Multi-antenna anti-interference method and device, storage medium and program product

Abstract

The invention discloses a multi-antenna anti-interference method, multi-antenna anti-interference equipment, a storage medium and a program product, and aims to solve the contradiction between poor performance of a sub-array architecture and overhigh calculation complexity of a full-array architecture. The method comprises the following steps: carrying out global synchronization processing by taking all array elements as a whole, and determining a shared synchronization point; the array is divided into a plurality of sub-arrays, each sub-array uses the synchronization point and independently calculates a set of anti-interference weights, and accordingly, sub-array output signals subjected to interference suppression are generated; and determining a group of optimal merging coefficients through an optimization algorithm with the purpose of maximizing the final signal quality, and performing weighted merging on all sub-array output signals to generate a final output signal. In addition, the method also comprises a mechanism for periodically updating an anti-interference weight and a merging coefficient. According to the method, through step-by-step processing and optimal combination, the calculation complexity is effectively reduced, meanwhile, the performance loss of a traditional sub-array scheme is avoided, and the anti-interference performance of a system in a dynamic complex environment is improved.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a multi-antenna anti-interference method, device, storage medium, and program product. Background Technology

[0002] In applications such as satellite navigation (GNSS) and unmanned aerial vehicle (UAV) communication, array antenna technology is commonly used for spatial filtering to suppress interference signals in order to ensure the reliability of the system in complex electromagnetic environments.

[0003] Currently, related technologies employ a full-array processing architecture, which processes the received signals from all antenna elements uniformly. Theoretically, this method can achieve optimal anti-interference performance. However, the computational complexity of its core algorithm is proportional to the cube of the number of array elements (O(N³)), resulting in enormous hardware resources and power consumption. This makes it generally impractical for cost- and power-sensitive embedded receivers. To address the excessive computational load of full-array processing, subarray processing can be used. This method divides a large array into several small subarrays, performing anti-interference calculations independently on each subarray, thus reducing the overall computational complexity.

[0004] However, while subarray architecture reduces complexity, it also introduces the drawback of decreased anti-interference performance. On one hand, dividing the array inherently limits the theoretical performance ceiling the system can achieve. On the other hand, after each subarray completes processing, a simple direct coherent addition method is typically used to synthesize the final output signal. In real-world scenarios where interference is spatially unevenly distributed, this simple addition indiscriminately mixes the heavily interfered subarray output signal with the relatively pure signal, thus contaminating the final result and resulting in suboptimal anti-interference performance.

[0005] Therefore, there is a question of how to achieve better anti-interference performance with acceptable computational complexity in order to solve the problem of poor performance of subarray architecture. Summary of the Invention

[0006] This application provides a multi-antenna anti-interference method, device, storage medium, and program product for achieving better anti-interference performance with acceptable computational complexity.

[0007] In a first aspect, this application provides an anti-interference method for an array antenna, applied to an array antenna system comprising multiple array elements, which are divided into at least two sub-arrays for signal processing. The method includes: the array antenna system synchronizing all array elements as a whole to determine a globally shared synchronization point indicating the position of the pilot signal; for each of the multiple sub-arrays, the array antenna system extracting the received pilot signal from the received signal using the synchronization point; for each of the multiple sub-arrays, the array antenna system calculating an independent set of anti-interference weights based on the received pilot signal and a preset original pilot signal; applying the anti-interference weights to the received signal of the corresponding sub-array to generate an interference-suppressed sub-array output signal; the array antenna system determining a set of optimal combining coefficients for combining the output signals of all sub-arrays using an optimization algorithm, wherein the goal of the optimization algorithm is to maximize the signal quality of the final combined signal; and the array antenna system using the optimal combining coefficients to perform weighted combining of the sub-array output signals to generate the final output signal.

[0008] The method provided in the above embodiments first performs signal synchronization processing on all array elements as a whole, utilizing the complete array aperture to obtain higher initial signal processing gain, thereby improving the estimation accuracy and reliability of the global shared synchronization point. This improves the array gain loss and unstable synchronization reference caused by the initial segmentation of the aperture in sub-array schemes in related technologies. Based on this high-precision synchronization, the array is further divided into multiple sub-arrays and anti-interference weights are calculated independently. This decomposes the original one-time high-order (N-order) matrix inversion operation into multiple parallel low-order (K-order) matrix operations, thereby reducing the peak computational load of the system and the requirements for hardware resources. This improves the problem that the full array scheme in related technologies is difficult to deploy on cost- and power-constrained devices due to its high computational complexity (O(N³)). Furthermore, this application does not use simple direct addition, but instead uses an optimization algorithm to determine the optimal merging coefficients for weighted merging. This weighting mechanism can dynamically adjust the contribution ratio of each signal in the final synthesized signal based on the real-time quality of each signal, thereby suppressing the pollution of the overall result by severely interfered paths. Compared with the shortcomings of subarray schemes in related technologies that limit performance due to indiscriminate merging, this application improves the signal-to-interference-plus-noise ratio of the final output signal.

[0009] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes cyclically performing the following steps at a preset update period to dynamically adapt to changes in the channel environment: During the update period, based on newly received signals, the array antenna system re-estimates a signal covariance matrix for each subarray; using the original pilot signals obtained during the update period, the array antenna system redetermines a steering vector for a desired signal for each subarray; based on the re-estimated signal covariance matrix and the redetermined steering vector, the array antenna system recalculates an independent set of anti-interference weights for each subarray. After the anti-interference weights for each subarray are updated, the optimal combining coefficients are updated by executing an optimization algorithm.

[0010] The above embodiments further introduce a mechanism for cyclically updating parameters at a preset period. By re-estimating the signal covariance matrix and desired signal steering vector of each subarray using the newly received signal in each update period, these two core parameters for calculating anti-interference performance can characterize the statistical properties of the current signal and interference environment in real time. Based on these real-time updated parameters, the anti-interference weights and optimal combining coefficients of each subarray are recalculated, allowing the interference suppression strategy of the entire array antenna system to adaptively adjust accordingly. Compared with related technologies that use fixed weights or coefficients, this dynamic update process does not lead to a decrease in anti-interference performance due to parameter mismatch when facing time-varying electromagnetic environments such as moving interference sources and changes in signal channels. This improves the robustness and environmental adaptability of the entire method in complex dynamic scenarios.

