A method, device, storage medium and electronic device for reducing radio frequency interference

Abstract

This specification discloses a method, device, storage medium and electronic device for radio frequency interference reduction. In the radio frequency interference reduction method provided in this specification, target observation data received by a large-scale antenna array is obtained; the target observation data is processed in blocks to obtain a plurality of block data; for each block data, the covariance matrix of the block data is determined; the covariance matrix is ​​decomposed by eigenvalues ​​to obtain each eigenvalue and each eigenvector of the covariance matrix of the block data, wherein each eigenvalue has and only has one corresponding eigenvector; the amount of interference is determined according to each eigenvalue, and an interference subspace is constructed based on the amount of interference and each eigenvector; an orthogonal projection matrix projected to the interference subspace is determined, and the orthogonal projection matrix is ​​used to perform interference reduction processing on the block data; each block data that has completed the interference reduction processing is merged to obtain noise reduction data.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method, device, storage medium and electronic device for reducing radio frequency interference. Background Art

[0002] Radio Frequency Interference (RFI) is one of the main challenges facing the field of radio astronomy. Its main source is electromagnetic equipment used in human daily life, industrial production and other activities. With the rapid development of information technology and manufacturing technology, the performance of radio astronomy equipment has been significantly improved in terms of sensitivity, receiving bandwidth and observation field of view, but at the same time, the sources of RFI have increased and its impact has become stronger. The intensity and time domain / spectral density of RFI will directly affect the observation results and reduce the quality of observation data. In particular, some RFI signals that vary with time will seriously affect time domain astronomical observations, such as the search for celestial bodies such as pulsars, fast radio bursts and radio transient sources. In order to cope with the increasingly complex characteristics of the RFI environment, real-time and active interference reduction is the key to reducing the impact of RFI and ensuring the operating efficiency and observation quality of radio telescopes.

[0003] At various stages of data collection, a variety of RFI mitigation measures have been developed to reduce or eliminate the impact of RFI, improve the signal-to-interference ratio, and thus improve the quality of data collection. Currently, the commonly used RF interference processing technologies mainly include the marker threshold method, the nulling method, spatial filtering, median filtering, adaptive filtering, etc. Although these technologies have certain interference reduction effects, they face challenges in computational efficiency in practical applications. The update and upgrade of observation equipment has brought wider bandwidth and higher resolution, resulting in a sharp increase in data volume, reaching TB or even PB levels. Existing algorithms are difficult to meet the needs of real-time processing when processing such a large data stream, which limits the effective deployment of these methods in practical applications.

[0004] Therefore, how to achieve fast and efficient RF interference reduction when receiving radio signals is an urgent problem to be solved. Summary of the invention

[0005] The present specification provides a method, device, storage medium and electronic device for radio frequency interference reduction, so as to at least partially solve the above-mentioned problems existing in the prior art.

[0006] This manual adopts the following technical solutions:

[0007] This specification provides a method for reducing radio frequency interference, including:

[0008] Acquire target observation data received by large-scale antenna arrays;

[0009] The target observation data is processed in blocks to obtain a plurality of block data;

[0010] For each block of data, determine the covariance matrix of the block of data;

[0011] Performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors of the covariance matrix of the block data, wherein each eigenvalue has only one corresponding eigenvector;

[0012] Determine the interference quantity according to each eigenvalue, and construct an interference subspace based on the interference quantity and each eigenvector;

[0013] Determine an orthogonal projection matrix projected onto the interference subspace, and use the orthogonal projection matrix to perform interference reduction processing on the block data;

[0014] The block data that have completed the interference reduction process are merged to obtain noise reduction data.

[0015] Optionally, the large-scale antenna array includes a plurality of receiving units;

[0016] Obtain target observation data received by a large-scale antenna array, including:

[0017] The observation data received by each receiving unit included in the large-scale antenna array is obtained and combined as the target observation data.

[0018] Optionally, before performing block processing on the observation data, the method further includes:

[0019] Determining whether the target observation data is complex;

[0020] In response to the target observation data not being a complex number, the target observation data is converted into a complex number.

