A large target RCS angle domain and frequency domain two-dimensional fast measurement method

Abstract

This invention relates to the field of large target RCS measurement technology, specifically to a two-dimensional fast measurement method for the RCS of large targets in the angular and frequency domains. The method constructs an equally spaced full-sampling measurement matrix P to obtain a sparse matrix R. Based on the sparse matrix R and the full-sampling signal, the target's rotation angle is scanned to obtain the measured value y. Finally, the full-sampling signal is reconstructed based on the measured value y to obtain the target's full-sampling RCS. The rotation-scanning sparse sampling method ensures that the sparse sampling frequency points are the same for each sampling angle, thus improving testing efficiency.

Description

Technical Field

[0001] This invention relates to the field of large target RCS measurement technology, and more specifically, to a two-dimensional rapid measurement method for the RCS of large targets in the angular and frequency domains. Background Technology

[0002] Modern warfare has entered the stealth era, and RCS measurement is crucial for the optimized design, finalization, qualification, mass production, and maintenance of full-scale stealth systems. To obtain a two-dimensional or three-dimensional image of the target, angular frequency sweep testing is required. Traditional sampling requires the angle sampling interval to meet the Nyquist sampling criterion. For example, measuring a 22m aircraft in an 80m anechoic chamber at the X-band (8.2GHz~12.4GHz), with a 360° azimuth angle, requires 13266 angular domain samples and 2240 frequency domain samples for full sampling, which is extremely time-consuming. Due to background clutter interference, existing RCS sampling technologies can only improve testing efficiency through sparse angular domain measurements, failing to address the problem of sparse frequency domain sampling. This results in relatively low RCS measurement efficiency, severely hindering the development, mass production, and maintenance of my country's stealth equipment. Summary of the Invention

[0003] This invention addresses the problem that existing RCS sampling techniques can only improve testing efficiency through sparse measurements in the angular domain and cannot handle sparse sampling in the frequency domain. It proposes a two-dimensional fast measurement method for the RCS of large targets in both the angular and frequency domains. This method constructs an equally spaced full-sampling measurement matrix P to obtain a sparse matrix R. Based on the sparse matrix R and the full-sampling signal, it sweeps the frequency of the target's rotation angle to obtain the measured value y. Finally, it reconstructs the full-sampling signal based on the measured value y to obtain the target's full-sampling RCS. The sparse sampling method using rotation angles ensures that the sparse sampling frequency points are the same for each sampling angle, thus improving testing efficiency.

[0004] The specific implementation details of this invention are as follows:

[0005] A two-dimensional fast measurement method for the RCS of a large target in the angular and frequency domains is proposed. First, the full-sampling angular interval and full-sampling frequency interval of the target are calculated to construct an equally spaced full-sampling measurement matrix P. Second, based on the equally spaced full-sampling measurement matrix P, the angular domain full-sampling quantity N and the frequency domain full-sampling quantity M are obtained, and an angular domain sparse matrix S and a frequency domain sparse matrix F are constructed. Then, based on the angular domain sparse matrix S and the frequency domain sparse matrix F, a sparse matrix R is constructed. Next, based on the sparse matrix R and the obtained full-sampling signal, the target rotation angle is scanned to obtain the measured value y. Finally, a sparse basis D is set, and the full-sampling signal is reconstructed based on the measured value y to obtain the target's full-sampling RCS.

[0006] To better realize the present invention, the following steps are further included:

[0007] Step 1: Set the test frequency, calculate the full sampling angle interval and the full sampling frequency interval of the target under test according to the Nyquist sampling theorem, and construct the equally spaced full sampling measurement matrix P;

[0008] Step 2: Based on the dimension of the equally spaced full sampling measurement matrix P, obtain the angular domain full sampling amount N and the frequency domain full sampling amount M, and determine the angular domain sparse sampling rate η and the frequency domain sparse sampling rate γ, construct the angular domain sparse matrix S and the frequency domain sparse matrix F, and construct the sparse matrix R based on the angular domain sparse matrix S and the frequency domain sparse matrix F.