[0011] In conjunction with some embodiments of the first aspect, in some embodiments, the optimization algorithm specifically includes: defining an error signal, which is the difference between the desired signal and the subarray output signal; using the mean square error of the error signal as the optimization objective to be minimized; solving for the weighted merging coefficients that minimize the mean square error by adjusting the weighted merging coefficients, and determining the value of the corresponding weighted merging coefficients as the optimal merging coefficients.

[0012] In the above embodiments, the optimal merging coefficients are solved by minimizing the mean square error between the desired signal and the actual merged output signal. This provides a clear and convergent mathematical criterion for the weighted merging process, ensuring that the obtained merging coefficients guarantee that the final synthesized signal is statistically closest to the desired signal. This approach improves the fidelity and accuracy of the final output signal.

[0013] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: the array antenna system further optimizing the optimal merging coefficients using a gradient optimization algorithm; the gradient optimization algorithm involves repeating the following operations until the power of the error signal meets a preset convergence condition to obtain the final optimal merging coefficients and initializing the optimal merging coefficients; weighting and merging all subarray output signals according to the current optimal merging coefficients to generate a temporary merged signal; comparing the merged signal with the original pilot signal to determine an error signal; calculating a gradient value of the optimal merging coefficients based on the error signal and all subarray output signals; and updating the optimal merging coefficients according to the gradient value.

[0014] The above embodiments further refine the process of finding the optimal merging coefficients, explicitly employing a gradient optimization algorithm to determine their values ​​through iterative approximation. This method transforms a complex optimization problem into a series of repetitive, low-complexity computational steps: calculating the gradient based on the current error and fine-tuning the merging coefficients along the gradient direction. This iterative optimization technique avoids the complex matrix inversions or high-dimensional analytical operations that might be involved in finding the optimal value, thus reducing the algorithm's hardware computing power requirements. Compared to schemes that require performing high-complexity analytical operations in one go, this gradient optimization method improves the algorithm's engineering feasibility and computational efficiency while ensuring that the final merging coefficients converge to the optimal solution. It is particularly suitable for dynamic application scenarios that require real-time, rapid updates of the merging coefficients in hardware.

[0015] In conjunction with some embodiments of the first aspect, in some embodiments, the array antenna system calculates an independent set of anti-interference weights for each of the multiple subarrays based on the received pilot signal and a preset original pilot signal. Specifically, this includes: the array antenna system determining the signal covariance matrix corresponding to each subarray based on the signal received by each subarray; wherein the signal covariance matrix is ​​obtained by taking the expected value of the product of the signal vector received by the corresponding subarray and its own conjugate transpose; the array antenna system determining the steering vector of the expected signal in the corresponding subarray based on the received pilot signal and the preset original pilot signal; the array antenna system multiplying the inverse matrix of the signal covariance matrix corresponding to each subarray with the steering vector to obtain a first intermediate result corresponding to each subarray; the array antenna system multiplying the conjugate transpose of the steering vector corresponding to each subarray with the first intermediate result to obtain a second intermediate result corresponding to each subarray; and the array antenna system dividing the first intermediate result corresponding to each subarray by the corresponding second intermediate result to obtain an independent anti-interference weight for each subarray.

[0016] The above embodiments further specify the generation process of the anti-interference weights for each subarray as a series of explicit mathematical operations. First, a covariance matrix characterizing the overall statistical properties of the signal, interference, and noise is constructed using the received signal. Then, the steering vector of the desired signal is determined by combining the pilot signal. Finally, the calculation is performed by multiplying the inverse of the covariance matrix with the steering vector. This series of operations essentially constitutes an implementation of the Minimum Variance Distortionless Response (MVDR) criterion. The generated anti-interference weights minimize the overall output power of the subarray while ensuring that the directional gain of the desired signal remains unchanged (i.e., without distortion). Since the overall output power consists of the desired signal, interference, and noise, under the condition that the desired signal power is fixed, this method is equivalent to suppressing the combined power of interference and noise to a minimum. This improves upon the problem in some related anti-interference methods that, while suppressing interference, may also cause attenuation or phase distortion of the desired signal itself, thus enhancing the extraction capability and fidelity of the desired signal in complex interference environments.

[0017] In conjunction with some embodiments of the first aspect, in some embodiments, the array antenna system applies an anti-interference weight to the signal received by the corresponding subarray for each of the plurality of subarrays to generate an interference-suppressed subarray output signal. Specifically, the array antenna system multiplies the conjugate transpose of the anti-interference weight corresponding to each subarray with the received signal vector to obtain the interference-suppressed subarray output signal.

[0018] The above embodiment combines multiple signals into a single, interference-suppressed output by multiplying the conjugate transpose of the anti-interference weights with the signal vector received by the subarray. This vector multiplication operation has a regular structure and is easily and efficiently implemented on a digital signal processor (DSP) or FPGA, thereby reducing the performance requirements and power consumption of the processing unit. Furthermore, since this operation is executed independently and in parallel on each subarray, a distributed processing architecture is naturally formed. Finally, this distributed architecture not only improves signal throughput through parallel processing but also enhances system scalability. When an antenna needs to be added, only the corresponding subarray processing module needs to be added, avoiding disruptive modifications to the core processing unit and reducing the maintenance cost throughout the system's lifecycle.

[0019] In conjunction with some embodiments of the first aspect, in some embodiments, the array antenna system uses the optimal combining coefficient to weight and combine the subarray output signals to generate the final output signal. Specifically, this includes: the array antenna system multiplying each subarray output signal by the corresponding optimal combining coefficient to obtain the weighted subarray signal corresponding to each subarray; and the array antenna system summing up the weighted subarray signals corresponding to all subarrays to generate the final output signal.

