A method for reconstructing antenna patterns in non-ideal UAV flight conditions

Abstract

The present invention relates to a method for reconstructing antenna patterns for unmanned aerial vehicles (UAVs) under non-ideal flight conditions, belonging to the field of radio. The method comprises the following steps: S1, the UAV flies on a preset spatial trajectory around the antenna to be tested, collects position data, attitude data, and received signal level data, and performs time synchronization and data alignment; S2, each actual sampling point is compared with its corresponding theoretical sampling point, the spatial offset and antenna pointing offset angle are calculated, and an error compensation model is constructed based on the spatial offset and antenna pointing offset angle; S3, the antenna to be tested is replaced with an antenna whose directional pattern actually needs to be tested, and steps S1 and S2 are repeated to complete data collection and data synchronization, perform error correction and interpolation, and obtain a reconstructed directional pattern. The present invention overcomes the dependence of traditional measurement schemes on platform stability, controls the measured error within 0.5dB, and meets the high-precision measurement requirements of 5G, radar, navigation, and other applications.

Description

Technical Field

[0001] The present invention relates to the field of wireless communications, and in particular to a method for reconstructing antenna patterns of unmanned aerial vehicles (UAVs) under non-ideal flight conditions. Background Art

[0002] Currently, drone systems used for antenna pattern measurement often assume an ideal and stable flight path. However, in practice, drones often experience trajectory deviations and attitude disturbances due to environmental factors. Existing systems fail to effectively address these errors, affecting the accuracy of measurement results. Consequently, traditional pattern measurement methods rely heavily on flight trajectory stability. Even the slightest deviation from the drone's trajectory can lead to pattern reconstruction errors. The lack of methods to compensate for and correct signal sampling errors in the presence of attitude disturbances and path deviations makes them incapable of meeting the high-precision requirements of antenna measurement tasks. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for reconstructing the antenna pattern of a UAV under non-ideal flight conditions, thereby solving the shortcomings of the prior art.

[0004] The purpose of the present invention is achieved through the following technical solutions: a method for reconstructing the antenna pattern of a UAV under non-ideal flight conditions, the reconstruction method comprising:

[0005] S1. The drone flies on a preset spatial trajectory around the antenna to be tested, collects position data, attitude data, and received signal level data, and performs time synchronization and data alignment;

[0006] S2. Compare each actual sampling point with its corresponding theoretical sampling point, calculate the spatial offset and antenna pointing offset angle, and build an error compensation model based on the spatial offset and antenna pointing offset angle;

[0007] S3. Replace the antenna to be tested with the antenna whose directional pattern actually needs to be tested, repeat steps S1 and S2 to complete data acquisition and data synchronization, and then perform error correction and interpolation to obtain a reconstructed directional pattern.

[0008] The time synchronization and data alignment include:

[0009] Select a unified reference time axis, use the data sampling data timestamp of the receiver module as a reference, and search for the corresponding position data and attitude data in the attitude data and position data sets. If not found, use the nearest 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 kth sampling data, pk is the position data of the kth sampling data, R k is the posture data of the kth sampling data, r k is the received signal level of the kth sampling data.

[0010] The S2 specifically includes the following contents:

[0011] 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 i k =arccos(d k ×V ideal,k / || d k ||×||V ideal,k ||), where ΔP k is the spatial offset of the kth sampling data, d k is the actual measured direction vector, V ideal,k is the ideal direction vector, Δ i k is the antenna pointing offset angle of the kth sampling data;

[0012] A standard antenna with a known directional pattern is used as the antenna to be tested. The theoretical received signal level r at each measurement position is calculated based on the radio wave propagation model and the directional pattern of the antenna to be tested. ideal,k , according to the actual measurement of the received signal level r of the data acquisition k , solve the error value between the actual received signal level and the theoretical received signal level as ε k =r k -r ideal,k , ε k is the error between the received signal level of the kth sample data and the theoretical received signal level;

[0013] Substitute the multivariate error compensation model to describe ε=α1|ΔP|+α2Δ i +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 i ;

[0014] The least squares method is used to fit and solve α1, α2 and β and store them, thus completing the error compensation model construction.

[0015] The S3 specifically includes the following contents:

[0016] Replace the antenna to be tested with the antenna whose radiation pattern actually needs to be tested, repeat steps S1 and S2, and calculate the spatial offset and 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. Substitute these into the error compensation model to correct the measured signal level.

[0017] For each theoretical point P ideal,j , based on the spatial Euclidean distance, search for k nearby sampling points in the actual point set, where the maximum search radius is r max ;

[0018] Using weighted average method Estimate the signal value of the theoretical point and complete the reconstruction of the antenna pattern, where r 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 point j, and the weight W ij Inverse distance squared weighting Make an estimate.

