A radio astronomy interference signal reduction method, device, system and computer equipment

Abstract

The application relates to a radio astronomy interference signal reduction method, device, system and computer equipment. The method comprises the following steps: acquiring at least two beam signals, and obtaining a covariance matrix between all beam signals according to the beam signals, wherein the beam signals comprise sub-signals from at least two directions; performing eigenvalue decomposition processing on the covariance matrix to obtain signal eigenvectors corresponding to the sub-signals and eigenvalues; extracting an interference eigenvector corresponding to an interference signal from the signal eigenvectors based on the eigenvalues; and obtaining a target reduction result of the interference signal according to the signal eigenvectors and the interference eigenvector. The method can solve the problem of low accuracy of radio astronomy interference signal reduction in related technologies.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, and in particular to a method, apparatus, system and computer equipment for reducing radio astronomy interference signals. Background Technology

[0002] In modern radio astronomy, the signals received by telescopes are inevitably subject to various forms of radio interference. For example, the Five-hundred-meter Aperture Spherical radio Telescope (FAST) operates at frequencies between 70 MHz and 3 GHz, a frequency band filled with a large number of radio services. Therefore, FAST is highly susceptible to interference from various radio signals during observations.

[0003] Currently, although electromagnetic environment protection zones have been established around astronomical telescopes such as FAST, effectively reducing the impact of surrounding ground interference sources on telescope observations, the extremely high sensitivity of FAST means that a certain amount of interference signals still exist in the observed data. Some of these interference signals are from known sources, such as satellite and civil aviation signals, while others are from unknown sources. This increases the difficulty of reducing interference in the acquired radio signals. In related technologies, the identification and labeling of these interference signals in the data typically relies on manual methods. This leads to inconsistent identification results and introduces human error, resulting in inconsistent interference signal results and low accuracy in interference signal reduction.

[0004] Currently, no effective solution has been proposed to address the issue of low accuracy in reducing radio astronomy interference signals in related technologies. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, system, and computer equipment for reducing radio astronomy interference signals to address the aforementioned technical problems.

[0006] Firstly, this application provides a method for reducing radio astronomy interference signals. The method includes:

[0007] Acquire at least two beam signals and obtain a covariance matrix for all beam signals based on the beam signals, wherein the beam signals include sub-signals from at least two directions;

[0008] The covariance matrix is ​​subjected to eigenvalue decomposition to obtain the signal feature vector and eigenvalue corresponding to the sub-signal. Based on the eigenvalue, the interference feature vector corresponding to the interference signal is extracted from the signal feature vector.

[0009] The target reduction result for the interference signal is obtained based on the signal feature vector and the interference feature vector.

[0010] In one embodiment, interference feature vectors form an interference subspace, and the target reduction result for the interference signal is obtained based on the signal feature vector and the interference feature vector, including:

[0011] The interference subspace projection matrix is ​​obtained based on the interference subspace calculation.

[0012] The covariance matrix is ​​transformed by projection based on the interference subspace projection matrix to obtain the covariance projection matrix;

[0013] Subtracting the covariance projection matrix from the covariance matrix yields the target reduction result.

[0014] In one embodiment, the covariance matrix is ​​subtracted from the covariance projection matrix to obtain the target reduction result, including:

[0015] Subtracting the covariance projection matrix from the covariance matrix yields the spatially filtered covariance matrix.

[0016] Signal recovery processing is performed based on the covariance matrix after spatial filtering to obtain the de-interference beam signal, and the target reduction result is obtained based on the de-interference beam signal.

[0017] In one embodiment, the interference feature vector corresponding to the interference signal is extracted from the signal feature vector based on the feature value, including:

[0018] Obtain the preset feature value threshold;

[0019] Each feature value is compared with a feature value threshold. Based on the comparison results, the interference feature value is determined from the feature values, and the corresponding interference feature vector is determined from the signal feature vector based on the interference feature value.

