A plane near-field fast measurement method of a large aperture antenna

Abstract

This invention provides a method for rapid planar near-field measurement of large-aperture antennas, relating to the field of antenna technology. The method obtains sparse field amplitude and phase information of several sampling surfaces in the near-field radiation region of the antenna under test through large-step uniform sampling. Simultaneously, it samples a finite number of precise field amplitude and phase information points for each sampling surface and obtains the field distribution characteristics by calculating the fluctuation of the amplitude at the sampling points. Based on the sparse field amplitude and phase information and field distribution characteristics of the sampling surfaces, it uses partitioned interpolation and field distribution calibration methods to obtain the initial electric field of each sampling surface. Based on planar wave spectrum theory, iterative numerical calculations are performed on the initial electric field to recover the near-field distribution of each sampling surface. Finally, the far-field radiation characteristics of the antenna are obtained through near-field-to-far-field transformation. This invention can significantly shorten the testing time while obtaining key indicators of the antenna's far-field characteristics, including the normalized radiation pattern main lobe and first sidelobe, 3dB beamwidth, sidelobe level, and axial ratio.

Description

Technical Field

[0001] This invention relates to the field of antenna technology, and in particular to a planar near-field rapid measurement method for large-aperture antennas. Background Technology

[0002] In recent years, with the rapid development of deep space exploration, satellite communication, and radar remote sensing, the demand for large-aperture antennas has been increasing. Their high gain, narrow beamwidth, and high resolution give them irreplaceable advantages in long-distance communication and high-precision imaging. Antenna performance directly determines the overall system effectiveness, making antenna testing a crucial step. Existing testing methods are mainly divided into two categories: direct methods, including far-field methods, focusing methods, and compact field methods; and indirect methods, namely near-field measurements. Compared to direct methods, near-field measurements have advantages such as smaller space requirements, higher accuracy, stronger environmental adaptability, and higher testing efficiency, and have become the mainstream technology. Based on the shape of the measurement surface, near-field measurements can be divided into planar, cylindrical, and spherical measurements. Among these, planar measurements are the most widely used due to their simple structure, high accuracy, wide applicability, and low cost.

[0003] Traditional planar near-field measurement methods, based on the Nyquist sampling theorem, acquire amplitude and phase information of the electric field in the near-field region and obtain far-field characteristics through near-far-field transformation. This method requires the sampling interval to be no more than half a wavelength, and as the antenna operating frequency increases, the number of sampling points surges, leading to a significant increase in test time. Long-term testing can easily cause amplitude and phase fluctuations in the output field of the transmitting system, affecting measurement accuracy.

[0004] Currently, the following two phase-free measurement methods are mainly used for near-field measurements of millimeter-wave antennas:

[0005] The first method is to recover the near field based on the iterative Fourier transform algorithm. For example, application number 201910632593.6 discloses a phase-free near-field antenna measurement method based on the iterative Fourier transform algorithm. This method constructs an initial field with a random phase and test amplitude, and optimizes the phase information by iterating between two planes and applying amplitude constraints. However, the iterative process is prone to getting trapped in local optima. To address this, application number 202310171476.0 discloses a phase-free planar near-field measurement method based on the conjugate gradient method. By introducing the conjugate gradient method, a global cost function is constructed to optimize the iteration direction, enhancing global convergence. However, this method still needs to meet the Nyquist sampling condition, which requires a huge time investment. Moreover, the randomly generated initial phase makes the amount of data to be optimized for large-aperture antennas quite large, consuming a lot of computing resources and reducing testing efficiency.

[0006] The second method optimizes the initial phase using the principle of uniqueness, and then recovers the near field using an iterative Fourier algorithm [Wang Yuan. Research on Phase-Free Near-Field Antenna Measurement Based on Interpolation Algorithm and Source Reconstruction Method [D]. Shaanxi: Xi'an University of Electronic Science and Technology, 2018.]. This method obtains the initial phase by constructing discrete equivalent surface current sources and combining them with particle swarm optimization algorithm to reconstruct a current distribution similar to the actual field distribution on the antenna aperture surface. However, the optimization problem is still non-convex and easily gets trapped in local optima; in addition, the discrete current source approximates the continuous field distribution with errors, and the number of current sources also affects the optimization effect. Summary of the Invention

[0007] To address the problems of cumbersome measurement processes, high time costs, and the tendency of phase optimization algorithms to get trapped in local optima in traditional phase-free testing for large-aperture array antennas, this invention proposes a rapid planar near-field measurement method for large-aperture antennas. This method can significantly shorten the testing time while obtaining key indicators of the antenna's far-field characteristics, including the normalized radiation pattern main lobe and first sidelobe, 3dB beamwidth, sidelobe level, and axial ratio.