[0009] Step 3: Based on the sparse matrix R and the acquired full-sample signal, sweep the frequency to sparsely measure the target rotation angle to obtain the measured value y;

[0010] Step 4: Set a sparse basis D, reconstruct the full-sample signal based on the measured value y, and obtain the target full-sample RCS.

[0011] To better realize the present invention, step 3 further includes the following steps:

[0012] Step 31: Construct the corner sweep sparse measurement matrix Φ based on the equally spaced full sampling measurement matrix P and the sparse matrix R;

[0013] Step 32: Based on the sparse matrix R, the sparse measurement matrix Φ for corner sweep frequency measurement, and the acquired full-sample signal, sweep frequency to measure the target corner and obtain the measured value y.

[0014] To better implement the present invention, step 4 is further defined as follows: setting a sparse basis D, calling the complex form of the l1_ls reconstruction algorithm, reconstructing the full-sample signal according to the measured value y, and obtaining the target full-sample RCS.

[0015] To better realize the present invention, after obtaining the full-sample signal in step 32, the full-sample signal is subjected to a one-dimensional Fourier transform to obtain a full-sample signal with time-domain background clutter filtered out.

[0016] To better realize the present invention, the two-dimensional fast measurement method for large target RCS in the angular and frequency domains further includes far-field RCS measurement and near-field RCS measurement.

[0017] To better implement the present invention, further, after acquiring the full-sample signal in step 32, a hardware background suppression system is set to amplify the full-sample signal;

[0018] The hardware background suppression system includes a microwave high-speed electronic switch, a low-noise amplifier, and a processor;

[0019] The microwave high-speed electronic switch receives a full-sample signal at its input terminal and its output terminal is connected to the processor.

[0020] The input terminal of the low-noise amplifier is connected to the output terminal of the processor, and the input terminal of the low-noise amplifier outputs the amplified full-sample signal.

[0021] The present invention has the following beneficial effects:

[0022] (1) The sparse sampling method of the corner sweep frequency proposed in this invention satisfies the constraint equidistant condition in both the frequency domain and the corner domain. For each sampling angle, the sparse sampling frequency points are the same, which improves the testing efficiency.

[0023] (2) By adding a hardware gate background interference suppression system, the present invention achieves simultaneous sparse sampling and high-precision reconstruction of the spatial scanning domain and frequency domain, further improving the testing efficiency of large target RCS. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the sparse sampling method in the angular domain.

[0025] Figure 2 This is a schematic diagram of a 7m airplane model.

[0026] Figure 3 This is a schematic diagram of a 7m aircraft model with a fully sampled two-dimensional image and no background interference.

[0027] Figure 4 This is a schematic diagram of a one-dimensional image of a 7m aircraft model measured in a compact field.

[0028] Figure 5 This is a schematic diagram of a 7m aircraft model reconstructed from a two-dimensional image using sparse sampling in the lower corner domain without temporal filtering.

[0029] Figure 6 This is a schematic diagram illustrating the consistency between the full-sample RCS and the sparse-sample reconstructed RCS of a 7m aircraft model without time-domain filtering.

[0030] Figure 7 This is a schematic diagram of a 2D image reconstructed from a 7m aircraft model using sparse sampling with temporal filtering.

[0031] Figure 8 The consistency between the full-sample RCS and the sparse-sample reconstructed RCS of the 7m aircraft model with time-domain filtering is shown.

[0032] Figure 9 This is a schematic diagram comparing the changes in the 10 GHz full-sample RCS and sparse-sample reconstructed RCS of a 7m aircraft model with time-domain filtering as a function of the test angle.

[0033] Figure 10 This is a schematic diagram of the corner sweep frequency sparse sampling method.

[0034] Figure 11 This is a schematic diagram of a 7m aircraft model with a fully sampled two-dimensional image and no background interference.

[0035] Figure 12 This is a schematic diagram of a one-dimensional image simulating a 7m aircraft model measured in a compact field.

[0036] Figure 13 It is a 2D image reconstructed from a 7m aircraft model using sparse sampling in the angular and frequency domains, containing background interference signals.

[0037] Figure 14 This is a schematic diagram showing the consistency between the full-sample RCS and the sparse-sample reconstructed RCS of a 7m aircraft model containing background interference signals.