[0020] In one embodiment, acquiring at least two beam signals further includes:

[0021] Acquire at least two initial beam signals;

[0022] Each initial beam signal is subjected to system noise calibration to obtain the beam signal; the system noise calibration includes removing the baseline from the beam signal.

[0023] In one embodiment, the beam signal includes frequency information, and a covariance matrix is ​​obtained based on the beam signal for all beam signals, including:

[0024] The beam signal is divided into at least one frequency channel based on the frequency information, and the frequency data corresponding to the frequency channel in the beam signal is extracted based on the frequency information.

[0025] The divided beam data is input into the corresponding frequency channel for calculation to obtain the covariance matrix.

[0026] In one embodiment, acquiring at least two beam signals includes:

[0027] Get the preset time interval;

[0028] At least two beam signals are acquired periodically based on time intervals.

[0029] Secondly, this application also provides a radio astronomy interference signal reduction device. The device includes:

[0030] An acquisition module is used to acquire at least two beam signals and obtain a covariance matrix for all beam signals based on the beam signals, wherein the beam signals include sub-signals from at least two directions;

[0031] The calculation module is used to perform eigenvalue decomposition on the covariance matrix to obtain the signal feature vector and eigenvalue corresponding to the sub-signal, and extract the interference feature vector corresponding to the interference signal from the signal feature vector based on the eigenvalue.

[0032] The generation module is used to obtain the target reduction result for the interference signal based on the signal feature vector and the interference feature vector.

[0033] Thirdly, this application also provides a radio astronomy interference signal reduction system. The system includes: a radio astronomy device and a main control device;

[0034] Radio astronomy equipment is used to receive signals from at least two beams and transmit the beam signals to the main control equipment;

[0035] A master control device, used to perform the steps of any one of the methods in the first aspect above.

[0036] Fourthly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0037] Acquire at least two beam signals and obtain a covariance matrix for all beam signals based on the beam signals, wherein the beam signals include sub-signals from at least two directions;

[0038] The covariance matrix is ​​subjected to eigenvalue decomposition to obtain the signal feature vector and eigenvalue corresponding to the sub-signal. Based on the eigenvalue, the interference feature vector corresponding to the interference signal is extracted from the signal feature vector.

[0039] The target reduction result for the interference signal is obtained based on the beam feature vector and the interference feature vector.

[0040] The aforementioned radio astronomy interference signal reduction method, apparatus, system, and computer equipment first calculate the covariance matrix between the acquired beam signals. Then, they perform eigenvalue decomposition on the covariance matrix to obtain eigenvectors and eigenvalues ​​of signals in different directions. Interference eigenvectors are then extracted based on the eigenvalues. Finally, the target reduction result is obtained based on the signal eigenvectors and interference eigenvectors. This application can remove interference signals using data from multiple beams without affecting the astronomical signals. Furthermore, because this application extracts the interference eigenvectors corresponding to the interference signals from the signal eigenvectors based on eigenvalues, it avoids errors and data loss caused by various factors to a certain extent, solving the problem of low accuracy in radio astronomy interference signal reduction in related technologies, and achieving a highly accurate and efficient radio astronomy interference signal reduction method. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating a radio astronomy interference signal reduction method in one embodiment;

[0042] Figure 2 This is a flowchart illustrating a radio astronomy interference signal reduction method in another embodiment;

[0043] Figure 3 This is a flowchart illustrating a preferred embodiment of a radio astronomy interference signal reduction method.

