A method, system, device and medium for reducing radio frequency interference

Abstract

This application relates to the technical field of radio astronomy signal processing, and discloses a radio frequency interference reduction method, system, device and medium. Among them, the method includes: obtaining target observation data; performing first segmentation processing on the target observation data to obtain a number of first segmented data; determining the amplitude mean value of each first segmented data, sorting according to the magnitude of the amplitude mean value to obtain a mean value sorting sequence; performing second segmentation processing on the mean value sorting sequence to obtain a number of second segmented data; if there are jump points in the second segmented data, determining the jump point with the smallest amplitude mean value as the target jump point; the target jump point is used to represent the existence of radio frequency interference; based on the target jump point, determining the noise floor sequence in the mean value 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 the processing efficiency to meet the real-time requirements.

Description

Technical Field

[0001] This application relates to the technical field of radio astronomy signal processing, and particularly to a radio frequency interference cancellation method, system, device and medium. Background Art

[0002] Radio telescopes detect weak electromagnetic signals from celestial bodies, and the typical value of their intensity is between -150 dBW / m² and -220 dBW / m². Therefore, the receiving sensitivity of radio telescopes is more than 50 decibels higher than that of other conventional electromagnetic signal receiving devices. Such a sensitive system faces very serious radio frequency interference (RFI). The main sources of RFI are man-made electromagnetic signals such as television broadcasts, satellite navigation, and wireless communication. RFI reduces the sensitivity of radio telescopes, affects target observation, increases the difficulty of detecting weak astronomical signals, and even causes data to be invalid.

[0003] Currently, there are various RFI cancellation technologies. Among them, the widely used one is the marking threshold method, which calculates a threshold in the time domain or frequency domain, and detects and eliminates or replaces the data exceeding the threshold as RFI. The key to the marking threshold method is how to calculate a robust threshold. Currently, the method based on the median has a good effect, which involves sorting operations. With the increase in the scale of radio telescope arrays and the progress of Graphics Processing Unit (GPU) technology, the application of GPUs in the field of real-time radio signal processing has gradually become the mainstream. The conventional median calculation method is difficult to give full play to the parallel computing advantage of GPUs and is not conducive to real-time processing. Summary of the Invention

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

[0005] To achieve the above object, the main technical solutions adopted in this application include:

[0006] In a first aspect, an embodiment of the present application provides a method for reducing radio frequency interference. The method includes: obtaining target observation data; performing a first segmentation process on the target observation data to obtain a plurality of first segmented data; determining the amplitude mean value of each of the first segmented data, and sorting them according to the magnitude of the amplitude mean value to obtain a mean value sorting sequence; performing a second segmentation process on the mean value sorting sequence to obtain a plurality of second segmented data; if there is a jump point in the second segmented data, determining the jump point with the smallest amplitude mean value as the target jump point; the target jump point is used to represent the existence of radio frequency interference; based on the target jump point, determining a background noise sequence in the mean value sorting sequence, so as to detect and reduce radio frequency interference based on the background noise sequence.

[0007] The method for reducing radio frequency interference provided in this embodiment includes: obtaining target observation data; performing a first segmentation process on the target observation data to obtain a plurality of first segmented data; determining the amplitude mean value of each of the first segmented data, and sorting them according to the magnitude of the amplitude mean value to obtain a mean value sorting sequence; performing a second segmentation process on the mean value sorting sequence to obtain a plurality of second segmented data; if there is a jump point in the second segmented data, determining the jump point with the smallest amplitude mean value as the target jump point; the target jump point is used to represent the existence of radio frequency interference; based on the target jump point, determining a background noise sequence in the mean value sorting sequence, so as to calculate the median based on the background noise sequence to detect and reduce radio frequency interference, which 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 the processing efficiency to meet the real-time requirement.

[0008] Optionally, the performing a first segmentation process on the target observation data specifically includes: determining a first segmentation length based on the data sampling rate and the shortest duration for reducing radio frequency interference; segmenting the target observation data based on the first segmentation length.

[0009] Optionally, determining the first segmentation length includes: calculating the product of the shortest duration and the data sampling rate, and performing a floor operation on the product; determining the result obtained by the floor operation as the first segmentation length.

