Method and apparatus for achieving station beam stability and sidelobe suppression

Abstract

The present disclosure relates to the technical field of radio astronomy, and particularly relates to a method and device for realizing station beam stability and sidelobe suppression. The method comprises: obtaining target layout information of a sparse antenna array, the target layout information being used to indicate spatial position coordinates of each antenna array element in the sparse antenna array; generating a weighting parameter of each antenna array element according to the target layout information, the weighting parameter being used to perform weighting processing on amplitudes and phases of signals received by the antenna array element to reduce sidelobe level; during a main beam dynamic scanning process, updating the weighting parameter of each antenna array element according to a main beam pointing change; and performing weighted synthesis on amplitudes and phases of signals received by each antenna array element according to the updated weighting parameter, and outputting a station beam after sidelobe suppression. Through real-time updating of the weighting parameter of each antenna array element and weighted synthesis during the main beam dynamic scanning process, the present disclosure realizes dynamic suppression of the sidelobe level and stability of the station beam output.

Description

Technical Field

[0001] This disclosure relates to the field of radio astronomy technology, and in particular to a method and apparatus for achieving station beam stability and sidelobe suppression. Background Technology

[0002] In radio astronomy observations, the Very High Frequency (VHF) band (approximately 50 MHz to 200 MHz) is crucial for studying scientific targets such as pulsars, solar radio bursts, the epoch of reionization (EOR), and neutral hydrogen spectral lines. To achieve sufficiently high sensitivity and angular resolution, antenna systems in this band typically employ large-scale arrays consisting of hundreds or thousands of antenna elements. However, large-scale arrays, if fully deployed, result in a massive number of elements, high system costs, and severe mutual coupling between elements. Therefore, sparse array techniques, by non-uniformly and sparsely distributing elements within a given aperture, can reduce the number of elements while maintaining high spatial resolution and survey speed.

[0003] Currently, sparsely distributed antenna arrays face the following technical challenges in radio astronomy applications: During dynamic scanning of the main beam, the weighting parameters are usually fixed or undergo only simple phase rotation with the pointing direction, failing to dynamically suppress sidelobe levels based on real-time pointing changes. This leads to a significant deterioration in the maximum sidelobe level (MSLL) when the beam scans to different azimuths or elevation angles. Interference noise from the far-field grating lobes and sidelobes severely contaminates the weak radio signals within the main lobe, resulting in decreased station beam stability and reduced observation data quality.

[0004] To address the aforementioned technical problems, no reasonable and effective method has yet been provided in the relevant technologies. Summary of the Invention

[0005] In view of this, this disclosure proposes a method and apparatus for achieving station beam stability and sidelobe suppression. By updating the weighting parameters of each antenna array element in real time and performing weighted synthesis during the dynamic scanning process of the main beam, dynamic suppression of sidelobe levels and stability of station beam output are achieved, thereby effectively reducing far-field grating lobe interference and improving the observation quality of weak radio signals.

[0006] According to one aspect of this disclosure, a method for achieving station beam stability and sidelobe suppression is provided, the method comprising:

[0007] Obtain the target layout information of the sparsely distributed antenna array, wherein the target layout information is used to indicate the spatial position coordinates of each antenna element in the sparsely distributed antenna array;

[0008] Based on the target layout information, weighting parameters are generated for each antenna element. These weighting parameters are used to weight the amplitude and phase of the received signal of the antenna element to reduce the sidelobe level.

[0009] During the dynamic scanning of the main beam, the weighting parameters of each antenna element are updated according to the change in the direction of the main beam.

[0010] The amplitude and phase of the received signals of each antenna element are weighted and synthesized according to the updated weighting parameters, and the station beam after sidelobe suppression is output.

[0011] In one possible implementation, generating weighted parameters for each antenna element based on the target layout information includes:

[0012] Based on the target layout information, a comprehensive weighting function is constructed on a two-dimensional plane. The comprehensive weighting function is a continuous two-dimensional function coupled with the spatial position of the array elements.

[0013] For each antenna element, the position coordinates of the antenna element are projected onto the two-dimensional plane to obtain the projected coordinates of the antenna element;

[0014] The weighted parameters of the antenna array elements are calculated using the integrated weighting function based on the projected coordinates of the antenna array elements.

[0015] In another possible implementation, the type of the integrated weighting function is determined based on the observation requirements of the sparsely distributed antenna array, and the type of the integrated weighting function includes at least one of the following: Taylor function weighting, Chebyshev function weighting, Hamming function weighting, cosine square function weighting, and uniform weighting.

[0016] In another possible implementation, updating the weighted parameters of each antenna element according to the change in the main beam pointing includes:

[0017] Based on the change in the main beam direction, the center frequency of each sub-band within the observation frequency band is determined; for each sub-band, the weighted parameters of each antenna element at the center frequency of the sub-band are recalculated based on the center frequency of the sub-band and the current main beam direction.

[0018] In another possible implementation, the method further includes:

[0019] A multi-error model is established, which is a mathematical model that comprehensively considers multiple error factors and is used to calculate the degree of deterioration of the antenna array pattern relative to the ideal pattern.

[0020] Based on the multi-error model, the degree of degradation of the radiation pattern corresponding to the station beam after sidelobe suppression is determined;

[0021] The degree of degradation is used as an error description and is output together with the station beam after sidelobe suppression.

[0022] In another possible implementation, the multi-error model is used to indicate at least two of the following four types of errors:

[0023] Array amplitude error, which is the gain amplitude deviation caused by the inconsistency between the antenna array elements and the receiver gain response;

[0024] Array phase error, which is the phase deviation caused by the inconsistency between the phase response of the antenna elements and the receiver;

[0025] Weighted frequency deviation, which is the deviation generated in channelization processing by replacing the actual response of the edge frequencies within the bandwidth with the center frequency as the weight;

[0026] The array element position error is the spatial coordinate deviation between the actual position and the ideal design position of the antenna array element caused by limitations in the antenna array installation process.

[0027] In another possible implementation, the method further includes:

[0028] Establish a database of bright source locations, which includes information on the distribution of sky background radiation and / or a model of the Milky Way's brightness temperature as a function of frequency;

[0029] Based on the observation time and the geographical latitude and longitude of the observation station, the azimuth of the bright source within the field of view of the sparsely distributed antenna array is calculated from the bright source location database;

[0030] When generating or updating the weighting parameters, a null point is set at the corresponding radiation pattern position according to the azimuth of the bright source in order to suppress the interference of the bright source on the observation.

[0031] According to another aspect of this disclosure, an apparatus for achieving station beam stabilization and sidelobe suppression is provided, the apparatus comprising:

[0032] The acquisition module is used to acquire the target layout information of the sparsely distributed antenna array, wherein the target layout information is used to indicate the spatial position coordinates of each antenna element in the sparsely distributed antenna array.

[0033] The generation module is used to generate weighted parameters for each antenna element based on the target layout information. The weighted parameters are used to weight the amplitude and phase of the received signal of the antenna element to reduce the sidelobe level.

[0034] An update module is used to update the weighted parameters of each antenna element according to the change in the direction of the main beam during the dynamic scanning of the main beam.

[0035] The output module is used to perform weighted synthesis of the amplitude and phase of the received signals of each antenna element according to the updated weighting parameters, and output the station beam after sidelobe suppression.

[0036] In one possible implementation, the generation module is further configured to:

[0037] Based on the target layout information, a comprehensive weighting function is constructed on a two-dimensional plane. The comprehensive weighting function is a continuous two-dimensional function coupled with the spatial position of the array elements.

[0038] For each antenna element, the position coordinates of the antenna element are projected onto the two-dimensional plane to obtain the projected coordinates of the antenna element;

[0039] The weighted parameters of the antenna array elements are calculated using the integrated weighting function based on the projected coordinates of the antenna array elements.

[0040] In another possible implementation, the type of the integrated weighting function is determined based on the observation requirements of the sparsely distributed antenna array, and the type of the integrated weighting function includes at least one of the following: Taylor function weighting, Chebyshev function weighting, Hamming function weighting, cosine square function weighting, and uniform weighting.

[0041] In another possible implementation, the update module is further configured to:

[0042] Based on the change in the main beam direction, the center frequency of each sub-band within the observation frequency band is determined; for each sub-band, the weighted parameters of each antenna element at the center frequency of the sub-band are recalculated based on the center frequency of the sub-band and the current main beam direction.

[0043] In another possible implementation, the device further includes:

[0044] The first module is used to establish a multi-error model, which is a mathematical model that comprehensively considers multiple error factors and is used to calculate the degree of deterioration of the antenna array pattern relative to the ideal pattern.

[0045] The determination module is used to determine the degree of degradation of the radiation pattern corresponding to the station beam after sidelobe suppression based on the multi-error model.

[0046] The output module is also used to output the degree of degradation as an error description, together with the station beam after sidelobe suppression.

[0047] In another possible implementation, the multi-error model is used to indicate at least two of the following four types of errors:

[0048] Array amplitude error, which is the gain amplitude deviation caused by the inconsistency between the antenna array elements and the receiver gain response;

[0049] Array phase error, which is the phase deviation caused by the inconsistency between the phase response of the antenna elements and the receiver;

[0050] Weighted frequency deviation, which is the deviation generated in channelization processing by replacing the actual response of the edge frequencies within the bandwidth with the center frequency as the weight;

[0051] The array element position error is the spatial coordinate deviation between the actual position and the ideal design position of the antenna array element caused by limitations in the antenna array installation process.

[0052] In another possible implementation, the device further includes:

[0053] The second module is used to establish a database of bright source locations, which includes information on the distribution of sky background radiation and / or a model of the Milky Way's brightness temperature as a function of frequency.

[0054] The calculation module is used to calculate the azimuth of the bright source within the field of view of the sparsely distributed antenna array from the bright source location database based on the observation time and the geographical latitude and longitude of the observation station.