[0008] The technical solution adopted in this invention is:

[0009] A rapid planar near-field measurement method for a large-aperture antenna is characterized by obtaining sparse field amplitude and phase information of several sampling surfaces in the near-field radiation region of the antenna under test through large-step uniform sampling; simultaneously, sampling a finite number of precise field amplitude and phase information for each sampling surface, and obtaining the field distribution characteristics by calculating the fluctuation of the amplitude at the sampling points; based on the sparse field amplitude and phase information and field distribution characteristics of the sampling surfaces, obtaining the initial electric field of each sampling surface using partition interpolation and field distribution calibration methods; numerically iterating the initial electric field based on planar wave spectrum theory to recover the near-field distribution of each sampling surface; and finally obtaining the far-field radiation characteristics of the antenna through near-field-far-field transformation.

[0010] Specifically, it includes the following steps:

[0011] Step 1. In the near-field region of the antenna under test, uniformly sample several surface data points and several line data points;

[0012] Step 1.1. At several planes at different distances from the aperture surface, uniformly sample the amplitude and phase data of the near field with a large step size;

[0013] Specifically, increasing the proportion of the central effective field region to the total sampling surface allows sparse sampling data to provide more effective field data with less distortion, and the interpolation strategy of this invention can obtain better initial values ​​for iteration. When the proportion of the central effective field region to the total sampling surface is greater than 70%, the sampling step size is selected as 5λ or more; when the proportion of the central effective field region to the total sampling surface is less than 70%, the sampling step size is selected as 2-5λ.

[0014] The continuity of the effective field distribution, the degree of data distortion, and the number of sampling points all affect the recovery result. If the effective field distribution is continuous and the data distortion is small, then two sampling surfaces should be selected; otherwise, the sampling step size should be reduced and the number of sampling surfaces increased.

[0015] To avoid spectral aliasing, the spacing between adjacent sampling planes is greater than 1λ;

[0016] Step 1.2. According to the Nyquist sampling theorem, sample several lines of data passing through the antenna element uniformly along the x and y directions on each sampling surface;

[0017] The field distribution characteristics of the sampling plane are obtained by analyzing the sampled line data; if the central effective field is continuous and the amplitude variation is less than 5dB, 3-5 symmetrical line data are sampled in the x and y directions respectively; otherwise, the number of sampled line data is increased to obtain the global characteristics of the electric field.

[0018] Acquiring multiple sampling surfaces can improve recovery accuracy, but it increases sampling time. Considering both recovery efficiency and accuracy, it is common practice to sparsely sample the electric fields of two planes and precisely sample four symmetrical lines in each of the x and y directions.

[0019] Step 2. Calculate the fluctuation of the amplitude of adjacent sampling points by the line data sampled in Step 1.2 to determine the range of the effective field. The defined range of the effective field will be used as a reference to correct the field after propagation.

[0020] Step 3. Select different interpolation strategies for the field distribution characteristics of different regions to obtain field data that satisfy the plane wave spectrum propagation conditions;

[0021] Step 3.1. Use bilinear interpolation on the sampling data within the effective field range of each sampling surface. After interpolation, the spacing should be consistent with the sampling step size of the line data, and the plane range covered by the data should remain unchanged.

[0022] Step 3.2. In the cutoff region outside the central effective field, the field within the rectangular ring surrounding the central field changes continuously, while the radial phase information exhibits abrupt changes. Therefore, to ensure a smooth and continuous field within the ring region, the line-sampled data within each rectangular ring is first expanded into column vectors according to their location, and then cubic spline interpolation is used to correct the data with missing phase features due to sparse sampling; the data outside the rectangular ring is supplemented using non-uniform interpolation.