[0010] Optionally, the method further includes: if there is no jump point in each of the second segmented data, determining the mean value sorting sequence as the background noise sequence.

[0011] 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, determining the amplitude mean value ratio between the second data point and the first data point, if the amplitude mean value ratio exceeds a set threshold, determining the second data point as the jump point.

[0012] Optionally, determining the background noise sequence in the mean sorting sequence based on the target jump point specifically includes: identifying the target index of the target jump point in the mean sorting sequence; based on the target index, extracting the sequence in the mean sorting sequence that is before the target index, and determining the extracted sequence as the background noise sequence, where the mean sorting sequence is sorted in ascending order of amplitude mean.

[0013] Optionally, detecting and reducing radio frequency interference based on the background noise sequence includes: determining the median of the amplitude means in the background noise sequence as the target threshold benchmark; based on the target threshold benchmark, determining the detection threshold; based on the detection threshold, detecting and reducing the radio frequency interference in the target observation data.

[0014] In a second aspect, an embodiment of the present application provides a radio frequency interference reduction system, the system includes: an acquisition module, configured to acquire target observation data; a first segmentation module, configured to perform a first segmentation process on the target observation data to obtain a plurality of first segmented data; a sorting module, configured to determine the amplitude means of the respective first segmented data and sort them according to the magnitude of the amplitude means to obtain a mean sorting sequence; a second segmentation module, configured to perform a second segmentation process on the mean sorting sequence to obtain a plurality of second segmented data; a determination module, configured to, 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 characterize the existence of radio frequency interference; a reduction module, configured to determine the background noise sequence in the mean sorting sequence based on the target jump point, so as to calculate the median based on the background noise sequence and then detect and reduce radio frequency interference.

[0015] In a third aspect, an embodiment of the present application provides a computer device, including: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the above-mentioned radio frequency interference reduction method.

[0016] 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 cause a computer to execute the above-mentioned radio frequency interference reduction method.

[0017] In a fifth aspect, an embodiment of the present application provides a computer program product, including computer instructions, and the computer instructions are used to cause a computer to execute the above-mentioned radio frequency interference reduction method. Description of the Drawings

[0018] To more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the accompanying drawings required for the description of the specific embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0019] Figure 1 It is a flowchart of the radio frequency interference reduction method provided by an embodiment of the present application;

[0020] Figure 2 It is a distribution diagram of the amplitude values of the simulation data provided by an embodiment of the present application;

[0021] Figure 3 It is a schematic diagram of the amplitude mean sequence provided by an embodiment of the present application;

[0022] Figure 4 It is a schematic diagram of the mean sorting sequence provided by an embodiment of the present application;

[0023] Figure 5 It is a schematic diagram of the detection situation of the jump point provided by an embodiment of the present application;

[0024] Figure 6 It is a schematic diagram of the estimation performance with the interference ratio less than 50% provided by an embodiment of the present application;

[0025] Figure 7 It is a schematic diagram of the estimation performance with the interference ratio of 60% provided by an embodiment of the present application;

[0026] Figure 8 It is a schematic diagram of the estimation performance with the interference ratio of 70% provided by an embodiment of the present application;

[0027] Figure 9 It is a schematic diagram of the estimation performance with the interference ratio of 80% provided by an embodiment of the present application;

[0028] Figure 10 It is a schematic diagram of the estimation performance with the interference ratio of 90% provided by an embodiment of the present application;

[0029] Figure 11 It is a schematic diagram of the time consumption of three algorithms under different data lengths provided by an embodiment of the present application;

[0030] Figure 12 It is a schematic diagram of the radio frequency interference reduction system provided by an embodiment of the present application;

[0031] Figure 13 It is a schematic diagram of the structure of a computer device provided by an embodiment of the present application. Specific Embodiments

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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 the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0036] 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. Here, the background noise refers to the background noise part in the signal. Based on this essence, this embodiment provides a method for reducing radio frequency interference. Please refer to Figure 1 , Figure 1 which is the flowchart of the radio frequency interference reduction method provided by the embodiment of the present application. As Figure 1 shown, this process includes the following steps:

[0037] Step S1, obtain target observation data.

