Spatial spectrum estimation and beam forming monitoring and direction finding method based on channel correction

Abstract

The invention discloses a monitoring direction-finding method for spatial spectrum estimation and beam forming based on channel correction, and relates to the technical field of wireless spectrum monitoring, and the method comprises the steps: carrying out the channel correction of a direction-finding system, so as to eliminate a phase error caused by the inconsistency between a plurality of direction-finding channels, acquiring real phase difference information of the antenna array receiving signals; receiving signals by using the antenna array subjected to channel correction, and performing spatial spectrum estimation through a multiple signal classification algorithm to obtain the direction of arrival of one or more incident signals; and selecting a desired signal direction according to the direction of arrival, carrying out adaptive beam forming by adopting a minimum variance criterion based on linear constraint, and carrying out weighting processing on an antenna array receiving signal so as to enhance a desired signal and suppress an interference signal. According to the invention, the direction finding precision and the signal enhancement capability are improved in a complex electromagnetic environment and a long-term operation condition.

Description

Technical Field

[0001] This application relates to the field of wireless spectrum monitoring technology, and in particular to a monitoring and direction finding method based on channel correction spatial spectrum estimation and beamforming. Background Technology

[0002] In the field of radio monitoring and direction finding, using antenna arrays to estimate the direction of arrival (DOA) of spatial signals is a key technology for signal localization and spectrum management. Among them, the multiple signal classification (MUSIC) algorithm based on spatial spectrum estimation has been widely used in modern direction finding systems due to its super-resolution capability, multi-signal processing capability, and high direction finding accuracy.

[0003] However, practical direction finding systems face several inherent challenges in engineering implementation. First, due to the non-ideal nature of antenna manufacturing processes, assembly tolerances, and passive RF front-end components such as filters, amplifiers, and mixers, it is difficult to guarantee the consistency of amplitude and phase between different direction finding channels. This channel inconsistency will drift with factors such as operating temperature and time, causing the array manifold to deviate from the theoretical model and severely degrading the accuracy of direction of arrival (DOA) estimation.

[0004] Secondly, while the MUSIC algorithm can effectively estimate the direction of arrival of multiple co-frequency signals, its output is only angular information and cannot directly extract or enhance useful signals from a specific direction. Especially in environments with strong interference, the desired signal may be submerged and difficult to demodulate or monitor. Furthermore, if the received data containing errors is directly used for adaptive beamforming, channel phase mismatch may cause beam pointing deviation or null shift, further reducing the system's anti-interference performance.

[0005] Therefore, existing direction finding systems based on spatial spectrum estimation generally suffer from unstable direction finding accuracy and weak signal enhancement capabilities under complex electromagnetic environments and long-term operating conditions, leaving room for improvement. Summary of the Invention

[0006] To address the shortcomings of existing technologies and improve direction finding accuracy and signal enhancement capabilities under complex electromagnetic environments and long-term operating conditions, this application provides a monitoring and direction finding method based on channel correction spatial spectrum estimation and beamforming.

[0007] Firstly, the objective of this invention is achieved through the following technical solution: Monitoring and direction finding methods based on channel-corrected spatial spectrum estimation and beamforming include: Channel correction is performed on the direction finding system to eliminate phase errors introduced by inconsistencies between multiple direction finding channels and to obtain the true phase difference information of the antenna array received signal; By using a channel-corrected antenna array to receive signals, spatial spectrum estimation is performed through a multiple signal classification algorithm to obtain the direction of arrival of one or more incident signals. The desired signal direction is selected based on the direction of arrival, and adaptive beamforming is performed using a linear constraint minimum variance criterion. The received signal from the antenna array is weighted to enhance the desired signal and suppress interference signals.

