Real-time beam forming and interference reduction method for phased array computing terminal

Abstract

The invention discloses a phased array computing terminal real-time beam forming and interference reduction method, which comprises the following steps of: 1, acquiring an observation signal by an observation target, calculating an independent narrow-band beam former for each sub-band channel for a channelized frequency domain baseband signal, and further realizing frequency domain real-time beam forming on all baseband signals at the same time, based on the dynamic change of the propagation environment, the beam forming weight is updated, the beam shape and direction are adjusted, and the maximum signal-to-noise ratio beam forming is realized; and 2, effective interference reduction is realized by adopting spatial filtering in phased array calculation terminal equipment, a spatial filter is constructed in combination with a subspace projection method, identification and elimination of interference signals are realized through calculation and characteristic decomposition of a covariance matrix, and finally filtered signal data are obtained. According to the method, a large number of independent tasks in matrix operation can be efficiently processed, so that the calculation time is remarkably shortened, and the observation capability of a system and the adaptability of an application scene are improved.

Description

Technical Field

[0001] The present invention relates to the field of radio astronomy technology, and is aimed at beamforming and radio frequency interference (RFI) suppression in a phased array computing terminal device, in particular to a phased array computing terminal real-time beamforming and interference reduction method. Background Art

[0002] Radio astronomy is a science that studies radio wave radiation in the universe. It studies the electromagnetic wave signals of natural celestial bodies in the universe by collecting and analyzing them. Phased array system is a cutting-edge radio observation equipment. Beamforming is a key technology to achieve beam pointing and gain adjustment of phased array antennas. Digital beamforming enables phased array systems to form multiple real-time beams, thereby expanding the field of view of the telescope. However, the accuracy of the beam synthesis coefficient is affected by RFI. Although there are many effective methods to eliminate radio interference, due to the geographical location of the observation equipment, the different scientific objectives, and the complexity and time-varying nature of RFI, there are few universal RFI elimination methods that are applicable to the data of all observation equipment.

[0003] At present, astronomical phased array systems are still at the forefront of international research. The leading ones in the world are the Parkes telescope and the Square Kilometer Array Pilot Project (ASKAP). Parkes and ASKAP have achieved 70×2 and 36×2 real-time beams respectively. In addition, ASKAP has also carried out experimental research on interference reduction and achieved certain results.

[0004] However, current astronomical phased array systems have limitations in data processing capabilities when faced with a large number of array elements, and the complexity of signal components increases. While removing RFI, it is easy to affect non-RFI data. There are also technical problems such as insufficient accuracy of real-time beamforming and insufficient flexibility of interference reduction algorithms. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a real-time beamforming and interference reduction method for a phased array computing terminal, which significantly expands the scale of data received, processed and output, shortens the processing time, and realizes flexible selection of frequency signals that meet the characteristics of the observed target. At the same time, it can avoid RFI of certain frequency channels, flexibly adjust the beam shape and direction, and improve the observation capability of the system and the adaptability of application scenarios.

[0006] To achieve the above object, the present invention provides a phased array computing terminal real-time beamforming and interference reduction method, the method comprising the following steps: Step 1: The observation target collects observation signals, and for the channelized frequency domain baseband signals, a separate narrowband beamformer is calculated for each sub-band channel, thereby realizing real-time frequency domain beamforming for all baseband signals at the same time, updating the beamforming weights based on the dynamic changes of the propagation environment, adjusting the beam shape and direction, and realizing beamforming with maximum signal-to-noise ratio; Step 2: Use spatial filtering in the phased array computing terminal device to achieve effective interference reduction. Combine the subspace projection method to construct a spatial filter. Calculate the covariance matrix and feature decomposition to identify and eliminate interference signals, and finally obtain filtered signal data.

[0007] Furthermore, the phased array computing terminal device includes a phased array receiving unit, a low noise amplifier group, an ADC+FPGA module, a GPU cluster, a multi-scientific target terminal, a pulsar terminal, a fast radio burst terminal and a spectral line terminal.

[0008] Furthermore, the phased array system in which the phased array computing terminal device is located is composed of 224 array elements, which will form 90×2 real-time beams.

[0009] Furthermore, in a GPU cluster, the intensive parallel computing advantages of the GPU and the complex process control advantages of the CPU are combined to achieve real-time data flow control and calculation.

[0010] Furthermore, the observation targets include pulsars and fast radio bursts, and the observation signals are point source signals.

