Radio frequency interference reduction method, system, equipment and medium

Abstract

The invention relates to the technical field of radio astronomical signal processing, and discloses a radio frequency interference reduction method, system and device and a medium, and the method comprises the steps: obtaining target observation data; performing first segment processing on the target observation data to obtain a plurality of first segment data; determining amplitude mean values of the first segment data, and sorting the first segment data according to the amplitude mean values to obtain a mean value sorting sequence; performing second segmentation processing on the mean value sorting sequence to obtain a plurality of second segment data; if the jump points exist in the second segment data, determining the jump point with the minimum amplitude mean value as a target jump point; the target jump point is used for representing the existence of radio frequency interference; and determining a bottom noise sequence in the mean sorting sequence based on the target jump point so as to reduce the radio frequency interference, thereby solving the technical problem of how to quickly, efficiently and precisely reduce the radio frequency interference, and achieving the technical effect of improving the processing efficiency to meet the real-time requirement.

Description

Technical Field

[0001] The present application relates to the technical field of radio astronomy signal processing, and in particular to a method, system, device and medium for reducing radio frequency interference. Background Art

[0002] Radio telescopes detect weak electromagnetic signals from celestial bodies, with typical intensities ranging from -150dBW / m² to -220dBW / m². Therefore, the receiving sensitivity of radio telescopes is more than 50 decibels higher than other conventional electromagnetic signal receiving devices. Such sensitive systems face serious radio frequency interference (RFI). The main source of RFI is artificially generated electromagnetic signals such as television broadcasts, satellite navigation, and wireless communications. RFI reduces the sensitivity of radio telescopes, affects target observations, increases the difficulty of discovering weak astronomical signals, and even renders data invalid.

[0003] There are many RFI reduction technologies, among which the most widely used is the marker threshold method, which calculates a threshold in the time domain or frequency domain, detects data exceeding the threshold as RFI and removes or replaces it. The key to the marker threshold method is how to calculate a robust threshold. Currently, the median-based method, which involves sorting operations, works well. With the increase in the size of radio telescope arrays and the advancement of graphics processing unit (GPU) technology, the application of GPU in the field of real-time processing of radio signals has gradually become mainstream. The conventional median calculation method is difficult to give full play to the parallel computing advantages of GPU and is not conducive to real-time processing. Summary of the invention

[0004] The present application provides a method, system, device and medium for reducing radio frequency interference, which solves the technical problem of how to quickly, efficiently and accurately estimate the median of data on a GPU to reduce radio frequency interference, and achieves the technical effect of improving processing efficiency to meet real-time requirements.

[0005] In order to achieve the above objectives, the main technical solutions adopted in this application include: In a first aspect, an embodiment of the present application provides a method for reducing radio frequency interference, the method comprising: acquiring target observation data; performing a first segmentation processing on the target observation data to obtain a number of first segmentation data; determining the amplitude mean of each of the first segmentation data, and sorting them according to the size of the amplitude mean to obtain a mean sorting sequence; performing a second segmentation processing on the mean sorting sequence to obtain a number of second segmentation data; if there is a jump point in the second segmentation data, determining the jump point with the smallest amplitude mean as the target jump point; the target jump point is used to characterize the presence of radio frequency interference; based on the target jump point, determining a background noise sequence in the mean sorting sequence, so as to detect and reduce radio frequency interference based on the background noise sequence.

[0006] The radio frequency interference reduction method provided in this embodiment includes: acquiring target observation data; performing a first segmentation processing on the target observation data to obtain a number of first segmentation data; determining the amplitude mean of each of the first segmentation data, and sorting them according to the size of the amplitude mean to obtain a mean sorting sequence; performing a second segmentation processing on the mean sorting sequence to obtain a number of second segmentation data; if there is a jump point in the second segmentation data, determining the jump point with the smallest amplitude mean as the target jump point; the target jump point is used to characterize the existence of radio frequency interference; based on the target jump point, determining the background noise sequence in the mean sorting sequence, so as to calculate the median based on the background noise sequence to detect and reduce radio frequency interference, solves the technical problem of how to quickly, efficiently and accurately reduce radio frequency interference on a GPU, and achieves the technical effect of improving processing efficiency to meet real-time requirements.