[0038] Figure 15 This is a schematic diagram of a one-dimensional image measured after adding a hardware gate background suppression system to a 7m aircraft model in a compressed field.

[0039] Figure 16 This is a schematic diagram of the angular and frequency domain sparse sampling reconstruction of a 7m aircraft model after adding a hardware gate background interference suppression system.

[0040] Figure 17 This is a schematic diagram showing the consistency between the full-sampled RCS and the sparse-sampled reconstructed RCS of the 7m aircraft model after adding the hardware gate background interference suppression system. Detailed Implementation

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments, and therefore should not be regarded as a limitation on the scope of protection. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set up," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0043] Example 1:

[0044] This embodiment proposes a two-dimensional fast measurement method for the RCS of a large target in the angular and frequency domains. First, the full-sampling angular interval and the full-sampling frequency interval of the target are calculated to construct an equally spaced full-sampling measurement matrix P. Second, based on the equally spaced full-sampling measurement matrix P, the angular domain full-sampling quantity N and the frequency domain full-sampling quantity M are obtained, and an angular domain sparse matrix S and a frequency domain sparse matrix F are constructed. Then, based on the angular domain sparse matrix S and the frequency domain sparse matrix F, a sparse matrix R is constructed. Next, based on the sparse matrix R and the obtained full-sampling signal, the target rotation angle is scanned using frequency sparse measurement to obtain the measured value y. Finally, a sparse basis D is set, and the full-sampling signal is reconstructed based on the measured value y to obtain the target's full-sampling RCS.

[0045] Furthermore, the specific steps include:

[0046] Step 1: Set the test frequency, calculate the full sampling angle interval and full sampling frequency interval of the target under test according to the Nyquist sampling theorem, and construct an equally spaced full sampling measurement matrix P.

[0047] Step 2: Based on the dimension of the equally spaced full-sampling measurement matrix P, obtain the angular domain full-sampling quantity N and the frequency domain full-sampling quantity M, and determine the angular domain sparse sampling rate η and the frequency domain sparse sampling rate γ. Construct the angular domain sparse matrix S and the frequency domain sparse matrix F, and construct the sparse matrix R based on the angular domain sparse matrix S and the frequency domain sparse matrix F.

[0048] Step 3: Based on the sparse matrix R and the acquired full-sample signal, sweep the frequency to sparsely measure the target rotation angle to obtain the measured value y.

[0049] Furthermore, step 3 specifically includes the following steps:

[0050] Step 31: Construct the corner sweep sparse measurement matrix Φ based on the equally spaced full sampling measurement matrix P and the sparse matrix R;

[0051] Step 32: Based on the sparse matrix R, the sparse measurement matrix Φ for corner sweep frequency measurement, and the acquired full-sample signal, sweep frequency to measure the target corner and obtain the measured value y.

[0052] Step 4: Set a sparse basis D, reconstruct the full-sample signal based on the measured value y, and obtain the target full-sample RCS.

[0053] Furthermore, the specific operation of step 4 is as follows: set a sparse basis D, and call the complex form l1_l s The reconstruction algorithm reconstructs the full-sample signal based on the measured value y to obtain the target full-sample RCS.

[0054] Furthermore, the RCS measurement is not limited to far-field RCS measurement, but also includes near-field RCS measurement.

[0055] Working principle: This embodiment constructs an equally spaced full-sampling measurement matrix P to obtain a sparse matrix R. Based on the sparse matrix R and the full-sampling signal, the target rotation angle is scanned to obtain the measurement value y. Finally, the full-sampling signal is reconstructed based on the measurement value y to obtain the target full-sampling RCS. The rotation angle frequency sweeping sparse sampling method has the same sparse sampling frequency for each sampling angle, which improves the testing efficiency.

[0056] Example 2:

[0057] Based on Embodiment 1 above, after obtaining the full-sample signal in step 32, this embodiment performs a one-dimensional Fourier transform on the full-sample signal to obtain a full-sample signal with time-domain background clutter filtered out.

[0058] Specifically, it includes the following steps.