[0011] Furthermore, the point source signal is processed by a high-speed digital receiving unit to complete the channelization operation, thereby obtaining a frequency domain baseband signal; the bandwidth of each channel will meet the narrowband constraint of beamforming; in the GPU cluster, the system calculates and configures the corresponding narrowband phase-shifted beamformer for each independent sub-band channel separately, realizing real-time frequency domain beamforming for all baseband signals at the same time.

[0012] Furthermore, the specific process of using maximum signal-to-noise ratio beamforming in step 1 is as follows: S1.1. Calculate the array covariance matrix for passive observation; S1.2. Calculate the array covariance matrix for active observation; S1.3. Calculate an optimal set of weights using the array covariance matrix during passive observation and the array covariance matrix during active observation; S1.4. Using the weight vector for beam synthesis; calculating the beam output; S1.5. Get the beamforming output and calculate the signal-to-noise ratio.

[0013] Furthermore, the array covariance matrix for passive observation is ; in and are the noise vector and its conjugate transpose respectively, and E[...] is the mean.

[0014] Furthermore, the array covariance matrix for active observation is ; in and are the signal vector and its conjugate transpose respectively.

[0015] Furthermore, using the optimal weight vector and narrowband signal matrix Calculate the beam output, the expression is: in, Represents the conjugate transpose of a matrix.

[0016] Beneficial effects: 1. The present invention designs a signal processing strategy for a phased array computing terminal device with 224 array elements and 90×2 real-time beams, including beamforming and interference suppression, to process high-throughput data in real time in a heterogeneous computing system based on a central processing unit and a graphics processing unit, significantly expanding the scale of data received, processed and output, and shortening the processing time.

[0017] 2. The interference reduction algorithm based on spatial filtering is combined with the subspace projection method to construct a spatial filter. It has strong adaptability in identifying RFI and ensures that the data processing efficiency is adapted to the requirements of real-time processing. While removing RFI, it reduces the impact on non-RFI data.

[0018] 3. For the channelized frequency domain baseband signal, a separate narrowband beamformer is calculated for each sub-band channel, thereby realizing real-time frequency domain beamforming for all baseband signals at the same time. This enables flexible selection of frequency signals that match the target characteristics and avoids RFI in certain frequency channels.

[0019] 4. Update the beamforming weights based on the dynamic changes of the propagation environment, and flexibly adjust the beam shape and direction. The present invention realizes the maximum signal-to-noise ratio beamforming, and can generate 90×2 (including horizontal and vertical polarization) beams at the same time. These real-time synthesized beams can be directed to different targets or cover a large area, which improves the observation capability of the system and the adaptability of application scenarios.

[0020] 5. Finally, the mutual coupling effect between receiving units will also increase the complexity of signal components. These factors pose challenges to the optimization of the algorithm itself and its real-time implementation. In this regard, the present invention realizes beamforming and interference suppression based on the beamforming method and spatial filtering method of maximum signal-to-noise ratio. On this basis, a layered and overlapping processing framework is designed to optimize the collaborative mechanism of beamforming and interference suppression. By reasonably allocating hardware resources and computing time, and dynamically scheduling computing tasks: tasks with dependencies are executed in sequence, and independent and unrelated tasks are executed in an overlapping manner, the overall processing efficiency is maximized, thereby realizing real-time processing of large-scale data, including two effects: one is to quickly process the received signal to ensure the timeliness of the data; the other is to dynamically calculate the covariance matrix of the received signal to reflect the changes in the propagation environment in real time, thereby improving the system's adaptive ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic diagram of the overall frame structure system architecture of the phased array system according to the present invention; Figure 2 It is a schematic diagram of the element distribution of the planar array; Figure 3 It is the principle diagram of beam synthesis; Figure 4 It is a flow chart of beam synthesis; Figure 5 Schematic diagram of spatial filtering data processing flow; Figure 6 It is a framework diagram of layered and overlapping processing. DETAILED DESCRIPTION

[0022] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0024] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] The following combination Figure 1-Figure 6 The specific embodiments of the present invention are described in detail. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0026] For observation data from astronomical radio telescopes, the phased array system is responsible for receiving signals and processing data in real time. Figure 1 As shown, the phased array system includes a phased array receiving unit, a low noise amplifier group, an ADC+FPGA module, a GPU cluster, a multi-science target terminal, a pulsar terminal, a fast radio burst terminal and a spectral line terminal.