[0007] Optionally, the first segmentation processing of the target observation data specifically includes: determining a first segment length based on a data sampling rate and a shortest duration for reducing radio frequency interference; and segmenting the target observation data based on the first segment length.

[0008] Optionally, determining the first segment length includes: calculating the product of the shortest duration and the data sampling rate, and rounding down the product; and determining the result obtained by rounding down as the first segment length.

[0009] Optionally, the method further includes: if no jump point exists in each of the second segmented data, determining the mean sorting sequence as a background noise sequence.

[0010] Optionally, the jump point in the second segmented data is determined in the following manner: for any adjacent first data point and second data point in the second segmented data, the amplitude mean ratio between the second data point and the first data point is determined; if the amplitude mean ratio exceeds a set threshold, the second data point is determined as a jump point.

[0011] Optionally, determining the background noise sequence in the mean sorted sequence based on the target jump point specifically includes: identifying a target index of the target jump point in the mean sorted sequence; based on the target index, extracting a sequence in the mean sorted sequence that is located before the target index, and determining the extracted sequence as the background noise sequence, wherein the mean sorted sequence is sorted from small to large according to the amplitude mean.

[0012] Optionally, detecting and eliminating radio frequency interference based on the background noise sequence includes: determining a median of amplitude means in the background noise sequence as a target threshold reference; determining a detection threshold based on the target threshold reference; and detecting and eliminating radio frequency interference in the target observation data based on the detection threshold.

[0013] In a second aspect, an embodiment of the present application provides a radio frequency interference reduction system, the system comprising: an acquisition module for acquiring target observation data; a first segmentation module for performing a first segmentation processing on the target observation data to obtain a number of first segmentation data; a sorting module for determining the amplitude mean of each first segmentation data, and sorting them according to the size of the amplitude mean to obtain a mean sorting sequence; a second segmentation module for performing a second segmentation processing on the mean sorting sequence to obtain a number of second segmentation data; a determination module for determining, if there is a jump point in the second segmentation data, the jump point with the smallest amplitude mean as the target jump point; the target jump point is used to characterize the presence of radio frequency interference; a reduction module for determining, based on the target jump point, a background noise sequence in the mean sorting sequence, so as to calculate the median based on the background noise sequence and thereby detect and reduce radio frequency interference.

[0014] In a third aspect, an embodiment of the present application provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the above-mentioned radio frequency interference reduction method by executing the computer instructions.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the above-mentioned radio frequency interference reduction method.

[0016] In a fifth aspect, an embodiment of the present application provides a computer program product, including computer instructions, wherein the computer instructions are used to enable a computer to execute the above-mentioned radio frequency interference reduction method. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A flowchart of a method for reducing radio frequency interference provided in an embodiment of the present application; Figure 2 A distribution diagram of the amplitude values ​​of the simulation data provided by the embodiment of the present application; Figure 3 A schematic diagram of an amplitude mean sequence provided in an embodiment of the present application; Figure 4 A schematic diagram of a mean sorting sequence provided in an embodiment of the present application; Figure 5 A schematic diagram of the detection of a jump point provided in an embodiment of the present application; Figure 6 A schematic diagram of the estimated performance when the interference ratio is less than 50% provided in an embodiment of the present application; Figure 7 A schematic diagram of the estimated performance with an interference ratio of 60% provided in an embodiment of the present application; Figure 8 A schematic diagram of the estimated performance with an interference ratio of 70% provided in an embodiment of the present application; Fig. 9 A schematic diagram of the estimated performance with an interference ratio of 80% provided in an embodiment of the present application; Fig.10 A schematic diagram of the estimated performance with an interference ratio of 90% provided in an embodiment of the present application; Fig.11 A schematic diagram of the time consumption of three algorithms under different data lengths provided in the embodiment of the present application; Fig.12 A schematic diagram of a radio frequency interference reduction system provided in an embodiment of the present application; Fig.13 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0020] Radio telescopes detect weak electromagnetic signals from celestial bodies, with typical intensities ranging from -150dBW / m² to -220dBW / m². Therefore, the receiving sensitivity of radio telescopes is more than 50 decibels higher than other conventional electromagnetic signal receiving devices. Such sensitive systems face serious radio frequency interference (RFI). The main source of RFI is artificially generated electromagnetic signals such as television broadcasts, satellite navigation, and wireless communications. RFI reduces the sensitivity of radio telescopes, affects target observations, increases the difficulty of discovering weak astronomical signals, and even renders data invalid.