[0038] Among them, the target observation data is obtained by observing and recording through any receiving unit in the receiving system. The target observation data reflects the radio frequency signal situation within a specific bandwidth. The target observation data can be the original signal data directly collected from the receiving system, retaining the original characteristics of the signal, or it can be the sub-band data after polyphase filtering processing. The receiving unit can be a receiving antenna in the receiving array, or it can be a receiving beam after digital beamforming. The receiving beam after digital beamforming refers to a specific spatial pointing formed through digital signal processing technology.

[0039] Step S3, perform a first segmentation process on the target observation data to obtain a number of first segmented data.

[0040] Among them, the segmentation lengths of the first segmented data are the same. In the GPU, multiple threads are used to process each first segmented data in parallel, which can give full play to the parallel computing advantage of the GPU and improve the GPU computing efficiency.

[0041] Step S5, determine the amplitude mean value of each first segmented data and sort them according to the magnitude of the amplitude mean value to obtain a mean sorting sequence.

[0042] Among them, the amplitude mean value refers to the mean value of the signal amplitude values. The amplitude mean values of the first segmented data in the target observation data constitute a mean sequence. Sorting according to the magnitude of the amplitude mean value, a mean sorting sequence is obtained. For example, perform a first segmentation process on the target observation data to obtain 10 segments of first segmented data. Calculate the amplitude mean value for each segment of the first segmented data respectively to obtain a mean sequence containing 10 amplitude mean values. Sorting according to the magnitude of the amplitude mean value, a mean sorting sequence can be obtained.

[0043] Step S7: Perform a second segmentation process on the mean sorted sequence to obtain several second segmented data.

[0044] Among them, the segmentation lengths of the second segmented data are the same. In the GPU, multiple threads are used to perform parallel processing on each second segmented data respectively, which can give full play to the advantages of GPU parallel computing and improve the GPU computing efficiency.

[0045] Step S9: If there are jump points in the second segmented data, determine the jump point with the minimum amplitude mean as the target jump point; the target jump point is used to represent the existence of radio frequency interference.

[0046] Among them, multiple threads are used in the GPU to perform parallel processing on each second segmented data to detect whether there are jump points. If multiple jump points are detected in the second segmented data, the jump point with the minimum amplitude mean is determined as the target jump point. The target jump point is used to represent the boundary between the background noise and radio frequency interference (RFI).

[0047] Step S11: Based on the target jump point, determine the background noise sequence in the mean sorted sequence to detect and reduce radio frequency interference based on the background noise sequence.

[0048] Generally, RFI will last for a period of time, 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. Taking the target jump point as the boundary, RFI and the background noise can be distinguished. The sample point sequence with an amplitude mean higher than the target jump point is considered as the interference sequence, and the sample point sequence with an amplitude mean lower than the target jump point is determined as the background noise sequence. The obtained background noise sequence can be used as a reference benchmark to effectively detect and eliminate radio frequency interference.

[0049] The radio frequency interference reduction method provided in this embodiment includes: obtaining target observation data; performing a first segmentation process on the target observation data to obtain several first segmented data; determining the amplitude mean of each first segmented data, sorting according to the magnitude of the amplitude mean to obtain a mean sorted sequence; performing a second segmentation process on the mean sorted sequence to obtain several second segmented data; if there are jump points in the second segmented data, determining the jump point with the minimum amplitude mean as the target jump point; the target jump point is used to represent the existence of radio frequency interference; based on the target jump point, determining the background noise sequence in the mean sorted 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 the processing efficiency to meet the real-time requirements.

[0050] In some embodiments, obtaining the target observation data specifically includes: obtaining the target observation data based on the radio frequency signals received by any receiving unit included in the large-scale antenna array.

[0051] Among them, the receiving unit may be a receiving antenna. For example, the receiving antenna converts the electromagnetic wave in space into an electrical signal, and target observation data can be obtained.

[0052] In some embodiments, performing a first segmentation process on the target observation data specifically includes: determining a first segmentation length based on the data sampling rate and the shortest duration for reducing radio frequency interference; segmenting the target observation data based on the first segmentation length.