[0023] Step 4. For the electric field data of each sampling surface obtained in Step 3.2, calculate the effective field range every ten lines of data in the x and y directions according to the method shown in Step 2, and correct it with reference to the effective field range; use non-uniform interpolation to supplement the error data caused by the field offset due to the interpolation method; replace the test data at the corresponding positions, and at this time the initial electric field to be recovered of each sampling surface is obtained;

[0024] Step 5. Based on the plane wave spectrum theory of fast Fourier transform, iteratively calculate the initial electric field of each sampling surface to recover the electric field of each sampling surface:

[0025] Step 5.1. Define the sampling surface closer to the antenna aperture surface as the first sampling surface and the other sampling surface as the second sampling surface; the iterative electric field Em2_1 of the second sampling surface is obtained by propagating the initial electric field Ep1 of the first sampling surface;

[0026] Step 5.2. For the iterative electric field Em2_1, calculate the range of the central effective field according to the method shown in Step 4, and correct the error data; use the test data of the second sampling surface in Step 1 to replace it at the corresponding position; at this time, the iteratively corrected electric field Em2 of the second sampling surface is obtained;

[0027] Step 5.3. Obtain the iterative electric field Em1_1 of the first sampling surface by propagating the corrected electric field Em2 of the second sampling surface;

[0028] Step 5.4. For the iterative electric field Em1_1, calculate the range of the central effective field according to the method shown in Step 4, and correct the error data; use the test data of the first sampling surface in Step 1 to replace it at the corresponding position; then, continue to propagate to the second sampling surface for iteration.

[0029] Step 5.5. The two propagation processes in steps 5.1-5.4 are considered as one cycle. The difference ΔFX between the vector correlation coefficients of the iterative electric field of the first sampling surface and the initial electric field in the two cycles is used as the criterion for whether the iterative operation terminates.

[0030] If the number of iterations reaches the set maximum number of iterations Q or the difference in vector correlation coefficients ΔFX is less than 1%, then the loop is exited and the electric field of the two sampling surfaces in the last iteration is output, which is the final recovered electric field.

[0031] Otherwise, continue iterating until the termination condition is met.

[0032] Step 6. Obtain the far-field radiation characteristics of the antenna under test from the recovered electric field of the first sampling surface using planar near-field and far-field transformation.

[0033] Working principle and testing time of this invention:

[0034] This method avoids getting trapped in local optima: by using large-step sampling, it significantly shortens the testing time and reduces the impact of output field fluctuations caused by long-term testing of the launch system. Numerical iteration based on test field data avoids the problem of relying on random initial phase optimization in traditional phase-free testing. A sparse multi-faceted-precise multi-line sampling strategy is adopted: combining large-step surface sampling with line sampling under the Nyquist criterion, it effectively reduces the number of sampling points while preserving key field distribution characteristics. A central effective field region identification and calibration method is introduced: by analyzing line data fluctuations, the effective region is automatically extracted for interpolation region division and post-propagation field calibration, improving the algorithm's adaptability. Different interpolation strategies are used for different regional field characteristics: enhancing the restoration accuracy of sparse sampled data and reducing interpolation errors.

[0035] Compared with the sampling time of 0.5λ step size in traditional near-field sampling tests, the method shown in this invention can complete the sampling of the electric field of a single sampling surface by sparsely sampling surface data with a step size of 5λ, which accounts for about 1% of the time, and then accurately sampling a limited number of line data with a step size of 0.5λ, thereby significantly shortening the overall test time.

[0036] Compared with current near-field testing methods without phase sampling surfaces, the present invention has the following advantages:

[0037] First, this invention overcomes the problems of phase-free planar near-field testing, which relies on initial values ​​and is prone to getting trapped in local optima. It employs large-step uniform sampling to rapidly acquire amplitude and phase data of the electric field of several sampling surfaces, significantly shortening the testing time, reducing the impact of output field fluctuations caused by long-term testing of the transmission system, and improving the measurement accuracy of the sampling surface electric field. By analyzing a finite number of precise data points to obtain the field distribution characteristics, and combining this with methods such as partitioned interpolation and field distribution calibration, it can provide highly accurate iterative initial values, avoid getting trapped in local optima, and save significant computational resources.