[0044] Figure 4 This is a structural block diagram of a radio astronomy interference signal reduction device in one embodiment;

[0045] Figure 5 This is a structural block diagram of a radio astronomy interference signal reduction system in one embodiment;

[0046] Figure 6 This is an internal structural diagram of a radio astronomy interference signal reduction computer device in one embodiment. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0048] In one embodiment, such as Figure 1As shown, a method for reducing radio astronomy interference signals is provided. In this embodiment, the method is applied to a terminal for illustration. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and can be implemented through the interaction between the terminal and the server. Figure 1 This is a flowchart of a radio astronomy interference signal reduction method according to an embodiment of this application, including the following steps:

[0049] Step S110: Acquire at least two beam signals and obtain the covariance matrix between all beam signals based on the beam signals, wherein the beam signals include sub-signals from at least two directions;

[0050] This involves acquiring beam signal data received by radio astronomy equipment such as multi-beam receivers of radio telescopes. Each beam signal can receive sub-signals from multiple directions, but with only one beam, it's impossible to distinguish the direction of each sub-signal. Therefore, by solving the covariance matrix of multiple beam signals and performing eigenvalue decomposition, the direction of the sub-signals can be obtained. Furthermore, multiple beam data recorded simultaneously can be imported into the same directory deployed on the aforementioned server or terminal as input files, using the astronomical standard .fits file format. The multi-beam receiver data includes time-frequency two-dimensional spectral data, polarization, and beam information. Specifically, taking the Five-hundred-meter Aperture Spherical radio Telescope (FAST) as an example, FAST's current 19-beam receiving system can simultaneously record data from 19 beams. After acquiring the beam signals, the covariance matrix between all beam signals is obtained. This covariance matrix is ​​defined as follows:

[0051]

[0052] Where C is the inter-beam covariance matrix. Let be the signal strength received by the i-th beam. It is conjugate to the signal. Each element in the covariance matrix is ​​the covariance between the beams, which contains the method factor and delay information of different beams to the signal. This information can reflect the direction of the signal, i.e., its spatial characteristics.

[0053] Step S120: Perform eigenvalue decomposition on the covariance matrix to obtain the signal feature vector and eigenvalue corresponding to the sub-signal, and extract the interference feature vector corresponding to the interference signal from the feature vector based on the eigenvalue.

[0054] The eigenvalue decomposition of the inter-beam covariance matrix can be expressed as:

[0055]

[0056] Where Q is the feature vector, Its conjugate transpose. The eigenvalues ​​are represented by the subscripts I and N, which indicate the interference and noise components, respectively. The standard deviation of noise power is represented by, where This refers to the feature subspace of the interference, where E represents the identity matrix, q represents the number of RFI (Radio Frequency Interference) components in the matrix, p is the total number of beams and also the order of the covariance matrix, and pq represents the remaining components, namely the astronomical signal and noise. After eigenvalue decomposition, the signal feature vector and eigenvalue corresponding to each direction are obtained. The eigenvalue represents the signal energy in the corresponding signal direction. Based on the eigenvalue, the interference feature vector corresponding to the interference signal is determined from the signal feature vector and extracted. In this application, based on the characteristics of astronomical signals and noise signals, it distinguishes whether a signal is an interference or noise signal, thereby removing interference signals without affecting the astronomical signal and preventing the loss of astronomical signal data. On the other hand, it can also quickly find noise signals, improving data processing efficiency.

[0057] Step S130: Obtain the target reduction result for the interference signal based on the signal feature vector and the interference feature vector.

[0058] The interference feature vector is a feature vector for the interference signal. The interference feature vector corresponding to the interference signal is extracted from the signal feature vector to obtain the target reduction result mentioned above. The target reduction result is the spatially filtered beam signal.

[0059] Through steps S110 to S130, the interference feature vector corresponding to the interference signal is determined and extracted from the signal feature vector using feature values. Then, the target reduction result for the interference signal is calculated based on the extracted interference feature vector and the signal feature vector. Determining the interference feature vector corresponding to the interference signal using feature values ​​eliminates the need for extensive manual identification and labeling of interference in the data, avoiding human error and inconsistent results. Furthermore, this application provides a method for efficiently and accurately reducing radio astronomy interference signals. The automatic determination of interference feature vectors and the reduction of interference signals not only reduce errors but also improve data processing efficiency.

