Noise reduction method, device and equipment for antenna performance test data, medium and product
By constructing a random acquisition matrix and expanding the noise vector through sparse equivalent and complex compressed sensing decomposition methods, the problem of environmental influence on active phased array antenna test data is solved, the signal-to-noise ratio and data quality are improved, and the accuracy of fault diagnosis is enhanced.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINESE PEOPLES LIBERATION ARMY UNIT 32181
- Filing Date
- 2026-02-03
- Publication Date
- 2026-05-15
AI Technical Summary
Existing test data on the radiation performance of active phased array antennas are greatly affected by the external environment, and existing data noise reduction methods are ineffective, resulting in low signal-to-noise ratios.
By constructing a random acquisition matrix using sparse equivalent original radiation performance test data, performing complex compressed sensing decomposition, expanding the noise vector and subtracting the expanded noise vector, the denoised radiation performance test data is obtained.
It effectively improved the signal-to-noise ratio and data quality of active phased array antenna radiation performance test data, and enhanced the accuracy of fault diagnosis.
Smart Images
Figure CN122045606A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of antenna performance testing, and in particular to a method, apparatus, equipment, medium, and product for denoising antenna performance test data. Background Technology
[0002] The radiation performance test data of active phased array antennas are greatly affected by the external environment. Existing data noise reduction methods are generally based on empirical information to estimate noise and then perform data noise reduction processing. However, this method is greatly affected by environmental and human experience factors, so the effect is poor. Summary of the Invention
[0003] The purpose of this application is to provide a method, apparatus, equipment, medium, and product for reducing noise in antenna performance test data, which can improve the signal-to-noise ratio and data quality of radiation performance test data of active phased array antennas.
[0004] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for denoising antenna performance test data, including: Obtain raw radiation performance test data of the source phased array antenna; The original radiation performance test data is subjected to sparse equivalence to obtain sparse test data. A random acquisition matrix is constructed based on the parameters of the source phased array antenna and the ambient noise and temperature. Based on sparse test data and a random sampling matrix, complex compressed sensing decomposition is performed to obtain a noise vector; The noise vector is expanded to obtain an expanded noise vector with the same dimension as the original radiation performance test data. The original radiation performance test data is subtracted from the extended noise vector to obtain the noise-reduced radiation performance test data.
[0005] Secondly, this application provides an antenna performance test data noise reduction device, comprising: The data acquisition module is used to acquire the raw radiation performance test data of the source phased array antenna; A sparse equivalence module is used to perform sparse equivalence on the original radiation performance test data to obtain sparse test data. The matrix construction module is used to construct a random acquisition matrix based on the parameters of the source phased array antenna and the ambient noise and temperature. The complex compressed sensing decomposition module is used to perform complex compressed sensing decomposition based on sparse test data and random acquisition matrix to obtain noise vectors; An extension module is used to extend the noise vector to obtain an extended noise vector with the same dimension as the original radiation performance test data; The noise reduction module is used to subtract the extended noise vector from the original radiation performance test data to obtain the noise-reduced radiation performance test data.
[0006] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the above-described method for denoising antenna performance test data.
[0007] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for denoising antenna performance test data.
[0008] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for denoising antenna performance test data.
[0009] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method, apparatus, device, medium, and product for denoising antenna performance test data. Based on the original radiation performance test data and ambient noise temperature, the noise is estimated to be consistent with environmental factors, which can effectively reduce the noise of test data and improve the signal-to-noise ratio and data quality of active phased array antenna radiation performance test data. Attached Figure Description
[0010] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0011] Figure 1 A flowchart illustrating a method for denoising antenna performance test data provided in an embodiment of this application; Figure 2 A functional module diagram of an antenna performance test data noise reduction device provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation
[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0013] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0014] To address the issue that active phased array antenna radiation performance test data is significantly affected by the external environment, resulting in low signal-to-noise ratio, in an exemplary embodiment, such as... Figure 1 As shown, this application provides a method for noise reduction of antenna performance test data. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, it includes the following steps 101 to 106. Wherein: Step 101: Obtain the raw radiation performance test data of the source phased array antenna.