[0038] Secondly, this invention enables rapid and efficient measurement of large-aperture antennas. Based on the field distribution characteristics analyzed from precise sampling line data, different interpolation strategies are adopted for different regions, resulting in relatively reliable initial values ​​for iteration. Furthermore, during each iteration, the field propagated to each surface is calibrated using a reference effective field region. This effectively corrects data with missing field features due to sparse sampling and errors caused by field shifts due to interpolation methods, thus accelerating the iteration convergence speed.

[0039] Third, this invention has good engineering applicability. The method has been successfully applied to the testing of Ka-band large-aperture strong-feed array antennas, and its recovery effect on Gaussian feed sources has been verified. Under low-frequency conditions, the phase is less affected by test time and test environment, thus enabling the sharing of high- and low-frequency antennas. Data is obtained through planar near-field uniform sampling, and most anechoic chambers are suitable for testing. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating the overall implementation of the present invention;

[0041] Figure 2 This is a sub-flowchart for determining the boundary of the central field in this invention;

[0042] Figure 3 This is a flowchart illustrating the implementation of the present invention for rapid measurement based on plane wave spectrum theory;

[0043] Figure 4 This is a schematic diagram of the experimental test of the present invention;

[0044] Figure 5 This is a diagram showing the recovery effect of vertical polarization of 5λ sparse sampling data;

[0045] Figure 6 This is a diagram showing the recovery effect of horizontal polarization of 5λ sparse sampling data;

[0046] Figure 7 This is a diagram showing the recovery effect of right-hand circular polarization of 5λ sparsely sampled data. Detailed Implementation

[0047] To more clearly illustrate the objectives and technical solutions of this invention, the present invention will be further described in detail below with reference to embodiments and accompanying drawings. The specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0048] Reference Figure 1 The large-aperture antenna planar near-field rapid measurement method provided in this example includes the following steps:

[0049] Step 1. In the near-field region of the antenna under test, uniformly sample several surface data points and several line data points. The sampling environment is as follows: Figure 4 .

[0050] (1.1) On two planes at different distances from the aperture plane, the amplitude and phase data of the near field are sampled uniformly with a large step size.

[0051] The antenna is a large-aperture, high-feed array antenna with an aperture area of ​​2.2 x 2.2 meters. 2 The sampling frequency band is 27-33GHz, the sampling plane is the xoy plane, the sampling plane is parallel to the antenna aperture plane and the sampling range is the same.

[0052] The sampling ranges Lx and Ly both satisfy greater than or equal to 2D*tanθ+a, where D is the maximum distance from the sampling surface to the antenna aperture surface, θ is the maximum reliable far-field angle, and a is the aperture surface width. A pre-scan is required before testing. The ranges of Lx and Ly are determined based on the sampling level difference between the effective field boundary and the sampling surface boundary being less than -10dB. In this embodiment, both Lx and Ly are 2.5m.

[0053] When the central effective field area accounts for more than 70% of the total sampling area, the sampling step size should be 5λ or greater; when the central effective field area accounts for less than 70% of the total sampling area, the sampling step size should be 2-5λ. The continuity of the effective field distribution, the degree of data distortion, and the number of sampling points all affect the recovery effect. If the effective field distribution is continuous and the data distortion is small, two sampling surfaces should be selected. Otherwise, the sampling step size should be reduced and the number of sampling surfaces increased. In this embodiment, two planes are sampled at distances of 4λ and 5.2λ from the aperture surface of the antenna under test, respectively, and the sampling step sizes dx and dy are both 5λ.

[0054] The first sampling plane amplitude A1 and phase P1 of the antenna under test are obtained, and the second sampling plane amplitude A2 and phase P2 are obtained. According to the electric field E = A·e j.P The electric fields E1 and E2 of the two sampling surfaces are obtained.

[0055] (1.2) According to the Nyquist sampling theorem, four line data L1_k and L2_k passing through the antenna element are uniformly sampled along the x and y directions on each sampling surface, with a sampling step size of 0.5λ.