[0053] Among them, the data sampling rate refers to the number of sample points collected per unit time. Calculating the product of the data sampling rate and the shortest duration for reducing radio frequency interference, and taking the floor of the product, the result can be used as the first segmentation length. The first segmentation length includes a plurality of sample point quantities. If the first segmentation length is set too large, each first-segment data contains too much signal data, and it may mix RFI and normal signals together. The characteristics of RFI will be "diluted" by a large amount of normal signals, resulting in its statistical characteristics (such as amplitude mean) becoming unclear and difficult to identify. If the first segmentation length is set too small, the target observation data is segmented into more segments of first-segment data. The increase in the number of segments of the first-segment data will make the subsequent processing steps complex and time-consuming. Based on the first segmentation length, performing a first segmentation process on the target observation data to obtain a number of first-segment data.

[0054] In some embodiments, determining the first segmentation length includes: calculating the product of the shortest duration and the data sampling rate, and performing a floor processing on the product; determining the result of the floor as the first segmentation length.

[0055] In some embodiments, if there are no jump points in each second-segment data, the mean sorting sequence is determined as the background noise sequence.

[0056] Specifically, performing a parallel search on each second-segment data, judging point by point in each second-segment data whether there is a jump point. If a jump is found, setting the flag corresponding to the second-segment data to the index number where the jump point is located, and at the same time ending the search for the second-segment data. If no jump point is found in the second-segment data, setting the corresponding flag to zero. If the flags of all second-segment 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.

[0057] In some embodiments, the jump points in the second-segment data are determined in the following manner: for any adjacent first data point and second data point in the second-segment data, calculating the amplitude mean ratio between the second data point and the first data point. If the amplitude mean ratio exceeds a set threshold, the second data point is determined as a jump point.

[0058] In the absence of RFI, the amplitude mean in the mean sorting sequence changes slowly and smoothly, the amplitude means of adjacent data points are close, and the ratio is approximately equal to 1. When the first segmentation length is set reasonably, if there is a jump point, it is considered that there is strong RFI.

[0059] In some embodiments, based on the target jump point, determining the background noise sequence in the mean sorting sequence specifically includes: identifying the target index of the target jump point in the mean sorting sequence; based on the target index, extracting the sequence in the mean sorting sequence that is before the target index, and determining the extracted sequence as the background noise sequence, where the mean sorting sequence is sorted in ascending order of amplitude mean.

[0060] If a jump point is detected, the mean sorting sequence needs to be divided into an interference signal sequence and a background noise sequence. In the mean sorting sequence, the target jump point is the demarcation point between the interference signal sequence (the sequence with RFI) and the background noise sequence. When the mean sorting sequence is sorted in ascending order of 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 sorting sequence with index numbers from 1 to 100 is extracted as the background noise sequence.

[0061] In some embodiments, reducing radio frequency interference based on the background noise sequence includes: determining the median of the amplitude means in the background noise sequence as the target threshold reference; based on the target threshold reference, determining the detection threshold, and detecting and reducing the radio frequency interference in the target observation data.

[0062] Specifically, if there is no jump point in the second-segment data, the mean sorting sequence is the background noise sequence. If the number of data samples in the mean sorting sequence is odd, the target threshold reference is the middle number in the mean sorting sequence. If the number of data samples in the mean sorting sequence is even, the target threshold reference is the average of the two middle numbers in the mean sorting sequence.

[0063] If there is a jump point in the second-segment data, the jump point with the smallest amplitude mean is determined as the target jump point. In the mean sorting 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 odd, the target threshold reference is the middle number in the background noise sequence. If the number of data samples in the background noise sequence is even, the target threshold reference is the average of the two middle numbers in the background noise sequence.

[0064] Among them, the target threshold benchmark can be used to detect RFI interference. Based on the target threshold benchmark, a detection threshold is determined. Based on the detection threshold, the interference can be eliminated by replacement or elimination.

[0065] In some embodiments, the RF interference reduction method provided by the embodiments of the present application specifically includes the following steps:

[0066] Step 1: Obtain target observation data, which is obtained by observing and recording the RF signals within the bandwidth by any unit in the receiving system. Among them, the receiving unit can be a receiving antenna in the 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.

[0067] Step 2: Set an input-output buffer in the GPU video memory space and write the target observation data stream into the input data buffer.

[0068] Step 3: Read the target observation data segment to be processed from the input data buffer. Assume that the data read length is N, and it is recorded as:

[0069] .