[0021] Optionally, converting the target observation data into a complex number specifically includes:

[0022] The target observation data is converted into complex numbers through Hilbert transform.

[0023] Optionally, the observation data is processed in blocks to obtain a plurality of block data, specifically including:

[0024] Dividing each receiving unit included in the large-scale antenna array to obtain a plurality of unit groups;

[0025] For each unit group, the observation data received by each receiving unit contained in the unit group are combined as one block data.

[0026] Optionally, determining the interference quantity according to the characteristic values ​​specifically includes:

[0027] Determine the mean and standard deviation of each characteristic value;

[0028] determining a feature threshold according to the mean and standard deviation;

[0029] The interference quantity is determined according to the characteristic values ​​and the characteristic threshold.

[0030] Optionally, constructing an interference subspace based on the interference quantity and each eigenvector specifically includes:

[0031] Selecting the eigenvalue with the largest interference quantity from among the eigenvalues ​​as the target eigenvalue;

[0032] The eigenvector corresponding to each target eigenvalue is determined as the target eigenvector, and the interference subspace is constructed using the target eigenvector.

[0033] This specification provides a radio frequency interference reduction device, the device comprising:

[0034] An acquisition module, used to acquire target observation data received by a large-scale antenna array;

[0035] A block module, used for performing block processing on the target observation data to obtain a plurality of block data;

[0036] A determination module, used for determining the covariance matrix of each block of data;

[0037] A decomposition module, used for performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors of the covariance matrix of the block data, wherein each eigenvalue has only one corresponding eigenvector;

[0038] A construction module, used to determine the interference quantity according to the eigenvalues, and construct an interference subspace based on the interference quantity and the eigenvectors;

[0039] A projection module, used to determine an orthogonal projection matrix projected onto the interference subspace, and perform interference reduction processing on the block data using the orthogonal projection matrix;

[0040] The merging module is used to merge the block data that have completed the interference reduction processing to obtain noise reduction data.

[0041] This specification provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned radio frequency interference reduction method is implemented.

[0042] This specification provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned radio frequency interference reduction method when executing the program.

[0043] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects:

[0044] In the radio frequency interference reduction method provided in the present specification, target observation data received by a large-scale antenna array is obtained; the target observation data is processed in blocks to obtain a plurality of block data; for each block data, the covariance matrix of the block data is determined; the covariance matrix is ​​decomposed into eigenvalues ​​to obtain each eigenvalue and each eigenvector of the covariance matrix of the block data, wherein each eigenvalue has one and only one corresponding eigenvector; the amount of interference is determined according to each eigenvalue, and an interference subspace is constructed based on the amount of interference and each eigenvector; an orthogonal projection matrix projected onto the interference subspace is determined, and the orthogonal projection matrix is ​​used to perform interference reduction processing on the block data; and each block data that has completed the interference reduction processing is merged to obtain noise reduction data.

[0045] When using the radio frequency interference reduction provided in this specification to reduce interference to radio astronomical signals, based on the block subspace projection method, the subspace projection technology can fully utilize the advantage of the large-scale receiving array multi-beam observation system to accurately capture the spatial characteristics of RFI, and achieve effective reduction of radio frequency interference in astronomical observation signals, thereby significantly improving the quality of observation data and the efficiency of scientific event search. In addition, this method significantly shortens the calculation time and improves the algorithm processing efficiency through the block parallel computing strategy. Combined with the dense parallel computing capability of the graphics processing unit, through the optimization of the calculation process control and process management scheduling strategy, it can quickly process large-scale data streams, realize rapid processing of RFI, and meet the high real-time requirements of actual engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation on this specification. In the drawings:

[0047] Figure 1 A flowchart of a method for reducing radio frequency interference in this specification;

[0048] Figure 2 It is a schematic diagram for comparing the time consumption of the eigenvalue decomposition process without using this method and using this method;

[0049] Figure 3A schematic diagram showing the comparison of signal spectra before and after interference reduction using this method;

[0050] Figure 4 A schematic diagram showing the energy distribution comparison before and after the interference reduction is performed using the method;

[0051] Figure 5 A schematic diagram of a radio frequency interference reduction device provided in this specification;

[0052] Figure 6 The corresponding Figure 1 Schematic diagram of electronic equipment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0054] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0055] Figure 1 The following is a flow chart of a method for reducing radio frequency interference in this specification, which specifically includes the following steps:

[0056] S100: Acquire target observation data received by a large-scale antenna array.