[0056] Step 2. By calculating the fluctuation of the amplitude of adjacent sampling points on the sampled line data, the range of the effective field is determined. The defined effective field range will be used as a reference to correct the field after propagation.

[0057] Reference Figure 2 Taking two planes as an example, this step is implemented as follows:

[0058] (2.1) Read the amplitude of four sampling points sequentially from the initial point of the line sampling data, and calculate the fluctuation of the amplitude of adjacent sampling points; if the fluctuation of the sampling data within the effective field range exceeds 5dB, the sampling data needs to be smoothed first.

[0059] The formula for calculating the amplitude fluctuation between adjacent sampling points is as follows:

[0060]

[0061] Where x(n) is the amplitude of the nth sampling point, This represents the average amplitude across all sampling points.

[0062] (2.2) If the difference between the two amplitude change rates before and after the sampling point is greater than 15% and the difference between the two consecutive amplitude change rates after the point is less than 5%, then output the amplitude and position of the left boundary point, and then read the subsequent data in sequence; if the difference between the two amplitude change rates before and after the sampling point is greater than 15% and the difference between the two amplitude change rates before the point is less than 5%, then output the amplitude and position of the right boundary point.

[0063] (2.3) Based on the boundary points on both sides of each line data determined in step (2.2), the area connected by these boundary points is identified as the range of the reference effective field.

[0064] Step 3. After the electric field has been processed by partition interpolation and field distribution calibration, the electric field of the sampling surface is recovered by iterating between the sampling planes using plane wave spectrum theory.

[0065] Reference Figure 3 Taking the two planes sampled in the embodiment as an example, this step is implemented as follows:

[0066] (3.1) Different interpolation strategies are used for different regions to obtain field data that meet the propagation conditions of the plane spectrum.

[0067] Bilinear interpolation is used for the sampling data within the effective field range of each sampling surface. After interpolation, the spacing is consistent with the sampling step size of the line data, and the plane range covered by the data remains unchanged.

[0068] In the cutoff region outside the effective field range, the field changes continuously within the rectangular ring surrounding the effective field, while the radial phase information exhibits abrupt changes. Therefore, to ensure a smooth and continuous field within the ring region, the line-sampled data within each rectangular ring is first expanded into column vectors according to their location, and then cubic spline interpolation is used to correct the data with missing phase features due to sparse sampling; the data outside the rectangular ring is supplemented using non-uniform interpolation.

[0069] (3.2) For error data caused by interpolation method to shift the effective field, the calibration shall be performed according to the reference effective field range.

[0070] After interpolating the electric field of each sampling surface obtained in step (3.1), calculate the range of the effective field every ten data lines in the x and y directions according to the method shown in step 2, and correct it with the reference effective field range; use non-uniform interpolation to supplement the error data of the effective field offset caused by the interpolation method; replace the sampling data at the corresponding positions to obtain the initial electric fields Ep1 and Ep2 to be recovered for the two sampling surfaces.

[0071] (3.3) Based on the plane wave spectrum theory of fast Fourier transform, the initial electric field of each sampling surface is iteratively calculated to obtain the recovered electric field of each sampling surface:

[0072] Plane wave propagation based on Fast Fourier Transform is expressed by the following formula:

[0073] E(x,y,d2)=IFFT2[FFT2[E(x,y,d1)]·exp(-jk z (d2-d1))]

[0074] Where j is the imaginary unit, k zd1 is the spatial wavenumber, FFT2 is the two-dimensional Fourier operator, IFFT2 is the two-dimensional Fourier inverse operator, d1 is the distance between the sampling plane before propagation and the aperture surface of the antenna under test, and d2 is the distance between the sampling plane after propagation and the aperture surface of the antenna under test.

[0075] To improve the accuracy of frequency domain propagation, the initial electric field is zero-filled, which expands a single dimension to four times the amount of data.

[0076] The iterative electric field Em2_1 of the second sampling surface is obtained by propagating the initial electric field Ep1 of the first sampling surface.