[0070] Step 4: Calculate the amplitude value of the target observation data according to the following formula:

[0071] .

[0072] Please refer to Figure 2 , Figure 2 , which is the amplitude value distribution diagram of the simulation data provided by the embodiments of the present application. As Figure 2 shown, the red area is RFI.

[0073] Step 5: Calculate the mean value in segments. Usually, RFI will last for a period of time and is significantly stronger than the background noise, and the amplitude mean will be significantly higher than the background noise. Based on this, set the first segment length to WinL_M. The first segment length needs to be set according to the duration of the interference signal in practice and is more suitable to match the minimum RFI duration.

[0074] For example, if the shortest duration of the RFI to be reduced is x seconds, a typical setting for the first segment length WinL_M can be WinL_M = floor(x * Fs). Among them, floor represents taking the integer downward, the data sampling rate is Fs, and the unit of the data sampling rate is Sps (the number of samples collected per second).

[0075] Divide the entire data of the target observation data with a data length of N into N_WinM segments of the first segment data. Among them,

[0076] ,

[0077] The above formula needs to be reasonably set to ensure exact division.

[0078] Utilize N_WinM threads within the GPU to parallel-compute the amplitude means of each first-segment data, obtaining an amplitude mean sequence M(k). Among them,

[0079] .

[0080] Please refer to Figure 3 , Figure 3 , which is a schematic diagram of the amplitude mean sequence provided by the embodiment of this application. Among them, each first-segment data contains 128 data samples.

[0081] Step Six: Sort the amplitude means M(k) in ascending order. The sorting method is not limited and can adopt merge sorting, bitonic sorting, etc. The sorted amplitude mean sequence is denoted as the mean sorting sequence r(k):

[0082] .

[0083] Please refer to Figure 4 , Figure 4 , which is a schematic diagram of the mean sorting sequence provided by the embodiment of this application.

[0084] Step Seven: Search for the target jump point. Perform segmented detection on the mean sorting sequence r(k). Perform a second-segment processing on the mean sorting sequence, set the second-segment length WinL_D, and divide the mean sorting sequence r(k) into N_WinD segments of second-segment data. Among them,

[0085] ,

[0086] The above formula needs to be reasonably set to ensure exact division. The second-segment length is usually reasonably set according to experience and data length.

[0087] Within the GPU, utilize N_WinD threads to perform a parallel search for jump points on the mean sorting sequence, and judge point by point in each second-segment data whether a jump point appears. Once a jump is found, set the flag corresponding to this second-segment data to the position where the jump point is located, and at the same time end the search for this second-segment data. If no jump point is found in this second-segment data, set the flag corresponding to this second-segment data to 0.

[0088] In the absence of RFI, the sorted amplitude means should increase slowly and steadily, the amplitude means of adjacent data samples are close, and the ratio is approximately equal to 1. If there is strong RFI and WinL is set reasonably, then there are jump points. Set a set threshold TH. When the following conditions are met, it is considered that a jump occurs at the i-th point:

[0089] 。

[0090] Please refer to Figure 5 , Figure 5 which is a schematic diagram of the detection situation of the jump points provided by the embodiment of the present application. As Figure 5 shown, TH is set to 1.1.

[0091] Step Eight: Traverse the flags of each second-segment data, and there are two situations:

[0092] Situation 1: If the flags of each second-segment data are all 0, it is considered that there is no strong interference. At this time, take the median of the mean sorting sequence as an approximation of the median of the background noise.

[0093] If N_WinM is odd, the approximation median of the background noise median is:

[0094] 。

[0095] If N_WinM is even, the approximation median of the background noise median is:

[0096] 。

[0097] Situation 2: There are jump points in the second-segment data. Take the jump point with the smallest amplitude mean as the target jump point. The position (i.e., the index number) where the target jump point is located is the boundary between the background noise and the interference. Assume the position of the target jump point is P hop , then it is considered that the sequence before the target jump point index is the background noise, and there is RFI in the sequence after the target jump point index. Take the median of the mean sorting sequence before the P hop point index as an approximation of the background noise median.

[0098] If P hop is odd, the approximation median of the background noise median is:

[0099] 。

[0100] If P hop is even, the approximation median of the background noise median is:

[0101] 。

[0102] Please refer to Figure 6 , Figure 6 which is a schematic diagram of the estimation performance with the interference ratio less than 50% provided by the embodiment of the present application.