[0027] The phased array receiving unit is responsible for receiving radio signals from celestial bodies and converting them into processable electronic signals. The weak radio signals are further enhanced by the low-noise amplifier group. After that, the signal enters the analog-to-digital converter (ADC) + field-programmable logic gate array (FPGA) module, where the analog signal is converted into a digital signal, and the signal is digitized and channelized. Next, the graphics processing unit (GPU) cluster is a hardware device that implements the algorithm and framework of the present invention, which includes a large number of GPUs and a small number of central processing units (CPUs). The CPU is used to allocate and schedule the graphics processing unit (GPU) to perform various computing tasks, and beamforming and interference reduction are completed here. In the signal processing process of beamforming and RFI suppression by the GPU cluster, large-scale matrix and vector operations are involved, including solving the covariance matrix of the signal between array elements, matrix eigendecomposition, and matrix multiplication and other high-complexity calculations. The GPU has a highly parallel computing capability and can efficiently process a large number of independent tasks in matrix operations, thereby significantly reducing the computing time. Finally, the processed data is output to the multi-science target terminal for special follow-up analysis for different targets.

[0028] The phased array computing terminal real-time beamforming and interference reduction method according to the present invention comprises the following steps: Step 1: The observation target collects observation signals, and for the channelized signals, calculates separate narrowband beamforming for each sub-band channel, thereby realizing beamforming for all signals at the same time. Based on the dynamic changes of the propagation environment, the beamforming weights are updated, and the beam shape and direction are adjusted to achieve maximum signal-to-noise ratio beamforming.

[0029] Step 2: Use spatial filtering in the phased array computing terminal device to achieve effective interference reduction. Combined with the subspace projection method, spatial filtering is implemented. The interference signal is identified and eliminated through the calculation of the covariance matrix and eigendecomposition, and finally the filtered signal data is obtained; Pulsars and fast radio bursts are the main observation targets of the system of the present invention. They are point source signals. The basic principle of narrowband point source maximum signal-to-noise ratio beamforming is as follows: First, we introduce the calculation method of the maximum signal-to-noise ratio. For a point source, the signal-to-noise ratio achieved by the beam is in: and are the beam weight vector and its conjugate transpose, is the signal covariance matrix, is the noise covariance matrix.

[0030] If the source is known or assumed to be a point source, then is a rank The matrix of in is the weight value that satisfies the maximum signal-to-noise ratio beamforming, is a constant generated by the normalization condition in the derivation of the formula, yes The inverse matrix of is the array response steering vector in the direction of the source.

[0031] Figure 2 The figure shows a schematic diagram of the array element distribution of a planar array. The figure shows an array arranged in a three-dimensional coordinate system, in which a rectangular coordinate system is used to identify the positions of the array elements and their relative relationships. Each black dot represents an array element, and an array element is a receiver for receiving signals. and A single arrow on an axis points in the positive direction of that axis. and Respectively represent the azimuth and elevation angle of the point source wave signal. For each array element, the steering vector element is calculated , whose expression is: in, is the signal wavelength. represents the horizontal coordinate of the array element, represents the ordinate of the array element, and (are integers greater than or equal to zero) together represent the position coordinates of the array element, for example, and The coordinates of the black dot on the intersection of the two axes are (0,0), the coordinates of the black dot adjacent to it in the positive direction of the x-axis are (1,0), and the coordinates of the black dot adjacent to it in the positive direction of the y-axis are (0,1); the actual spacing of the array elements represented by the two black dots with coordinates (0,0) and (1,0) is The actual spacing of the array elements represented by the two black dots with coordinates (0,0) and (0,1) is . Calculate the Value composition . This way you can The expression of is used to calculate the weight value of the maximum signal-to-noise ratio beamforming.

[0032] During the radio observation process, the receiver continuously collects signal streams that change over time. By sampling the signal stream at fixed time intervals, a uniformly distributed discrete electrical signal is obtained to form signal data. In the digital terminal data stream targeted by the present invention, after the signal passes through the high-speed digital receiving unit, the channelization operation is completed, and the broadband signal is decomposed into multiple narrowband signals with different center frequencies. Each sub-channel corresponds to a narrowband signal, and its bandwidth satisfies the narrowband constraint conditions of beamforming. Then, the data of each channel is input into the GPU cluster. In the GPU cluster, the system will calculate the beamforming separately for each independent sub-channel, so that real-time beamforming of all baseband signals can be achieved simultaneously.