[0060] In one embodiment, the interference feature vectors constitute an interference subspace, and the above-mentioned radio astronomy interference signal reduction method further includes the following steps:

[0061] The interference subspace projection matrix is ​​calculated based on the interference subspace; the covariance matrix is ​​transformed by projection based on the interference subspace projection matrix to obtain the covariance projection matrix; the covariance projection matrix is ​​subtracted from the covariance matrix to obtain the target reduction result.

[0062] Specifically, the projection matrix of the interference subspace, calculated based on the interference subspace, is calculated using the following formula:

[0063]

[0064] Represents the projection matrix of the interference subspace, since It is a unit vector, therefore it simplifies to Furthermore, after obtaining the interference subspace projection matrix, the covariance matrix is ​​transformed based on the interference subspace projection matrix to obtain the covariance projection matrix, which is obtained by multiplying the covariance matrix by the interference subspace projection matrix. In this process, after extracting the interference feature vector corresponding to the interference signal, the projection matrix of the interference subspace is obtained using the definition of projection in linear algebra, which is equivalent to constructing a spatial filter for the acquired beam signal. Subtracting the component of the interference subspace projection direction from the aforementioned inter-beam covariance matrix yields the spatially filtered inter-beam covariance matrix, which is the target reduction result for the interference signal. Specifically, the subtraction of the interference subspace projection direction component from the aforementioned inter-beam covariance matrix can be expressed as:

[0065]

[0066] in, This is the inter-beam covariance matrix after spatial filtering, and the values ​​on its diagonal are the squares of the values ​​of each beam after spatial filtering. Let C be the projection matrix of the interference subspace, and C be the covariance matrix between the beams. It can be seen that this application obtains the projection matrix of the interference subspace through the basic projection definition, which is equivalent to constructing a spatial filter. This method can quickly and in real-time reduce the components of the interference signal without affecting the astronomical signal. In this application, the projection matrix of the interference subspace is selected, and multiplying it by its covariance matrix yields the components of the covariance matrix in the direction of the interference subspace. Subtracting these components is equivalent to removing the interference signal. Utilizing the projection definition allows for convenient and rapid removal of the interference components from the covariance matrix between beams, facilitating subsequent steps where the interference reduction result is obtained based on the covariance matrix with the interference components removed.

[0067] In one embodiment, the above-mentioned radio astronomy interference signal reduction method further includes:

[0068] Subtracting the covariance projection matrix from the covariance matrix yields the spatially filtered covariance matrix.

[0069] Signal recovery processing is performed based on the covariance matrix after spatial filtering to obtain the de-interference beam signal, and the target reduction result is obtained based on the de-interference beam signal.

[0070] Specifically, after obtaining the spatially filtered covariance matrix, signal recovery processing is performed based on this spatially filtered covariance matrix to obtain the spatially filtered result. The signal of each beam is recovered from the covariance matrix after removing interference components, and written into a standard .fits file to obtain the spatially filtered result. The diagonal of the covariance matrix represents the autocorrelation of each beam; taking the square root of the diagonal of the filtered covariance matrix recovers the signal of each beam. It should be noted that in actual operation, after acquiring multiple beam signals, the beam signals need to be divided into different frequency channels according to the frequency information in the beam signals, and calculations are performed independently for each channel. After completing the above calculations, the de-interference beam signal is obtained. This de-interference beam signal is the calculation result for each beam channel. The calculation results of all beam channels are summarized and recombined to obtain the aforementioned target reduction result.

[0071] In one embodiment, the above-mentioned radio astronomy interference signal reduction method further includes:

[0072] Obtain the preset feature value threshold;

[0073] Each feature value is compared with a feature value threshold. Based on the comparison results, the interference feature value is determined from the feature values, and the corresponding interference feature vector is determined from the signal feature vector based on the interference feature value.