[0059] Step 1: Construct the angular domain sparse measurement matrix

[0060] Select the test frequency, calculate the full sampling angle interval and frequency interval of the target under test according to the Nyquist sampling theorem, and determine the equal-interval full sampling measurement matrix P by combining the sampling frequency range and the angular domain range.

[0061]

[0062] Where P has a dimension of N×M, N is the angular domain sampling rate, and M is the frequency domain sampling rate. θ i For the sampling position of the i-th corner domain, f i Let i be the i-th frequency domain sampling position.

[0063] Determine the sparse sampling rate of the angular domain η , η= B / N, where B is the sparse sampling rate of the angular domain. <N;

[0064] A sparse matrix S is generated based on the sparse sampling amount in the angular domain. S has a dimension of 1×N, and its elements take only 0 and 1 values. The number of 1s is a random sample amount B, and the 0s and 1s in S are randomly arranged. The frequency domain matrix is ​​F, with a dimension of 1×M. All elements in F take the value 1. The sparse matrix R is:

[0065]

[0066] [ ] T This is the transpose of the matrix.

[0067] The sparse measurement matrix of the angular domain is Φ=P·R, where · represents matrix dot product (multiplication of corresponding elements), that is, sampling is only performed at the positions corresponding to non-zero elements in the Φ matrix.

[0068] Instruction manual attached Figure 1 This is a schematic diagram of a sampling method for angular domain sparse measurement. Figure 1 For example, η =0.5, N=16, M=17, B=8, S=[0 0 1 0 0 1 1 0 1 0 1 0 1 0 1 1], F=[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11]. Wherein Figure 1 Hollow circles represent full sampling, and black dots represent sparse sampling.

[0069] Step 2: Angular domain sparse measurement.

[0070] Based on the sparse measurement matrix determined in step 1, the target angle is swept frequency to obtain the measured value y. Let the full-sample signal be x, then y = x·R.

[0071] Step 3: Time-domain filtering.

[0072] Perform a one-dimensional Fourier transform on the frequency sampling data corresponding to each angle in step 2, and then perform time-domain filtering to remove background clutter.

[0073] Step 4: Sparse Reconstruction.

[0074] Choose a sparse base (dictionary) D, and then use complex numbers to form... l 1 _l s The reconstruction algorithm solves the following equation to reconstruct the full-sample signal.

[0075]

[0076] In the formula, α is the sparse coding of the fully sampled signal x under the sparse basis D.

[0077] The RCS measurement is not limited to far-field RCS measurement, but also includes near-field RCS measurement.

[0078] The sparse basis (dictionary) D can be a Fourier transform basis, or it can be trained by machine learning to produce an overcomplete dictionary of the batch target.

[0079] It should be noted that the larger the sparse sampling rate, the higher the reconstruction accuracy, but the longer the reconstruction time. The selection of the sampling rate needs to be determined comprehensively based on both the target reconstruction accuracy and the reconstruction time.

