope of this application. The following further describes the present invention with reference to the accompanying drawings.

[0013] The present invention specifically relates to a method for reconstructing an antenna pattern in non-ideal flight conditions of an unmanned aerial vehicle (UAV). In a non-ideal flight state where the UAV's trajectory or attitude deviates, it can still reconstruct a high-precision antenna pattern. The UAV carries a measurement payload to fly along a predetermined trajectory to collect wireless signals emitted by the antenna under test. During the collection process, the measurements of the measurement payload are transmitted back to the ground control computer in real time via the wireless link between the remote control and the UAV, or can be stored in the measurement payload storage device.

[0014] Among them, the measurement payload includes: an antenna, a polarization motor, a pitch motor, an azimuth motor, a receiving module, and a control board. The antenna is connected to the polarization motor, the polarization motor is respectively connected to the receiver module and the control board, and the azimuth motor and the pitch motor are connected to the control board.

[0015] As Figure 1 shown, it specifically includes the following content: Step 1: Flight data and signal collection; The UAV flies along a preset spatial trajectory around the antenna under test, and the collected information includes position data, attitude data, and received signal level data. Among them, for the position data: collect the high-precision three-dimensional coordinates (longitude, latitude, altitude) output by the UAV's RTK module and the data sampling timestamp. For the attitude data: the azimuth, pitch, and roll angles of the azimuth motor, pitch motor, and polarization motor, and the data sampling timestamp. For the received signal level data: the signal level value measured by the receiver module and the data sampling timestamp.

[0016] Step 2: Time synchronization and data alignment; Due to the asynchronous sampling of each module, it is necessary to uniformly construct standardized sampling points. The steps include: selecting a unified reference time axis, using the data sampling timestamp of the receiver module as a reference, finding the corresponding position data and attitude data in the attitude data and position data sets, and for those that cannot be found, the nearest neighbor principle or linear interpolation is used. Form a unified sampling data format (t k , p k , R k , r k ), where t k is the timestamp of the k-th sampling data, p k is the position data of the k-th sampling data, R k is the attitude data of the k-th sampling data, and r k is the received signal level of the k-th sampling data.

[0017] Step 3: Construct an error correction model through the trajectory and attitude deviation; Compare each actual sampling point with its corresponding theoretical sampling point, and calculate the spatial offset ΔP k = P(t k ) - P ideal,k , calculate the antenna pointing offset angle Δ θ k = arccos(d k × V ideal,k / ||d k || × ||V ideal,k ||), where ΔP k is the spatial offset of the k-th sampling data, d k is the actually measured direction vector, V ideal,k is the ideal direction vector, and Δ θ k is the antenna pointing offset angle of the k-th sampling data; Use a standard antenna with a known radiation pattern as the antenna under test, calculate the theoretical received signal level r ideal,k at each theoretical measurement position according to the radio wave propagation model combined with the radiation pattern of the antenna under test, and according to the actually measured received signal level r k in data acquisition, solve the error value between the actual received signal level and the theoretical received signal level as ε k = r k - r ideal,k , and ε k is the error value between the received signal level of the k-th sampling data and the theoretical received signal level; Substitute into the multi-error compensation model description to get ε = α1|ΔP| + α2Δ θ+β, based on the theoretical flight estimate and the position and attitude data of the actual flight trajectory, the spatial offset ΔP and the antenna pointing offset angle Δ θ ; The least square method is used to fit and solve α1, α2 and β and store them. The error compensation model is now built. α1 represents the position offset sensitivity coefficient, α2 represents the antenna pointing offset sensitivity coefficient, and β represents the constant bias.

[0018] Step 4: remapping and interpolation compensation; Replace the antenna to be tested with the antenna whose radiation pattern actually needs to be tested, repeat steps 1 and 2, calculate the spatial offset and antenna pointing offset angle according to the position information and attitude data in the measured data and the position information and attitude data of the theoretical flight trajectory, and substitute them into the error compensation model to correct the measured signal level; For each theoretical point P ideal,j , based on the spatial Euclidean distance, search for k nearby neighboring sampling points in the actual point set, where the maximum search radius is r max ; Using weighted average method Estimate the signal value at the theoretical point and complete the reconstruction of the antenna pattern. The pattern is the mapping relationship between the position and the spatial signal level value. The process of estimating the theoretical signal level value is the process of pattern reconstruction. i is the data of the measured signal level after being corrected by the error compensation model. is the signal strength value after error correction and interpolation at the jth point, and the weight W ij Inverse distance square weighting Make an estimate, It is a non-zero minimum value to prevent the denominator from being 0 when the measured signal level is equal to the theoretical level.

[0019] The present invention quantifies the deviation of the actual position and attitude data of the drone, and constructs a correction model based on the relationship between the position offset distance, the pointing deviation angle and the received signal level error. The least squares method is used to fit the model, and the inverse distance square weight is used to perform weighted interpolation on the error-corrected received signal level to improve the accuracy of pattern reconstruction.

[0020] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used for various other combinations, modifications and improvements, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art do not deviate from the spirit and scope of the present invention, and should be within the scope of protection of the claims attached to the present invention.