[0077] For the iterative electric field Em2_1, the effective field range is calculated according to the method shown in step 2, and the error data is corrected; the sampling data E2 and L2_k from the second sampling surface are used to replace the data at the corresponding positions. At this time, the iteratively corrected electric field Em2 of the second sampling surface is obtained.

[0078] The iterative electric field Em1_1 of the first sampling surface is obtained by propagating the modified electric field Em2 of the second sampling surface.

[0079] For the iterative electric field Em1, the range of the central field is calculated according to the method shown in step 2, and the error data is corrected; the test data E1 and L1_k from the first sampling surface are used to replace the data at the corresponding positions. Then, the iteration continues to the second sampling surface.

[0080] The two propagation processes in step (3.3) are considered as one loop. The difference ΔFX between the vector correlation coefficients of the iterative electric field of the first sampling surface and the initial electric field in the two loops is used as the criterion for whether the iterative operation terminates.

[0081] The vector correlation coefficient FX is calculated using the following formula:

[0082]

[0083] Where Ed1 and Ed2 represent the electric field after iteration and the electric field before iteration on the same sampling surface, respectively. Let |i,j| denote the conjugate of the complex field, |.| denotes taking the magnitude of the vector, and (i,j) denotes the position of the sampling point on the plane.

[0084] If the number of iterations reaches the set maximum number of iterations Q or the difference in vector correlation coefficients ΔFX is less than 1%, then the loop will exit and the electric field of the two sampling surfaces in the last loop will be output, which is the final recovered electric field.

[0085] Otherwise, return to (3.3) and continue iterating until the termination condition is met.

[0086] Step 4. Using the planar near-field and far-field transformation theory, the far-field radiation characteristics of the antenna under test are obtained from the final recovered electric field of the first sampling surface.

[0087] The formula used for planar near-field and far-field transformation is as follows:

[0088]

[0089]

[0090] in, These represent the coordinates of the antenna under test located in the spherical coordinate system. The element values ​​in the far-field pattern along the θ and Φ directions, where j is the imaginary unit, k is the wave number, and A x A y These represent the plane wave spectrum components of the antenna under test along the x and y directions, respectively.

[0091] The effectiveness of this invention can be further demonstrated through the following experiments.

[0092] The above method will be used to reconstruct the area sampling data with a sampling step size of 5λ, and the main indicators of the standard far-field radiation characteristics obtained with a sampling step size of 0.5λ will be compared, such as the normalized far-field radiation pattern, sidelobe level, 3dB beamwidth, and axial ratio. The reconstruction error of the normalized radiation pattern is calculated according to the following formula: error = 20log(||Eh| - |Ec||), where |Eh| and |Ec| are the normalized amplitudes of the reconstructed far-field and standard far-field obtained by near-field transformation, respectively.

[0093] Verification 1: The near field was reconstructed using sparse sampling data with a vertical polarization step size of 5λ, and the following results were obtained: Figure 5 The recovery effect diagram is shown, and the main indicators of the antenna's far-field characteristics are compared. Figure 5 In the above, (a) and (b) represent the standard amplitude and the recovered amplitude, (c) and (d) represent the standard phase and the recovered phase, with a vector correlation coefficient of 0.9846; (e) shows the E-plane normalized radiation pattern and the recovery error, with the recovery error being less than -49.881dB, the standard 3dB beamwidth being 0.2286, the recovered 3dB beamwidth being 0.2321, the standard sidelobe level being -37.439dB, and the recovered sidelobe level being -38.115dB; (f) shows the H-plane normalized radiation pattern and the recovery error, with the recovery error being less than -58.618dB, the standard 3dB beamwidth being 0.1481, the recovered 3dB beamwidth being 0.1517, the standard sidelobe level being -64.731dB, and the recovered sidelobe level being -63.832dB; the main indicators of the recovered far-field are in good agreement with the standard values.