[0080] Working principle: This embodiment uses the simulation of RCS testing of an aircraft model in a compressed darkroom as an example. The aircraft model is 7m long. Figure 2As shown, the scattering source is located only at the hollow circle. The anechoic chamber is 20m long, and the test frequency range is 8~12GHz. According to the Nyquist sampling criterion, the angular domain sampling interval is 0.1°, and the frequency sampling interval is 0.0075GHz. The angular domain test range is ±10°, that is, the full-sampling angular domain sampling quantity is N=201, and the frequency domain sampling quantity is M=535. Its full-sampling two-dimensional image without background interference is as follows. Figure 3 As shown. To simulate the background of a compact field anechoic chamber, typical background interference signals from direct leakage, the lower edge of the reflecting surface, and the echo from the back wall were set to be 60dB, 30dB, and 20dB higher than the target signal, respectively. Their one-dimensional images are shown below. Figure 4 As shown. Figure 4 The horizontal axis represents the location of the background signal scattering source, and the vertical axis represents the background signal scattering intensity. This measured signal contains typical background interference signals of a compact field (direct leakage, echo from the edge of the reflecting surface, and echo from the back wall). The sparse sampling rate is set to η=0.6, i.e., the sampling volume is 60% of the full sampling volume. A sparse measurement matrix in the angular domain is constructed using step 1. Figure 1 The angular domain sparse sampling method shown is used, with a sampling size of B=120. First, the signal containing background interference is directly sparsely reconstructed. Then, step 4 is used to reconstruct 201 angular domain full-sample values ​​from the 120 angular domain sparse sample values. Figure 5 The result of reconstructing a two-dimensional image containing background interference signals. Figure 6 It is the consistency between the full-sample RCS and the sparse-sampled reconstructed RCS containing background interference signals. Figure 6 The x-axis is composed of the normalized scattering intensity of the fully sampled 2D image, ordered from smallest to largest. The y-axis is the normalized scattering intensity of the reconstructed 2D image corresponding to the same scattering point within this order. Higher sparse reconstruction accuracy results in a more concentrated distribution of the consistent points between the fully sampled RCS and the sparse reconstructed RCS around a line with a slope of 1. In the ideal case of error-free reconstruction, the consistent distribution points of both the fully sampled RCS and the sparse reconstructed RCS fall on a line with a slope of 1. Figure 6 As can be seen, due to the presence of background interference signals, the direct reconstruction result has strong interference and large error. Before signal reconstruction, a one-dimensional Fourier transform and time-domain filtering are performed on the frequency sampling data corresponding to each angle in step 3 to filter out background clutter. Then, step 4 is used to reconstruct the 201 full-sample values ​​in the angular domain. Figure 7 For a two-dimensional image with sparse reconstruction after temporal filtering, and Figure 3 The full-sample two-dimensional image without background interference is consistent. Figure 8 It is the consistency between the full-sampled RCS and the sparse-sampled reconstructed RCS (including temporal filtering), and Figure 6 In comparison, the consistency distribution points of the full-sample RCS and the sparse reconstructed RCS after time-domain filtering are more concentrated on a straight line with a slope of 1, and the reconstruction accuracy is significantly improved. Figure 9This is a comparison of the RCS reconstructed from full sampling and sparse sampling at 10 GHz with the change of the test angle (including time-domain filtering). The sparse reconstruction result is consistent with the full sampling reconstruction result. In summary, using the angular domain fast measurement method disclosed in this invention, only 60% of the full sampling amount needs to be sampled to reconstruct the target RCS with high accuracy, improving the measurement efficiency by 40%.

[0081] The other parts of this embodiment are the same as those in Embodiment 1 above, so they will not be described again.

[0082] Example 3:

[0083] Based on any one of Embodiments 1-2 above, this embodiment, after obtaining the full-sample signal in step 32, sets up a hardware background suppression system to amplify the full-sample signal.

[0084] The hardware background suppression system includes a microwave high-speed electronic switch, a low-noise amplifier, and a processor;

[0085] The microwave high-speed electronic switch receives a full-sample signal at its input terminal and its output terminal is connected to the processor.

[0086] The input terminal of the low-noise amplifier is connected to the output terminal of the processor, and the input terminal of the low-noise amplifier outputs the amplified full-sample signal.

[0087] The processor in this embodiment uses an FPGA development board.

[0088] Specifically, the following steps are included:

[0089] Step 1: Construct a sparse measurement matrix

[0090] Select the test frequency, calculate the full-sampling angular interval and frequency interval of the target under test according to the Nyquist sampling theorem, and determine the equally spaced full-sampling measurement matrix P by combining the sampling frequency range and angular domain range.

[0091]

[0092] Where P has dimensions N×M, N is the full sampling rate in the angular domain, and M is the full sampling rate in the frequency domain. θ i f is the sampling position of the i-th corner domain. i Let i be the i-th frequency domain sampling position.

[0093] Determine the sparse sampling rate η for the angular domain, η = B / N, where B is the sparse sampling amount for the angular domain. <N;

[0094] Determine the frequency domain sparse sampling rate γ, γ = C / M, where C is the frequency domain sparse sampling amount. <M;

[0095] A sparse matrix S is generated based on the sparse sampling amount of the angular domain, where the dimension of S is 1×N, the elements in S take only 0 and 1 values, the number of 1s is the random sampling amount B of the angular domain, and the 0s and 1s in S are randomly arranged.