[0057] All steps in the radio frequency interference reduction method provided in this specification can be implemented by any electronic device with computing functions, such as a terminal, a server, etc. For ease of understanding, this specification mainly uses the server in the ground observation station as an example for explanation.

[0058] This method is mainly used to reduce the radio frequency interference contained in the radio astronomy signal received by the antenna. Based on this, the target observation data received by the large-scale antenna array can be first obtained in this step. Large-scale antenna arrays are usually composed of a large number of antennas. The substantial increase in the number of antennas enables the system to form a narrower beam, improve spatial resolution, enhance signal gain, and reduce interference. Each antenna is a separate receiving unit, that is, a large-scale antenna array can usually contain several receiving units.

[0059] Each receiving unit can receive observation data separately. Therefore, when acquiring target observation data received by a large-scale antenna array, it is possible to specifically acquire observation data received by each receiving unit included in the large-scale antenna array and combine them as target observation data.

[0060] In this specification, the received target observation data can be expressed as x(t):

[0061]

[0062] in, It is a radio astronomy signal. It's radio frequency interference. is the system noise.

[0063] The received target observation data may be stored in a cache of the server for use in subsequent steps.

[0064] S102: performing block processing on the target observation data to obtain a plurality of block data.

[0065] In this step, the target observation data obtained in step S100 can be processed into blocks to obtain multiple block data. The number of block data and the amount of data contained in each block data may vary depending on the specific situation and requirements of the application, and this specification does not impose specific restrictions on this.

[0066] Additionally, in order to achieve a better interference reduction effect in subsequent steps, all target observation data may be converted into complex data before starting this step. Specifically, before the observation data is processed in blocks, it may be determined whether the target observation data is a complex number; in response to the target observation data not being a complex number, the target observation data is converted into a complex number.

[0067] The above judgment needs to be made for each piece of target observation data. If the data is complex, no processing is performed on the data; if the data is not complex, the data is converted into complex data. In the conversion process, the target observation data can be converted into complex numbers in any way. For example, the target observation data can be converted into complex numbers through Hilbert transform. The formula is as follows:

[0068]

[0069] in, is the target observation data of the real signal, is the transformed complex signal, yes The Hilbert transform of ,symbol" ” indicates convolution.

[0070] Furthermore, in the case where the method uses a large-scale antenna array as a receiving device, since it contains a large number of receiving units, when the target observation data received by the large-scale antenna array is divided into blocks, the blocks can be divided based on the receiving units contained therein. Specifically, the receiving units contained in the large-scale antenna array can be divided to obtain a plurality of unit groups; for each unit group, the observation data received by each receiving unit contained in the unit group is combined as a block data.

[0071] In order to ensure that the processing speed of each block data obtained after the block is similar in the subsequent processing, when the target observation data is blocked based on the receiving unit, the average blocking method can be used to allocate the same number of receiving units to each unit group. Specifically, the blocking strategy of the target observation data can be based on the position arrangement of the receiving units in the large-scale receiving array, and the receiving units are evenly blocked, so that the positions of the receiving units in each unit group are adjacent; when the total number of receiving units can be divided by the number of receiving units in a unit group, there are no repeated receiving units between the unit groups; when the total number of receiving units cannot be divided by the number of receiving units in a unit group, there are repeated receiving units between the unit groups, and these repeated receiving units are selected from the units adjacent to the positions of other receiving units in the block.

[0072] S104: For each block of data, determine the covariance matrix of the block of data.

[0073] In this method, the subspace projection method is mainly used to reduce the radio frequency interference of each block data. Steps S104 to S110 of this specification will specifically provide a specific method for reducing interference on a block data. In actual application, each block data can be processed for interference reduction according to the method given in steps S104 to S110 of this specification, and the interference reduction processing of each block data can be performed simultaneously.