[0015] Step 102: Perform sparse equivalence on the original radiation performance test data to obtain sparse test data.
[0016] Step 103: Construct a random acquisition matrix based on the parameters of the source phased array antenna and the ambient noise temperature.
[0017] Step 104: Perform complex compressed sensing decomposition based on sparse test data and random sampling matrix to obtain noise vector.
[0018] Step 105: Expand the noise vector to obtain an expanded noise vector with the same dimension as the original radiation performance test data.
[0019] Step 106: Subtract the extended noise vector from the original radiation performance test data to obtain the noise-reduced radiation performance test data.
[0020] By performing steps 101 to 106 above, the test data was subjected to sparse equivalence, and a random Gaussian sampling matrix was designed based on the radiation characteristics of the test antenna. The noise signal was reproduced using the collected sparse data, and then the reproduced noise signal was subtracted from the original signal to complete the noise reduction process, thereby improving the signal-to-noise ratio of the test data.
[0021] In another exemplary embodiment of this application, the sparse test data is represented as follows: Y=[y1,y2,y3,…,y n ]; In the formula, Y represents sparse test data, n represents the angular range covered by the antenna radiation, and y1, y2, y3, y n These represent the sparse test data for the first, second, third, and nth angular ranges covered by the antenna radiation, respectively.
[0022] For example, n is typically 180, indicating that the coverage area of the antenna radiation performance is divided into 180 parts, with one part representing 1 degree.
[0023] In another exemplary embodiment of this application, a random acquisition matrix A is constructed based on antenna parameters. A is related to the azimuth dimension x, the elevation dimension y, the sensitivity k of the testing instrument, and the ambient noise temperature h. Here, x, y, and k are known parameters, and the ambient noise temperature h needs to be obtained by testing with a dedicated ambient noise temperature measuring instrument. Once the above parameter information is obtained, A can be constructed. Then, step 103 above may include: constructing a random acquisition matrix A=[a...] based on the azimuth dimension x, the elevation dimension y, the sensitivity k of the testing instrument, and the ambient noise temperature. 11 ,a 12 ,…a 1n ;a 21 ,a 22 ,…a 2n ;….;a m1 ,a m2 ,…,a mn ]; where A represents the random sampling matrix, m represents the number of pure noise points in the test data, n represents the angular range covered by the antenna radiation, and a ij This represents the element in the i-th row and j-th column of the randomly selected matrix, where i = 1, 2, ..., m, j = 1, 2, ..., n.
[0024] In the random sampling matrix A, because matrix element a ij Since the ambient noise temperature h is affected, the output of the noise temperature measuring instrument should be monitored in real time during each measurement, and the average value of the measurement process should be taken as a. ij The calculation parameters are optimized to reduce the impact of environmental noise on the measurement.
[0025] In another exemplary embodiment of this application, based on sparse test data and a random sampling matrix, the formula is used... The noise vector is obtained by formula Z = [z1, z2, ..., zm], where z1, z2, and zm represent the noise quantities of the first, second, and m-th dimensions of the noise vector, respectively; A represents the random sampling matrix, and Y represents the sparse test data.
[0026] Z represents the amount of noise contained in Y in this measurement, and Z is an m-dimensional noise vector.
[0027] In another exemplary embodiment of this application, Z is expanded into an n-dimensional original noise vector, and the expansion process of the noise vector can be replaced by the following steps 201 to 203.
[0028] Step 201: When the index of the element in the extended noise vector satisfies g=1,2,…,(m-1), the element in the extended noise vector is obtained using the formula zkg=(zg+z(g+1)) / I; where m represents the number of pure noise points in the test data, zkg represents the g-th element in the extended noise vector, zg represents the g-th noise quantity in the noise vector, z(g+1) represents the g+1-th noise quantity in the noise vector, and I represents the number of g.