[0033] like Figure 3 The figure shows the principle of beamforming. The oblique solid line represents the observation source, which indicates the astronomical signal from the outer space and is the desired receiving object of the astronomical observation equipment. The oblique dotted line represents the interference source, which indicates the signal transmitted by artificial satellites, radio and television, aircraft and mobile communication base stations. The triangle represents the array element, that is, the receiver, which has random noise itself, such as the mutual coupling effect between array elements. Both of these are signals that the astronomical observation equipment does not want to receive. Each array element receives signals from the observation source and the interference source, and noise will be mixed in during the transmission process of the hardware system. These three together constitute the receiving signal of each array element. represents the weight factor corresponding to different receiver signals. The core principle of beamforming is summarized as the weighted sum of the received signals of each array element ( Figure 3 middle represents the summation. Due to the nature of matrix multiplication, this summation operation is implicit in the operation of S1.4. below). This "weight" is the weight factor, which can be constructed in many ways. The present invention adopts the maximum signal-to-noise ratio method. Calculate the beam synthesis to get the result . Figure 4 This is the process of beamforming. Specifically, for the array element, the maximum signal-to-noise ratio beamforming process in step 1 is: S1.1. Calculation of the array covariance matrix for passive observations ; in, and are the noise vector and its conjugate transpose respectively, and E[...] is the mean.

[0034] S1.2. Calculation of the array covariance matrix for active observations ; in, and are the signal vector and its conjugate transpose respectively.

[0035] S1.3. Use and Calculate the optimal set of weights; The signal covariance matrix and the noise covariance matrix Perform eigenvalue decomposition: in is the eigenvalue matrix, is the eigenvector matrix, and eig means calculating the eigenvalues ​​and eigenvectors of the matrix.

[0036] There is a one-to-one correspondence between eigenvalues ​​and eigenvectors. The size of the eigenvalue means the importance of the corresponding eigenvector in the signal space. Sort and extract the eigenvector corresponding to the maximum eigenvalue as the optimal weight vector , In practical applications, the beamforming weights are constantly calculated and updated.

[0037] S1.4. Using the weight vector for beam synthesis; Using the optimal weight vector and narrowband signal matrix (The matrix composed of all array element receiving signals) calculates the beam output, and its expression is: in, Represents the conjugate transpose of a matrix.

[0038] S1.5. Obtain beam synthesis output.

[0039] This is the result of beamforming, which reflects the strength of the signal in different directions. This is the formation of a single beam. By performing the above homogeneous operations in parallel on different array element combinations, multiple beams can be generated simultaneously. Finally, different beam data are stored separately as input based on RFI suppression calculation.

[0040] While beamforming occupies some processing units in the GPU cluster for calculation, another part of the processing units performs RFI suppression processing.

[0041] Among them, the data processing flow of interference reduction based on spatial filtering is as follows: Figure 5 As shown. For a large number of interference sources with unknown positions in space, it is necessary to adaptively estimate the spatial characteristics of the interference. The specific process of step 2 is as follows: S2.1. Data preprocessing. The data of each beam is stored in matrix form. The purpose of preprocessing these data is to ensure the accuracy of the data in a scientific sense. Preprocessing is to uniformly correct the elements in the signal matrix. The signal matrix of each beam after preprocessing is used as the input data for the next step of calculation to calculate the covariance matrix. Including S2.1.1- S2.1.3: S2.1.1 Calibration The data is calibrated to eliminate the differences in the telescope system over time and frequency. What the radio telescope actually measures is the power of the electrical signal recorded by the receiver, which is called raw data. Converting it into a meaningful physical quantity, temperature, is achieved using noise tube calibration.

[0042] S2.1.2 Bandpass removal Bandpass and baseline include the differences between different beams, for example, in on-source and off-source observation mode, the median is subtracted from the input data at fixed intervals to remove the bandpass.

[0043] S2.1.3 Baseline removal Cut the data into sub-bands by frequency, fit the baseline using methods such as second and third order polynomials, and select the better one using the chi-square test.

[0044] S2.2. Calculate the covariance matrix.