[0020] The above embodiment utilizes a weighted summation based on optimal combining coefficients determined according to signal quality. This mechanism, by assigning higher weights to subarray signals with higher signal-to-noise ratios (SNR), achieves secondary purification during signal combining, thereby further improving the SNR of the final output signal. Simultaneously, the "multiplication and accumulation" form used in this combining operation has low computational overhead and low latency, ensuring high efficiency throughout the entire processing chain. This scheme, combined with the interference suppression stage within the subarray, constitutes a cascaded processing mode of "first local interference suppression, then global optimization." This mode decomposes and executes complex optimization problems, enabling the system to achieve higher quality output while maintaining the simplicity of the processing architecture.

[0021] In a second aspect, embodiments of this application provide an array antenna device, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions, and the one or more processors call the computer instructions to cause the array antenna device to perform the method described in the first aspect and any possible implementation thereof.

[0022] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on an array antenna device, cause the array antenna device to perform the method described in the first aspect and any possible implementation thereof.

[0023] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on an array antenna device, cause the array antenna device to perform the method described in the first aspect and any possible implementation thereof.

[0024] It is understood that the array antenna device provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0025] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. This application reconciles the inherent physical contradiction between "coherent merging of the entire aperture for gain" (merging) and "spatial diversity of subarrays for robust interference resistance" (dispersion) in array antennas by designing a dynamic, phased processing architecture. This architecture intelligently applies the most suitable strategy at different stages of the signal processing flow: in the initial synchronization stage, a "merging" strategy is adopted, utilizing the complete array aperture to accurately lock onto weak signals with the highest gain; in the interference suppression stage, a "dispersion" strategy is adopted, decomposing the array into multiple subarrays for parallel processing, flexibly and robustly addressing complex interference with uneven spatial distribution through a "divide and conquer" approach. This dynamic switching and step-by-step execution mode overcomes the performance bottleneck of existing static architectures that must make static trade-offs between "gain" and "robustness."

[0026] 2. This application achieves optimal integration of processing results from various channels by introducing intelligent merging based on an optimization algorithm. After each subarray completes local interference suppression, this application abandons the simple direct addition method used in related subarray schemes. Instead, it uses an optimization algorithm aimed at maximizing the final signal quality to determine a set of optimal merging coefficients for weighted merging. This intelligent merging dynamically assigns weights based on the real-time quality of the output signals of each subarray, fundamentally avoiding the indiscriminate mixing of heavily contaminated signals into the final result. This achieves a merging gain of "1+1>2," significantly improving the signal-to-interference-plus-noise ratio (SNR) of the final output signal.

[0027] 3. This application decomposes high-order operations into parallel low-order operations and employs an efficient iterative algorithm, ensuring low computational complexity and high environmental adaptability in engineering applications. This two-level processing architecture decomposes the original one-time high-order (N³) matrix inversion into multiple parallel low-order operations, significantly reducing the peak demand on hardware computing power and power consumption. Simultaneously, the periodic parameter update and gradient iterative optimization mechanism ensures that the system can adapt to dynamically changing environments in real time. This allows this architecture, which balances "gain" and "robustness," to be effectively deployed and applied on cost- and power-sensitive embedded platforms. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating an anti-interference method for an array antenna in an embodiment of this application. Figure 2 This is a schematic diagram of the subarray division of eight array elements in an embodiment of this application; Figure 3 This is a flowchart illustrating the adaptive update method in an embodiment of this application; Figure 4 This is a flowchart illustrating the method for establishing the final merging coefficients in an embodiment of this application; Figure 5This is a schematic diagram of the physical device structure of an array antenna device in the embodiments of this application. Detailed Implementation

[0029] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0030] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0031] To facilitate understanding, the application scenarios of the embodiments of this application are described below.

[0032] The technical solutions of this application are mainly applied to wireless systems that use array antennas for communication or navigation, such as GNSS receivers, UAV data links, and radar systems. These applications share the common characteristic of requiring high-reliability signal reception in complex electromagnetic environments. In this technical field, spatial filtering techniques are commonly used to suppress interference signals. Existing technologies mainly employ two mainstream architectures: one is a full-array processing architecture, which performs unified processing of the received signals from all antenna elements; the other is a subarray processing architecture derived to reduce complexity, which divides a large array into several small subarrays that independently perform anti-interference calculations.

[0033] However, both of the aforementioned existing technical architectures have significant shortcomings. While full-array processing theoretically offers the best performance, the computational complexity of its core algorithm is proportional to the cube of the number of array elements (O(N³)), resulting in substantial hardware resource overhead and power consumption. Subarray architecture, while reducing computational load, sacrifices interference immunity.

[0034] To address the aforementioned problems, this application provides an innovative multi-antenna anti-jamming method based on diversity spatial notch filtering. This method does not simply trade off between the full array and subarrays, but rather cleverly combines the advantages of both.

[0035] For ease of understanding, the method provided in this implementation is described in process below. Please refer to [link / reference]. Figure 1This is a flowchart illustrating an anti-interference method for an array antenna in an embodiment of this application.

[0036] S101. The array antenna system performs signal synchronization processing on all array elements as a whole, and determines a globally shared synchronization point that indicates the position of the pilot signal.

[0037] An array antenna system is an integrated receiving system composed of multiple independent antenna elements (i.e., array elements) arranged in a specific geometric structure. It is used for the joint processing of signals received by each array element, achieving spatial filtering, beamforming, and signal gain enhancement. For example, a circular array containing eight antenna elements is a typical array antenna system. Signal synchronization processing refers to a series of operations performed to determine the precise start time of a specific data frame or symbol in the received signal. This is used to align the received signal stream, whose time reference is unknown, with a locally known reference template, providing a precise time reference for subsequent demodulation and decoding. The synchronization point is the result of the operation—a time reference point. As a high-precision "time stamp," the synchronization point indicates the exact position of a key signal (such as a pilot) in the data stream and is shared by all subsequent processing units (such as various subarrays) within the system. For example, in a digitized signal sequence, determining the 1st, 483rd, 520th sampling point as the start point of a navigation message subframe uses this index number as the globally shared synchronization point. A pilot signal is a specific sequence with a known structure and content pre-embedded in the transmitted signal by the transmitting end. It does not carry user data; its function is to act as a "lighthouse" for the receiver, assisting the receiver in completing key tasks such as carrier synchronization, channel estimation, and time synchronization.