[0029] Step 202: When the indices of the elements in the extended noise vector satisfy g=m,(m+1),…,n, use the formula zkg=( … …,m))), obtains the elements in the extended noise vector; where rand(1,2,…,m) means randomly selecting a number from 1,2,…,m, with each number having an equal probability of appearing. z(rand(1,2,…,m)) represents the rand(1,2,…,m)-th dimension of the noise vector.
[0030] Step 203: Construct an extended noise vector from all the elements obtained in the extended noise vector; the extended noise vector is represented as Z_k=[zk1,zk2,…,zkn]; where Z_k represents the extended noise vector, and zk1, zk2, and zkn represent the noise quantities in the first, second, and nth dimensions of the extended noise vector, respectively.
[0031] In another exemplary embodiment of this application, data denoising is performed using an extended n-dimensional noise vector. The denoising process is: Y_n = Y - Z_k. Wherein, Y_n represents the antenna performance test data after denoising.
[0032] In another exemplary embodiment of this application, after step 106 above, the method may further include: calculating the signal-to-noise ratio of the original radiation performance test data and the noise-reduced radiation performance test data respectively, and comparing the noise reduction effect.
[0033] Assuming the data vector is R, the formula for calculating the signal-to-noise ratio is SNratio=max(abs(R)) / average(abs(R)), where SNratio represents the signal-to-noise ratio, abs(R) is the modulo operation on all data in the data vector, max() represents the maximum value operation, and average() represents the average value operation.
[0034] The method of this application estimates noise through test data. The estimated noise is consistent with environmental factors, and the noise value matches the environment. This method can effectively reduce noise in test data, improve the signal-to-noise ratio of test data, enhance the data quality of test data, and make fault diagnosis based on test data more accurate.
[0035] Based on the same inventive concept, this application also provides an antenna performance test data noise reduction device for implementing the antenna performance test data noise reduction method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more antenna performance test data noise reduction device embodiments provided below can be found in the limitations of the antenna performance test data noise reduction method described above, and will not be repeated here.
[0036] In one exemplary embodiment, such as Figure 2 As shown, an antenna performance test data noise reduction device is provided, comprising: a data acquisition module, a sparse equivalent module, a matrix construction module, a complex compressed sensing decomposition module, an extension module, and a noise reduction module.
[0037] The system includes a data acquisition module for acquiring raw radiation performance test data of a source phased array antenna; a sparse equivalence module for performing sparse equivalence on the raw radiation performance test data to obtain sparse test data; a matrix construction module for constructing a random acquisition matrix based on the parameters of the source phased array antenna and the ambient noise temperature; a complex compressed sensing decomposition module for performing complex compressed sensing decomposition based on the sparse test data and the random acquisition matrix to obtain a noise vector; an expansion module for expanding the noise vector to obtain an expanded noise vector with the same dimension as the raw radiation performance test data; and a noise reduction module for subtracting the expanded noise vector from the raw radiation performance test data to obtain denoised radiation performance test data.
[0038] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores denoised radiation performance test data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for denoising antenna performance test data.
[0039] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.
[0040] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0041] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0042] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.
[0043] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0044] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0045] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0046] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for denoising antenna performance test data, characterized in that, include: Obtain raw radiation performance test data of the source phased array antenna; The original radiation performance test data is subjected to sparse equivalence to obtain sparse test data. A random acquisition matrix is constructed based on the parameters of the source phased array antenna and the ambient noise and temperature. Based on sparse test data and a random sampling matrix, complex compressed sensing decomposition is performed to obtain a noise vector; The noise vector is expanded to obtain an expanded noise vector with the same dimension as the original radiation performance test data. The original radiation performance test data is subtracted from the extended noise vector to obtain the noise-reduced radiation performance test data.