[0074] Specifically, this application determines whether a component in the beam signal is an interference signal based on the magnitude of its eigenvalue. Astronomical signals are generally very weak and buried in noise, while interference signals are very strong. When interference is present, the largest eigenvalue will be many orders of magnitude higher than other signal eigenvalues. The threshold value for this eigenvalue is generally related to the actual interference intensity; typically, the eigenvalue of an interference signal is several orders of magnitude or even hundreds of orders of magnitude higher than other eigenvalues. The threshold value needs to be determined based on the actual application environment. Therefore, based on this principle, each eigenvalue is compared with a preset eigenvalue threshold. If the eigenvalue is greater than the threshold, it can be determined that the eigenvalue is an interference eigenvalue, and the corresponding interference eigenvector is determined from the signal eigenvector based on the interference eigenvalue. Therefore, this application utilizes the characteristics of astronomical signals and interference signals to separate interference components in the beam, thereby achieving interference reduction. This enables the recovery of useful data obscured by interference, improving the data quality and observation efficiency of the telescope. Furthermore, the interference reduction method in this application does not require human intervention to mark and reduce interference signals, which not only reduces labor costs but also avoids errors and data loss caused by human factors.

[0075] In one embodiment, a method for reducing radio astronomy interference signals is provided. Figure 2 The flowchart of another radio astronomy interference signal reduction method according to this application is shown in the figure. The process includes the following steps:

[0076] Step S210: Acquire at least two initial beam signals; perform system noise calibration on each initial beam signal to obtain a beam signal; wherein, the system noise calibration includes removing the baseline from the beam signal.

[0077] Specifically, after acquiring multiple beam data, system noise calibration is required. This is achieved by removing baselines from the beam signals to eliminate the influence of differences between different beams. Baseline removal methods include asymmetric penalized least squares. First, an initial value is given. Then, the difference between the fitted baseline and the original data, as well as the smoothness of the baseline, are calculated based on this initial value. The parameters are then updated, and a new baseline is calculated. The parameter updates can use the sigmoid function. This process is repeated until a threshold is met or the maximum number of iterations is reached. By iteratively changing the parameters, the fitted baseline gradually approaches the original data and becomes smoother, thus calibrating the acquired original beam signals for system noise and removing the influence of differences between different beams. This facilitates subsequent steps such as the identification and elimination of interference signals.

[0078] In one embodiment, the above-mentioned radio astronomy interference signal reduction method further includes:

[0079] The beam signal is divided into multiple frequency channels based on the frequency information, and the frequency data corresponding to the frequency channels in the beam signal is extracted based on the frequency information.

[0080] The beam data is input into the corresponding frequency channel for calculation to obtain the covariance matrix.

[0081] Specifically, taking FAST as an example, its 19-beam receiver, with each beam acting as a feedhorn, simultaneously receives signals from the sky, resulting in 19 signals. After sampling, 19 one-dimensional time series are obtained. Each of these 19 time series undergoes a Fast Fourier Transform (FFT) to obtain signals from different frequency channels, transforming them into 19 two-dimensional matrices (time vertically, frequency horizontally). Time is referred to as sampling, and frequency as a channel. Finally, data from the same frequency channels within the 19 beams are extracted. The above calculations are performed independently for each frequency channel, including solving the covariance matrix, finding the interference subspace, and performing spatial filtering. After each channel's calculations are completed, filtered data is obtained. The filtered data from all channels are then aggregated and reassembled to obtain the interference-free beam signal. This method allows for calculations on the acquired beam signal from a frequency perspective. Performing independent calculations on data with the same frequency enables faster and more accurate removal of interference feature vectors. After the calculations are completed, the filtered data are aggregated and reassembled, improving both computational efficiency and accuracy.

[0082] In one embodiment, the above-mentioned radio astronomy interference signal reduction method further includes:

[0083] Get the preset time interval;

[0084] The at least two beam signals are periodically acquired based on the time interval.