[0038] The above steps are carried out after the array antenna system is powered on and starts receiving signals, which is the starting link of the entire signal processing chain. Specifically, in this step, a "combining" strategy is first adopted. In terms of the operation process, the system does not process the signals of each sub-array separately, but first coherently combines the digital signals received by all N array elements once. By adding the N signals in phase, the power of the desired pilot signal can be enhanced by N² times (theoretical value), while the power of the uncorrelated noise of each path only increases by N times, so that the signal-to-noise ratio of the combined signal can be increased by up to N times. Based on this significantly enhanced high signal-to-noise ratio signal, the system then executes the correlation peak search algorithm, that is, slides it with the original pilot signal stored locally to find the peak of the correlation value. Due to the more concentrated signal energy, this correlation peak will become extremely sharp and prominent, and is very easy to distinguish from the noise floor. The moment corresponding to this peak is determined as the globally shared synchronization point. This step obtains a more accurate and robust time reference than the synchronization point obtained by independent synchronization on a single sub-array (the number of array elements k < N). By fully utilizing the complete array aperture at the initial stage of the processing flow, it provides a solid and unified "starting line" for the parallel processing of all subsequent sub-arrays, reduces the performance loss caused by inconsistent synchronization errors of each sub-array, and improves the capture ability and reliability of the entire system in a weak signal environment.

[0039] S102. For each of the multiple sub-arrays in the array antenna system, using the synchronization point, extract the received pilot signal from the signals received by each.

[0040] Among them, a sub-array refers to several smaller array element groups divided from the entire antenna array at the signal processing level, providing independent computing units for subsequent parallel anti-interference processing. In this application, an array antenna with N array elements is logically divided into M (M ≥ 2) sub-arrays. For example, an 8-element antenna can be divided into two sub-arrays each containing 4 array elements. Physically, this design follows two core criteria: First, the array elements within each sub-array are arranged at equal intervals; Second, the center-to-center distance between adjacent sub-arrays is set to be greater than half of the working signal wavelength (λ / 2), and reference can be made to Figure 2 , which is a schematic diagram of the division of an 8-element sub-array. The equal-spacing design within the sub-array ensures the regularity of the signal steering vector and simplifies the algorithm complexity of calculating the anti-interference weight value at the sub-array level. More importantly, it is the sub-array spacing greater than half a wavelength, which actively utilizes the spatial decorrelation effect to ensure that each sub-array can obtain interference signals that are statistically uncorrelated with other sub-arrays. This input difference guaranteed by the physical layout is the physical premise for each sub-array to generate different anti-interference weight vectors, and it provides the diverse input basis necessary for the subsequent global optimal combining algorithm to achieve excellent anti-interference performance.

[0041] This step, executed immediately after global synchronization and before each subarray independently calculates its anti-interference weights, is a crucial step in data distribution and preparation, reflecting the beginning of the "separation" phase in this application's "merge first, then separate" strategy. Operationally, the system controller broadcasts the unique synchronization point value obtained in S101 (e.g., sample index number n_peak) to all parallel subarray processing units. Each subarray processing unit then extracts a data segment of length L (where L is the known pilot signal length) from its own independent received signal data buffer, starting from n_peak. Logically, although all subarrays use the same starting point, because their data sources are different original received signals, the resulting "received pilot signal" vector composed of the L extracted samples is unique to each subarray. This unique version more accurately reflects the unique channel response (phase rotation and amplitude attenuation) experienced by the desired signal upon reaching that specific subarray, as well as the specific interference and noise superimposed on the subarray's spatial region. This step generates a customized "exam paper" (received pilot signal) for each subarray. The difference between this exam paper and the "standard answer" (original pilot signal) contains all the information needed to solve the interference problem faced by that subarray. This ensures that the subsequent anti-interference weight calculation (S103) is based on the most realistic local signal environment of each subarray, rather than a fuzzy global average, thus laying the data foundation for achieving accurate and efficient "divide and conquer" interference suppression.

[0042] It should be noted that this application can also achieve precise extraction of the received pilot signals of each subarray using synchronization points in various ways. For example, optionally, a block copying method based on direct memory address offset can be used. The system allocates an independent circular data buffer for each of the M subarrays to store the signal sample streams received and digitized by their respective array elements in real time. Once the global synchronization point n_peak is determined, this value is regarded as a base address offset. Then, a master process or a dedicated DMA (direct memory access) controller is triggered, which performs M memory block copying operations in parallel. For the i-th subarray (i=1 to M), the controller calculates the starting memory address of its received pilot as Buffer_Base_Addr_i + (n_peak%Buffer_Size_i) * Sample_Size. Then, starting from this starting address, L samples (a total of L * Sample_Size bytes) are copied continuously to the "received pilot storage area" pre-allocated for the i-th subarray. This process is performed simultaneously on all subarrays, making great use of the data parallel processing capabilities of modern processors to accurately extract all pilot signals with minimal CPU intervention and extremely high speed. Understandably, other methods can also be used to extract pilot signals from each subarray signal using global synchronization points; this is not limited here.

[0043] S103. For each of the multiple subarrays, the array antenna system calculates an independent set of anti-interference weights based on the received pilot signal and the preset original pilot signal.

[0044] Among them, the anti-interference weights represent a set of complex weighted coefficient vectors specifically for a certain subarray. By performing specific weighted summation on the signals received by each array element in the subarray, a gain beam pointing towards the desired signal is formed in the spatial dimension, while simultaneously forming nulls in the direction of the interference signal, thereby achieving spatial filtering and suppressing interference while enhancing the desired signal.