[0096] A sparse frequency domain matrix F is generated based on the sparse frequency domain sampling amount. The dimension of F is 1×M, and the elements in F take only 0 and 1 values. The number of 1s is the random frequency domain sampling amount C, and the 0s and 1s in F are randomly arranged.

[0097] The sparse matrix R is:

[0098]

[0099] [ ] T This is the transpose of the matrix.

[0100] The sparse measurement matrix for corner sweep frequency is Φ=P·R, where · represents matrix dot product (multiplication of corresponding elements), meaning that sampling is only performed at the positions corresponding to non-zero elements in the Φ matrix.

[0101] Instruction manual attached Figure 10 This is a schematic diagram of a sampling method for sparse measurement with corner sweep frequency. Figure 10 For example, with N=16, M=17, B=9, and C=10, the angular domain and frequency domain sparse matrices are as follows:

[0102] S=[1 1 0 0 1 1 0 0 1 0 1 1 1 0 0 1], F=[1 1 0 0 0 1 0 1 1 1 0 1 1 1 10 0].

[0103] Step 2: Build a hardware gate background suppression system.

[0104] The hardware gate suppression system consists of a microwave high-speed electronic switch, a low-noise amplifier, an FPGA development board, a power supply, and RF connection cables. The echo signal received by the receiving feed antenna is fed into the microwave high-speed electronic switch, serving as a time-selection hardware gate for the echo signal. The time domain required for hardware gate selection is controlled by the FPGA development board. The low-noise amplifier then amplifies the selected echo signal before sending it to the receiver's input port.

[0105] Step 3: Sparse measurement.

[0106] A hardware-gated background suppression system is introduced between the transceiver antennas. Based on the sparse measurement matrix determined in step 1, a frequency sweep sparse measurement is performed on the target corner to obtain the measured value y. Let the full-sample signal be x, then y = x·R.

[0107] Step 4: Sparse Reconstruction.

[0108] Choose a sparse base (dictionary) D, and then use complex numbers to form... l1 _l s The reconstruction algorithm solves the following equation to reconstruct the full-sample signal using sparse measurements y. .

[0109]

[0110] In the formula, α is the sparse coding of the fully sampled signal x under the sparse basis D.

[0111] The RCS measurement is not limited to far-field RCS measurement, but also includes near-field RCS measurement.

[0112] The sparse basis (dictionary) D can be selected as a Fourier transform basis. For batch production targets, an overcomplete dictionary can also be trained using machine learning methods.

[0113] The hardware gate background suppression system consists of RF switches, FPGA, low-noise amplifier, power supply, etc. Its purpose is to suppress the impact of background noise interference on frequency domain sparse reconstruction by controlling the timing of the RF switches connected to the transceiver antenna through FPGA.

[0114] It should be noted that the presence of background interference makes the frequency domain signal no longer sparse, and background interference must be suppressed to achieve frequency domain compression. For the measurement of the RCS of large targets, the measurement process is time-consuming, and the RF module exhibits significant drift. Commonly used background cancellation methods cannot effectively suppress background interference clutter, so hardware gates must be used to suppress background clutter interference. Furthermore, the larger the sparse sampling rate, the higher the reconstruction accuracy, but the longer the reconstruction time. The selection of the sampling rate needs to be determined comprehensively based on the target reconstruction accuracy and reconstruction time.

[0115] Existing methods for improving RCS testing efficiency only involve sparse sampling of the spatial scanning domain (angular domain or near-field planar scanning domain), failing to reconstruct the frequency domain through sparse sampling, resulting in low testing efficiency. The method proposed in this invention, by adding a hardware-gated background interference suppression system, enables simultaneous sparse sampling and high-precision reconstruction of both the spatial scanning domain and the frequency domain, further improving the testing efficiency of large target RCS.