[0085] Specifically, since the signal source corresponding to the acquired beam signal may be moving, the beam signal collected in this application also needs to be updated at a preset time interval to adapt to the new signal environment. This time interval can be preset by the operator according to the actual situation; for example, it can be preset to about 40 seconds, during which the interference signal is relatively stable and the filtering is more accurate. Furthermore, during the periodic acquisition of the multiple beam signals through the above steps, interference signals in the beam signals can be removed in real time using any of the above method embodiments, ultimately obtaining the corresponding target elimination result for the interference signal. The periodic acquisition of beam signals in this application is better suited to practical application environments, avoiding errors caused by signal source movement, and allowing the filtering results to be updated promptly as the signal source changes.

[0086] This embodiment also provides a specific embodiment of a radio astronomy interference signal reduction method, such as... Figure 3 As shown, Figure 3 This is a flowchart illustrating a preferred embodiment of a radio astronomy interference signal reduction method.

[0087] First, data from the FAST multi-beam receiver is used as input, including two-dimensional time-frequency spectral data, polarization, and beam information. Data from all 19 FAST beams recorded simultaneously are imported into the same directory as input files, using the astronomical standard .fits file format, and spatial filtering parameters are set. System noise is calibrated for the data from different beams separately to remove the influence of differences between beams on the signal. System noise calibration can use baseline removal methods, such as asymmetric penalized least squares. Given an initial value, the difference between the fitted baseline and the original data, as well as the smoothness of the baseline, are calculated, and parameters are updated to calculate a new baseline. Parameter updates can use the sigmoid function. The above calculations are repeated until a threshold is met or the maximum number of iterations is reached. After the system noise calibration is completed, the files of all beams are read simultaneously, and the data is divided according to frequency channels. The calculation of each channel is performed independently. Specifically, each beam of the beam receiver is equivalent to a feed source, receiving signals from the sky. Taking FAST as an example, after sampling, 19 one-dimensional time series are obtained. Each of the 19 time series undergoes a fast Fourier transform to obtain signals of different frequency channels, which are transformed into 19 two-dimensional matrices (time is vertical and frequency is horizontal). Time is the sampling, and frequency is the channel. Data of the same frequency channel in the 19 beams is extracted for subsequent calculations. The calculation of each channel is performed independently.

[0088] Then, the covariance matrix between each channel beam is calculated and its eigenvalues ​​are decomposed to obtain the signal eigenvectors and eigenvalues, where the eigenvalues ​​represent the signal energy in the corresponding signal direction. The components belonging to the interference signal in the feature space are identified, and the projection matrix of the interference subspace is obtained using the definition of projection in linear algebra. This is equivalent to constructing a spatial filter for the acquired beam signal. Specifically, the components belonging to the interference signal in the feature space are determined by their eigenvalues. Since astronomical signals are very weak and generally buried in noise, while interference signals are very strong, their largest eigenvalue will be much higher than others when interference is present. This characteristic is used to find the components belonging to the interference signal in the feature space. Then, the components projected in the interference subspace are subtracted from the inter-beam covariance matrix to obtain the spatially filtered inter-beam covariance matrix.

[0089] Finally, the intensity of each beam signal is recovered from the inter-beam covariance matrix. After repeating the above steps for all channels, the complete filtered data is obtained. This data is then aggregated and reassembled to obtain the target reduction result, which at this point refers to the beam signal after removing the interference signal.

[0090] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0091] Based on the same inventive concept, this application also provides a radio astronomy interference signal reduction device for implementing the radio astronomy interference signal reduction method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more embodiments of the radio astronomy interference signal reduction device provided below can be found in the limitations of the radio astronomy interference signal reduction method described above, and will not be repeated here.