Claims

1. An antenna pattern reconstruction method for drones in non-ideal flight conditions, characterized in that: The reconstruction method includes: S1. The drone flies along a preset spatial trajectory around the antenna to be measured, collects position data, attitude data, and received signal level data, and performs time synchronization and data alignment. S2. Compare each actual sampling point with its corresponding theoretical sampling point, calculate the spatial offset and the antenna pointing offset angle, and construct an error compensation model based on the spatial offset and the antenna pointing offset angle. S3. Replace the antenna to be measured with the antenna for which the radiation pattern actually needs to be tested. After repeating the steps of S1 and S2 to complete data collection and data synchronization, perform error correction and interpolation to obtain the reconstructed radiation pattern.

2. The method for reconstructing an antenna radiation pattern in the case of non-ideal flight of an unmanned aerial vehicle according to claim 1, wherein: The time synchronization and data alignment include: Select a unified reference time axis. With the data sampling timestamp of the receiver module as the reference, search for the corresponding position data and attitude data in the attitude data and position data sets. If no data is found, use the principle of proximity or linear interpolation to form a unified sampling data format (t k , p k , R k , r k ), where t k is the timestamp of the k-th sampling data, p k is the position data of the k-th sampling data, R k is the attitude data of the k-th sampling data, and r k is the received signal level of the k-th sampling data.

3. A method for reconstructing an antenna radiation pattern in the case of non-ideal flight of an unmanned aerial vehicle according to claim 2, characterized in that: The specific content of S2 includes the following: Compare each actual sampling point with its corresponding theoretical sampling point and calculate the spatial offset ΔP k =P(t k )-P ideal,k , calculate the antenna pointing offset angle Δ θ k =arccos(d k ×V ideal,k / || d k ||×||V ideal,k ||), where ΔP k is the spatial offset of the k-th sampling data, d k is the actually measured direction vector, V ideal,k is the ideal direction vector, Δ θ k is the antenna pointing offset angle of the k-th sampling data; Use a standard antenna with a known radiation pattern as the antenna under test, and calculate the theoretical received signal level r at each measurement position according to the radio wave propagation model in combination with the radiation pattern of the antenna under test ideal,k , and actually measure the received signal level r according to data acquisition k , and solve the error value between the actual received signal level and the theoretical received signal level as ε k =r k -r ideal,k , where ε k is the error value between the received signal level of the k-th sampled data and the theoretical received signal level; Bringing in the multi - error compensation model description gives ε = α1|ΔP|+α2Δ θ +β. According to the position data and attitude data of the theoretical flight estimate and the actual flight trajectory, the spatial offset ΔP and the antenna pointing offset angle Δ θ ; Use the least squares method to perform fitting to solve for α1, α2, and β and store them. Thus, the construction of the error compensation model is completed. α1 represents the position offset sensitivity coefficient, α2 represents the antenna pointing offset sensitivity coefficient, and β represents the constant bias.

4. A method for reconstructing an antenna radiation pattern in the case of non-ideal flight of an unmanned aerial vehicle according to claim 3, characterized in that: The specific content of S3 includes the following: Replace the antenna to be measured with the antenna for which the radiation pattern actually needs to be tested. Repeat the steps of S1 and S2. Calculate the spatial offset and the antenna pointing offset angle based on the position information and attitude data in the measured data and the position information and attitude data of the theoretical flight trajectory, and substitute them into the error compensation model to correct the measured signal level. For each theoretical point P ideal,j , search for k nearby sampled points in the actual point set based on the spatial Euclidean distance, where the maximum search radius is r max ; Adopt the weighted average method Estimate the signal value of the theoretical point to complete the reconstruction of the antenna pattern, where r i is the data after the measured signal level is corrected by the error compensation model is the signal strength value after error correction and interpolation at the j-th point, and the weight W ij Adopt the inverse distance squared weight for estimation is a non-zero minimum value 5. A method for reconstructing an antenna pattern in non-ideal flight conditions of an unmanned aerial vehicle according to claim 1, characterized in that: The drone carries a measurement payload and flies along a predetermined trajectory to collect the wireless signals emitted by the antenna to be measured. During the collection process, the measurements of the measurement payload are transmitted in real-time to the ground control computer via the wireless link between the remote controller and the drone. The measurement payload includes: an antenna, a polarization motor, a pitch motor, a azimuth motor, a receiving module, and a control board. The antenna is connected to the polarization motor, the polarization motor is respectively connected to the receiver module and the control board, and the azimuth motor and the pitch motor are connected to the control board.

6. A method for reconstructing an antenna pattern in the case of non-ideal flight of a drone according to claim 5, characterized in that: The position data includes: collecting the three-dimensional data of the high-precision longitude, latitude, and altitude output by the RTK module of the drone, as well as the data sampling timestamp. The attitude data includes: the azimuth, pitch angle, and roll angle of the azimuth motor, pitch motor, and polarization motor, as well as the data sampling timestamp. The received signal level data includes: the signal level value measured by the receiver module and the data sampling timestamp.

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