[0055] The setting module is used to set a null point at the corresponding radiation pattern position according to the azimuth of the bright source when generating or updating the weighting parameters, so as to suppress the interference of the bright source on the observation.

[0056] According to another aspect of this disclosure, an apparatus for achieving station beam stabilization and sidelobe suppression is provided, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described method.

[0057] According to another aspect of this disclosure, a non-volatile computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described method.

[0058] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0059] This embodiment generates weighted parameters for each antenna element based on pre-acquired target layout information of a sparsely distributed antenna array to reduce sidelobe levels. During the dynamic scanning of the main beam, the weighted parameters of each element are updated in real-time according to changes in pointing. Finally, amplitude and phase weighted synthesis is performed based on the updated weighted parameters to output a station beam with sidelobe suppression. Compared to related technologies where weighted parameters are fixed or only undergo simple phase rotation with pointing, this embodiment dynamically optimizes sidelobe suppression performance when the beam scans to different azimuth and elevation angles, effectively avoiding a sharp deterioration in sidelobe levels due to pointing changes, thereby significantly suppressing interference noise from far-field grating lobes and sidelobes. Simultaneously, because the weighted parameters are updated in real-time with pointing, the main beam maintains a consistent shape during scanning, avoiding beam distortion or broadening, improving the stability of the station beam, and providing reliable data quality for observations of weak radio signals such as pulsars and cosmic reionization. Furthermore, actively implementing sidelobe suppression during beamforming reduces the computational burden on interference removal algorithms in subsequent imaging processing and provides good array configuration adaptability.

[0060] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0061] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0062] Figure 1 A flowchart illustrating a method for achieving station beam stability and sidelobe suppression provided by an exemplary embodiment of this disclosure is shown.

[0063] Figure 2 A flowchart illustrating a method for achieving station beam stability and sidelobe suppression provided by another exemplary embodiment of this disclosure is shown.

[0064] Figure 3 A flowchart illustrating a method for achieving station beam stability and sidelobe suppression provided by another exemplary embodiment of this disclosure is shown.

[0065] Figure 4 A flowchart illustrating a method for achieving station beam stability and sidelobe suppression provided by another exemplary embodiment of this disclosure is shown.

[0066] Figure 5 A schematic diagram of a device for achieving station beam stabilization and sidelobe suppression, provided by an exemplary embodiment of this disclosure, is shown.

[0067] Figure 6 This is a block diagram illustrating an apparatus according to an exemplary embodiment. Detailed Implementation

[0068] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0069] As used herein, the terms “comprising,” “including,” “having,” or variations thereof are open-ended and include one or more of the stated features, integrals, elements, steps, components, or functions, but do not exclude the presence or addition of one or more other features, integrals, elements, steps, components, functions, or groups thereof.

[0070] When an element is referred to as “connected,” “coupled,” “responding,” or a variation thereof relative to another element, it may be directly connected, coupled, or responding to another element, or there may be an intermediate element present.

[0071] Although the terms first, second, third, etc., may be used herein to describe various elements / operations, these elements / operations should not be limited by these terms. These terms are used only to distinguish one element / operation from another. Therefore, without departing from the teachings of the conception of embodiments of this disclosure, a first element / operation in some embodiments may be referred to as a second element / operation in other embodiments.

[0072] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0073] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0074] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant regions.

[0075] In radio astronomy observations, the signals being measured are generally weak, and noise introduced by the far-field grating lobes of the antenna array can significantly contaminate the main lobe received data. According to relevant literature, Far Sidelobe Confusion Noise (FSCN) can be represented as the convolution of the brightness functions of multiple interfering sources with the array's point spread function (PSF). Current processing methods typically employ the dirty graph cleansing (CLEAN) algorithm to iteratively remove the brightest interfering sources to reduce FSCN. However, effective implementation of this method requires precise knowledge of the position and amplitude relationship between the interfering sources and the PSF, and demands that the highest sidelobe level of the array be at least 30 dB lower than the main lobe level to effectively suppress radiation interference from strong sources such as the Milky Way's center.

[0076] To address these needs, existing studies have simulated and compared the low sidelobe performance of Taylor weighting and Chebyshev synthesis weighting. Results show that Chebyshev synthesis can suppress sidelobe levels by more than 30 dB, effectively suppressing most strong source interference, while Taylor weighting struggles to achieve the same level of suppression. However, this research only explored a few weighting algorithms, and a systematic survey of the performance of a wider range of synthesis weighting algorithms is lacking. Especially for broadband and randomly non-uniformly distributed sparse arrays in the VHF band, pattern synthesis under different frequencies, numbers of array elements, and weighting schemes is more complex. The beam profile of a sparse array cannot be approximated analytically like that of a regular array; high-precision simulation based on the actual sparse layout is necessary. Furthermore, broadband observations often span 3–4 octaves, with significant differences in beam profiles across different frequency bands. Multi-band imaging requires acquiring beam information for each frequency band, leading to massive computational simulation demands.

[0077] For large arrays containing numerous antenna elements, position, phase, and amplitude errors all negatively impact the radiation pattern. Compared to uniform arrays, the aperiodic arrangement of sparsely distributed arrays makes the effects of these errors more pronounced. However, most simulations in existing research are based on idealized assumptions, neglecting various error factors, and have not yet systematically established a comprehensive model integrating errors such as position, phase, amplitude, and weighted frequency deviations. In practical systems, it is crucial to consider and model these errors, but such modeling and analysis remains a gap in the field of radio astronomy and involves enormous computational demands.

[0078] On the other hand, digital channelization in broadband arrays divides the observation band into multiple sub-bands. In engineering implementations, weighting parameters are typically calculated using the center frequency of each sub-band and applied across the entire sub-band bandwidth. The resulting pointing accuracy deviation between the upper and lower limit frequencies and the center frequency, and the extent to which this deviation affects the sub-band edge frequencies, have been preliminarily studied in uniform arrays, but no relevant literature has been reported in broadband sparse arrays. Furthermore, the selection of sub-band bandwidth is directly constrained by the processing power of the front-end Field-Programmable Gate Array (FPGA), the system's transmission capacity, and data storage capacity, forming mutually coupled engineering constraints.

[0079] The multi-frequency radiation model of the sky is a key factor determining the success or failure of VHF band antenna array observations. As the brightness temperature model of the antenna array shows, the sources of sky background radiation are complex, including both natural strong radio sources such as galactic synchrotron radiation and extragalactic radio sources, as well as man-made radio interference. Some studies have proposed using multiple sky background models to accurately calculate background radiation, and subsequent array verification further used open-source code (PyGSM) to simulate and obtain the antenna array temperature at different scanning angles. Radio sky models exhibit different behaviors at different frequencies. For broadband antenna arrays, solving the FSCN at different frequencies requires combining sub-channel partitioning in digital signal processing. Taking the low-band antenna (LBA) segment (10–80 MHz) of a low-frequency array (LOFAR) as an example, after two channelizations, it is divided into 512 sub-channels, each with a bandwidth of 195 kHz or 156 kHz. Ideally, pattern synthesis and low-sidelobe design should be performed separately on each sub-channel, which undoubtedly places extremely high demands on computing power and communication capabilities.

[0080] Further research suggests that a more accurate radio sky background model should be used, fully considering the sky distribution characteristics and frequency correlations of galactic radiation and multi-source radiation. Solving the PSF (Power-Side Array) for each sub-channel and providing the corresponding error would provide strong support for precise pointing of each channel and the CLEAN procedure in inter-station interferometry, but this also presents a massive computational challenge. Furthermore, the combined effect of the sky's bright source distribution and the far-field grating lobes of the antenna array pattern can cause cycle slips in the system temperature during scanning, leading to instability in the overall array measurement temperature. Existing simulation studies show that in regular arrays with an element spacing of one wavelength and Taylor weighting, the system temperature changes most drastically within the 110°–120° scan angle range. These cycle slips are fatal for observing weak signals such as pulsars, high-redshift neutral hydrogen, and cosmic rays. Without precise analysis and suppression during beamforming, instrumental effects can easily be misinterpreted as inherent variations in celestial signals, leading to erroneous astronomical conclusions. Therefore, it is necessary to accurately model and analyze the above coupling relationship during beamforming to reduce the impact of cycle slip on the quality of observation data.

[0081] To achieve dynamic suppression of strong interference signals and improve beam stability for weak source observations, this disclosure proposes to introduce real-time dynamic null technology during scanning, based on the antenna array temperature definition formula and current research results on beamforming and sidelobe cancellation, and to construct a dedicated anti-interference scheme for narrowband radio interference. Through these methods, the effective beam of the antenna array can always be precisely aligned with the observation target, actively suppressing the influence of strong interference sources such as bright sources, thereby significantly improving the signal-to-noise ratio of weak source observation data. Simultaneously, the suppression effect can be quantitatively verified by tracking constant flow sources (such as calibration sources or cold air regions). For observations such as solar radio bursts, the scientific objective is more concerned with the spectral morphology changes at the burst moment; the impact of bright source interference itself on these observations is relatively small. Therefore, different observation targets have different requirements for beamforming and interference suppression, and a suitable trade-off can be achieved between the observation target and beam stability. Based on relevant requirements, the different types of observation sources impose different levels of stability requirements on antenna beamforming, which need to be specifically analyzed according to specific scientific objectives in the research, thereby effectively optimizing the system resources such as back-end computing, storage, and configuration of the antenna array.