[0092] In one embodiment, such as Figure 4 As shown, a radio astronomy interference signal reduction device is provided, comprising: an acquisition module 41, a calculation module 42, and a generation module 43, wherein:

[0093] Acquisition module 41 is used to acquire at least two beam signals and obtain a covariance matrix between all beam signals based on the beam signals, wherein the beam signals include sub-signals from at least two directions;

[0094] Calculation module 42 is used to perform eigenvalue decomposition on the covariance matrix to obtain the signal feature vector and eigenvalue corresponding to the sub-signal, and extract the interference feature vector corresponding to the interference signal from the signal feature vector based on the eigenvalue.

[0095] The generation module 43 is used to obtain the target reduction result for the interference signal based on the signal feature vector and the interference feature vector.

[0096] Specifically, taking the FAST radio telescope as an example, the acquisition module 41 acquires 19 beam signals and obtains the covariance matrix between the beam signals. The beam signals include two-dimensional time-frequency spectral data, polarization, and beam information. Various parameters for spatial filtering are set, including the processing time, frequency, polarization range, and result storage path. The acquisition module 41 sends the covariance matrix corresponding to the beam signals to the calculation module 42. The calculation module 42 performs eigenvalue decomposition on the covariance matrix to obtain signal feature vectors and eigenvalues ​​corresponding to signals in different directions. Based on the eigenvalues, the corresponding interference feature vectors of the interference signals are extracted from the signal feature vectors. Specifically, since astronomical signals are generally very weak and buried in noise, their eigenvalues ​​are generally small. Interference signals, on the other hand, are very strong; when interference exists, the largest eigenvalue will be much higher than others. Based on the significant differences in characteristics between astronomical signals and interference signals, the interference signal is identified based on the eigenvalues. After extracting the interference feature vector corresponding to the interference signal, the calculation module 42 sends the interference feature vector and the signal feature vector to the generation module 43. The generation module 43 obtains the target reduction result for the interference signal based on the signal feature vector and the interference feature vector. The target reduction result is the inter-beam covariance matrix after spatial filtering.

[0097] The aforementioned radio astronomy interference signal reduction device achieves several advantages. First, based on the obvious difference in characteristic values ​​between interference signals and astronomical signals, it can quickly and efficiently identify and extract interference signals from the acquired beam signals with a low probability of error. Second, existing technologies typically rely on significant human effort to identify and label interference signals in the data, leading to inconsistent identification results and introducing human error. This application completely eliminates the need for human intervention in identifying interference signals, thus resolving the errors caused by human intervention. It enables the removal of interference signals using data from multiple beam signals without affecting the astronomical signals and can, to some extent, recover useful signals buried by interference, preventing errors and data loss caused by human factors.

[0098] Each module in the aforementioned radio astronomy interference signal reduction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0099] In one embodiment, a radio astronomy interference signal reduction system is provided, the structural block diagram of which is shown below. Figure 5As shown, the system consists of a radio astronomy instrument and a main control system, specifically...

[0100] Radio astronomy equipment used to receive signals from at least two beams and transmit those signals to a main control device;

[0101] The main control device is used to execute the steps of any radio astronomy interference signal reduction method.

[0102] Specifically, after acquiring the beam signal transmitted by the radio astronomy equipment, the main control device obtains the covariance matrix among all beam signals; it performs eigenvalue decomposition on the covariance matrix to obtain signal feature vectors and eigenvalues ​​corresponding to signals in different directions; based on the eigenvalues, it extracts the interference feature vector corresponding to the interference signal from the signal feature vectors; and based on the signal feature vector and the interference feature vector, it obtains the target reduction result for the interference signal. The main control device can be a server, computer, main control chip, or other hardware device used to control the radio astronomy interference signal reduction process. Further, the main control device may include a processor, memory, and network interface connected via a system bus. The processor of the main control device provides computing and control capabilities. The memory of the main control device includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the main control device stores data on the radio astronomy interference signal reduction method. The network interface of the main control device is used for communication with external terminals via a network connection. When executed by a processor, the computer program implements a method for reducing radio astronomy interference signals. In some embodiments, the master control device can communicate with the radio astronomy equipment via a transmission device; in other embodiments, the master control device can be directly integrated into the radio astronomy equipment.