[0094] Verification 2: The near field was recovered using sparse sampling data with a horizontal polarization step size of 5λ, and the following results were obtained: Figure 6 The recovery effect diagram is shown, and the main indicators of the antenna's far-field characteristics are compared. Figure 6In the above, (a) and (b) represent the standard amplitude and the recovered amplitude, (c) and (d) represent the standard phase and the recovered phase, with a vector correlation coefficient of 0.9818; (e) shows the E-plane normalized radiation pattern and the recovery error, with the recovery error being less than -49.398 dB, the standard 3 dB beamwidth being 0.2277, the recovered 3 dB beamwidth being 0.2314, the standard sidelobe level being -37.313 dB, and the recovered sidelobe level being -37.915 dB; (f) shows the H-plane normalized radiation pattern and the recovery error, with the recovery error being less than -56.726 dB, the standard 3 dB beamwidth being 0.1485, the recovered 3 dB beamwidth being 0.1523, the standard sidelobe level being -64.414 dB, and the recovered sidelobe level being -64.685 dB; the main indicators of the recovered far-field are in good agreement with the standard values.

[0095] Verification 3: Using the recovered electric field obtained from Verification 1 and Verification 2, the following was obtained: Figure 7 The right-hand circularly polarized wave shown is a key indicator for comparing the far-field characteristics of antennas. Figure 7 In the above, (a) shows the normalized radiation pattern and recovery error at an azimuth angle of 90 degrees. The standard 3dB beamwidth is 0.2282, the recovered 3dB beamwidth is 0.2319, the standard sidelobe level is -37.376dB, the recovered sidelobe level is -38.015dB, and the recovery error is less than -48.094dB; (b) shows the normalized radiation pattern and recovery error at an azimuth angle of 0 degrees. The standard 3dB beamwidth is 0.1483, the recovered 3dB beamwidth is 0.1519, the standard sidelobe level is -64.581dB, the recovered sidelobe level is -64.318dB, and the recovery error is less than -57.38dB; (c) shows the axial ratio at an azimuth angle of 90 degrees. At 0 degrees in the maximum radiation direction, the standard axial ratio is 0dB, and the recovered axial ratio is 0.02dB. The main indicators of the recovered far field are in good agreement with the standard values.

[0096] This embodiment completes the circular polarization near-field test of a large-aperture, high-feed array antenna in approximately 7% of the time using a traditional planar near-field testing method. Specifically, sparse sampling of the two planes with a step size of 5λ accounts for approximately 4.3%; precise sampling of four lines of data in each of the x and y directions of the two planes with a step size of 0.5λ accounts for approximately 2.7%.

[0097] Additional notes on the recovery effect of this method:

[0098] The recovery results of this embodiment match the standard value well. The reasons are as follows: First, the effective field area in the center accounts for 77.5% of the total sampling area, and the interpolation strategy of this invention can obtain better initial values ​​for iteration; Second, the antenna has a very narrow beam and a concentrated field distribution.

[0099] Based on this method, multiple planar near-field rapid measurements of Ka-band large-aperture array antennas have been completed, and the main far-field indicators can be recovered in all cases. This indicates that the method can significantly reduce the test time while reducing the impact of output field fluctuations caused by long-term testing of the transmitting system, quickly obtain the radiated electric field characteristics of the antenna under test, and effectively recover the far-field characteristics of the antenna based on this data.

Claims

1. A planar near-field rapid measurement method for a large-aperture antenna, characterized in that, The planar near-field rapid measurement method includes: obtaining sparse field amplitude and phase information of several sampling surfaces in the near-field radiation region of the antenna under test through large-step uniform sampling; simultaneously, sampling a finite number of precise field amplitude and phase information for each sampling surface, and obtaining field distribution characteristics by calculating the fluctuation degree of the amplitude at the sampling points; based on the sparse field amplitude and phase information and field distribution characteristics of the sampling surfaces, obtaining the initial electric field of each sampling surface using partition interpolation and field distribution calibration methods; performing numerical iteration on the initial electric field based on planar wave spectrum theory to recover the near-field distribution of each sampling surface; and finally obtaining the far-field radiation characteristics of the antenna through near-field-far-field transformation. Specifically, the steps include the following: Step 1. In the near-field region of the antenna under test, uniformly sample several surface data points and several line data points; Step 1.

1. At several planes at different distances from the aperture surface, uniformly sample the amplitude and phase data of the near field with a large step size; When the effective field area at the center accounts for more than 70% of the total sampling area, the sampling step size should be 5λ or more; when the effective field area at the center accounts for less than 70% of the total sampling area, the sampling step size should be 2-5λ. If the effective field distribution is continuous and the data distortion is small, then select two sampling surfaces; otherwise, reduce the sampling step size and increase the number of sampling surfaces. Step 1.