[0082] Based on the above analysis, this disclosure further investigates the antenna array beamforming coefficients using an optimized sparse array layout scheme. Considering the multi-band segmentation characteristics of broadband observation signals, the antenna array radiation pattern at different elevation angles within each frequency band is calculated, resulting in various weighting / shaping schemes to further suppress the highest sidelobe level and far-field grating lobes. Furthermore, an error model for the antenna array radiation pattern is studied, comprehensively considering various practical factors. This model enables quantitative calculation of key indicators such as the main lobe descent, sidelobe level distribution, and pointing deviation of the entire antenna array radiation pattern at different frequencies under conditions including element position errors, amplitude and phase errors, and upper and lower limit frequency deviations within the channelized frequency range.

[0083] One of the core advantages of array antennas lies in their ability to achieve precise and controllable weighting, thereby producing radiation patterns with extremely low sidelobes or patterns very close to a specific shape. Based on the relationship between the antenna array aperture illumination function and the far-field sidelobes, the attenuation characteristics of the far-field sidelobes are controlled by the derivative of the aperture field weighting function. Therefore, this disclosure proposes to conduct research on low-sidelobe technology from the following aspects.

[0084] First, we attempt to synthesize the radiation pattern using various antenna array synthesis algorithms and compare and analyze the intrinsic relationship between antenna array efficiency and maximum sidelobe level (MSLL) suppression. Traditional research shows that under uniform weighting with discontinuities in the function and its derivative, the far-field sidelobes exhibit a specific attenuation law with changes in spatial angle; when using trigonometric function weighting, the far-field sidelobes change rapidly; while when using cosine square weighting, the sidelobes attenuate even more rapidly. For complex problems, such as multi-objective optimization and simultaneous optimization of multiple array parameters, it is often difficult to find a definite analytical synthesis formula. With the development of computer technology and the continuous improvement of the performance requirements for array antenna radiation patterns, numerical optimization methods are now mostly used to solve synthesis problems. Therefore, in the in-depth research on low sidelobe technology, the embodiments of this disclosure use different analytical polynomial structures to implement the weighting function, and solve for the weighting parameters of each antenna element at each frequency point based on constraints such as antenna main lobe width and maximum sidelobe level.

[0085] In recent years, a wide variety of beamforming optimization algorithms have emerged, mainly including Chebyshev synthesis, Taylor synthesis, Fourier transform, Woodward synthesis, staggered projection, and power pattern synthesis, among which Chebyshev synthesis, Taylor synthesis, and their improved versions are the most widely used. This disclosure will systematically study the beamforming effects of various algorithms, particularly addressing the specific requirements of radio astronomy observations, seeking the optimal balance between sidelobe suppression, beam stability, and sensitivity variation.

[0086] Secondly, this disclosure also considers the beamforming scheme effects under different pointing angles and wavelengths, and evaluates them based on radio astronomy requirements, focusing on the following two aspects. First, it examines the deterioration of the overall antenna array's MSLL (Maximum Saturation Linear Liberty) with changing pointing angles, paying particular attention to situations where the MSLL is difficult to suppress through weighted synthesis at low elevation angles. Based on this, the optimal observable sky area of ​​the antenna array is determined. Furthermore, based on different radio source observation requirements (such as sensitivity to sidelobe levels and tolerance for gain changes caused by beamforming), different weighting functions are optimized and compared. Second, it integrates existing VHF band survey data to study the impact of sky background radiation distribution on the overall layout effect. By analyzing the bright source distribution, the convolution effect of the point spread function after antenna array synthesis with the sky background is calculated, thereby evaluating the station beam stability under different frequency bands and providing a basis for system design and observation strategy formulation.

[0087] This disclosure presents a method and apparatus for achieving station beam stability and sidelobe suppression. By generating weighted parameters based on the target layout and updating the amplitude and phase weights of each array element in real time according to the pointing change during the dynamic scanning of the main beam, the station beam after sidelobe suppression is output, thereby effectively reducing far-field grating lobe interference and improving the observation performance and beam stability of sparsely distributed antenna arrays in the VHF band.

[0088] The following describes, using several exemplary embodiments, the methods for achieving station beam stability and sidelobe suppression provided in the embodiments of this disclosure.

[0089] Please refer to Figure 1 This document illustrates a flowchart of a method for achieving station beam stability and sidelobe suppression according to an exemplary embodiment of the present disclosure. This embodiment uses the method in a computing device as an example for illustration. The method includes the following steps.

[0090] Step 101: Obtain the target layout information of the sparsely distributed antenna array. The target layout information is used to indicate the spatial position coordinates of each antenna element in the sparsely distributed antenna array.

[0091] A sparsely distributed antenna array is an antenna array in which the antenna elements are arranged in a non-uniform, non-periodic, sparse distribution within a given array aperture. Compared with a regular, fully distributed array, a sparsely distributed array can achieve lower sidelobe levels and higher spatial resolution with fewer elements, while reducing element mutual coupling and system cost.

[0092] The target layout information is a set of data describing the spatial arrangement of all antenna elements in a sparsely distributed antenna array. It typically includes the number of elements, the number of each element, and the spatial coordinates of each element relative to the array reference point. This layout information can be obtained by solving optimization algorithms (such as genetic algorithms, 0-1 programming, etc.) under constraints such as minimum spacing and maximum aperture.

[0093] Spatial location coordinates represent the geometric position of each antenna element in three-dimensional space. For planar arrays, two-dimensional Cartesian coordinates (xi,yi,0) or polar coordinates (ri,θi) are typically used; for actual site locations, altitude coordinates may also be included. The origin of the coordinate system is generally taken as the geometric center of the array or a corner point.

[0094] In some embodiments, the target layout information is obtained in several possible ways, including: reading from a pre-generated layout file (such as a text file, JSON, or HDF5 format); retrieving from a relational or non-relational database; calculating in real time by running an online layout optimization algorithm (such as an improved genetic algorithm or a 0-1 programming algorithm); and manually inputting via host computer software or a configuration file. This disclosure does not limit the scope of these methods.

[0095] Step 102: Based on the target layout information, generate weighted parameters for each antenna element. The weighted parameters are used to weight the amplitude and phase of the received signal of the antenna element to reduce the sidelobe level.

[0096] The weighting parameters are complex weights for each antenna element in a specific frequency channel. These parameters are used to adjust the amplitude and phase of the received signal for each element to achieve beam pointing control, sidelobe level suppression, and null notch filtering.

[0097] Amplitude refers to the scaling factor of the signal gain, used to control the contribution of array elements to the signal sensitivity; phase refers to the phase shift corresponding to the signal delay, used to compensate for the spatial path difference between different array elements to achieve in-phase superposition.

[0098] The weighting process involves multiplying the time-domain or frequency-domain signal received by each antenna element by its corresponding complex weighting parameter, thereby changing the radiation pattern characteristics of the synthesized beam.

[0099] The sidelobe level is the ratio of the maximum radiation intensity outside the main lobe in the antenna pattern to the peak value of the main lobe, usually expressed in decibels (dB). Reducing the sidelobe level can effectively suppress interference signals from the sidelobe direction and background noise from the sky.

[0100] In some embodiments, the weighting parameters are generated in several possible ways: In one possible implementation, a low sidelobe synthesis algorithm is used to calculate the weighting parameters, such as Taylor synthesis, Chebyshev synthesis, Fourier transform, Woodward synthesis, and staggered projection; in another possible implementation, an optimization algorithm (genetic algorithm, particle swarm optimization, differential evolution algorithm) is used to solve for the weighting parameters under multiple objective constraints such as sidelobe level, beamwidth, and null depth. This disclosure does not limit the scope of the embodiments.

[0101] Step 103: During the dynamic scanning of the main beam, update the weighted parameters of each antenna element according to the change in the direction of the main beam.

[0102] Dynamic scanning of the main beam is the process by which the receiving main lobe of an antenna array changes continuously or in steps in space, aiming to track moving radio sources (such as the sun or pulsars) or scan the sky according to a predetermined plan.

[0103] The main beam pointing change is a dynamic change in the azimuth angle φ and elevation angle θ of the beam pointing over time. The pointing change can be driven by the astronomical observation plan or controlled by feedback from a real-time tracking algorithm.

[0104] The update involves recalculating or adjusting the weighting parameters of each antenna element based on the new main beam pointing angle to maintain correct beam pointing and meet the requirements for sidelobe suppression performance.

[0105] In some embodiments, the weighting parameters are updated in several possible ways: In one possible implementation, a real-time analytical calculation method is used: based on the new beam pointing angle (including azimuth and elevation angles) and the wavelength corresponding to the current sub-band center frequency, the phase weight of each antenna element is recalculated using the steering vector formula. The core of this formula is that the phase difference is determined by dividing twice pi by the wavelength and then multiplying by the sum of the products of the element position coordinates and the cosine of the pointing angle direction. The amplitude weight can be directly obtained by mapping the element positions based on different low sidelobe functions (e.g., Taylor weighting). In another possible implementation, a lookup table method is used: the weighting parameters under different discrete pointing angle grids are pre-calculated and stored. During beam scanning, the weighting parameters corresponding to the current pointing direction are quickly obtained by looking up the table and combining it with interpolation. This disclosure does not limit the scope of this method.

[0106] Step 104: Based on the updated weighting parameters, the amplitude and phase of the received signals of each antenna element are weighted and synthesized to output the station beam after sidelobe suppression.

[0107] Weighted combining refers to performing complex multiplication on the signals received by each antenna element (which have undergone down-conversion, sampling, and channelization) with their corresponding updated complex weighting parameters. The results of all complex multiplications for all elements are then summed to form one or more beam domain output signals. In this process, the signal received by each element is weighted independently, and the summed output is the combined result for that beam direction.