[0021] Radio Frequency Interference (RFI) reduction is an important guarantee for effective radio astronomy observation. There are currently a variety of RFI reduction techniques, among which the most widely used is the marker threshold method. By calculating a threshold in the time domain or frequency domain, the data exceeding the threshold is detected as RFI and removed or replaced. The key to the marker threshold method is how to calculate a robust threshold. Currently, the median-based method has a better effect, which involves sorting operations. With the increase in the size of radio telescope arrays and the advancement of graphics processing unit (GPU) technology, the application of GPU in the field of real-time processing of radio signals has gradually become mainstream. Conventional median calculation methods are difficult to give full play to the advantages of GPU parallel computing and are not conducive to real-time processing. To solve this problem, a block clipping and re-sorting method is proposed in the relevant literature (Wu Zhenpeng et al. "Median calculation algorithm based on GPU in OLAP." Journal of Shandong University: Engineering Edition 51.3 (2021): 8.), which realizes parallel operation to a certain extent, but there are still many local sorting and one global sorting in the algorithm, which is not efficient. At the same time, the breakdown threshold of the median is 50%, which means that when the interference ratio is greater than 50%, the marking threshold method based on the median will fail. To overcome these problems, it is necessary to develop a calculation method with high accuracy, strong real-time performance, and no breakdown threshold to meet the needs of real-time RFI marking and removal.

[0022] An embodiment of the present application provides a method for reducing radio frequency interference. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0023] In the application field of radio astronomy signal interference detection and marking, the essence of obtaining a robust threshold is to exclude RFI interference and calculate the median of the background noise, where the background noise refers to the part of the background noise in the signal. Based on this essence, this embodiment provides a method for reducing radio frequency interference. Please refer to Figure 1 , Figure 1 A flowchart of a method for reducing radio frequency interference provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the process includes the following steps: Step S1, obtaining target observation data.

[0024] Among them, the target observation data is obtained by observing and recording any receiving unit in the receiving system. The target observation data reflects the RF signal situation within a specific bandwidth. The target observation data can be the original signal data collected directly from the receiving system, retaining the original characteristics of the signal, or it can be the sub-band data after multi-phase filtering. The receiving unit can be a receiving antenna in a receiving array, or it can be a receiving beam after digital beamforming. The receiving beam after digital beamforming refers to a specific spatial direction formed by digital signal processing technology.

[0025] Step S3, performing first segmentation processing on the target observation data to obtain a plurality of first segmented data.

[0026] The segment lengths of the first segment data are consistent. In the GPU, multiple threads are used to process the first segment data in parallel, which can give full play to the advantages of GPU parallel computing and improve GPU computing efficiency.

[0027] Step S5, determining the amplitude mean of each first segment data, and sorting them according to the amplitude mean values ​​to obtain a mean sorting sequence.