[0074] Based on the idea of ​​using subspace projection to achieve interference reduction, the covariance matrix of each block data can be first determined in this step. In a specific embodiment, let the large-scale receiving array contain N receiving units, there are L sampling points to be observed, and the block processing divides the target observation data into K block data. At this time, each block data can be represented in matrix form as , k=1, 2, ..., K. Each block data Contains M×L dimensional data. Among them, ,symbol" " means rounding up. Block data The covariance matrix of can be expressed as , calculated as follows:

[0075]

[0076] in, express The transposed matrix, covariance matrix It is an M×M square matrix.

[0077] S106: Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors of the covariance matrix of the block data, wherein each eigenvalue has one and only one corresponding eigenvector.

[0078] After the covariance matrix of the block data is determined in step S104, the determined covariance matrix can be further subjected to eigenvalue decomposition in this step to obtain the eigenvalues ​​and eigenvectors of the covariance matrix. Eigenvalue decomposition decomposes a square matrix into a product form of a matrix consisting of its eigenvalues ​​and corresponding eigenvectors.

[0079] The specific expression of eigenvalue decomposition is as follows:

[0080]

[0081] Among them, V is an M×M square matrix, and each column is a covariance matrix A feature vector of is the transposed matrix of V, is an M×M diagonal matrix, and the elements on its diagonal are the corresponding eigenvalues.

[0082] In this method, the covariance matrix can be obtained through eigenvalue decomposition There are M eigenvalues ​​and corresponding M eigenvectors of , where each eigenvalue corresponds to each eigenvector one by one.

[0083] S108: Determine the interference quantity according to the eigenvalues, and construct an interference subspace based on the interference quantity and the eigenvectors.

[0084] In step S106, the covariance matrix is ​​completed. After the eigenvalue decomposition is performed to obtain its eigenvalues ​​and eigenvectors, the determined eigenvalues ​​and eigenvectors can be used to construct an interference subspace for achieving interference reduction in this step.

[0085] According to the determined eigenvalues, the number of interferences present can be determined first. Specifically, the mean μ and standard deviation σ of the eigenvalues ​​can be determined; the characteristic threshold can be determined according to the mean and standard deviation; and the number of interferences can be determined according to the eigenvalues ​​and the characteristic threshold.

[0086] According to the covariance matrix The mean and standard deviation of each eigenvalue can be calculated. Further, using the mean and standard deviation of each eigenvalue, the characteristic threshold can be determined according to the pre-set rules, and the interference quantity can be further determined. Among them, the pre-set rules can be changed according to different specific needs. For example, the characteristic threshold can be set to μ+3σ, and the number of eigenvalues ​​greater than the characteristic threshold can be counted as the interference quantity p.

[0087] After determining the interference quantity p, an interference subspace can be further constructed based on the interference quantity p. Specifically, the eigenvalue with the largest interference quantity can be selected from the eigenvalues ​​as the target eigenvalue; the eigenvector corresponding to each target eigenvalue is determined as the target eigenvector, and the interference subspace is constructed using the target eigenvector.

[0088] In this method, the p eigenvalues ​​greater than the characteristic threshold, that is, the eigenvectors corresponding to the p largest eigenvalues, are considered to be eigenvectors related to interference, and based on this, the interference subspace A is constructed. Among them, A=V[:,1:p], which means that A represents a matrix composed of the eigenvectors corresponding to the selected p eigenvalues ​​as column vectors.

[0089] S110: Determine an orthogonal projection matrix projected onto the interference subspace, and use the orthogonal projection matrix to perform interference reduction processing on the block data.

[0090] After the interference subspace is constructed in step S108, the interference subspace can be used in this step to complete interference reduction of the block data.

[0091] In this step, the orthogonal projection matrix projected to the interference subspace can be first determined as follows:

[0092]

[0093] in, is the orthographic projection matrix, is the transposed matrix of the interference subspace A.

[0094] Then, the orthogonal projection matrix can be used to block the data according to the following formula Perform interference reduction processing:

[0095]

[0096] in, For block data The data obtained after completing the interference reduction process.