[0108] A station beam is the received beam output formed by beam combining a single antenna station. The antenna station is a subarray composed of multiple antenna elements arranged in a specific sparse layout. By applying amplitude and phase weighting parameters to each element, the signals received by each element are weighted and combined to form a spatial radiation pattern with specific directionality, beamwidth, gain, and sidelobe characteristics. The station beam is the core scientific data output from the antenna station and can be directly used for subsequent imaging, spectrum analysis, or as input for interferometry. Its stability and sidelobe performance directly affect the quality and reliability of the observation data. In this embodiment, the station beam after sidelobe suppression is designed through optimized weighting parameters, resulting in a synthesized beam pattern with a lower maximum sidelobe level and faster far-sidelobe attenuation, thereby effectively suppressing interference signals from the sidelobes and grating lobes, as well as sky background radiation.

[0109] In some embodiments, the weighted synthesis method includes the following possible implementations: In one possible implementation, digital-domain time-domain synthesis: in an FPGA or DSP, complex multiplication and accumulation operations are performed on the digitized time-domain signal of each array element, which can be processed in parallel pipelined mode; In another possible implementation, frequency-domain sub-band synthesis: after channelization processing, the frequency-domain data of each sub-band are weighted and summed, and then inversely transformed to obtain the time-domain beam; In another possible implementation, two-stage sub-array synthesis: first, several adjacent array elements are combined into sub-arrays for initial weighted synthesis, and then the outputs of each sub-array are weighted and synthesized a second time to reduce computational complexity; In another possible implementation, partially adaptive beamforming: based on low sidelobe weighting, nulls are adaptively added to the interference direction to achieve robust beamforming. This disclosure does not limit the scope of the embodiments.

[0110] In some embodiments, the output method of the station beam includes the following possible implementations: In one possible implementation, the synthesized digital baseband signal is transmitted to a storage server or real-time processor via a high-speed serial interface; in another possible implementation, it is output to a spectrum analyzer, pulsar searcher, or VLBI data recording terminal; in yet another possible implementation, metadata such as beam pattern and sensitivity are displayed in real time and recorded to an observation log file. This disclosure does not limit these implementations.

[0111] In summary, the method for achieving station beam stability and sidelobe suppression provided in this disclosure generates weighted parameters for each antenna element based on pre-acquired target layout information of a sparsely distributed antenna array to reduce sidelobe levels. During the dynamic scanning of the main beam, the weighted parameters of each element are updated in real-time according to pointing changes. Finally, amplitude and phase weighted synthesis is performed based on the updated weighted parameters to output the station beam with sidelobe suppression. Compared to related technologies where weighted parameters are fixed or only undergo simple phase rotation with pointing, this disclosure dynamically optimizes sidelobe suppression performance when the beam scans to different azimuth and elevation angles, effectively avoiding a sharp deterioration in sidelobe levels due to pointing changes, thereby significantly suppressing interference noise from far-field grating lobes and sidelobes. Simultaneously, because the weighted parameters are updated in real-time with pointing, the main beam maintains a consistent shape during scanning, avoiding beam distortion or broadening, improving station beam stability, and providing reliable data quality for the observation of weak radio signals such as pulsars and cosmic reionization. Furthermore, actively suppressing sidelobes during beamforming can reduce the computational burden on interference removal algorithms in subsequent imaging processing and provides good array configuration adaptability.

[0112] Please refer to Figure 2 This document illustrates a flowchart of a method for achieving station beam stability and sidelobe suppression according to another exemplary embodiment of the present disclosure, which is used as an example in a computing device. The method includes the following steps.

[0113] Step 201: Obtain the target layout information of the sparsely distributed antenna array. The target layout information is used to indicate the spatial position coordinates of each antenna element in the sparsely distributed antenna array.

[0114] It should be noted that the relevant details of this step can be found in the descriptions in the above embodiments, and will not be repeated here.

[0115] Step 202: Based on the target layout information, construct a comprehensive weighting function on a two-dimensional plane. The comprehensive weighting function is a continuous two-dimensional function coupled with the spatial position of the array elements.

[0116] The two-dimensional plane is the spatial plane where the sparsely distributed antenna array is located (usually the ground plane or parallel to the tangent plane of the site), used to project the spatial coordinates of the array elements and then perform weighted function calculations.

[0117] The synthesis weighting function is a continuous scalar function defined on a two-dimensional plane. Its value varies with position and is used to describe the amplitude / phase distribution across the antenna array aperture. For low sidelobe designs, the value of this function at the location of an element is the amplitude weighting coefficient or phase weighting reference for that element.

[0118] The output value of the weighting function coupled with the spatial position of the array element is directly related to the specific spatial coordinates of the array element. Array elements at different positions obtain different weighting values.

[0119] In some embodiments, the type of the comprehensive weighting function is determined based on the observation requirements of the sparsely distributed antenna array, and the type of the comprehensive weighting function includes at least one of the following: Taylor function weighting, Chebyshev function weighting, Hamming function weighting, cosine square function weighting, and uniform weighting.

[0120] The observation requirements are the beam performance requirements of radio astronomy scientific targets. For example, observations of pulsars and EOR require extremely low sidelobes (e.g., ≤-30dB); observations of solar radio bursts can tolerate higher sidelobes; and VLBI requires stable beam pointing. Different requirements correspond to different weighting function types.

[0121] In some embodiments, the functional expression of the comprehensive weighting function can be established on a two-dimensional plane according to the selected weighting function type (Taylor, Chebyshev, etc.) and its parameters (such as sidelobe suppression degree, main lobe broadening factor). The construction methods of the comprehensive weighting function include the following possible implementations: in one possible implementation, it is directly defined using mathematical formulas (such as spatial discrete sampling of Taylor distribution); in another possible implementation, weighted values ​​on a discrete grid are generated using numerical methods and then fitted to a continuous function; in yet another possible implementation, a one-dimensional distribution is generated by calling existing library functions and then extended to a two-dimensional separable function. This disclosure does not limit these implementations.

[0122] Step 203: For each antenna element, project the position coordinates of the antenna element onto a two-dimensional plane to obtain the projected coordinates of the antenna element.

[0123] The projected coordinates are the orthogonal projection of the spatial position coordinates of the antenna elements onto a two-dimensional plane. In some embodiments, the projection is implemented as follows: if the array elements are already located on the same plane, the projected coordinates are equal to the original coordinates, and no additional calculation is required. If the array elements have height differences due to terrain undulations or installation errors, the height component can be ignored (z is set to 0) or the elements can be projected onto a reference plane along the normal direction.

[0124] Step 204: Based on the projected coordinates of the antenna array elements, the weighted parameters of the antenna array elements are calculated using a comprehensive weighting function.

[0125] The weighting parameters are complex weights for each antenna element in a specific frequency channel. The amplitude part is determined by the value of the comprehensive weighting function, while the phase part can be initially set to 0 (subsequently determined by the beam pointing) or given by the function.

[0126] In some embodiments, the calculation of weighting parameters includes several possible implementations: In one possible implementation, the function is directly substituted: the projected coordinates are substituted into the comprehensive weighting function to obtain the function value at that location, which is used as the magnitude weighting coefficient. For separable functions, the weighting values ​​in the x-direction and y-direction are calculated first, and then multiplied. In another possible implementation, numerical interpolation is used: if the weighting function is stored in discrete grid form, the value at the array element location is obtained through bilinear interpolation. This disclosure does not limit the scope of the implementation.

[0127] Step 205: During the dynamic scanning of the main beam, determine the center frequency of each sub-band within the observation frequency band based on the change in the direction of the main beam.

[0128] Dynamic scanning of the main beam involves continuously or incrementally changing the direction of the main lobe of the receiving beam of the antenna array in space to track radio sources or scan the sky area according to a plan.

[0129] The main beam pointing direction changes as the azimuth and elevation angles of the beam direction change over time.

[0130] The observation frequency band is the entire frequency range in which the sparsely distributed antenna array operates, such as 50MHz to 200MHz in the VHF band.

[0131] A subband is a narrowband channel divided into several subbands after the observed frequency band is channelized in the digital back-end (FPGA). Each subband is characterized by its center frequency. For example, LOFAR divides the bandwidth into 512 subbands, each with a bandwidth of approximately 195 kHz.

[0132] The center frequency is the mid-frequency value fc of each sub-band passband, used to approximately represent the wavelength of all frequency components within that sub-band.

[0133] In some embodiments, determining the center frequency of each sub-band within the observation band includes several possible implementations: In one possible implementation, a pre-defined list of sub-band center frequencies is read from an observation plan or configuration file. In another possible implementation, channelization parameters (such as start frequency, sub-band width, and sub-band number) reported by the front-end FPGA are received in real time. In yet another possible implementation, the subset of sub-bands to be calculated is dynamically selected based on the system clock and observation mode. This disclosure does not limit these implementations.

[0134] Step 206: For each sub-band, based on the sub-band's center frequency and the current main beam pointing, recalculate the weighted parameters of each antenna element at the sub-band's center frequency.

[0135] Based on the current azimuth and elevation angles of the main beam and the wavelength corresponding to the center frequency of the sub-band, the weighted parameters of each antenna element are recalculated. The phase update requires spatial path difference compensation, which is determined by the element's position coordinates, the direction cosine of the pointing angle, and the wavelength. This compensation can be expressed as the phase difference being proportional to the projected distance from the element's position to the wavefront normal. The amplitude update can be performed in two ways: either remain unchanged, using the amplitude coefficients previously calculated based on the integrated weighting function; or make minor adjustments to the amplitude according to frequency changes to accommodate differences between sub-bands.

[0136] Step 207: The amplitude and phase of the received signals of each antenna element are weighted and synthesized according to the updated weighting parameters, and the station beam after sidelobe suppression is output.

[0137] Weighted synthesis is achieved by multiplying and accumulating the signal received by each antenna element (after down-conversion, sampling, and channelization) with the updated complex weighting parameters to form the beam domain output.

[0138] The station beam is the output signal and corresponding radiation pattern of a single antenna station beam synthesized.

[0139] After sidelobe suppression, the highest sidelobe level of the synthesized beam is lower than the preset index, and the far-field grating lobe is effectively suppressed.