[0045] For each beam signal matrix after preprocessing, solve the covariance between beam signals and construct the covariance matrix , whose expression is: in, Indicates beam signal, It's expectation. yes The complex conjugate of . Each element in represents the dot product of different beam signal combinations. This matrix is ​​the key foundation of spatial filtering technology and contains the correlation characteristics of beam data in the spatial dimension, especially the characteristics of signal and interference.

[0046] S2.3. Feature decomposition, distinguishing the interference subspace from the target signal subspace; Since the covariance matrix The astronomical signal term, interference term, and receiver noise term in each element of are additively combined, so the covariance matrix can be divided into an astronomical signal matrix, an interference matrix, and a noise matrix, corresponding to , and In antenna array applications, the integration time of the covariance matrix should be short enough so that the celestial signal part can be ignored relative to interference and noise. Then: In view of the symmetry of the covariance matrix, the singular value decomposition method is used to decompose its eigenvalues, thereby decomposing it into the product of eigenvalues ​​and eigenvectors to extract spatial information. With the help of eigendecomposition technology, the interference subspace and the target signal subspace are identified and distinguished, laying the foundation for subsequent interference suppression.

[0047] S2.4. Interference subspace removal. Use the interference projection matrix to construct a filter to remove the interference subspace.

[0048] After obtaining the covariance matrix and eigendecomposition, the present invention uses the interference projection matrix to construct the filter. Assume is the known interference space structure, and the covariance matrix containing interference and noise becomes: in represents the variance of the noise, represents an identity matrix.

[0049] The projection matrix in the interference space is defined as: Here and represent the transposed matrix and the inverse matrix respectively.

[0050] Using the result of eigenvalue decomposition, through the projection matrix Remove the interference subspace. Specifically, calculate the projection matrix of the interference signal and remove the interference component from the covariance matrix to obtain the filtered covariance matrix that does not contain RFI: This can significantly reduce the impact of interference.

[0051] S2.5. Reconstruct data from the covariance matrix. Extract the diagonal elements from the covariance matrix after removing interference and reconstruct the original data after interference suppression. Reconstruct data from the covariance matrix.

[0052] From the covariance matrix after removing interference The diagonal elements are extracted from the signal, which represent the autocorrelation components of the signal, so that the original data after interference suppression can be reconstructed. The reconstructed data is then stored in a standard format for subsequent in-depth analysis and processing.

[0053] S2.6. Residual calculation and threshold setting. Calculate the residual between the spatially filtered data and the original input data, set the threshold, retain the data that meets the threshold limit and remove the data that does not meet the threshold limit.

[0054] Calculate the residual between the spatially filtered data and the original input data , and use the mean of the residuals With standard deviation Multiply by the set adjustment factor To set the threshold , which is expressed as: The data that meets the threshold limit is retained and the data that does not meet the threshold limit is eliminated, thereby effectively identifying and marking the remaining low-intensity interference areas.

[0055] S2.7. Spatial filtering result output The final spatial filtering result is saved in .npz format as the basic file for subsequent data analysis and processing. This file records the data after spatial filtering, achieving the suppression of interference signals and the enhancement of target signals.

[0056] The whole process realizes the identification and elimination of interference signals through the calculation of covariance matrix and eigendecomposition, and finally obtains filtered high-quality signal data. This method is suitable for signal extraction in large-scale arrays and complex interference.

[0057] The present invention improves the real-time processing strategy of beamforming and interference reduction. The present invention layers and overlaps tasks, that is, logically divides tasks according to the dependencies between them, executes tasks with dependencies in layers and in order, and overlaps tasks without dependencies, thereby achieving parallelization and improving processing speed. Figure 6 The figure shows the framework diagram of layered and overlapping processing, where the arrows pointing from the outside to the input layer represent the input data, which is the narrowband signal data of each channel, and the arrows pointing from the output layer to the outside represent the output data, which is the signal of each beam after interference elimination. Other arrows represent the flow relationship between data input and output within the framework. The specific implementation architecture is as follows: Input layer: The CPU is responsible for receiving the narrowband signal data of each channel and distributing it to the GPU node; Processing layer: In the processing layer, GPU nodes are mainly responsible for executing three types of core tasks. When executing for the first time, Task 1, Task 2, and Task 3 are started in sequence; thereafter, homogeneous operations between different tasks and within the same task are calculated in parallel. The processing layer adopts a pipeline architecture, starting from the data at a certain moment, including: Task 1: Calculate the covariance matrix of active and passive observations, and calculate the weight factors of different array element combinations; Task 2: Calculate the narrowband signal data of each channel to calculate the beamforming results; Task 3: Perform spatial filtering on the input data to suppress interference, and start the above operation at the next moment, where "next moment" only represents the logical order, corresponding to the output of beamforming being the input of interference reduction; And so on, a pipeline is formed in terms of timing. The CPU dynamically schedules each GPU to perform different tasks.