[0103] Please refer to Figure 7 , Figure 7Schematic diagram of the estimated performance with an interference ratio of 60% provided by the embodiments of this application.

[0104] Please refer to Figure 8 , Figure 8 Schematic diagram of the estimated performance with an interference ratio of 70% provided by the embodiments of this application.

[0105] Please refer to Figure 9 , Figure 9 Schematic diagram of the estimated performance with an interference ratio of 80% provided by the embodiments of this application.

[0106] Please refer to Figure 10 , Figure 10 Schematic diagram of the estimated performance with an interference ratio of 90% provided by the embodiments of this application.

[0107] Please refer to Figure 11 , Figure 11 Schematic diagram of the time consumption of three algorithms under different data lengths provided by the embodiments of this application. Among them, Thrust is a high-level parallel algorithm library based on CUDA, provided by NVIDIA. Thrust provides a variety of efficient parallel algorithms, and 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 Science 51.3 (2021): 8.). Figure 11 compares the time consumption of three different algorithms for calculating the median on the same GPU (NVIDIA RTX3060).

[0108] The radio frequency interference reduction method proposed by the embodiments of this application makes full use of the parallel processing ability of the GPU. The most time-consuming part is the sorting link in step six. Relatively speaking, in directly obtaining the median through global sorting, the amount of data participating in the sorting is N. In the literature ("Wu Zhenpeng, et al. 'Median calculation algorithm based on GPU in OLAP.' Journal of Shandong University: Engineering Science 51.3 (2021): 8."), the amount of data for global sorting in the GPU-optimized algorithm > N / 2. The amount of data for parameter sorting in the algorithm proposed by the embodiments of this application is N_WinM, where,

[0109] ,

[0110] while is usually much greater than 2. Generally, Setting it to 64 or larger is reasonable, so the sorting time consumption will be greatly reduced. At the same time, in the radio frequency interference cancellation method provided by the embodiments of the present application, by judging the jump point to select the region where the background noise is located, the problem of facing the 50% breakdown threshold when directly using the median is avoided, and the detection ability for different interferences is enhanced. In the case of sufficiently long data, in theory, a relatively reasonable median approximation value can be obtained under any interference ratio situation.

[0111] Please refer to Figure 12 , Figure 12 which is a schematic diagram of the radio frequency interference cancellation system provided by the embodiments of the present application. As Figure 12 shown, the radio frequency interference cancellation system includes: an acquisition module, configured to acquire target observation data; a first segmentation module, configured to perform a first segmentation process on the target observation data to obtain a plurality of first segmented data; a sorting module, configured to determine the amplitude mean value of each first segmented data, sort according to the magnitude of the amplitude mean value to obtain a mean sorting sequence; a second segmentation module, configured to perform a second segmentation process on the mean sorting sequence to obtain a plurality of second segmented data; a determination module, configured to, if there is a jump point in the second segmented data, determine the jump point with the smallest amplitude mean value as the target jump point; the target jump point is used to represent the existence of radio frequency interference; a cancellation module, configured to determine a background noise sequence in the mean sorting sequence based on the target jump point, so as to detect and cancel radio frequency interference based on the background noise sequence.

[0112] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding embodiments above, and will not be repeated here.

[0113] The radio frequency interference cancellation system in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0114] Please refer to Figure 13 , Figure 13 which is a schematic diagram of a computer device provided by the embodiments of the present application. As Figure 13As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 13 In the figure, a processor 10 is taken as an example.

[0115] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above-mentioned hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device can be a complex programmable logic device, a field-programmable gate array, a generic array logic, or any combination thereof.

[0116] Among them, 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 embodiments.

[0117] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device. In addition, the memory 20 can include a high-speed random access memory and can also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0118] The memory 20 can include a volatile memory, such as a random access memory; the memory can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 can also include a combination of the above types of memories.

[0119] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0120] The embodiments of the present application also provide a computer-readable storage medium. The methods according to the embodiments of the present application can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the methods described herein can be stored as 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 memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned 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, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0121] The embodiments of the present application provide a computer program product. The computer program product includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the 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 methods of any embodiment of the present application.