[0140] Optionally, to achieve the generation and updating of the above weighting parameters, this embodiment of the disclosure can employ a graphics processing unit (GPU) and FPGA collaborative computing platform. Illustratively, the GPU can be an NVIDIA TESLA V100 32G model, which has 5120 computing cores, a double-precision floating-point performance of 7 teraflops per second, a single-precision floating-point performance of 14 teraflops, and a memory capacity of 32GB. Based on the number of subbands after the entire observation passband is channelized by the front end, a corresponding number of threads are established in the GPU. Each thread corresponds to the calculation of the weighting factor and radiation pattern for one channel (subband) frequency. The FPGA can be a ZYNQ7000 series high-performance chip, which integrates a high-speed gigabit Ethernet (GigE) interface and utilizes its embedded ARM Cortex-A9 processor for data distribution. The format of each frame of data transmitted to the FPGA can be defined as shown in Table 1.

[0141] Table 1

[0142]

[0143] in, The number of array antennas, This represents the number of channelizations.

[0144] In summary, the embodiments of this disclosure further specify the generation process of weighting parameters by constructing a continuous integrated weighting function coupled with the spatial position of the array elements on a two-dimensional plane, and calculating the weighting parameters after projecting and mapping the coordinates of each antenna array element. This enables the generated amplitude weighting coefficients to accurately match the non-uniform distribution position characteristics of the sparse array, avoiding the approximation error caused by the assumption of a regular array. Thus, while maintaining low sidelobe performance, it ensures the smooth transition and optimal efficiency of the aperture field of the large-aperture sparse array.

[0145] This disclosure also specifies the type of comprehensive weighting function, including Taylor function weighting, Chebyshev function weighting, Hamming function weighting, cosine square function weighting, and uniform weighting, and allows selection based on observation requirements. This limitation enables the antenna array to flexibly switch weighting schemes for different scientific observation targets: for example, for observations of cosmic reionization or pulsars that are extremely sensitive to sidelobes, Chebyshev weighting can be selected to obtain a sidelobe suppression degree of up to 30 dB or more; for targets that are not sensitive to interference, such as solar radio bursts, Taylor weighting can be used to achieve a balance between the width of the sidelobes and the main lobe, thereby optimizing the observation performance under limited resources.

[0146] This embodiment further specifies the updating of weighting parameters during the main beam scanning process by determining the center frequency of each sub-band based on pointing changes, and then recalculating the weighting parameters based on the sub-band center frequency and the current pointing. Since the channelization processing of broadband radio astronomy arrays divides the observation frequency band into numerous narrow sub-bands, each with a different wavelength, sharing the same set of weighting parameters across all frequencies would lead to significant pointing deviations and sidelobe rise at the sub-band edges. This weighting method effectively eliminates weighting frequency deviation by independently calculating the weighting parameters at the center frequency of each sub-band, ensuring the accuracy of beam pointing and the consistency of sidelobe suppression across the entire frequency band, and is particularly suitable for broadband observations in the VHF band with a ratio as high as 4:1.

[0147] In some embodiments, such as Figure 3 As shown, the above method also includes the following steps:

[0148] Step 301: Establish a multi-error model. The multi-error model is a mathematical model that comprehensively considers multiple error factors and is used to calculate the degree of deterioration of the antenna array pattern relative to the ideal pattern.

[0149] The multi-error model is a mathematical expression or numerical simulation framework that integrates at least two error factors such as array amplitude error, array phase error, weighted frequency deviation, and array element position error. Its output is a maladaptive estimate of the radiation pattern performance indicators (such as the highest sidelobe level, pointing accuracy, gain loss, etc.) relative to the ideal error-free case.

[0150] Various error factors are sources of deviation from the ideal design in actual antenna array systems due to hardware inconsistencies, environmental changes, installation techniques, and digital processing approximations. For VHF sparse arrays, these typically include amplitude inconsistencies, phase inconsistencies, sub-band weighted approximations, and element geometric position deviations.

[0151] The radiation pattern of an antenna array is a spatial distribution function of the radiation or reception capability of the antenna array in the far field region, usually expressed as power or gain.

[0152] An ideal radiation pattern is a theoretical radiation pattern calculated by an array synthesis algorithm under the ideal conditions that all antenna elements have consistent amplitude responses, perfectly matched phase responses, precise element positions at the target coordinates, and weighted parameters that exactly match each frequency point.

[0153] The degree of degradation is an indicator of the performance degradation of the actual radiation pattern relative to the ideal radiation pattern. It can be quantitatively expressed as the increase in the highest sidelobe level (ΔMSLL, in dB), pointing error (in degrees), main lobe width expansion ratio, gain decrease (dB), etc.

[0154] In some embodiments, the establishment of the multi-error model includes several possible implementations: In one possible implementation, analytical modeling is used: based on the probability distribution of the errors (such as Gaussian distribution or uniform distribution) and the array geometry, an approximate analytical expression for the statistical expectation of the radiation pattern is derived, which is applicable to the estimation of sidelobe deterioration in regular arrays. In another possible implementation, numerical simulation modeling is used: a large number of random error samples are generated in a computer (Monte Carlo method), and the actual radiation pattern is calculated for each sample to obtain the probability distribution and upper and lower limits of the deterioration degree. Since sparse arrays lack analytical solutions, this study adopts this method. This disclosure does not limit the scope of the embodiments.

[0155] The quality of an antenna array's radiation pattern also depends on various errors, which are rarely considered in current radio astronomy antenna array designs. These errors mainly include amplitude error, phase error, and weighted frequency deviation.

[0156] Optionally, the multi-error model is used to indicate at least two of the following four types of errors: 1. Array amplitude error, which is the gain amplitude deviation caused by the inconsistency between the antenna elements and the receiver gain response; 2. Array phase error, which is the phase deviation caused by the inconsistency between the antenna elements and the receiver phase response; 3. Weighted frequency deviation, which is the deviation caused by replacing the actual response of the edge frequencies within the bandwidth with the center frequency in the channelization process; 4. Element position error, which is the spatial coordinate deviation between the actual position and the ideal design position of the antenna elements caused by the limitations of the antenna array installation process.

[0157] Array amplitude error refers to the multiplicative deviation between the output signal amplitude of each channel and its theoretical value caused by inconsistencies in the gain response of antenna elements and their receiving links (including low-noise amplifiers, mixers, analog-to-digital converters, etc.). This error is mainly caused by factors such as ambient temperature changes, receiver amplitude stability, component aging, and manufacturing tolerances. Generally, the amplitude error can be considered independent of the signal's direction of arrival. By measuring the amplitude difference between each pair of antennas, the range of this error can be determined. This embodiment of the disclosure intends to establish an amplitude error model based on this.

[0158] Array phase error refers to the additive deviation between the phase of the output signal of each channel and the theoretical value caused by the inconsistency in the phase response of the antenna elements and their receiving links. This error is mainly caused by factors such as changes in ambient temperature, receiver phase stability, differences in feeder length, local oscillator phase noise, and temperature-induced time delay drift. Generally, the phase error can be considered independent of the direction of signal arrival. By measuring the phase difference between each pair of antennas, the range of this error can be determined. Based on this, the embodiments of this disclosure intend to establish a phase error model.

[0159] Weighted frequency bias refers to the phase pointing error and amplitude weighting mismatch introduced at the upper and lower edge frequencies of a subband during broadband observation, where a digital array uses the same set of weighting parameters calculated based on the subband center frequency for beamforming of all frequency components within each subband. This bias increases with the increase of the subband's relative bandwidth (the ratio of bandwidth to center frequency).

[0160] In practical ultra-wideband antenna arrays, digital arrays can form beams with specific directions by applying phase weights to signals of different frequencies. However, the system typically cannot configure weights individually for each frequency. Instead, it calculates weighting parameters based on the center frequency within a subband and applies them to the entire subband bandwidth, resulting in weighting bias at non-center frequencies. Therefore, it is necessary to establish this error model for analysis.

[0161] So, the center frequency formed by the channelization processing performed on the front-end preprocessing FPGA The bandwidth is BW, and its upper limit frequency is: The lower limit frequency is: Substituting the two frequencies mentioned above into the calculations for the radiation pattern and antenna array integration, the antenna radiation pattern is re-obtained, and the changes in sidelobe levels are obtained, i.e.: and the gain change at MSLL .So The degree of degradation is a function of BW. In this embodiment of the disclosure, we aim to find the functional relationship under different layout and synthesis conditions: Error explanations are provided in the final radiation pattern calculation.

[0162] Element position error refers to the three-dimensional offset between the actual spatial coordinates of the antenna element and its ideal design position, including lateral and longitudinal offsets in the horizontal plane and height offset in the vertical direction. This error is mainly caused by factors such as construction measurement accuracy, mechanical structure deformation, and foundation settlement. For sparse arrays, position errors can disrupt the phase relationship upon which the sparse distribution depends, leading to an unpredictable increase in sidelobe levels. Since the phase of the weighted parameters for each element given by array synthesis is calculated based on the ideal position, position errors will cause changes in the actual array radiation pattern, potentially resulting in a higher sidelobe level than the simulation results. This embodiment proposes to set a range for element position errors, consider the degree of degradation caused by the upper and lower limits of this error range during radiation pattern calculation, and add error descriptions to the final radiation pattern data.

[0163] The four error models mentioned above (array amplitude error, array phase error, weighted frequency deviation, and element position error) are all established under the condition of considering only one type of error, while in actual antenna arrays, all kinds of errors exist simultaneously. Therefore, this embodiment of the disclosure will establish a comprehensive pattern error model. All of the above errors are expressed as complex exponential functions of random errors. For uniform arrays, the MSLL can be approximated by mathematical analytical methods; however, for sparse arrays with non-uniformly arranged elements, it is difficult to use analytical methods to locate the position of the sidelobe level and solve for the MSLL. Therefore, this embodiment of the disclosure intends to use computer simulation to artificially incorporate the above multiple error models into the predefined sparse array simulation calculation program, and set the upper and lower limits of each error by combining measurement and prediction, and calculate the possible degree of deterioration respectively.