[0097] S112: merging the block data that have completed the interference reduction process to obtain noise reduction data.

[0098] As described in step S104, steps S104 to S110 in this specification are specific operations for performing interference reduction processing on a block of data. In the process of performing interference reduction processing on the target observation data as a whole, each block of data needs to complete interference reduction according to the method given in steps S104 to S110.

[0099] When all block data have completed the interference reduction processing, finally in this step, all block data that have completed the interference reduction processing can be merged to obtain noise reduction data, that is, target observation data that have completed the interference reduction processing.

[0100] Among them, when merging, the merging strategy can be selected according to whether there are overlapping receiving units between each unit group during division: when there are no overlapping receiving units between unit groups, the block data are merged according to the correspondence between the serial number of each receiving unit and the overall sorting during division; when there are overlapping receiving units between unit groups, the non-overlapping receiving units are merged according to the correspondence between the serial number of the receiving unit and the overall sorting, and the overlapping receiving units select the unit group with the closest spatial distance to join. If the distance is equal, any unit group is selected to join, and the corresponding relationship between the serial number of the receiving unit in the selected unit group and the overall sorting is used to merge, so as to obtain the time series data of all the receiving units after noise reduction.

[0101] When using the radio frequency interference reduction provided in this specification to reduce interference to radio astronomical signals, based on the block subspace projection method, the subspace projection technology can fully utilize the advantage of the large-scale receiving array multi-beam observation system to accurately capture the spatial characteristics of RFI, and achieve effective reduction of radio frequency interference in astronomical observation signals, thereby significantly improving the quality of observation data and the efficiency of scientific event search. In addition, this method significantly shortens the calculation time and improves the algorithm processing efficiency through the block parallel computing strategy. Combined with the dense parallel computing capability of the graphics processing unit, through the optimization of the calculation process control and process management scheduling strategy, it can quickly process large-scale data streams, realize rapid processing of RFI, and meet the high real-time requirements of actual engineering applications.

[0102] Figure 2 The figure is a time-consuming comparison diagram of the eigenvalue decomposition process without using this method and with using this method. When the target observation data contains 1 target source signal and 1 interference signal, the time consumption of the block method without using this method and the block method with this method is compared when calculating the eigenvalue decomposition. Figure 2 As shown in Figure 2, it can be seen that the data block method of this method significantly reduces the computational time of eigenvalue decomposition and greatly improves the computational efficiency.

[0103] FIG3 is a schematic diagram showing the comparison of signal spectra before and after the interference reduction by using this method. Figure 4This is a schematic diagram of energy distribution comparison before and after interference reduction using this method. Figure 3 and Figure 4 It shows that this method can effectively reduce the intensity of the interference signal contained in the target observation data, while having almost no effect on the source signal itself.

[0104] In summary, this method can not only quickly obtain the results of interference reduction, but also achieve effective RFI interference suppression, significantly improving the signal-to-interference ratio of real-time observation data of RF signals. At the same time, each block of data in this method supports parallel processing, which effectively solves the problems of low efficiency and insufficient real-time processing capability in existing RFI reduction technologies.

[0105] The above is the radio frequency interference reduction method provided in this specification. Based on the same idea, this specification also provides a corresponding radio frequency interference reduction device, such as Figure 5 shown.

[0106] Figure 5 A schematic diagram of a radio frequency interference reduction device provided in this specification specifically includes:

[0107] An acquisition module 200 is used to acquire target observation data received by a large-scale antenna array;

[0108] A block module 202 is used to process the target observation data in blocks to obtain a plurality of block data;

[0109] A determination module 204 is used to determine the covariance matrix of each block of data;

[0110] A decomposition module 206 is used to perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors of the covariance matrix of the block data, wherein each eigenvalue has only one corresponding eigenvector;

[0111] A construction module 208, configured to determine the interference quantity according to the eigenvalues, and construct an interference subspace based on the interference quantity and the eigenvectors;

[0112] A projection module 210 is used to determine an orthogonal projection matrix projected onto the interference subspace, and perform interference reduction processing on the block data using the orthogonal projection matrix;

[0113] The merging module 212 is used to merge the block data that have completed the interference reduction process to obtain noise reduction data.