[0019] The UAV carries a measurement payload and flies along a predetermined trajectory to collect wireless signals transmitted by the antenna to be measured. During the collection process, the measurement of the measurement payload is summarized and transmitted back to the ground measurement and control computer in real time via the wireless line between the remote controller and the UAV.

[0020] The measurement load 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 connected to the receiver module and the control board respectively, and the azimuth motor and the pitch motor are connected to the control board.

[0021] The position data includes: collecting high-precision three-dimensional data of longitude, latitude and altitude output by the drone RTK module, as well as the data sampling timestamp;

[0022] The attitude data includes: the azimuth motor, pitch motor, polarization motor, azimuth, pitch angle and roll angle and data sampling timestamp;

[0023] The received signal level data includes: a signal level value measured by a receiver module and a data sampling timestamp.

[0024] The present invention has the following advantages: a method for reconstructing antenna patterns in non-ideal UAV flight conditions, which can effectively reconstruct accurate antenna patterns even when there are 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 traditional measurement scheme's dependence on platform stability, and controls the measured error within 0.5dB, meeting the high-precision measurement requirements of 5G, radar, navigation, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application provided below in conjunction with the drawings is not intended to limit the scope of protection of the present application for which protection is claimed, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application. The present invention is further described below in conjunction with the drawings.

[0027] The present invention specifically relates to a method for reconstructing antenna patterns of unmanned aerial vehicles (UAVs) under non-ideal flight conditions. Under non-ideal flight conditions in which the trajectory or attitude of the UAV deviates, a high-precision antenna pattern can still be reconstructed. A UAV equipped with a measurement payload flies along a predetermined trajectory to collect wireless signals emitted by an antenna to be measured. During the collection process, the measurement of the measurement payload is transmitted back to a ground measurement and control computer in real time via a wireless link between a remote controller and the UAV, and can also be stored in a measurement payload storage device.

[0028] Among them, the measurement payload includes: antenna, polarization motor, pitch motor, azimuth motor, receiving module and control board; the antenna is connected to the polarization motor, the polarization motor is connected to the receiver module and control board respectively, and the azimuth motor and pitch motor are connected to the control board.

[0029] like Figure 1 As shown, specifically including the following:

[0030] Step 1: Flight data and signal collection;

[0031] The drone flies on a preset spatial trajectory around the antenna to be tested, and the information collected includes position data, attitude data, and received signal level data;

[0032] Among them, position data: collects high-precision three-dimensional coordinates (longitude, latitude, altitude) and data sampling timestamp output by the drone RTK module;

[0033] Attitude data: azimuth, pitch, and roll angles of the azimuth motor, pitch motor, and polarization motor, as well as data sampling timestamps;

[0034] Received signal level data: The receiver module measures the signal level value and data sampling timestamp.

[0035] Step 2: Time synchronization and data alignment;

[0036] Since the sampling of each module is asynchronous, a standardized sampling point needs to be uniformly constructed. The steps include: selecting a unified reference time axis, using the data sampling data timestamp of the receiver module as a reference, finding the corresponding position data and attitude data in the attitude data and position data set, and using the nearest principle or linear interpolation if it cannot be found. Form a unified sampling data format (t k ,p k ,R k ,r k ), where t k is the timestamp of the kth sampling data, p k is the position data of the kth sampling data, R k is the posture data of the kth sampling data, r k is the received signal level of the kth sampling data.

[0037] Step 3: Construct an error correction model based on trajectory and posture deviation;

[0038] 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 i k =arccos(d k ×V ideal,k / || d k ||×||V ideal,k ||), where ΔP k is the spatial offset of the kth sampling data, d k is the actual measured direction vector, V ideal,k is the ideal direction vector, Δ i k is the antenna pointing offset angle of the kth sampling data;

[0039] A standard antenna with a known directional pattern is used as the antenna to be tested. The theoretical received signal level r at each measurement position is calculated based on the radio wave propagation model and the directional pattern of the antenna to be tested. ideal,k , according to the actual measurement of the received signal level r of the data acquisition k , solve the error value between the actual received signal level and the theoretical received signal level as ε k =r k -r ideal,k , εk is the error between the received signal level of the kth sample data and the theoretical received signal level;

[0040] Substitute the multivariate error compensation model to describe ε=α1|ΔP|+α2Δ i +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 i ;

[0041] The least squares method is used for fitting to solve α1, α2 and β and store them. The error compensation model is now constructed. α1 represents the position offset sensitivity coefficient, α2 represents the antenna pointing offset sensitivity coefficient, and β represents the constant bias.

[0042] Step 4: remapping and interpolation compensation;

[0043] Replace the antenna to be tested with the antenna whose radiation pattern needs to be tested. Repeat steps 1 and 2. Calculate the spatial offset and antenna pointing offset angle based on the position and attitude data from the measured data and the position and attitude data from the theoretical flight trajectory. Substitute these into the error compensation model to correct the measured signal level.