[0164] Based on a determined sparse array layout scheme, the radiation patterns at different frequencies across the entire observation band can be quickly calculated by comprehensively considering observation requirements. The array factors corresponding to each frequency are then obtained through an array synthesis algorithm, enabling rapid transfer of these factors. By establishing a VHF antenna array synthesis error model, considering factors such as amplitude error, phase error, position error, and weighted frequency deviation, the calculated array factors are further corrected. The ultimate goal is to achieve simultaneous observation and calculation of relevant performance indicators of the antenna array within the observation band, meeting the requirements of knowing, solving, and maintaining stability for multiple antenna array indicators.

[0165] The following is an illustrative example of how the error models are established:

[0166] Suppose a given m The antenna pattern of a sparsely distributed antenna array consisting of n antenna elements under ideal conditions. , The pitch angle, The azimuth angle is the actual steering vector when the array has amplitude errors. for:

[0167]

[0168] Let be the amplitude error matrix of the array. This represents the amplitude error (not necessarily regularly distributed) of the array element located in the m-th row and n-th column of a two-dimensional plane. In this study, receiver errors can be factored into the antenna pattern.

[0169] Therefore, the array pattern function under the influence of array amplitude error can be rewritten based on the simplified pattern expression as follows:

[0170]

[0171] in, The array pattern function represents the entire planar array in any direction in space. The orientation pattern function; N is the number of array elements in one dimension (e.g., the x-axis direction); M is the number of array elements in another dimension (e.g., the y-axis direction); Let be the radiation pattern of the array elements, representing the radiation characteristics of the (m,n)th individual antenna element. Assume the radiation patterns of all antenna elements are... All are consistent; The weighting factor for each element using a low sidelobe design is denoted by λ, which is the complex weight applied to the (m,n)th antenna element. λ is the operating wavelength. The position coordinates (or spacing accumulation) of the nth array element in the x-axis direction; Let be the position coordinates of the m-th array element along the y-axis; The beam pointing angle is the target direction that the antenna's main beam is intended to be aligned with.

[0172] Suppose a given m A sparsely distributed antenna array consisting of n antenna elements has the following position vectors for its elements: When the array has a phase error, the actual steering vector for:

[0173]

[0174] in, This is the array steering vector under ideal conditions.

[0175] Let be the amplitude error matrix of the array. Let represent the phase error (not necessarily regularly distributed) of the array element in the i-th row and j-th column of the two-dimensional plane. Based on this, the array pattern function under the influence of array phase error can be expressed as:

[0176]

[0177] Assuming the bandwidth of a digital channel in an array is BW, then the maximum weighted frequency deviation of the system is BW / 2. Let it be... ,set up

[0178]

[0179] but:

[0180]

[0181] The array pattern function after introducing weighted frequency bias can be expressed as:

[0182]

[0183] Among them, f H f is the upper limit frequency of the entire observation band; L is the lower limit frequency of the entire observation band; q is the ratio of the maximum frequency deviation to the upper limit frequency; N B N is the ratio of the upper frequency limit to the lower frequency limit. f f is the upper limit frequency of the entire observation band. H The ratio to the center frequency of the channel.

[0184] Suppose a given m A sparsely distributed antenna array consisting of n antenna elements has the following position vectors for its elements: When the array has position errors, the actual position vector for:

[0185]

[0186] Wherein, the position deviation vector of the (m,n)th antenna element It can be decomposed into row direction deviation. Column direction deviation .

[0187] The array pattern function after introducing element position errors can be expressed as:

[0188]

[0189] Where, d n Let be the column-direction position coordinates of the nth antenna element under ideal conditions; d represents the positional error offset of the nth column antenna element in the column direction; m Let be the row direction position coordinates of the m-th row antenna element under ideal conditions; This represents the positional error offset of the m-th row antenna element in the row direction.

[0190] By integrating the aforementioned various errors into a single array pattern calculation function, a comprehensive calculation model with multiple error parameters is formed. Based on this model, the potential deterioration of the radiation pattern under various error combinations is calculated according to the upper and lower limits of each error (which can be determined by a combination of measurement and prediction), thereby achieving a comprehensive evaluation of the antenna array's radiation pattern performance.

[0191] Step 302: Based on the multi-error model, determine the degree of degradation of the radiation pattern corresponding to the station beam after sidelobe suppression.

[0192] The station beam after sidelobe suppression is the actual output of the large-scale antenna array station-level beam signal and its corresponding radiation pattern according to the above method (generating weighting parameters, updating during scanning, and weighted synthesis). This beam has already adopted low sidelobe weighting (such as Taylor, Chebyshev, etc.) during the design phase.

[0193] In some embodiments, the determination of the degree of pattern deterioration includes the following possible implementations: In one possible implementation, Monte Carlo sampling: Based on the probability distribution (or value range) of each error factor in the multi-error model, multiple sets of random error instances are generated. For each set of instances, the station beam pattern is recalculated (including array element position correction, amplitude and phase error substitution, and weighted frequency deviation correction), and indicators such as MSLL and pointing deviation are extracted. Then, the mean, standard deviation, and worst-case scenario (such as the maximum possible MSLL increment) of the deterioration degree are statistically calculated. In another possible implementation, worst-case analysis: Without considering the probability distribution, each error is simultaneously set to extreme values ​​(such as the combination of the maximum positive deviation and negative deviation), and the pattern is calculated to obtain the upper limit boundary of the deterioration degree. In another possible implementation, sensitivity analysis: Only one error factor is changed each time, while other factors are fixed at nominal values. The slope of the influence of this error on each indicator of the pattern is calculated separately, and finally, the overall deterioration degree is estimated using linear superposition. This disclosure does not limit the specific implementation.

[0194] Step 303: The degree of degradation is used as an error description and output together with the station beam after sidelobe suppression.

[0195] Error descriptions are a set of fields appended to the observation data stream or metadata, describing the degree of deviation between the current station's beam pattern and the ideal design. These descriptions can be used for correction or weighting during subsequent processing such as interferometric imaging, spectrum analysis, and interference removal.

[0196] In some embodiments, the output can be implemented in several ways, including: In one possible implementation, as metadata appended: when storing beam data, the degradation level is embedded in the file header, or transmitted in parallel with scientific data via a custom field carrying error descriptions in a real-time communication protocol. In another possible implementation, independent log output: the degradation level is written to a separate log file or database table by time, direction, and subband, and then associated with the beam data by timestamp during subsequent processing. In another possible implementation, real-time visualization: the current radiation pattern and its degradation curve are displayed on the monitoring interface for operators to assess data quality. In yet another possible implementation, automatic calibration triggering: when the degradation level exceeds a preset threshold (e.g., MSLL increment > 5dB), the system automatically generates an alarm and triggers online calibration or a rerun of layout optimization.

[0197] In some embodiments, such as Figure 4 As shown, the above method also includes the following steps:

[0198] Step 401: Establish a database of bright source locations, which includes information on the distribution of sky background radiation and / or a model of the Milky Way's brightness temperature as a function of frequency.

[0199] The Bright Source Location Database is a structured dataset stored on a computer-readable medium, used to record the spatial coordinates, radiation intensity, and spectral characteristics of strongly radiating celestial objects (bright sources) in the sky. Bright sources include the center of the Milky Way, supernova remnants, radio galaxies, pulsars, etc. Data sources include observation catalogs from low-frequency sky surveys, global sky models, and empirical formulas for the brightness temperature of the Milky Way.

[0200] The information on the distribution of the celestial background radiation describes the spatial distribution of radiance (unit: Jy / beam or Kelvin) in various directions (expressed in right ascension and declination) across the entire celestial sphere at a specific frequency or frequency range. This distribution is composed of multiple components, including galactic synchrotron radiation, free-to-free radiation, and extragalactic radio background radiation.

[0201] The model for the Milky Way's brightness temperature as a function of frequency is a mathematical expression derived from fitting measured data, describing the quantitative relationship between brightness temperature and frequency along the galactic direction. For example, it employs an empirical formula showing a power-law variation of brightness temperature with frequency in higher galactic latitude regions. This model is used to estimate the intensity of the Milky Way's background radiation at any VHF frequency.

[0202] In some embodiments, the establishment of the brightness source location database includes, but is not limited to, the following steps: downloading low-frequency survey data from a public data source; generating a full-sky brightness temperature cube with a specified frequency range and spatial resolution using open-source code; and storing the fused different models and survey results in a unified format into a local file system or in-memory database.

[0203] Step 402: Calculate the azimuth of the bright source within the field of view of the sparsely distributed antenna array from the bright source location database based on the observation time and the geographical latitude and longitude of the observation station.

[0204] The observation time is the absolute moment when the radio astronomy observation is performed, usually using Coordinated Universal Time (UTC) or Julian Day. This parameter is used to calculate the change in the position of celestial objects due to the Earth's rotation.

[0205] The geographical latitude and longitude of the observatory are the geographical latitude (north positive, south negative, in degrees) and longitude (east positive, west negative, in degrees) of the Earth's surface where the sparsely distributed antenna array is located, and sometimes altitude is also required. This parameter is used to convert the celestial equatorial coordinates (right ascension, declination) to the horizontal coordinates (azimuth, elevation) of the observatory.

[0206] The field of view of a sparsely distributed antenna array is the area of ​​the sky that the array can effectively receive signals from. It is typically defined as the solid angle region covered by the half-power beamwidth, but in some contexts it can also refer to the upper half-space including all sidelobes. The size of the field of view varies with the direction and frequency of the main beam.