[0114] Optionally, the large-scale antenna array includes a plurality of receiving units;

[0115] The acquisition module 200 is specifically used to acquire the observation data received by each receiving unit included in the large-scale antenna array, and merge them as target observation data;

[0116] Optionally, the device further includes a judgment module 214, which is specifically configured to judge whether the target observation data is a complex number; in response to the target observation data not being a complex number, convert the target observation data into a complex number.

[0117] Optionally, the judgment module 214 is specifically configured to convert the target observation data into a complex number through a Hilbert transform.

[0118] Optionally, the blocking module 202 is specifically used to divide the receiving units included in the large-scale antenna array to obtain a plurality of unit groups; for each unit group, the observation data received by the receiving units included in the unit group are combined as a block data.

[0119] Optionally, the construction module 208 is specifically used to determine the mean and standard deviation of each characteristic value; determine the characteristic threshold according to the mean and standard deviation; and determine the interference quantity according to each characteristic value and the characteristic threshold.

[0120] Optionally, the construction module 208 is specifically used to select the eigenvalue with the largest number of interferences from the eigenvalues ​​as the target eigenvalue; determine the eigenvector corresponding to each target eigenvalue as the target eigenvector, and use the target eigenvector to construct the interference subspace.

[0121] This specification also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 Provides methods for reducing RF interference.

[0122] This manual also provides Figure 6 The schematic structure diagram of the electronic device shown in FIG. Figure 6 As mentioned above, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 Of course, in addition to the software implementation, this specification does not exclude other implementations, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0123] For the improvement of a technology, it can be clearly distinguished whether it is a hardware improvement (for example, improvement of the circuit structure of diodes, transistors, switches, etc.) or a software improvement (improvement of the method flow). However, with the development of technology, many improvements of the method flow today can be regarded as direct improvements of the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that the improvement of a method flow cannot be implemented with a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming themselves, without having to ask chip manufacturers to design and make dedicated integrated circuit chips. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0124] The controller may be implemented in any suitable manner, for example, the controller may take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (e.g., software or firmware) executable by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, and the memory controller may also be implemented as part of the control logic of the memory. It is also known to those skilled in the art that, in addition to implementing the controller in a purely computer-readable program code manner, the controller may be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, such a controller may be considered as a hardware component, and the devices for implementing various functions included therein may also be considered as structures within the hardware component. Or even, the devices for implementing various functions may be considered as both software modules for implementing the method and structures within the hardware component.

[0125] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0126] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0127] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0128] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0129] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0131] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0132] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0133] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0134] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0135] It should be understood by those skilled in the art that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0137] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0138] The above description is only an embodiment of this specification and is not intended to limit this specification. For those skilled in the art, this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this specification should be included in the scope of the claims of this application.

Claims

1. A method for reducing radio frequency interference, characterized in that: include: Acquire target observation data received by large-scale antenna arrays; The target observation data is processed in blocks to obtain a plurality of block data; For each block of data, determine the covariance matrix of the block of data; Performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors of the covariance matrix of the block data, wherein each eigenvalue has only one corresponding eigenvector; Determine the interference quantity according to each eigenvalue, and construct an interference subspace based on the interference quantity and each eigenvector; Determine an orthogonal projection matrix projected onto the interference subspace, and use the orthogonal projection matrix to perform interference reduction processing on the block data; Merging the block data after the interference reduction process to obtain noise reduction data; The determination of the interference quantity according to the characteristic values ​​specifically includes: Determine the mean and standard deviation of each characteristic value; determining a feature threshold based on the mean and standard deviation; Determine the interference quantity according to each characteristic value and the characteristic threshold; The large-scale antenna array comprises a plurality of receiving units; Obtain target observation data received by a large-scale antenna array, including: Obtain observation data received by each receiving unit included in the large-scale antenna array and merge them as target observation data; The observation data is processed in blocks to obtain a number of block data, specifically including: Dividing each receiving unit included in the large-scale antenna array to obtain a plurality of unit groups; For each unit group, the observation data received by each receiving unit contained in the unit group are combined as a block data; The receiving units included in the large-scale antenna array are divided to obtain a plurality of unit groups, specifically including: According to the position sequence of each receiving unit included in the large-scale antenna array, the receiving units are evenly divided to obtain a plurality of unit groups; In response to the number of the receiving units being an integer multiple of the number of the unit groups, there are no repeated receiving units between the unit groups; In response to the number of the receiving units not being an integer multiple of the number of the unit groups, there are repeated receiving units between the unit groups, and the repeated receiving units are sequentially adjacent to other receiving units in the unit group where the receiving units are located; The block data that have completed the interference reduction process are merged to obtain noise reduction data, specifically including: For each receiving unit, if the receiving unit is not a repeated receiving unit, determine the data of the receiving unit as block data belonging to the unit group to which the receiving unit belongs; If the receiving unit is a repeated receiving unit, the receiving unit is determined as the block data belonging to the unit group with the closest position sequence distance to the receiving unit; The block data after the interference reduction is merged to obtain noise reduction data.