[0045] This step provides diverse and high-quality inputs for subsequent steps (such as S104) to generate a globally optimal anti-interference solution. The core task of this step is to independently perform a "correction" calculation for each of the M subarrays. Specifically, this involves using a "preset original pilot signal" as a reference: this signal represents the ideal "correct answer" without any interference or noise; and using the "received pilot signal of the subarray" as the object: this signal is the "actual answer" mixed with unique interference and noise from the spatial location of the subarray. The core calculation logic is to find and solve for an anti-interference weight vector using an algorithm (such as the minimum mean square error criterion). The role of this vector is to maximize the elimination of interference and noise when applied to this "actual answer" (received pilot signal), making its processing result mathematically infinitely close to the "correct answer" (preset original pilot signal). Since the interference received by each subarray (errors on the "actual answer") is different, the "error correction scheme" (anti-interference weight vector) tailored for each subarray will inevitably be different. The system thus obtains a set of M distinct local optima. Each anti-interference weight vector encodes an optimal spatial filtering strategy for a specific local interference environment. This set, containing multiple high-quality and significantly differentiated anti-interference weight vectors, provides an indispensable and rich foundation for subsequent global optimization or fusion steps, and is an important part of this application's achievement of improved anti-interference performance.

[0046] In some embodiments, the step of calculating an independent set of anti-interference weights specifically includes: the array antenna system determining the signal covariance matrix corresponding to each subarray based on the signal received by each subarray; wherein, the signal covariance matrix is ​​obtained by taking the expected value of the product of the signal vector received by the corresponding subarray and its own conjugate transpose; the array antenna system determining the steering vector of the expected signal in the corresponding subarray based on the received pilot signal and the preset original pilot signal; the array antenna system multiplying the inverse matrix of the signal covariance matrix corresponding to each subarray with the steering vector to obtain a first intermediate result corresponding to each subarray; the array antenna system multiplying the conjugate transpose of the steering vector corresponding to each subarray with the first intermediate result to obtain a second intermediate result corresponding to each subarray; the array antenna system dividing the first intermediate result corresponding to each subarray by the corresponding second intermediate result to obtain an independent anti-interference weight corresponding to each subarray.

[0047] The calculation steps in the above embodiments can be expressed by the following formula: ; Here, the signal covariance matrix (R) is represented as a mathematical matrix, which is obtained by taking the signal vector (y) received by a specific subarray and its conjugate transpose (R). The product of ) is obtained by taking the expected value, i.e. This matrix, in a physical sense, comprehensively describes the power, source direction, and correlation of all signals (including the desired signal, various interfering signals, and background noise) in the electromagnetic environment of the subarray; it is a "statistical snapshot" of the local signal environment. The steering vector (h) is a complex vector that precisely describes the phase relationship when the desired signal arrives at each antenna element of the subarray at a specific angle. It is equivalent to the "spatial fingerprint" of the desired signal on the subarray, used to "inform" the algorithm of the target signal characteristics to be protected and enhanced. First intermediate result ( The vector is obtained by multiplying the inverse of the signal covariance matrix by the steering vector. It is used to represent the "whitening" of the steering vector of the desired signal, after considering all interference and noise statistical characteristics, thus initially forming a filtering direction that suppresses interference and aligns with the desired signal. The second intermediate result ( The value is a scalar obtained by multiplying the conjugate transpose of the steering vector by the first intermediate result. It represents the power response in the direction of the desired signal after the initial filtering described above. The anti-interference weight (w) is a set of complex numbers that are calculated and applied to the received signal of each antenna element in the subarray. It is used to perform a weighted summation of the signals. Its core function is to adjust the amplitude and phase of the received signal of each antenna element to achieve beam alignment enhancement of the desired signal, while forming nulls in the direction of the interference signal, thereby maximizing the output signal-to-interference-plus-noise ratio.

[0048] Specifically, the calculation process in this embodiment is a rigorous optimization solution process based on the MVDR criterion. The system first obtains the signal vector y containing all spatial information received by a specific subarray over a period of time. Based on the known direction information of the desired signal, the theoretical response mode of the desired signal on the subarray, i.e., the steering vector h, is determined. Next, the algorithm performs the crucial matrix inversion operation to obtain R⁻¹, which is equivalent to deconstructing the statistical structure of interference and noise. Then, R⁻¹ is multiplied by h to obtain the first intermediate result. The physical meaning of this operation is to find the optimal direction that preserves the desired signal h in the background of "whitened" interference. To ensure that the gain for the desired signal is 1 (i.e., a distortion-free response), the algorithm further calculates the second intermediate result, which is a normalization factor. Finally, dividing the first intermediate result by the second intermediate result yields the final anti-interference weight w. The generated weights w guide the subarray to adaptively form extremely deep nulls in the directions of arrival of various interfering signals while maintaining the desired signal direction gain. This achieves maximum suppression of local interference by minimizing the total output power (variance). This step does not seek a globally uniform set of weights, but rather performs an independent, complete MVDR optimal solution calculation for each subarray based on its own actual received data. This generates multiple distinct local optimal solutions for the system. This diversity of solutions is the cornerstone for achieving breakthrough anti-interference effects in subsequent steps.

[0049] It should be noted that this application can also achieve the purpose of calculating independent anti-interference weights in various ways. For example, optionally, the Least Mean Square (LMS) adaptive algorithm can be used. This method does not require direct calculation and inverse covariance matrix calculation, but approximates the optimal weights through iteration. First, the anti-interference weight vector w(n) of the subarray is initialized to a zero vector. Then, at each sampling time n, the output signal z(n) of the array is calculated using the current weight w(n) and the received signal vector y(n). The output signal is compared with the preset original pilot signal (desired signal) d(n) to obtain the error signal e(n) = d(n) - z(n). Finally, based on this error signal, the weight vector is iteratively updated according to the update rule w(n+1) = w(n) + μ*y(n)*e*(n), where μ is the step size factor that determines the convergence speed and stability. By repeatedly performing this process, the weight vector w will be automatically adjusted until the mean square value of the error e(n) reaches its minimum. This w is the desired anti-interference weight. This method has low computational complexity, is easy to implement in hardware, and is especially suitable for dynamically changing interference environments. It is understood that other methods can also be used to obtain independent anti-interference weights, such as the sampling matrix inversion (SMI) method, etc., which are not limited here.