[0207] The azimuth of the light source is the horizontal coordinate of the light source relative to the observation station at a given observation time and location, specifically including the azimuth angle (0° to 360°, measured clockwise from north) and the elevation angle (0° for the ground plane and 90° for the zenith).

[0208] In some embodiments, the calculation of the azimuth of bright sources within the field of view of a sparsely distributed antenna array includes, but is not limited to, the following steps: inputting the observation time and the latitude and longitude of the station location; reading the equatorial coordinates of each bright source from the bright source location database; converting the equatorial coordinates to horizontal coordinates using an astronomical coordinate transformation library; filtering out bright sources located within the field of view by combining the current main beam pointing of the antenna array (if known); and outputting the azimuth and elevation angles of each bright source.

[0209] Step 403: When generating or updating the weighted parameters, a null point is set at the corresponding radiation pattern position according to the azimuth of the bright source in order to suppress the interference of the bright source on the observation.

[0210] The weighted parameters are generated during the initial configuration or reconfiguration phase by calculating the complex weights (amplitude and phase) of each antenna element based on the target layout, operating frequency, and observation requirements of the sparsely distributed antenna array.

[0211] The weighting parameters are updated to recalculate or adjust them when the main beam is dynamically scanned, subbands are switched, or the spatiotemporal environment changes, in order to maintain correct beam pointing and sidelobe suppression performance.

[0212] The radiation pattern position is defined in a two-dimensional spherical coordinate system consisting of elevation and azimuth angles, representing the far-field radiation pattern function of the antenna array. A specific point on the radiation pattern corresponds to the azimuth of a bright source.

[0213] A null is an artificially constructed region of minimum gain in the radiation pattern. Within this region, the receiving gain of the antenna array is reduced to a level far below the main lobe peak (typically required to be at least 30 dB below the main lobe), thereby effectively suppressing signals from that direction. The depth and width of the null can be adjusted according to the radiation intensity and angular size of the bright source.

[0214] Suppressing the interference of bright sources on observation involves reducing the sensitivity of the antenna array in the direction of the bright source, reducing the energy emitted or radiated by the bright source that enters the main channel of the receiving system, and preventing the bright source from generating false structures in imaging, raising the noise floor in the spectrum, or causing sidelobe confusion noise.

[0215] In some embodiments, setting null points at corresponding radiation pattern locations can be achieved by incorporating constraints during the weighted parameter solution process. Optionally, this includes, but is not limited to, the following steps: employing a penalty function method to add a penalty term for the gain in the brightness source direction to the optimization objective (e.g., minimizing the highest sidelobe level); directly solving for the weighted vector that satisfies preset conditions using a linear constraint minimum variance or zero-forcing method; and for the basic weights already obtained through Taylor or Chebyshev synthesis, using feature projection or subspace orthogonalization techniques to embed null points without significantly altering the main lobe shape. All calculations can be performed in parallel on a GPU, with the null point positions updated in real-time as the brightness source orientation changes.

[0216] In performing the above steps of pattern synthesis, weighted parameter update, and error analysis, the following databases are required to provide basic data support to ensure the accuracy and efficiency of the calculations.

[0217] The first is the database of bright source locations. In the VHF band, strong background sources in the sky (including radio interference) are the main potential influencing factors for antenna array observations. Therefore, in the aforementioned constraints, it is necessary to consider constraints that enable the pattern response to achieve a specific degree of suppression at certain azimuth and elevation angles, i.e., setting "null points." The study will fully consider the distribution characteristics of strong radiation sources in the sky background. For example, existing studies have established empirical formulas for the variation of the Milky Way's brightness temperature with frequency in higher galactic latitude regions. The sky background radiation distribution information under different flux conditions given by different observations can be used to "subtract" the influence of bright sources at the pattern synthesis level. Therefore, it is necessary to clearly know the spatial distribution of bright sources over time at the geographical latitude and longitude of the observation station and utilize it in pattern simulation. By referring to currently available sky model simulation code, the distribution of bright sources in the sky at different times and locations can be generated. In addition, the low-frequency sky survey project has also released a batch of low-frequency sky survey data, which are available for download.

[0218] The second is a database of radiation patterns for each antenna element at different frequencies. This database stores the element radiation pattern data for each antenna element at different frequencies. For VHF band antennas, each element can be considered to have the same radiation characteristics, so it is only necessary to store the element radiation patterns at different frequencies. This data can be obtained through electromagnetic simulation software (such as High Frequency Structure Simulator (HFSS), Computer Simulation Technology (CST), etc.), stored in the computer during the antenna design and simulation phase, and retrieved according to the frequency index when calculating the antenna array radiation pattern.

[0219] The third is a multi-error parameter database. This database is established based on the error range of actual measurement data of the antenna array, covering amplitude error, phase error, upper and lower limit frequency deviations caused by front-end channelization design, and position accuracy errors estimated during antenna element installation at different frequencies. This database is also called according to frequency index during radiation pattern simulation, providing input parameters for the construction of error models and the analysis of the degree of degradation.

[0220] Finally, the study employed a nighttime tracking method to construct spatial null points, thereby verifying the suppression effect on far-field sidelobes and the system stability. Based on schemes recommended in existing literature, three cold-air regions were identified as stable radiation source areas. By tracking these regions with an antenna array and analyzing the fluctuations in the observed data, information such as the degree of sidelobe level suppression and changes in the antenna array temperature could be detected.

[0221] Building upon this foundation, by synthesizing the characteristics of radio sources, the lowest sidelobes can be achieved and the antenna array pattern can be reconstructed in real time. Low sidelobe synthesis techniques for antenna patterns are widely used in regular arrays, but while research has been conducted on sparse arrays, it remains limited. The main challenge lies in the fact that current synthesis techniques primarily use generation functions based on regular arrays, whose weighting parameters possess good analytical solvability. However, in sparse arrays, the accuracy and range of the independent variables in the array pattern synthesis function need to be adjusted according to the sparse distribution pattern and spatial interference suppression requirements. This is precisely the focus of this embodiment. Leveraging the parallel computing capabilities of GPUs, the above analysis and weighting function generation can be rapidly implemented. Furthermore, by effectively classifying the types of observation sources in conjunction with actual radio astronomy practices, the complexity and data volume of beamforming calculations can be reduced to some extent.

[0222] In the actual data processing and analysis, multiple constraint parameters are first input based on the antenna array conditions, including antenna array layout size, operating frequency band, maximum and minimum spacing between array elements, and the direction of arrival of the main interference waves, and an optimization objective function is established. Then, the optimal antenna array layout scheme is selected by combining various optimization algorithms with the constraint conditions. Based on this layout scheme, different antenna array synthesis methods are studied: first, with the antenna array pointing towards the zenith as the initial state, the optimal antenna array pattern synthesis scheme is solved under the constraint of the optimization objective function. Then, the weighted parameters for different frequency bands are calculated in the GPU and sent to the FPGA through a high-speed network card. Subsequently, the multi-error model and the multi-frequency radiation model of the sky background are substituted to solve for the point PSF at multiple frequencies and stored. Finally, the beam pointing direction is switched according to the antenna array pointing requirements, and the above steps three and four are repeated to update the array element synthesis weighted parameters in real time and send them down through the high-speed network.

[0223] To verify the correctness and feasibility of the above scheme, experiments are required. Since VHF antenna arrays occupy a large area, global array measurement is difficult to achieve within the project execution cycle. Therefore, a scaled-down measurement method is proposed, using high-frequency antennas to replace the VHF antennas in the simulation, thereby significantly reducing the physical size of the entire antenna array. The experiment uses a maturely designed approximately 8 GHz microstrip antenna. A scaled-down array is constructed in a microwave anechoic chamber, and the radiation pattern of the antenna array is obtained through far-field measurements to verify the correctness of the layout scheme and algorithm. The relevant microstrip antenna, receiver, and channelizer hardware have been maturely developed by collaborating units. The receiver can output intermediate frequency signals, and its output bandwidth can be set to be consistent with that of the VHF band receiver, facilitating seamless integration with the actual VHF system in the future. The experiment consists of two sub-steps: first, the acquired signals are weighted by channel to verify the correctness of the radiation pattern; then, the acquisition card is connected to verify the distribution effect of the weighting parameters. In the high-frequency band, the number of antenna elements can be appropriately reduced. Multi-channel acquisition and weighting operations are implemented within a System on a Programmable Chip (SOPC) to analyze the overall radiation pattern effect. In addition, in the real-scale array, the stability of beamforming will be verified by using the cold air region recommended by existing simulation experiments. By tracking the stable cold air radiation source, information such as the degree of sidelobe level suppression and system temperature changes will be detected based on the fluctuations of the observation data.

[0224] In summary, this embodiment of the present disclosure further introduces a multi-error model. By pre-establishing a mathematical model that comprehensively considers various error factors, it determines the degree of degradation of the actual output station beam pattern relative to the ideal pattern, and outputs this degradation degree as an error description along with the beam data. This approach allows observers to clearly understand the extent to which the current data is affected by factors such as array element position errors and amplitude-phase inconsistencies. This enables quantitative correction of instrument effects in subsequent interferometric imaging, CLEAN processing, or scientific analysis, avoiding the misinterpretation of systematic errors as changes in celestial signals, and significantly improving the reliability and interpretability of radio astronomy data.

[0225] This disclosure further defines four error types included in the multi-error model: array amplitude error, array phase error, weighted frequency deviation, and element position error, requiring at least two of these to be included. This definition identifies the error sources that require focused calibration and modeling for practical systems, giving the error model clear physical meaning and engineering feasibility. By evaluating the degree of pattern degradation caused by these errors individually or in combination, guidance can be provided for antenna array installation accuracy tolerance design, receiver channel consistency calibration strategies, and the rational selection of subband bandwidth. This allows for controllable margin management of sidelobe stability and station beam quality during the system design phase.