[0116] Traditional sparse reconstruction requires sampling to satisfy the Restricted Isometry Property (RIP), meaning that the sampling positions must be at unequal intervals. For a two-dimensional measurement matrix composed of frequency and angular domains, satisfying the RIP condition is not conducive to practical measurement implementation. For example... Figure 1 As shown, the sparse sampling method of corner sweep frequency proposed in this invention satisfies the RIP condition in both the frequency domain and the corner domain. For each sampling angle, the sparse sampling frequency points are the same. This sampling method is easy to implement in actual measurement.

[0117] Working principle: This embodiment uses the simulation of RCS testing of an aircraft model in a compressed darkroom as an example. The aircraft model is 7m long. Figure 2 As shown, the scattering source is located only at the hollow circle. The anechoic chamber is 20m long, and the test frequency range is 8~12GHz. According to the Nyquist sampling criterion, the angular domain sampling interval is 0.1°, and the frequency sampling interval is 0.0075GHz. For ease of data processing, the angular domain test range is selected as ±10°, i.e., the full-sampling angular domain sampling quantity is N=201, and the frequency domain sampling quantity is M=535. Its full-sampling two-dimensional image without background interference is shown below. Figure 11 To simulate the background of a compressed-field anechoic chamber, typical background interference signals were set to be 60dB, 30dB, and 20dB higher than the target signal at the direct leakage of the background interference signal, the lower edge of the reflecting surface, and the back wall, respectively. The one-dimensional image of the background was as follows: Figure 12 As shown. The sparse sampling fast measurement method disclosed in this invention improves testing efficiency. The corner domain sampling rate is set to η=0.7, and the frequency domain sampling rate is set to γ=0.7, meaning the sparse sampling amount is 49% of the full sampling amount. A corner-sweeping frequency sparse measurement matrix is ​​constructed using step 1, and... Figure 10 The sampling method shown is a sparse sampling method in both the angular and frequency domains, where the angular domain sampling amount is B=140 and the frequency domain sampling amount is C=375. The sparse measurements containing background interference are then used to reconstruct the full-sampled signal using the method in step 4. The reconstructed full-sampled two-dimensional image is shown below. Figure 13 The consistency between full-sampled RCS and sparse-reconstructed RCS is as follows: Figure 14 As shown, Figure 14 The x-axis is composed of the normalized scattering intensity of the fully sampled 2D image, ordered from smallest to largest. The y-axis is the normalized scattering intensity of the reconstructed 2D image corresponding to the same scattering point within this order. Higher sparse reconstruction accuracy results in a more concentrated distribution of the consistent points between the fully sampled RCS and the sparse reconstructed RCS around a line with a slope of 1. In the ideal case of error-free reconstruction, the consistent distribution points of both the fully sampled RCS and the sparse reconstructed RCS fall on a line with a slope of 1. Figure 13 , Figure 14 As can be seen, due to background interference, direct sparse sampling and reconstruction in the frequency domain results in poor accuracy, and the reconstructed 2D image exhibits aliasing. To address the impact of background interference on frequency-domain sparse reconstruction, a hardware-gated background interference suppression system is required to suppress the background interference signal. In the simulation, this case demonstrates the function of the hardware-gated background interference suppression system by increasing the attenuation of the background interference signal intensity. Figure 15 This is a one-dimensional image of the background signal after suppression (the background signal is 20dB lower than the target). The suppressed background signal is sparsely sampled in both the angular and frequency domains at a sampling rate of 0.7, and the fully sampled signal is reconstructed using the method in step 4. The reconstructed two-dimensional image is shown below. Figure 16 ,and Figure 11 The RCS of the fully sampled 2D image without background interference is consistent. The consistency between the RCS of the fully sampled image and the RCS of the sparse reconstruction is as follows: Figure 17 As shown, Figure 17 The x-axis is composed of the normalized scattering intensities of the fully sampled 2D images sorted from smallest to largest, and the y-axis is composed of the normalized scattering intensities of the reconstructed 2D images corresponding to the same scattering point within that sorted image. Figure 14 In comparison, the consistency distribution points of the sparse reconstructed RCS and the full-sampled RCS after adding a hardware gate background interference suppression system are more concentrated on a straight line with a slope of 1, significantly improving the reconstruction accuracy. In summary, the two-dimensional fast measurement method for large target RCS in the angular and frequency domains disclosed in this invention only requires sampling 49% of the full-sampled amount to achieve high-precision sparse reconstruction of the target RCS, improving measurement efficiency by approximately 50%.