[0058] Decision-making layer: The CPU adjusts the weight factor in real time according to the processing results, and determines whether the maximum number of iterations has been reached or other termination conditions have been met.

[0059] Output layer: Generates interference-mitigated multi-beam signals and transmits them to the multi-science target terminal.

[0060] The technical advantages of the present invention are as follows: The present invention designs a signal processing strategy for a phased array computing terminal device with 224 array elements and 90×2 (including dual-polarization) real-time beams, including beam synthesis and interference suppression. These two signal processing operations are performed by Figure 1The "GPU cluster" in the system completes the calculation. For the phased array system, the amount of data processed by its digital back-end operation is directly proportional to the number of array elements, the number of beams and the instantaneous bandwidth of the signal. The phased array system targeted by the present invention has an array element and data scale that other existing astronomical phased array systems have not achieved. Therefore, the present invention designs a strategy that can cope with the requirements of data processing on this scale, and can also be migrated and applied to other similar systems. The advantage is that it can not only quickly process the received signal, but also continuously calculate the covariance matrix of the received signal, reflecting the dynamic changes of the propagation environment, so that the system can perform beam synthesis and interference reduction calculations in real time in complex and changeable scenarios.

[0061] The present invention proposes an efficient real-time signal processing architecture. On the basis of retaining the core of beamforming based on maximum signal-to-noise ratio and interference reduction algorithm based on spatial filtering, the specific tasks in the two algorithms are layered and overlapped to fully realize the parallelization of tasks and improve the data processing speed. In the architecture, the CPU is responsible for receiving and distributing narrowband signal data to GPU nodes. The GPU nodes rely on the pipeline architecture to execute three types of core tasks in parallel: one is to calculate the covariance matrix and its weight factor during active and passive observations; the second is to perform beamforming; the third is to implement spatial filtering to suppress interference and continuously start subsequent operations. The decision layer dynamically adjusts the weight factor by the CPU, and judges the processing iteration conditions, and finally generates multi-beam signal data after interference reduction. This framework makes full use of the dense parallel computing advantages of the GPU and the complex process control advantages of the CPU: the CPU is mainly responsible for complex logical operations such as process control, such as monitoring the data flow of the port and distributing it to different GPU cards for processing; the GPU is responsible for large batches of matrix operations in the algorithm process, such as solving the covariance matrix of the signal between array elements, matrix eigendecomposition and matrix multiplication. Efficient dynamic scheduling is achieved.

[0062] The broadband signal is divided into multiple sub-band channels, and a separate narrowband beamforming is calculated for each sub-band channel, thereby realizing real-time beamforming in the frequency domain for all baseband signals at the same time. This enables flexible selection and observation of frequency signals that match the target characteristics, while avoiding RFI in certain frequency channels.

[0063] Update beamforming weights based on dynamic changes in the propagation environment, and flexibly adjust beam shape and direction. In the process of propagating to ground receivers on Earth, astronomical signals from distant space will be affected by various factors, such as interference from passing aircraft and satellites, random noise from the hardware equipment itself, and changes in propagation channel conditions. These factors cause the amplitude and phase characteristics of the received signal to change randomly. Using the covariance matrix, the covariance of different received signals is analyzed, the statistical characteristics of these signals are quantified, and information about various components in the propagation environment is provided, thereby identifying and eliminating unnecessary signal components. By continuously calculating the covariance matrix, the system continuously updates the estimate of the propagation environment characteristics based on the new data received, and the dynamic update can capture the real-time changes in the propagation environment. For example, when the change in the short-term covariance matrix is ​​detected to be significantly higher than the long-term covariance matrix, it may be an environmental mutation caused by sudden interference such as passing aircraft. Such abnormal signals are considered interference and need to be subtracted from the signal data. The present invention realizes maximum signal-to-noise ratio beam synthesis and can generate 90×2 (including dual-polarization) beams simultaneously. These real-time synthesized beams can be directed to different targets or cover a large area, thereby improving the observation capability of the system and the adaptability to application scenarios.