[0028] Among them, the amplitude mean refers to the mean of the signal amplitude value. The amplitude means of each first segmented data in the target observation data constitute a mean sequence. Arrange according to the size of the amplitude mean to obtain a mean sorting sequence. For example, the target observation data is processed by the first segmentation to obtain 10 first segmented data. The amplitude mean is calculated for each first segmented data to obtain a mean sequence containing 10 amplitude means. Sorting according to the size of the amplitude mean can obtain a mean sorting sequence.

[0029] Step S7, performing second segmentation processing on the mean sorting sequence to obtain a plurality of second segmented data.

[0030] The segment lengths of the second segment data are consistent. In the GPU, multiple threads are used to process the second segment data in parallel, which can give full play to the advantages of GPU parallel computing and improve GPU computing efficiency.

[0031] Step S9: If there is a jump point in the second segmented data, determine the jump point with the smallest amplitude mean as the target jump point; the target jump point is used to indicate the existence of radio frequency interference.

[0032] In which, multiple threads are used in the GPU to process each second segmented data in parallel to detect whether there is a jump point. If multiple jump points are detected in the second segmented data, the jump point with the smallest amplitude average is determined as the target jump point. The target jump point is used to characterize the boundary between the background noise and radio frequency interference (RFI).

[0033] Step S11 : determining a background noise sequence in a mean sorting sequence based on a target transition point, so as to detect and reduce radio frequency interference based on the background noise sequence.

[0034] Usually, RFI will last for a period of time, and the amplitude mean of RFI is significantly stronger than the background noise, while astronomical signals are usually weaker than the background noise and hidden under the background noise. The target jump point is used as the boundary to distinguish RFI from the background noise. The sample point sequence with an amplitude mean higher than the target jump point is considered to be an interference sequence, and the sample point sequence with an amplitude mean lower than the target jump point is determined to be a background noise sequence. The obtained background noise sequence can be used as a reference benchmark to effectively detect and eliminate RF interference.

[0035] The radio frequency interference reduction method provided in this embodiment includes: acquiring target observation data; performing a first segmentation processing on the target observation data to obtain a number of first segmentation data; determining the amplitude mean of each first segmentation data, sorting according to the size of the amplitude mean, and obtaining a mean sorting sequence; performing a second segmentation processing on the mean sorting sequence to obtain a number of second segmentation data; if there is a jump point in the second segmentation data, determining the jump point with the smallest amplitude mean as the target jump point; the target jump point is used to characterize the existence of radio frequency interference; based on the target jump point, determining the background noise sequence in the mean sorting sequence to reduce radio frequency interference, solving the technical problem of how to quickly, efficiently and accurately reduce radio frequency interference, and achieving the technical effect of improving processing efficiency to meet real-time requirements.

[0036] In some embodiments, acquiring the target observation data specifically includes: obtaining the target observation data based on a radio frequency signal received by any receiving unit included in the large-scale antenna array.

[0037] The receiving unit may be a receiving antenna. For example, the receiving antenna converts electromagnetic waves in space into electrical signals to obtain target observation data.

[0038] In some embodiments, the target observation data is subjected to a first segmentation process, specifically including: determining a first segment length based on a data sampling rate and a minimum duration for reducing radio frequency interference; and segmenting the target observation data based on the first segment length.

[0039] Among them, the data sampling rate refers to the number of samples collected per unit time. The product of the data sampling rate and the shortest duration of the radio frequency interference reduction is calculated, and the result obtained by rounding down the product can be used as the first segment length. The first segment length includes multiple sample numbers. If the first segment length is set too large, each first segment data contains too much signal data, which may mix RFI and normal signals together. The characteristics of RFI will be "diluted" by a large number of normal signals, causing its statistical characteristics (such as amplitude mean) to become unclear and difficult to identify. If the first segment length is set too small, the target observation data is divided into more first segment data. The increase in the number of segments of the first segment data will make the subsequent processing steps complicated and time-consuming. Based on the first segment length, the target observation data is subjected to first segment processing to obtain a number of first segment data.

[0040] In some embodiments, determining the first segment length includes: calculating the product of the shortest duration and the data sampling rate, and rounding down the product; and determining the result obtained by rounding down as the first segment length.