[0050] S104. For each of the multiple subarrays, the array antenna system applies anti-interference weights to the signals received by the corresponding subarray to generate the subarray output signal after interference suppression.

[0051] This step, executed immediately after weight generation, continues throughout the entire communication process, continuously processing the received data stream. The system applies customized anti-interference weights for each subarray to the raw, real-time received signal, performing spatial filtering through a precise weighted summation operation. This step ultimately yields multiple high-quality output signals (each corresponding to a subarray) that have undergone interference suppression. This signifies that the system has successfully transformed the noisy raw signal into multiple "clean" candidate signals, each representing the optimal solution obtainable from the local perspective of its corresponding subarray. This high-quality and differentiated set of optimal solutions provides an unprecedented, crucial, and rich data foundation for subsequently achieving globally optimal performance.

[0052] In some embodiments, the array antenna system multiplies the conjugate transpose of the anti-interference weights corresponding to each subarray with the received signal vector to obtain the subarray output signal after interference suppression.

[0053] Specifically, the steps in the above embodiments can be represented as follows: Taking a subarray with M antenna elements as an example, its weight vector is: At a certain moment, the signal vector received by n is Therefore, the weight vector after the conjugate transpose is a row vector. The asterisk (*) represents the conjugate. The multiplication operation expands to: For signal components from the desired signal direction, the design of the weight w ensures that the phases of each signal are aligned during addition, achieving coherent accumulation and thus strengthening the energy of the desired signal. For signal components from the interference direction, the weight w introduces a specific phase shift, causing the phases of each interference signal to be opposite during addition, achieving incoherent cancellation and thus forming a null in the interference direction.

[0054] It should be noted that this application can also achieve the purpose of multiplying the conjugate transpose of the weights with the signal vector in various ways. For example, optionally, it can be implemented using a fully hardware pipeline based on an Application-Specific Integrated Circuit (ASIC). In this way, the entire inner product operation is embedded in the physical circuit on the chip. The M complex data streams of the signal vector y and the M complex coefficients of the weight vector w are fed in parallel into M specially designed "Complex Multiply-Accumulate" (ComplexMAC) units. Next, each MAC unit, internally, first inverts the imaginary part of the input weights to obtain their conjugate, then uses four built-in real multipliers and two adders / subtractors to perform complex multiplication, and finally feeds the result into an accumulator. Then, the outputs of these M MAC units are efficiently aggregated through a hardware structure called an "addition tree." This addition tree consists of multi-level adders and can accumulate the M data streams into the final scalar result z within a few clock cycles. The entire process resembles a factory assembly line, with data flowing in and results flowing out continuously. Processing latency is compressed to the nanosecond level, meeting the most demanding real-time communication and radar processing requirements. Understandably, this multiplication operation can also be implemented in other ways, such as by directly performing weighted summation in the radio frequency or intermediate frequency domain using phase shifters and synthesizer networks in analog circuits; this is not a limitation here.

[0055] S105. The array antenna system uses an optimization algorithm to determine a set of optimal combining coefficients for combining the output signals of all subarrays. The goal of the optimization algorithm is to maximize the signal quality of the final combined signal.

[0056] The optimal merging coefficients are represented by a complex vector with a dimension equal to the number of subarrays. Each element in the vector is a complex weight used to adjust the amplitude and phase of the output signal of the k-th subarray. This step is crucial for optimizing the global performance of the entire array antenna system. It takes the multiple signals output from all subarrays after local anti-interference as input, and uses an optimization algorithm to calculate a set of precise, complex-form optimal merging coefficients with the sole objective of maximizing the signal quality of the final merged signal. Technically, this set of coefficients represents the best weighting scheme for the final fusion of the output signals from each subarray. It assigns an amplitude and phase adjustment value to each subarray output signal, ensuring that when they are merged into a single signal stream, the desired signal achieves maximum coherent accumulation, while residual interference and noise are maximally incoherently canceled, thus maximizing the theoretically maximum signal-to-noise ratio (SNR) of the final output signal.

[0057] S106. The array antenna system uses the optimal combining coefficient to weight and combine the output signals of the subarrays to generate the final output signal.

[0058] In some embodiments, the array antenna system multiplies the output signal of each subarray with the corresponding optimal combining coefficient to obtain the weighted subarray signal corresponding to each subarray; the array antenna system sums up the weighted subarray signals corresponding to all subarrays to generate the final output signal.

[0059] The above embodiment is the final step before signal output. Specifically, for the output signal of the k-th subarray and its corresponding optimal combining coefficient, the system performs multiplication to obtain a weighted signal, and then accumulates all weighted signals. By adjusting the amplitude and phase of the coefficients, the desired signal components in each signal are enhanced in phase during addition, while interference and noise cancel each other out due to phase disorder, thereby improving the signal-to-interference-plus-noise ratio of the final output signal.

[0060] The above embodiments illustrate the static process of a single execution of this method. However, in dynamically changing environments such as moving interference sources, fixed anti-interference parameters will gradually become ineffective. To address this issue, this application introduces a periodic adaptive update mechanism to ensure continuous optimal anti-interference performance by refreshing key parameters in real time.

[0061] The following provides a more detailed description of the process of the method provided in this implementation. Please refer to [link / reference]. Figure 3 This is a flowchart illustrating the adaptive update method in an embodiment of this application.

[0062] S301. During the update cycle, based on the newly received signal, the array antenna system re-estimates a signal covariance matrix for each subarray. This step, executed at the beginning of each update cycle, is the first action to process a new batch of data, aiming to provide the latest input for subsequent adaptive calculations. Specifically, when the set update cycle (e.g., 10 milliseconds) ends, the system marks all N signal samples x(t) collected within that cycle as a "new sample block." It then discards or overwrites the previously calculated and used old covariance matrix, and uses only this "new sample block" to calculate a completely new covariance matrix. This step keeps the system's understanding synchronized with the rapidly changing real-world environment. If an interference source moves between two cycles, the old matrix will lead to incorrect interference suppression. This step, through forced re-estimation, ensures that the newly calculated covariance matrix accurately reflects the new location information of the interference source, thereby improving the continued effectiveness of subsequent anti-interference processing (such as S302 and subsequent steps) and mitigating the problem of drastic performance degradation of static solutions in dynamic scenarios.