[0044] For each theoretical point P ideal,j , based on the spatial Euclidean distance, search for k nearby sampling points in the actual point set, where the maximum search radius is r max ;

[0045] Using weighted average method Estimate the signal value of 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 point j, and the weight W ij Inverse distance squared 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.

[0046] This method quantifies the deviation between the actual position and attitude data of the drone and constructs a correction model based on the relationship between position offset distance, pointing deviation angle, and received signal level error. The model is solved using the least squares method, and inverse distance squared weighting is used to perform weighted interpolation on the error-corrected received signal levels, improving the accuracy of pattern reconstruction.

[0047] The foregoing description is merely 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 construed as excluding other embodiments. Rather, the present invention is capable of various other combinations, modifications, and improvements, and is capable of modifications within the scope of the concepts described herein, through the above teachings, or through techniques or knowledge in the relevant fields. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention are intended to be within the scope of the appended claims.

Claims

1. A method for reconstructing antenna patterns for unmanned aerial vehicles (UAVs) under non-ideal flight conditions, characterized by: The reconstruction method comprises: S1. The drone flies on a preset spatial trajectory around the antenna to be tested, 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 antenna pointing offset angle, and build an error compensation model based on the spatial offset and antenna pointing offset angle; S3, replacing the antenna to be tested with the antenna whose directional pattern actually needs to be tested, repeating steps S1 and S2 to complete data acquisition and data synchronization, and then performing error correction and interpolation to obtain a reconstructed directional pattern; The S2 specifically includes the following contents: 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 kth sampling data, d k is the actual measured direction vector, V ideal,k is the ideal direction vector, Δ θ k is the antenna pointing offset angle of the kth sampling data, t k is the timestamp of the kth sampling data; A standard antenna with a known directional pattern is used as the antenna to be tested. The theoretical received signal level r at each measurement position is calculated based on the radio wave propagation model and the directional pattern of the antenna to be tested. ideal,k , according to the actual measurement of the received signal level r of the data acquisition k , solve the error value between the actual received signal level and the theoretical received signal level as ε k =r k -r ideal,k , ε k is the error between the received signal level of the kth sample data and the theoretical received signal level; Substitute the multivariate error compensation model to describe ε=α1|ΔP|+α2Δ θ +β, based on the position data and attitude data of the theoretical flight trajectory and the actual flight trajectory, the spatial offset ΔP and the antenna pointing offset angle Δ θ ; The least squares method is used for fitting to solve α1, α2 and β and store them. The error compensation model is now constructed. α1 represents the position offset sensitivity coefficient, α2 represents the antenna pointing offset sensitivity coefficient, and β represents the constant bias.

2. The method for reconstructing antenna patterns for unmanned aerial vehicles under non-ideal flight conditions according to claim 1, characterized in that: The time synchronization and data alignment include: Select a unified reference time axis, use the data sampling data timestamp of the receiver module as a reference, and search for the corresponding position data and attitude data in the attitude data and position data sets. If not found, use the nearest 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 kth sampling data, p k is the position data of the kth sampling data, R k is the posture data of the kth sampling data, r k is the received signal level of the kth sampling data.

3. The method for reconstructing antenna patterns for unmanned aerial vehicles under non-ideal flight conditions according to claim 1, characterized in that: The S3 specifically includes the following contents: Replace the antenna to be tested with the antenna whose radiation pattern actually needs to be tested, repeat steps S1 and S2, and calculate the spatial offset and 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. Substitute these 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 sampling points in the actual point set, where the maximum search radius is r max ; Using weighted average method Estimate the signal value of the theoretical point and complete the reconstruction of the antenna pattern, where r 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 point j, and the weight W ij Inverse distance squared weighting Make an estimate, is a non-zero minimum value.

4. The method for reconstructing antenna patterns for unmanned aerial vehicles under non-ideal flight conditions according to claim 1, characterized in that: The UAV carries a measurement payload and flies along a predetermined trajectory to collect wireless signals transmitted by the antenna to be measured. During the collection process, the measurement of the measurement payload is summarized and transmitted back to the ground measurement and control computer in real time via the wireless line between the remote controller and the UAV. The measurement load 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 connected to the receiver module and the control board respectively, and the azimuth motor and the pitch motor are connected to the control board.

5. The method for reconstructing antenna patterns for unmanned aerial vehicles under non-ideal flight conditions according to claim 4, characterized in that: The position data includes: collecting high-precision three-dimensional data of longitude, latitude and altitude output by the drone RTK module, as well as the data sampling timestamp; The attitude data includes: the azimuth motor, pitch motor, polarization motor, azimuth, pitch angle and roll angle and data sampling timestamp; The received signal level data includes: a signal level value measured by a receiver module and a data sampling timestamp.

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