[0041] In some embodiments, if no jump point exists in each second segment data, the mean sorting sequence is determined as a background noise sequence.

[0042] Specifically, each second segmented data is searched in parallel, and it is determined point by point whether a jump point appears in each second segmented data. If a jump point is found, the flag corresponding to the second segmented data is set to the index number where the jump point is located, and the search of the second segmented data is terminated at the same time. If no jump point is found in the second segmented data, the corresponding flag is set to zero. If the flags of each second segmented data are all zero, it is considered that there is no strong interference, and the mean sorting sequence is determined as the background noise sequence.

[0043] In some embodiments, the jump point in the second segmented data is determined in the following manner: for any adjacent first data point and second data point in the second segmented data, the amplitude mean ratio between the second data point and the first data point is calculated; if the amplitude mean ratio exceeds a set threshold, the second data point is determined as a jump point.

[0044] In the absence of RFI, the amplitude mean in the mean sorting sequence changes slowly and steadily, the amplitude means of adjacent data points are close, and the ratio is approximately equal to 1. If the first segment length is set reasonably and there is a jump point, it is considered that there is a strong RFI.

[0045] In some embodiments, based on the target jump point, a background noise sequence in the mean sorted sequence is determined, specifically including: identifying a target index of the target jump point in the mean sorted sequence; based on the target index, extracting a sequence in the mean sorted sequence that is located before the target index, and determining the extracted sequence as the background noise sequence, wherein the mean sorted sequence is sorted from small to large according to the amplitude mean.

[0046] If a jump point is detected, the mean sorted sequence needs to be divided into an interference signal sequence and a background noise sequence. In the mean sorted sequence, the target jump point is the dividing point between the interference signal sequence (sequence with RFI) and the background noise sequence. When the mean sorted sequence is sorted from small to large according to the amplitude mean, based on the target index of the target jump point, the sequence before the target index is extracted as the background noise sequence, and the sequence after the target index is determined as the interference signal sequence with RFI interference. The sequence before the target index refers to the sequence before the target index number of the target jump point. For example, if the target index number is 100, the mean sorted sequence with index numbers between 1 and 100 is extracted as the background noise sequence.

[0047] In some embodiments, reducing radio frequency interference based on a background noise sequence includes: determining a median of amplitude means in the background noise sequence as a target threshold reference; determining a detection threshold based on the target threshold reference, and detecting and reducing radio frequency interference in target observation data.

[0048] Specifically, if there is no jump point in the second segmented data, the mean sorting sequence is a background noise sequence. If the number of data sample points in the mean sorting sequence is an odd number, the target threshold reference is the middle number in the mean sorting sequence. If the number of data sample points in the mean sorting sequence is an even number, the target threshold reference is the average of the two middle numbers in the mean sorting sequence.

[0049] If there is a jump point in the second segmented data, the jump point with the smallest amplitude mean is determined as the target jump point. In the mean sorted sequence arranged in ascending order, the sequence before the target index of the target jump point is determined as the background noise sequence. If the number of data samples in the background noise sequence is an odd number, the target threshold reference is the middle number of the background noise sequence. If the number of data samples in the background noise sequence is an even number, the target threshold reference is the average of the two middle numbers of the background noise sequence.

[0050] The target threshold reference may be used to detect RFI interference. Based on the target threshold reference, a detection threshold is determined. Based on the detection threshold, interference may be eliminated by replacement or elimination.

[0051] In some embodiments, the radio frequency interference reduction method provided in the embodiments of the present application specifically includes the following steps: Step 1: Obtain target observation data. The target observation data is obtained by observing and recording the RF signal within the bandwidth by any unit in the receiving system. The receiving unit can be a receiving antenna in a receiving array or a receiving beam after digital beamforming. The target observation data can be the original acquisition data or the sub-band data after polyphase filtering.