[0063] S302. Using the original pilot signal obtained during the update cycle, the array antenna system redetermines a steering vector of the desired signal for each subarray. S303. Based on the re-estimated signal covariance matrix and the re-determined steering vector, the array antenna system recalculates an independent set of anti-interference weights for each subarray.

[0064] S304. After updating the anti-interference weights of each subarray, the optimal merging coefficients are updated by executing an optimization algorithm.

[0065] The above three steps form a tightly linked calculation process, constituting the core execution link of the "dynamic adaptive" loop in this embodiment. First, S302 uses the pilot sequence in the newly received raw data to measure a new steering vector for each subarray individually. This ensures that the system is always accurately aligned with the current position of the desired signal. S303 takes the new covariance matrix obtained in S301 and the new steering vector obtained in S302 as inputs, and for each subarray, calculates a completely new set of anti-interference weights w_new by solving a constrained optimization problem. This refreshes the interference suppression capability of each subarray to the optimal state for the current interference environment. Finally, since w_new for all subarrays has been updated, the signal-to-interference-plus-noise ratio and other characteristics of their output signals have changed. Therefore, S304 must rerun the top-level optimization algorithm based on the new output states of these subarrays to obtain a new set of optimal merging coefficients. This entire cascaded update ensures the system's global optimality in the face of environmental changes. Through forced, chain-like recalculation, all key parameters in the entire signal processing chain can be refreshed synchronously, all pointing to the same optimization goal—maximizing the output signal-to-interference-plus-noise ratio at the current moment.

[0066] The above embodiments establish a framework for periodic updates, the core of which lies in updating the merging coefficients in real time through an optimization algorithm. To elaborate on this optimization process, the next embodiment will define in detail the mathematical criteria of the optimization algorithm, i.e., how to solve for the optimal merging coefficients.

[0067] The following is a more detailed description of the process. Please refer to [link / reference]. Figure 4 This is a flowchart illustrating the method for establishing the final merging coefficients in an embodiment of this application.

[0068] S401. Define an error signal, which is the difference between the desired signal and the subarray output signal; Among them, the error signal is the core feedback quantity driving the entire optimization algorithm. Mathematically, it represents the instantaneous difference between the "actual merging result" and the "ideal expected target". The expected signal refers to the original pilot signal stored locally by the system and generated without any distortion. It serves as the unique and absolutely correct "reference benchmark" in the algorithm.

[0069] Specifically, the system aligns the current temporary merged signal with the ideal desired signal (original pilot) in time and performs a subtraction operation, which generates an error signal. The energy (power) of this error signal is proportional to the "error level" of the current merged coefficients. This step provides an indispensable feedback signal for the entire closed-loop adaptive algorithm, acting like a sensor to monitor the deviation between the system output and the ideal target in real time and quantify this deviation. Without this error signal, subsequent gradient calculations (S408) and coefficient updates (S409), and the entire efficient iterative optimization process, cannot be established.

[0070] S402. The mean square error of the error signal is used as the optimization objective to be minimized. S403. By adjusting the weighted merging coefficients, the weighted merging coefficients that minimize the mean square error are obtained, and the corresponding weighted merging coefficients are determined as the optimal merging coefficients.

[0071] Specifically, S402 first transforms the instantaneous error signal generated in S401 into a smooth cost function representing average performance by calculating its mean square value. S403 then explicitly states that the algorithm's ultimate goal is to find the minimum value of this cost function by adjusting the weighted merging coefficients. This optimization objective can be directly expressed as the formula:

[0072] in, Let represent the optimal weighted merging coefficient vector to be solved, which contains the optimal complex weights corresponding to the output signals of each subarray. d(n) represents the desired signal at discrete time n, which in one embodiment of the invention is a preset, known original pilot signal. Y(n) represents the input signal column vector composed of the output signals of all M subarrays at the same discrete time n. This represents a temporary column vector of merging coefficients, which are the variables that the optimization problem seeks to find. The entire optimization process aims to find a set of merging coefficients. Through this coefficient The signals Y(n) received from all subarrays are linearly weighted and summed to obtain a combined output signal. The goal of this process is to minimize the error energy (achieved by |·|²) between this combined output signal and the ideal expected signal d(n) in a statistically average sense (implemented by the expectation operator E[·]). Ultimately, the coefficient vector that achieves this minimization objective is... This is the optimal merging coefficient that this invention aims to determine. .

[0073] S404. The array antenna system further optimizes the optimal combining coefficients using a gradient optimization algorithm. The gradient optimization algorithm involves repeating the following operations until the power of the error signal meets a preset convergence condition to obtain the final optimal combining coefficients: initializing the optimal combining coefficients; weighting and combining all subarray output signals according to the current optimal combining coefficients to generate a temporary combined signal; comparing the combined signal with the original pilot signal to determine an error signal; calculating a gradient value for the optimal combining coefficients based on the error signal and all subarray output signals; and updating the optimal combining coefficients according to the gradient value.

[0074] Specifically, this step establishes a closed-loop feedback mechanism: First, the optimal merging coefficients obtained from S403 are initialized as the starting point; then, the current coefficients are used to merge the subarray signals and compared with the pilot signals to generate an error; next, the gradient of the cost function is calculated based on this error and the subarray signals; finally, the merging coefficients are fine-tuned and updated based on this gradient value. This "merge-compare-update" cycle is repeated continuously until the error meets the convergence condition. The gradient optimization algorithm described in this step has a flexible and efficient engineering relationship with the aforementioned analytical method. The two work together to form a "coarse adjustment first, fine adjustment later" working mechanism. However, when the analytical method cannot directly solve for the optimal merging coefficients due to excessively high problem dimensionality, unknown channel environment, or dynamic changes, the gradient descent method can also serve as a more adaptive and robust independent solution, finding the optimal solution through adaptive iteration.

[0075] The array antenna device in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference]. Figure 5 This is a schematic diagram of the physical device structure of an array antenna device in the embodiments of this application.