[0103] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for reducing radio astronomy interference signals. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0104] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0105] Those skilled in the art should understand that the technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for reducing radio astronomy interference signals, characterized in that, The method includes: Acquire at least two beam signals and obtain a covariance matrix for all the beam signals based on the beam signals, wherein the beam signals include sub-signals from at least two directions; The covariance matrix is ​​subjected to eigenvalue decomposition to obtain the signal feature vector and eigenvalue corresponding to the sub-signal. Based on the eigenvalue, the interference feature vector corresponding to the interference signal is extracted from the signal feature vector; wherein, the interference feature vector forms an interference subspace. Obtaining the target reduction result for the interference signal based on the signal feature vector and the interference feature vector includes: calculating the interference subspace projection matrix based on the interference subspace; performing projection transformation on the covariance matrix based on the interference subspace projection matrix to obtain the covariance projection matrix; and subtracting the covariance projection matrix from the covariance matrix to obtain the target reduction result.

2. The method according to claim 1, characterized in that, The step of subtracting the covariance projection matrix from the covariance matrix to obtain the target reduction result includes: Subtracting the covariance projection matrix from the covariance matrix yields the spatially filtered covariance matrix. Signal recovery processing is performed based on the spatially filtered covariance matrix to obtain a de-interference beam signal, and the target reduction result is obtained based on the de-interference beam signal.

3. The method according to claim 1, characterized in that, Based on the feature values, the interference feature vector corresponding to the interference signal is extracted from the signal feature vector, including: Obtain the preset feature value threshold; Each of the feature values ​​is compared with the feature value threshold, and an interference feature value is determined from the feature values ​​based on the comparison results. The corresponding interference feature vector is then determined from the signal feature vector based on the interference feature value.

4. The method according to claim 1, characterized in that, The acquisition of at least two beam signals also includes: Acquire at least two initial beam signals; Each initial beam signal is subjected to system noise calibration to obtain the beam signal; wherein, the system noise calibration includes removing the baseline from the beam signal.

5. The method according to claim 1, characterized in that, The beam signal includes frequency information, and obtaining the covariance matrix among all the beam signals based on the beam signal includes: The beam signal is divided into at least one frequency channel according to the frequency information, and frequency data corresponding to the frequency channel in the beam signal is extracted according to the frequency information; the frequency data is input into the corresponding frequency channel for calculation to obtain the covariance matrix.

6. The method according to any one of claims 1 to 5, characterized in that, The acquisition of at least two beam signals also includes: Get the preset time interval; The at least two beam signals are periodically acquired based on the time interval.

7. A radio astronomy interference signal reduction device, characterized in that, The device includes: An acquisition module is configured to acquire at least two beam signals and obtain a covariance matrix for all the beam signals based on the beam signals, wherein the beam signals include sub-signals from at least two directions; The calculation module is used to perform eigenvalue decomposition on the covariance matrix to obtain the signal feature vector and eigenvalue corresponding to the sub-signal, and extract the interference feature vector corresponding to the interference signal from the signal feature vector based on the eigenvalue; wherein the interference feature vectors form an interference subspace; The generation module is used to obtain a target reduction result for the interference signal based on the signal feature vector and the interference feature vector; including: calculating an interference subspace projection matrix based on the interference subspace; performing a projection transformation on the covariance matrix based on the interference subspace projection matrix to obtain a covariance projection matrix; and subtracting the covariance projection matrix from the covariance matrix to obtain the target reduction result.

8. A radio astronomy interference signal reduction system, characterized in that, The system includes: radio astronomy equipment and main control equipment; The radio astronomy equipment is used to receive at least two beam signals and transmit the beam signals to the main control device; The main control device is used to perform the steps of the method as described in any one of claims 1 to 6.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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