[0052] Step 2: Set up input and output buffers in the GPU memory space and write the target observation data stream into the input data buffer.

[0053] Step 3: Read the target observation data segment to be processed from the input data buffer. Suppose the read length is N, and record it as: .

[0054] Step 4: Calculate the amplitude value of the target observation data according to the following formula: .

[0055] Please refer to Figure 2 , Figure 2 The amplitude value distribution diagram of the simulation data provided in the embodiment of the present application is as follows: Figure 2 As shown, the red area is RFI.

[0056] Step 5: Calculate the mean value in segments. Usually, RFI will last for a period of time and will be significantly stronger than the background noise. The amplitude mean value will be significantly higher than the background noise. Based on this, set the length of the first segment to WinL_M. The length of the first segment needs to be set according to the actual duration of the interference signal, and it is more appropriate to match the minimum RFI duration.

[0057] For example, if the minimum duration of RFI to be reduced is x seconds, the typical setting of the first segment length WinL_M can be WinL_M=floor(x*Fs), where floor means rounding down to an integer, the data sampling rate is Fs, and the unit of the data sampling rate is Sps (the number of samples collected per second).

[0058] The entire segment of the target observation data with a data length of N is divided into the first segment data of N_WinM segments. Among them, , The above formula needs to be set reasonably to ensure divisibility.

[0059] In the GPU, N_WinM threads are used to parallelly calculate the amplitude mean of each first segment data to obtain an amplitude mean sequence M(k). .

[0060] Please refer to Figure 3 , Figure 3 A schematic diagram of an amplitude mean value sequence provided in an embodiment of the present application, wherein each first segmented data contains 128 data sample points.

[0061] Step 6: Sort the amplitude mean values ​​M(k) from small to large. The sorting method is not limited. Merge sort, bitonic sort, etc. can be used. The sorted amplitude mean sequence is recorded as the mean sorting sequence r(k): .

[0062] Please refer to Figure 4 , Figure 4 A schematic diagram of a mean sorting sequence provided in an embodiment of the present application.

[0063] Step 7: Search for target jump point. Perform segment detection on the mean sorting sequence r(k). Perform second segment processing on the mean sorting sequence, set the second segment length WinL_D, and divide the mean sorting sequence r(k) into second segment data of N_WinD segments. Among them, , The above formula needs to be set reasonably to ensure divisibility. The second segment length is usually set reasonably based on experience and data length.

[0064] In the GPU, N_WinD threads are used to search for the jump point of the mean sorting sequence in parallel, and whether the jump point appears in each second segmented data is determined point by point. Once a jump is found, the flag corresponding to the second segmented data is set to the position where the jump point is located, and the search for the second segmented data is ended at the same time. If no jump point is found in the second segmented data, the flag corresponding to the second segmented data is set to 0.

[0065] In the absence of RFI, the amplitude mean after sorting should increase slowly and steadily, the amplitude mean of adjacent data samples is close, and the ratio is approximately equal to 1. If there is a strong RFI and WinL is set reasonably, there is a jump point. Set the threshold TH. When the following conditions are met, it is considered that the i-th point has a jump: .

[0066] Please refer to Figure 5 , Figure 5 A schematic diagram of the detection of the jump point provided in the embodiment of the present application. Figure 5As shown, TH is set to 1.1.

[0067] Step 8: Traverse the flags of each second segment data. There are two situations: Case 1: If the flags of the second segmented data are all 0, it is considered that there is no strong interference. In this case, the median of the mean sorting sequence is taken as the approximate value of the median of the background noise.

[0068] If N_WinM is an odd number, the approximate value of the median of the background noise is: .

[0069] If N_WinM is an even number, the approximate value of the median of the background noise is: .

[0070] Case 2: There is a jump point in the second segment data. The jump point with the smallest amplitude mean is the target jump point. The location of the target jump point (i.e., the index number) is the boundary between the background noise and the interference. Assume that the location of the target jump point is P hop , then the sequence before the target jump point index is considered to be background noise, and there is RFI in the sequence after the target jump point index. hop The median of the mean-sorted sequence before the point index is an approximation of the median of the noise floor.