[0064] The interference reduction algorithm based on spatial filtering is implemented, and the spatial filtering is realized by combining the subspace projection method. It has strong adaptability when identifying RFI, and at the same time ensures that the data processing efficiency is adapted to the requirements of real-time processing.

[0065] Any process or method described in the flowchart of the present invention or in other ways herein can be understood as a module, fragment or part of a code including one or more executable instructions for implementing the steps of a specific logical function or process, which can be implemented in any computer-readable medium for use by an instruction execution system, device or equipment, and the computer-readable medium can be any medium containing storage, communication, propagation or transmission programs for use by execution systems, devices or equipment, including read-only memories, magnetic disks or optical disks, etc.

[0066] In the description of this specification, the description with reference to the terms "embodiment", "example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. In addition, those skilled in the art can combine or combine different embodiments or examples described in this specification and the features therein without causing any contradiction.

[0067] Although the above contents have shown and described the embodiments of the present invention, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in the field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A phased array computing terminal real-time beamforming and interference reduction method, characterized in that: The method comprises the following steps: Step 1: The observation target collects observation signals, and for the channelized frequency domain baseband signals, a separate narrowband beamformer is calculated for each sub-band channel, thereby realizing real-time frequency domain beamforming for all baseband signals at the same time, updating the beamforming weights based on the dynamic changes of the propagation environment, adjusting the beam shape and direction, and realizing beamforming with maximum signal-to-noise ratio; Step 2: Use spatial filtering in the phased array computing terminal device to achieve effective interference reduction. Combine the subspace projection method to construct a spatial filter. Calculate the covariance matrix and feature decomposition to identify and eliminate interference signals, and finally obtain filtered signal data.

2. The real-time beamforming and interference reduction method for a phased array computing terminal according to claim 1, characterized in that: The phased array computing terminal device includes a phased array receiving unit, a low noise amplifier group, an ADC+FPGA module, a GPU cluster, a multi-scientific target terminal, a pulsar terminal, a fast radio burst terminal and a spectral line terminal.

3. The real-time beamforming and interference reduction method for a phased array computing terminal according to claim 2, characterized in that: The phased array system where the phased array computing terminal device is located is composed of 224 array elements, which will form 90×2 real-time beams.

4. The real-time beamforming and interference reduction method for a phased array computing terminal according to claim 2, characterized in that: In a GPU cluster, the intensive parallel computing advantages of the GPU and the complex process control advantages of the CPU are combined to achieve real-time data flow control and calculation.

5. The real-time beamforming and interference reduction method for a phased array computing terminal according to claim 1, characterized in that: The observation targets include pulsars and fast radio bursts, and the observation signals are point source signals.

6. The real-time beamforming and interference reduction method for a phased array computing terminal according to claim 5, characterized in that: The point source signal is processed by a high-speed digital receiving unit to complete the channelization operation, thereby obtaining a frequency domain baseband signal; the bandwidth of each channel will meet the narrowband constraint of beamforming; in the GPU cluster, the system calculates and configures the corresponding narrowband phase-shifted beamformer for each independent sub-band channel separately, realizing real-time frequency domain beamforming for all baseband signals at the same time.

7. The phased array computing terminal real-time beamforming and interference reduction method according to claim 6, characterized in that: Step 1: Use maximum signal-to-noise ratio beamforming. The specific process is as follows: S1.

1. Calculate the array covariance matrix for passive observation; S1.

2. Calculate the array covariance matrix for active observation; S1.

3. Calculate an optimal set of weights using the array covariance matrix during passive observation and the array covariance matrix during active observation; S1.

4. Using the weight vector for beam synthesis; calculating the beam output; S1.

5. Get the beamforming output and calculate the signal-to-noise ratio.

8. The real-time beamforming and interference reduction method for a phased array computing terminal according to claim 7, characterized in that: Array covariance matrix for passive observation ; in and are the noise vector and its conjugate transpose respectively, and E[...] is the mean.

9. The real-time beamforming and interference reduction method for a phased array computing terminal according to claim 5, characterized in that: Array covariance matrix for active observation ; in, and are the signal vector and its conjugate transpose respectively.

10. The real-time beamforming and interference reduction method for a phased array computing terminal according to claim 5, characterized in that: Using the optimal weight vector and narrowband signal matrix Calculate the beam output, the expression is: in, Represents the conjugate transpose of a matrix.

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