[0076] It should be noted that, Figure 5 The structure of the array antenna device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0077] like Figure 5As shown, the array antenna device includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes based on a program stored in Read-Only Memory (ROM) 502 or a program loaded from storage section 508 into Random Access Memory (RAM) 503, such as performing the methods described in the above embodiments. The RAM 503 also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An Input / Output (I / O) interface 505 is also connected to the bus 504.

[0078] The following components are connected to I / O interface 505: input section 506 including audio input devices, push-button switches, etc.; output section 507 including liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 508 including hard disks, etc.; and communication section 509 including network interface cards such as LAN (Local Area Network) cards, modems, etc. Communication section 509 performs communication processing via a network such as the Internet. Drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on drive 510 as needed so that computer programs read from them can be installed into storage section 508 as needed.

[0079] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs the various functions defined in the present invention.

[0080] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0081] 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 invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0082] Specifically, the array antenna device in this embodiment includes a processor and a memory. The memory stores a computer program, and when the computer program is executed by the processor, it implements the array antenna anti-interference method provided in the above embodiment.

[0083] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the array antenna device described in the above embodiments; or it may exist independently and not assembled into the array antenna device. The storage medium carries one or more computer programs that, when executed by a processor of the array antenna device, cause the array antenna device to implement the array antenna anti-interference method provided in the above embodiments.

[0084] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0085] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0086] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for anti-interference of an array antenna, characterized in that, Applied to an array antenna system, the array antenna system comprising multiple array elements, the multiple array elements being divided into at least two sub-arrays for signal processing, the method comprising: The array antenna system processes all array elements as a whole for signal synchronization, and determines a globally shared synchronization point that indicates the position of the pilot signal; For each of the multiple subarrays, the array antenna system uses the synchronization point to extract the received pilot signal from the received signal. For each of the plurality of subarrays, the array antenna system calculates an independent set of anti-interference weights based on the received pilot signal and the preset original pilot signal. For each of the plurality of subarrays, the array antenna system applies an anti-interference weight to the signal received by the corresponding subarray to generate an interference-suppressed subarray output signal. The array antenna system uses an optimization algorithm to determine a set of optimal combining coefficients for combining the output signals of all subarrays. The goal of the optimization algorithm is to maximize the signal quality of the final combined signal. The array antenna system uses the optimal combining coefficient to weight and combine the output signals of the subarray to generate the final output signal.

2. The method according to claim 1, characterized in that, The method further includes cyclically executing the following steps at a preset update cycle to dynamically adapt to changes in the channel environment: During the update period, based on the newly received signals, the array antenna system re-estimates a signal covariance matrix for each subarray; Using the original pilot signal obtained during the update cycle, the array antenna system redetermines a steering vector for a desired signal for each subarray; Based on the re-estimated signal covariance matrix and the re-determined steering vector, the array antenna system recalculates an independent set of anti-interference weights for each subarray; After the anti-interference weights of each subarray are updated, the optimal merging coefficients are updated by executing the optimization algorithm.

3. The method according to claim 1, characterized in that, The optimization algorithm specifically includes: Define an error signal, which is the difference between the desired signal and the signal obtained by combining the output signals of all subarrays; The mean square error of the error signal is taken as the optimization objective to be minimized. By adjusting the weighted merging coefficients, the weighted merging coefficients that minimize the mean square error are obtained, and the corresponding weighted merging coefficient values ​​are determined as the optimal merging coefficients.

4. The method according to claim 3, characterized in that, The method further includes: The array antenna system further optimizes the optimal combining coefficients using a gradient optimization algorithm. The gradient optimization algorithm involves repeating the following operations until the power of the error signal meets a preset convergence condition, thus obtaining the final optimal combining coefficients: Initialize the optimal merging coefficients; Based on the current optimal merging coefficients, the output signals of all subarrays are weighted and merged to generate a temporary merged signal; The combined signal is compared with the original pilot signal to determine an error signal; Based on the error signal and the output signals of all subarrays, a gradient value of the optimal merging coefficient is calculated; The optimal merging coefficients are updated based on the gradient value.

5. The method according to claim 1, characterized in that, For each of the plurality of subarrays, the array antenna system calculates an independent set of anti-interference weights based on the received pilot signal and a preset original pilot signal, specifically including: The array antenna system determines the signal covariance matrix corresponding to each subarray based on the signal received by each subarray; wherein, the signal covariance matrix is ​​obtained by taking the expected value of the product of the signal vector received by the corresponding subarray and its own conjugate transpose. The array antenna system determines the steering vector of the desired signal in the corresponding subarray based on the received pilot signal and the preset original pilot signal; The array antenna system multiplies the inverse of the signal covariance matrix corresponding to each subarray with the steering vector to obtain the first intermediate result corresponding to each subarray. The array antenna system multiplies the conjugate transpose of the steering vector corresponding to each subarray with the first intermediate result to obtain the second intermediate result corresponding to each subarray; The array antenna system divides the first intermediate result corresponding to each subarray by the corresponding second intermediate result to obtain an independent anti-interference weight for each subarray.

6. The method according to claim 1, characterized in that, The array antenna system applies anti-interference weights to the signal received by the corresponding subarray for each of the plurality of subarrays, generating an interference-suppressed subarray output signal, specifically including: The array antenna system multiplies the conjugate transpose of the anti-interference weights corresponding to each subarray with the received signal vector to obtain the subarray output signal after interference suppression.

7. The method according to claim 1, characterized in that, The array antenna system uses the optimal combining coefficients to weight and combine the output signals of the subarrays to generate the final output signal, specifically including: The array antenna system multiplies the output signal of each subarray with the corresponding optimal combining coefficient to obtain the weighted subarray signal for each subarray. The array antenna system sums up the weighted subarray signals corresponding to all subarrays to generate the final output signal.

8. An array antenna device, characterized in that, The array antenna device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the array antenna device to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the array antenna device, it causes the array antenna device to perform the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the array antenna device, it causes the array antenna device to perform the method as described in any one of claims 1-7.

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