[0071] If P hop is an odd number, then the approximate value of the median of the background noise is: .

[0072] If P hop is an even number, then the approximate value of the median of the background noise is: .

[0073] Please refer to Figure 6 , Figure 6 A schematic diagram of the estimated performance when the interference ratio is less than 50% provided in an embodiment of the present application.

[0074] Please refer to Figure 7 , Figure 7 A schematic diagram of the estimated performance with an interference ratio of 60% provided in an embodiment of the present application.

[0075] Please refer to Figure 8 , Figure 8 A schematic diagram of the estimated performance with an interference ratio of 70% provided in an embodiment of the present application.

[0076] Please refer to Fig. 9 , Fig. 9 A schematic diagram of the estimated performance with an interference ratio of 80% provided in an embodiment of the present application.

[0077] Please refer to Fig.10 , Fig.10 A schematic diagram of the estimated performance with an interference ratio of 90% provided in an embodiment of the present application.

[0078] Please refer to Fig.11 , Fig.11 The time-consuming diagram of the three algorithms under different data lengths provided for the embodiments of the present application. Among them, Thrust is an advanced parallel algorithm library based on CUDA, provided by NVIDIA. Thrust provides a variety of efficient parallel algorithms, among which the sorting algorithm is one of its core functions. The GPU median determination method based on block clipping refers to the median calculation method provided in the literature (Wu Zhenpeng et al. "Median calculation algorithm based on GPU in OLAP." Journal of Shandong University: Engineering Edition 51.3 (2021): 8.). Fig.11 The figure compares the time taken to calculate the median using three different algorithms on the same GPU (NVIDIA RTX3060).

[0079] The RF interference reduction method proposed in the embodiment of the present application makes full use of the parallel processing capability of the GPU. The most time-consuming part is the sorting link in step 6. Relatively speaking, the amount of data involved in the direct global sorting to obtain the median is N. In the literature ("Wu Zhenpeng et al. "Median calculation algorithm based on GPU in OLAP." Journal of Shandong University: Engineering Edition 51.3(2021):8."), the amount of data for global sorting in the GPU optimization algorithm is >N / 2. The amount of data for algorithm parameter sorting proposed in the embodiment of the present application is N_WinM, where, , and Usually much larger than 2. In general, It is reasonable to set it to 64 or more, so the sorting time will be greatly reduced. At the same time, the RF interference reduction method provided in the embodiment of the present application selects the area where the background noise is located by judging the jump point, avoiding the problem of 50% breakdown threshold faced by directly using the median, and enhancing the detection ability of different interferences. If the data is long enough, in theory, a more reasonable median approximation can be obtained under any interference ratio.

[0080] Please refer to Fig.12 , Fig.12 A schematic diagram of a radio frequency interference reduction system provided in an embodiment of the present application is shown in FIG. Fig.12As shown, the radio frequency interference reduction system includes: an acquisition module, which is used to acquire target observation data; a first segmentation module, which is used to perform a first segmentation processing on the target observation data to obtain a number of first segmentation data; a sorting module, which is used to determine the amplitude mean of each first segmentation data, sort them according to the size of the amplitude mean, and obtain a mean sorting sequence; a second segmentation module, which is used to perform a second segmentation processing on the mean sorting sequence to obtain a number of second segmentation data; a determination module, which is used to determine the jump point with the smallest amplitude mean as the target jump point if there is a jump point in the second segmentation data; the target jump point is used to characterize the existence of radio frequency interference; and a reduction module, which is used to determine the background noise sequence in the mean sorting sequence based on the target jump point, so as to detect and reduce radio frequency interference based on the background noise sequence.

[0081] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0082] The RF interference mitigation system in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0083] See also Fig.13 , Fig.13 A schematic diagram of the structure of a computer device provided in an embodiment of the present application is shown in FIG. Fig.13 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.13 A processor 10 is taken as an example.