[0122] Although the embodiments of the present application are described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations all fall within the scope defined by the appended claims.

[0123] The systems, modules, or units illustrated in the above embodiments can be specifically implemented by a computer chip or an entity, or by a product with certain functions. A typical implementation device is a computer. Specifically, the computer can 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.

[0124] For the convenience of description, when describing the above devices, they are described separately as various units according to 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.

[0125] Those skilled in the art should understand that the embodiments of this application can be provided as a method, system, or computer program product. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can 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.) that contain computer-usable program code.

[0126] This application is described with reference to the flowcharts and / or block diagrams of methods, systems, and computer program products according to the embodiments of this application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows 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 the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0127] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0128] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0129] It should also be noted that the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity, or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such a process, method, commodity, or device. Without further limitations, the element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, commodity, or device including the said element.

[0130] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and reference can be made to the relevant part of the method embodiment for the relevant content.

[0131] The above description is only for the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

[0132] Although the embodiments of the present application are described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A radio frequency interference reduction method, characterized in that, The method includes: Obtaining target observation data; Performing first segmentation processing on the target observation data to obtain a number of first segmented data; Determining the amplitude mean value of each of the first segmented data, and sorting them in ascending order of the amplitude mean value to obtain a mean sorting sequence; Performing second segmentation processing on the mean sorting sequence to obtain a number of second segmented data; If there is a jump point in the second segmented data, determining the jump point with the smallest amplitude mean value 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 the background noise sequence in the mean sorting sequence, including: identifying the target index of the target jump point in the mean sorting sequence; based on the target index, extracting the sequence in the mean sorting sequence before the target index, and determining the extracted sequence as the background noise sequence, so as to detect and eliminate radio frequency interference based on the background noise sequence, including: determining the median of the amplitude mean values in the background noise sequence as the target threshold reference; based on the target threshold reference, detecting and eliminating radio frequency interference in the target observation data.

2. The method according to claim 1, characterized in that Performing first segmentation processing on the target observation data specifically includes: Determining the first segmentation length based on the data sampling rate and the shortest duration for eliminating radio frequency interference; Segmenting the target observation data based on the first segmentation length.

3. The method according to claim 2, wherein Determining the first segmentation length includes: Calculating the product of the shortest duration and the data sampling rate, and performing a floor operation on the product; Determining the result obtained by the floor operation as the first segmentation length.

4. The method according to claim 1, wherein The method further includes: If there is no jump point in each of the second segmented data, determining the mean sorting sequence as the background noise sequence.

5. The method according to claim 1, characterized in that, 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, determining the amplitude mean ratio between the second data point and the first data point. If the amplitude mean ratio exceeds a set threshold, determining the second data point as the jump point.

6. The method according to claim 1, wherein Detecting and eliminating radio frequency interference based on the background noise sequence includes: Determining the median of the amplitude mean values in the background noise sequence as the target threshold reference; Determining the detection threshold based on the target threshold reference; Detecting and eliminating radio frequency interference in the target observation data based on the detection threshold.

7. A radio frequency interference reduction system capable of implementing the radio frequency interference reduction method described in any one of claims 1 to 6, characterized in that, The system includes: An acquisition module for acquiring target observation data; A first segmentation module for performing first segmentation processing on the target observation data to obtain a number of first segmented data; A sorting module for determining the amplitude mean value of each of the first segmented data and sorting them according to the magnitude of the amplitude mean value to obtain a mean sorting sequence; A second segmentation module for performing second segmentation processing on the mean sorting sequence to obtain a number of second segmented data; A determination module for, if there is a jump point in the second segmented data, determining the jump point with the smallest amplitude mean value as the target jump point; the target jump point is used to characterize the presence of radio frequency interference; A subtraction module, configured to determine a noise floor sequence in the mean sorting sequence based on the target jump point, so as to detect and subtract radio frequency interference based on the noise floor sequence.

8. A computer device, characterized in that, It includes: A memory and a processor, which are communicatively connected to each other. Computer instructions are stored in the memory, and the processor executes the computer instructions to perform the radio frequency interference subtraction method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to perform the radio frequency interference subtraction method according to any one of claims 1 to 6.

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