[0226] This embodiment also establishes a bright source location database and calculates the horizontal coordinates of bright sources within the field of view in real time using observation time and station latitude and longitude. Then, it actively sets null points at corresponding positions in the radiation pattern when generating or updating weighting parameters. This scheme directly targets strong radio objects, the most significant source of interference in radio astronomy observations. By adaptively aligning the null points of the radiation pattern with these bright sources, the radiation energy from these sources cannot enter the main receiving channel, thus significantly reducing sidelobe confusion noise and false imaging signals caused by bright sources. Simultaneously, since the null point setting is integrated into the dynamic generation of weighting parameters, when the main beam scanning or Earth's rotation causes a bright source to move out of the field of view, the system can release null resources or reconfigure null positions in real time. This achieves closed-loop coupling between the sky background radiation model and reconfigurable beamforming, effectively suppressing system temperature cycle slips caused by bright sources entering far-field sidelobes, and providing clean antenna temperature profiles for the observation of weak signals such as pulsars and neutral hydrogen.

[0227] The following are device embodiments of the present disclosure. For parts not described in detail in the device embodiments, please refer to the technical details disclosed in the above method embodiments.

[0228] Please refer to Figure 5 This illustration shows a schematic diagram of an apparatus for achieving station beam stabilization and sidelobe suppression, provided by an exemplary embodiment of this disclosure. The apparatus can be implemented, in whole or in part, through software, hardware, or a combination of both, as a computing device. The apparatus includes: an acquisition module 510, a generation module 520, an update module 530, and an output module 540.

[0229] The acquisition module 510 is used to acquire the target layout information of the sparsely distributed antenna array. The target layout information is used to indicate the spatial position coordinates of each antenna element in the sparsely distributed antenna array.

[0230] The generation module 520 is used to generate weighted parameters for each antenna element based on the target layout information. The weighted parameters are used to weight the amplitude and phase of the received signal of the antenna element to reduce the sidelobe level.

[0231] The update module 530 is used to update the weighted parameters of each antenna element according to the change in the direction of the main beam during the dynamic scanning of the main beam.

[0232] The output module 540 is used to perform weighted synthesis of the amplitude and phase of the received signals of each antenna array element according to the updated weighting parameters, and output the station beam after sidelobe suppression.

[0233] In one possible implementation, the generation module 520 is also used for:

[0234] Based on the target layout information, a comprehensive weighting function is constructed on a two-dimensional plane. The comprehensive weighting function is a continuous two-dimensional function coupled with the spatial position of the array elements.

[0235] For each antenna element, the position coordinates of the antenna element are projected onto a two-dimensional plane to obtain the projected coordinates of the antenna element.

[0236] The weighted parameters of the antenna array elements are calculated using a comprehensive weighting function based on their projected coordinates.

[0237] In another possible implementation, the type of the integrated weighting function is determined based on the observation requirements of the sparsely distributed antenna array. The type of integrated weighting function includes at least one of the following: Taylor function weighting, Chebyshev function weighting, Hamming function weighting, cosine square function weighting, and uniform weighting.

[0238] In another possible implementation, update module 530 is also used for:

[0239] Based on the change in the main beam direction, the center frequency of each sub-band within the observation frequency band is determined; for each sub-band, the weighted parameters of each antenna element at the center frequency of the sub-band are recalculated based on the center frequency of the sub-band and the current main beam direction.

[0240] In another possible implementation, the device also includes:

[0241] The first module is used to establish a multi-error model, which is a mathematical model that comprehensively considers various error factors and is used to calculate the degree of deterioration of the antenna array pattern relative to the ideal pattern.

[0242] The determination module is used to determine the degree of degradation of the radiation pattern corresponding to the station beam after sidelobe suppression, based on the multi-error model.

[0243] The output module 540 is also used to output the degree of degradation as an error description, along with the station beam after sidelobe suppression.

[0244] In another possible implementation, a multi-error model is used to indicate at least two of the following four types of errors:

[0245] Array amplitude error is the gain amplitude deviation caused by the inconsistency between the antenna elements and the receiver gain response.

[0246] Array phase error is the phase deviation caused by the inconsistency between the phase response of the antenna elements and the receiver.

[0247] Weighted frequency deviation is the deviation generated in channelization processing by weighting the center frequency instead of the actual response of the edge frequencies within the bandwidth.

[0248] Element position error refers to the spatial coordinate deviation between the actual position and the ideal design position of the antenna elements due to limitations in the antenna array installation process.

[0249] In another possible implementation, the device also includes:

[0250] The second module is used to establish a database of bright source locations, which includes information on the distribution of sky background radiation and / or a model of how the brightness temperature of the Milky Way varies with frequency.

[0251] The calculation module is used to calculate the azimuth of bright sources within the field of view of the sparsely distributed antenna array based on the observation time and the geographical latitude and longitude of the observation station.

[0252] The setting module is used to set null points at the corresponding radiation pattern positions based on the azimuth of the bright source when generating or updating weighted parameters, in order to suppress the interference of the bright source on the observation.

[0253] It should be noted that the above embodiments only illustrate the division of the above functional modules when implementing the device. In actual applications, the above functions can be assigned to different functional modules according to actual needs, that is, the content structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0254] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0255] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0256] This disclosure also provides an apparatus for achieving station beam stabilization and sidelobe suppression, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0257] This disclosure also provides a non-volatile computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0258] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0259] Figure 6This is a block diagram illustrating an apparatus 1900 according to an exemplary embodiment. For example, apparatus 1900 may be provided as a server or terminal device. (Refer to...) Figure 6 The apparatus 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0260] Device 1900 may also include a power supply component 1926 configured to perform power management of device 1900, a wired or wireless network interface 1950 configured to connect device 1900 to a network, and an input / output interface 1958 (I / O interface). Device 1900 can operate on an operating system, such as Windows Server, stored in memory 1932. TM macOS X TM Unix TM Linux TM FreeBSD TM Or similar.

[0261] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of the device 1900 to perform the above-described method.

[0262] Computer-readable storage media can be tangible devices capable of holding and storing programs / instructions used by instruction execution devices. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0263] The computer program (or computer-readable program instructions) described herein can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage medium in the respective computing / processing device.

[0264] The computer program (or computer program instructions) used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions to implement various aspects of this disclosure.

[0265] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0266] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0267] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0268] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0269] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for achieving station beam stability and sidelobe suppression, characterized in that, The method includes: Obtain the target layout information of the sparsely distributed antenna array, wherein the target layout information is used to indicate the spatial position coordinates of each antenna element in the sparsely distributed antenna array; Based on the target layout information, weighting parameters are generated for each antenna element. These weighting parameters are used to weight the amplitude and phase of the received signal of the antenna element to reduce the sidelobe level. During the dynamic scanning of the main beam, the weighting parameters of each antenna element are updated according to the change in the direction of the main beam. The amplitude and phase of the received signals of each antenna element are weighted and synthesized according to the updated weighting parameters, and the station beam after sidelobe suppression is output.

2. The method according to claim 1, characterized in that, The step of generating weighted parameters for each antenna element based on the target layout information includes: Based on the target layout information, a comprehensive weighting function is constructed on a two-dimensional plane. The comprehensive weighting function is a continuous two-dimensional function coupled with the spatial position of the array elements. For each antenna element, the position coordinates of the antenna element are projected onto the two-dimensional plane to obtain the projected coordinates of the antenna element; The weighted parameters of the antenna array elements are calculated using the integrated weighting function based on the projected coordinates of the antenna array elements.

3. The method according to claim 2, characterized in that, The type of the integrated weighting function is determined based on the observation requirements of the sparsely distributed antenna array. The type of the integrated weighting function includes at least one of the following: Taylor function weighting, Chebyshev function weighting, Hamming function weighting, cosine square function weighting, and uniform weighting.

4. The method according to claim 1, characterized in that, The step of updating the weighted parameters of each antenna element according to the change in the main beam direction includes: Based on the change in the main beam direction, the center frequency of each sub-band within the observation frequency band is determined; for each sub-band, the weighted parameters of each antenna element at the center frequency of the sub-band are recalculated based on the center frequency of the sub-band and the current main beam direction.

5. The method according to claim 4, characterized in that, The method further includes: A multi-error model is established, which is a mathematical model that comprehensively considers multiple error factors and is used to calculate the degree of deterioration of the antenna array pattern relative to the ideal pattern. Based on the multi-error model, the degree of degradation of the radiation pattern corresponding to the station beam after sidelobe suppression is determined; The degree of degradation is used as an error description and is output together with the station beam after sidelobe suppression.

6. The method according to claim 5, characterized in that, The multi-error model is used to indicate at least two of the following four types of errors: Array amplitude error, which is the gain amplitude deviation caused by the inconsistency between the antenna array elements and the receiver gain response; Array phase error, which is the phase deviation caused by the inconsistency between the phase response of the antenna elements and the receiver; Weighted frequency deviation, which is the deviation generated in channelization processing by replacing the actual response of the edge frequencies within the bandwidth with the center frequency weighted; The array element position error is the spatial coordinate deviation between the actual position and the ideal design position of the antenna array element caused by limitations in the antenna array installation process.

7. The method according to claim 1, characterized in that, The method further includes: Establish a database of bright source locations, which includes information on the distribution of sky background radiation and / or a model of the Milky Way's brightness temperature as a function of frequency; Based on the observation time and the geographical latitude and longitude of the observation station, the azimuth of the bright source within the field of view of the sparsely distributed antenna array is calculated from the bright source location database; When generating or updating the weighting parameters, a null point is set at the corresponding radiation pattern position according to the azimuth of the bright source in order to suppress the interference of the bright source on the observation.

8. A device for achieving station beam stabilization and sidelobe suppression, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

Images