2. According to the Nyquist sampling theorem, sample several lines of data passing through the antenna element uniformly along the x and y directions on each sampling surface; The field distribution characteristics of the sampling plane are obtained by analyzing the sampled line data; if the central effective field is continuous and the amplitude variation is less than 5 dB, 3-5 symmetrical line data are sampled in the x and y directions respectively; otherwise, the number of sampled line data is increased to obtain the global characteristics of the electric field. Step 2. Calculate the fluctuation of the amplitude of adjacent sampling points for the line data sampled in Step 1.2, determine the range of the effective field, and use it as a reference to correct the field after propagation; Step 3. Select different interpolation strategies for the field distribution characteristics of different regions to obtain the electric field that satisfies the propagation conditions of the plane wave spectrum; Step 4. For the electric fields that satisfy the planar wave propagation conditions of each sampling surface obtained in Step 3, the effective field range is recalculated every ten lines of data in the x and y directions. The range of the recalculated effective field is corrected according to the reference effective field range, and non-uniform interpolation is used to supplement the error data caused by the interpolation method to obtain the corrected electric field. The sampled data is replaced at the corresponding electric field location to obtain the initial electric field to be recovered for each sampled surface; Step 5. Based on the plane wave spectrum theory of fast Fourier transform, perform iterative calculations on the initial electric field of each sampling surface, and obtain the final recovered electric field after the iteration is completed; Specifically, the steps include the following: Step 5.

1. Define the sampling surface closer to the antenna aperture surface as the first sampling surface and the other sampling surface as the second sampling surface; the iterative electric field Em2_1 of the second sampling surface is obtained by propagating the initial electric field Ep1 of the first sampling surface; Step 5.

2. For the iterative electric field Em2_1, calculate the effective field range and correct the error data according to the method shown in Step 4; replace the sampling data of the second sampling surface in Step 1 at the corresponding positions; at this time, the iteratively corrected electric field Em2 of the second sampling surface is obtained; Step 5.

3. Obtain the iterative electric field Em1_1 of the first sampling surface by propagating the corrected electric field Em2 of the second sampling surface; Step 5.

4. For the iterative electric field Em1_1, calculate the effective field range and correct the error data according to the method shown in Step 4; replace the corresponding positions with the sampling data of the first sampling surface in Step 1; then, continue to propagate to the second sampling surface for iteration; Step 5.

5. The two propagation processes in steps 5.1-5.4 are considered as one cycle. The difference ΔFX between the vector correlation coefficients of the iterative electric field of the first sampling surface and the initial electric field in the two cycles is used as the criterion for determining whether the iterative operation terminates. If the number of iterations reaches the set maximum number of iterations Q or the difference in vector correlation coefficients ΔFX is less than 1%, then the loop is exited and the electric fields of the two sampling surfaces obtained in the last iteration are output, which is the final recovered electric field. Otherwise, continue iterating until the termination condition is met; Step 6. Obtain the far-field radiation characteristics of the antenna under test from the final recovered electric field of the sampling surface using planar near-field and far-field transformation.

2. The planar near-field rapid measurement method for a large-aperture antenna as described in claim 1, characterized in that, Step 3 includes the following steps: Step 3.

1. Use bilinear interpolation on the sampling data within the effective field range of each sampling surface. After interpolation, the spacing should be consistent with the sampling step size of the line data, and the plane range covered by the data should remain unchanged. Step 3.

2. In the cutoff region outside the effective field range, first expand the line sampling data of each rectangular ring region surrounding the effective field range into column vectors according to their positions, and then use cubic spline interpolation to correct the data with missing phase features caused by sparse sampling; the data outside the rectangular ring is supplemented by non-uniform interpolation; the data in the cutoff region is interpolated at intervals consistent with the line data sampling step size.

3. A planar near-field rapid measurement method for a large-aperture antenna as described in claim 1 or 2, characterized in that, The distance between adjacent sampling surfaces is greater than 1λ.

Images