[0084] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0085] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0086] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0087] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0088] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0089] The embodiment of the present application also provides a computer-readable storage medium. The above method according to the embodiment of the present application can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0090] The embodiment of the present application provides a computer program product, which includes computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method of any embodiment of the present application.

[0091] Although the embodiments of the present application are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

[0092] The systems, 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.

[0093] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0094] 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.

[0095] The present application is described with reference to the flowcharts and / or block diagrams of the methods, systems and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks 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.

[0096] 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 including 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.

[0097] 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.

[0098] 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, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity or device including the element.

[0099] 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.

[0100] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0101] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for reducing radio frequency interference, characterized in that: The method comprises: Obtain target observation data; Performing first segmentation processing on the target observation data to obtain a plurality of first segmentation data; Determine the amplitude mean of each of the first segmented data, and sort them according to the magnitude of the amplitude mean to obtain a mean sorting sequence; Performing second segmentation processing on the mean sorting sequence to obtain a plurality of second segmentation data; If there is a jump point in the second segmented data, determine the jump point with the smallest amplitude mean as the target jump point; the target jump point is used to indicate the existence of radio frequency interference; Based on the target transition point, a background noise sequence in the mean sorting sequence is determined to detect and reduce radio frequency interference based on the background noise sequence.

2. The method according to claim 1, characterized in that The target observation data is subjected to a first segmentation process, specifically comprising: Determine the length of the first segment based on the data sampling rate and the shortest duration of radio frequency interference reduction; The target observation data is segmented based on the first segment length.

3. The method according to claim 2, characterized in that Determining the length of the first segment includes: Calculating the product of the shortest duration and the data sampling rate, and rounding down the product; A result obtained by rounding down is determined as the first segment length.

4. The method according to claim 1, characterized in that: The method further comprises: If no jump point exists in each of the second segmented data, the mean sorting sequence is determined as a background noise sequence.

5. The method according to claim 1, characterized in that The jump point in the second segment data is determined in the following manner: For any adjacent first data point and second data point in the second segmented data, determine the amplitude mean ratio between the second data point and the first data point; if the amplitude mean ratio exceeds a set threshold, determine the second data point as a jump point.

6. The method according to claim 1 or 5, characterized in that: The step of determining the background noise sequence in the mean sorting sequence based on the target jump point specifically includes: Identify a target index of the target transition point in the mean sorted sequence; Based on the target index, a sequence before the target index in the mean sorting sequence is extracted, and the extracted sequence is determined as a background noise sequence, wherein the mean sorting sequence is sorted from small to large according to the amplitude mean.

7. The method according to claim 1, characterized in that Detecting and reducing radio frequency interference based on the background noise sequence includes: Determine the median of the amplitude mean in the background noise sequence as the target threshold reference; Determining a detection threshold based on the target threshold reference; Based on a detection threshold, radio frequency interference in the target observation data is detected and eliminated.

8. A radio frequency interference mitigation system, characterized in that: The system comprises: An acquisition module is used to acquire target observation data; A first segmentation module, used for performing a first segmentation process on the target observation data to obtain a plurality of first segmentation data; A sorting module, used to determine the amplitude mean of each of the first segmented data, and sort them according to the amplitude mean to obtain a mean sorting sequence; A second segmentation module, used for performing a second segmentation process on the mean sorting sequence to obtain a plurality of second segmentation data; A determination module, configured to determine, if a jump point exists in the second segmented data, a jump point with a minimum amplitude mean as a target jump point; the target jump point is used to characterize the existence of radio frequency interference; The mitigation module is configured to determine a background noise sequence in the mean sorting sequence based on the target transition point, so as to detect and mitigate radio frequency interference based on the background noise sequence.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the radio frequency interference reduction method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the radio frequency interference reduction method according to any one of claims 1 to 7.

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