[0118] The other parts of this embodiment are the same as any one of the above embodiments 1-2, so they will not be described again.

[0119] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications or equivalent changes made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A two-dimensional fast measurement method for the RCS of large targets in the angular and frequency domains, characterized in that, First, calculate the full-sampling angle interval and the full-sampling frequency interval of the target under test, and construct an equally spaced full-sampling measurement matrix P. Second, based on the equally spaced full-sampling measurement matrix P, obtain the angular domain full-sampling quantity N and the frequency domain full-sampling quantity M, and construct an angular domain sparse matrix S and a frequency domain sparse matrix F. Then, based on the angular domain sparse matrix S and the frequency domain sparse matrix F, construct a sparse matrix R. Next, based on the sparse matrix R and the obtained full-sampling signal, sweep the frequency sparse measurement of the target rotation angle to obtain the measured value y. Finally, set a sparse basis D, and reconstruct the full-sampling signal based on the measured value y to obtain the target full-sampling RCS.

2. The two-dimensional fast measurement method for the RCS of a large target in the angular and frequency domains according to claim 1, characterized in that, Specifically, the following steps are included: Step 1: Set the test frequency, calculate the full sampling angle interval and the full sampling frequency interval of the target under test according to the Nyquist sampling theorem, and construct the equally spaced full sampling measurement matrix P; Step 2: Based on the dimension of the equally spaced full sampling measurement matrix P, obtain the angular domain full sampling amount N and the frequency domain full sampling amount M, and determine the angular domain sparse sampling rate η and the frequency domain sparse sampling rate γ, construct the angular domain sparse matrix S and the frequency domain sparse matrix F, and construct the sparse matrix R based on the angular domain sparse matrix S and the frequency domain sparse matrix F. Step 3: Based on the sparse matrix R and the acquired full-sample signal, sweep the frequency to sparsely measure the target rotation angle to obtain the measured value y; Step 4: Set a sparse basis D, reconstruct the full-sample signal based on the measured value y, and obtain the target full-sample RCS.

3. The two-dimensional rapid measurement method for the RCS of a large target in the angular and frequency domains according to claim 2, characterized in that, Step 3 specifically includes the following steps: Step 31: Construct the corner sweep sparse measurement matrix Φ based on the equally spaced full sampling measurement matrix P and the sparse matrix R; Step 32: Based on the sparse matrix R, the sparse measurement matrix Φ for corner sweep frequency measurement, and the acquired full-sample signal, sweep frequency to measure the target corner and obtain the measured value y.

4. The two-dimensional rapid measurement method for the RCS of a large target in the angular and frequency domains according to claim 2, characterized in that, The specific operation of step 4 is as follows: set a sparse basis D, and call the complex form... l 1 _l s The reconstruction algorithm reconstructs the full-sample signal based on the measured value y to obtain the target full-sample RCS.

5. A two-dimensional rapid measurement method for the RCS of a large target in the angular and frequency domains according to claim 3, characterized in that, After obtaining the full-sampled signal in step 32, the full-sampled signal is subjected to a one-dimensional Fourier transform to obtain a full-sampled signal with time-domain background clutter filtered out.

6. The two-dimensional rapid measurement method for the RCS of a large target in the angular and frequency domains according to any one of claims 1-5, characterized in that, The two-dimensional fast measurement method for large target RCS in the angular and frequency domains includes far-field RCS measurement and near-field RCS measurement.

7. A two-dimensional rapid measurement method for the RCS of a large target in the angular and frequency domains according to claim 3, characterized in that, After acquiring the full-sample signal in step 32, a hardware background suppression system is set to amplify the full-sample signal; The hardware background suppression system includes a microwave high-speed electronic switch, a low-noise amplifier, and a processor; The microwave high-speed electronic switch receives a full-sample signal at its input terminal and its output terminal is connected to the processor. The input terminal of the low-noise amplifier is connected to the output terminal of the processor, and the input terminal of the low-noise amplifier outputs the amplified full-sample signal.

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