2. The antenna performance test data noise reduction method according to claim 1, characterized in that, The sparse test data is represented as follows: Y = [y1,y2,y3,…,y n ]; In the formula, Y represents sparse test data, n represents the angular range covered by the antenna radiation, and y1, y2, y3, y n These represent the sparse test data for the first, second, third, and nth angular ranges covered by the antenna radiation, respectively.
3. The antenna performance test data noise reduction method according to claim 1, characterized in that, Based on the parameters of the source phased array antenna and the ambient noise and temperature, a random acquisition matrix is constructed, specifically including: Based on the antenna's azimuth dimension, elevation dimension, instrument sensitivity, and ambient noise and temperature, a random acquisition matrix is constructed as follows: A = [a 11 ,a 12 ,…a 1n ;a 21 ,a 22 ,…a 2n ;….;a m1 ,a m2 ,…,a mn ]; Where A represents the random sampling matrix, m represents the number of pure noise points in the test data, n represents the angular range covered by the antenna radiation, and a ij This represents the element in the i-th row and j-th column of the randomly selected matrix, where i = 1, 2, ..., m, j = 1, 2, ..., n.
4. The antenna performance test data noise reduction method according to claim 1, characterized in that, Based on sparse test data and a random sampling matrix, complex compressed sensing decomposition is performed to obtain a noise vector, specifically including: Based on sparse test data and a random sampling matrix, using the formula The noise vector is obtained by formula Z = [z1, z2, ..., zm], where z1, z2, and zm represent the noise quantities of the first, second, and m-th dimensions of the noise vector, respectively; A represents the random sampling matrix, and Y represents the sparse test data.
5. The method for reducing noise in antenna performance test data according to claim 1, characterized in that, Expanding the noise vector to obtain an expanded noise vector with the same dimension as the original radiation performance test data specifically includes: When the index of the elements in the extended noise vector satisfies g=1,2,…,(m-1), the elements in the extended noise vector are obtained using the formula zkg=(zg+z(g+1)) / I; where m represents the number of pure noise points in the test data, zkg represents the g-th element in the extended noise vector, zg represents the g-th noise quantity in the noise vector, z(g+1) represents the (g+1)-th noise quantity in the noise vector, and I represents the number of g. When the indices of the elements in the extended noise vector satisfy g=m,(m+1),…,n, the formula zkg=( … …,m))), obtain the elements in the extended noise vector; where rand(1,2,…,m) represents randomly selecting a number from 1,2,…,m, and z(rand(1,2,…,m)) represents the rand(1,2,…,m)-th dimension of the noise vector; The elements of all the obtained extended noise vectors are used to form an extended noise vector; the extended noise vector is represented as Z_k=[zk1,zk2,…,zkn]; where Z_k represents the extended noise vector, and zk1, zk2, and zkn represent the noise quantities of the first, second, and nth dimensions of the extended noise vector, respectively.
6. The antenna performance test data noise reduction method according to claim 1, characterized in that, Also includes: Calculate the signal-to-noise ratio of the original radiation performance test data and the radiation performance test data after noise reduction, and compare the noise reduction effect.
7. A noise reduction device for antenna performance test data, characterized in that, include: The data acquisition module is used to acquire the raw radiation performance test data of the source phased array antenna; A sparse equivalence module is used to perform sparse equivalence on the original radiation performance test data to obtain sparse test data. The matrix construction module is used to construct a random acquisition matrix based on the parameters of the source phased array antenna and the ambient noise and temperature. The complex compressed sensing decomposition module is used to perform complex compressed sensing decomposition based on sparse test data and random acquisition matrix to obtain noise vectors; An extension module is used to extend the noise vector to obtain an extended noise vector with the same dimension as the original radiation performance test data; The noise reduction module is used to subtract the extended noise vector from the original radiation performance test data to obtain the noise-reduced radiation performance test data.
8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the antenna performance test data noise reduction method according to any one of claims 1-6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for denoising antenna performance test data as described in any one of claims 1-6.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for denoising antenna performance test data as described in any one of claims 1-6.