2. The method according to claim 1, characterized in that Before the observed data is processed in blocks, the method further includes: Determining whether the target observation data is complex; In response to the target observation data not being a complex number, the target observation data is converted into a complex number.

3. The method according to claim 2, characterized in that Converting the target observation data into complex numbers specifically includes: The target observation data is converted into complex numbers through Hilbert transform.

4. The method according to claim 1, characterized in that Constructing an interference subspace based on the interference quantity and each eigenvector specifically includes: Selecting the eigenvalue with the largest interference quantity from among the eigenvalues ​​as the target eigenvalue; The eigenvector corresponding to each target eigenvalue is determined as the target eigenvector, and the interference subspace is constructed using the target eigenvector.

5. A radio frequency interference reduction device, characterized in that: include: An acquisition module, used to acquire target observation data received by a large-scale antenna array; A block module, used for performing block processing on the target observation data to obtain a plurality of block data; A determination module, used for determining the covariance matrix of each block of data; A decomposition module, used for performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors of the covariance matrix of the block data, wherein each eigenvalue has only one corresponding eigenvector; A construction module, used to determine the interference quantity according to the eigenvalues, and construct an interference subspace based on the interference quantity and the eigenvectors; A projection module, used to determine an orthogonal projection matrix projected onto the interference subspace, and perform interference reduction processing on the block data using the orthogonal projection matrix; A merging module, used for merging the block data after the interference reduction process to obtain noise reduction data; The construction module is specifically used to determine the mean and standard deviation of each characteristic value; determine the characteristic threshold according to the mean and standard deviation; determine the interference quantity according to each characteristic value and the characteristic threshold; The large-scale antenna array comprises a plurality of receiving units; The acquisition module is specifically used to acquire the observation data received by each receiving unit included in the large-scale antenna array, and merge them as target observation data; The block module is specifically used to divide the receiving units included in the large-scale antenna array to obtain a plurality of unit groups; for each unit group, the observation data received by the receiving units included in the unit group are combined as a block data; The block division module is specifically used to evenly divide the receiving units contained in the large-scale antenna array according to the position sequence of the receiving units to obtain a plurality of unit groups; in response to the number of the receiving units being an integer multiple of the number of the unit groups, there are no repeated receiving units between the unit groups; in response to the number of the receiving units not being an integer multiple of the number of the unit groups, there are repeated receiving units between the unit groups, and the repeated receiving units are adjacent to other receiving units in the unit group where they are located in sequence; The merging module is specifically used to, for each receiving unit, if the receiving unit is not a repeated receiving unit, determine the data of the receiving unit as block data belonging to the unit group to which the receiving unit belongs; if the receiving unit is a repeated receiving unit, determine the receiving unit as block data belonging to the unit group with the closest position sequence distance to the receiving unit; merge the block data that have completed the interference reduction to obtain noise reduction data.

6. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of claims 1 to 4 is implemented.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method described in any one of claims 1 to 4 is implemented.

Images