[0008] By employing the above technical solutions, a monitoring and direction-finding method integrating channel correction, super-resolution direction finding, and adaptive beamforming is provided. In practical applications, by injecting a calibration signal with the same frequency and phase and calculating the calibration phase difference, the difference between this and the measured phase difference can be used to accurately eliminate system errors introduced by hardware differences, thereby obtaining the true phase difference information of the antenna array received signal and effectively eliminating phase errors caused by channel inconsistency. Then, based on the channel-corrected data, the Multiple Signal Classification (MUSIC) algorithm is applied to significantly improve the accuracy and robustness of direction-of-arrival estimation, effectively distinguishing multiple incident signals with overlapping frequencies, breaking through the Rayleigh resolution limit of traditional direction-finding methods, and achieving high-precision, multi-signal super-resolution direction finding. Based on the direction-of-arrival information output by MUSIC, adaptive beamforming is performed using the Linear Constrained Minimum Variance (LCMV) criterion, dynamically adjusting the array weighting to ensure the main lobe is precisely aligned with the desired signal direction and the null is aligned with the interference direction, thereby enhancing the target signal while effectively suppressing interference and improving the output signal-to-interference-plus-noise ratio, thus realizing an integrated mechanism of spatial filtering and signal enhancement. This application overcomes the performance degradation problems caused by temperature drift and device aging in actual service environments by adopting a "calibration first, then direction finding, and finally synthesis" direction finding process. This enables the system to maintain stable direction finding and listening capabilities in complex electromagnetic environments and during long-term operation. It thus solves the problems of traditional space spectrum direction finding systems being sensitive to model distortion and unable to extract signal characteristic parameters, achieving the technical effect of improving direction finding accuracy and signal enhancement capabilities under complex electromagnetic environments and long-term operating conditions.

[0009] In a preferred embodiment of this application: the direction-finding antenna of the direction-finding system includes a direction-finding antenna array and a power divider; the channel correction of the direction-finding system includes: It receives radio frequency signals propagating in space and receives them through multiple antenna elements arranged in a set geometric structure in a direction-finding antenna array; The signals received by each antenna element are down-converted, filtered, and analog-to-digital converted to obtain a digital intermediate frequency signal. A single-carrier calibration signal is generated based on the built-in calibration source module, and the single-carrier calibration signal is distributed to each direction finding channel through a power divider. The calibration response signal of each direction finding channel is collected. The phase difference between any two direction finding channels is calculated, and the phase difference includes the error introduced by the channel hardware inconsistency. According to the formula Calculate the original phase difference between direction-finding channel i and direction-finding channel j, where Let φ be the true phase of the signals received by direction-finding channel i and direction-finding channel j, respectively; i φ j These represent the phase errors introduced by the inconsistency between direction finding channels i and j, respectively. Using one of the direction-finding channels as the reference channel, the true relative phase difference after eliminating channel inconsistency errors is obtained through differential calculation: This is to achieve effective correction of phase errors between direction-finding channels.

[0010] By adopting the above technical solution, the single-carrier calibration signal is distributed to each direction-finding channel via a power divider, ensuring that the single-carrier calibration signal is in phase and frequency. After the single-carrier signal is generated by the built-in calibration source, it is synchronously fed into all channels via the power divider, and the response of each channel is collected to extract the phase difference. This design utilizes the same signal source to eliminate differences in external propagation paths, ensuring that the measured phase difference only reflects channel hardware inconsistencies. Compared to field calibration or offline calibration, the channel correction scheme of this application can be completed in real time within the system without additional equipment, and can effectively separate channel errors from the true signal phase.

[0011] In a preferred example, this application associates the calculated true relative phase difference at each frequency and angle with the corresponding frequency and azimuth parameters to construct and store an antenna sample correction database, so as to perform real-time correction of the measured phase data during actual direction finding. The channel correction step further includes: selecting a calibration mode or a direction finding mode through the display and control software; in calibration mode, collecting calibration signal responses to update the antenna sample database; and in direction finding mode, calling sample data of the corresponding frequency and angle to correct the measured phase difference.

[0012] By adopting the above technical solution, an "antenna sample calibration database" is constructed, which stores single-carrier calibration signals at various frequencies and angles. By storing the actual relative phase difference at different frequencies and angles in association with parameters, the system can quickly look up and call the corresponding calibration data according to the current signal frequency and preliminary azimuth during actual direction finding, thereby achieving dynamic and accurate phase compensation. Compared with single-frequency calibration, this application covers a wide bandwidth and omnidirectional space, effectively addressing the calibration deviation caused by frequency selectivity and direction dependence, and significantly improving the stability and adaptability of the system in multi-band and multi-directional scenarios, especially suitable for broadband monitoring tasks.

[0013] In a preferred embodiment of this application, the array received signal model of the antenna array is: X(t) = AS(t) + n(t) Where X(t) is an N×1 array data vector, X(t) = [x1(t), x2(t), ..., x N(t)] T n(t) is the array noise vector, n(t) = [n1(t), n2(t), ..., n N (t)] T S(t) is the complex envelope vector of the signal, S(t) = [s0(t), s1(t), ..., s P (t)] T S k (t) represents the complex envelope of the k-th source; A is the array manifold matrix. in This is the steering vector of the k-th source; R is the array radius, and θ is the source elevation angle. λ is the azimuth angle, and λ is the wavelength. The angle between the nth element and the x-axis

[0014] By adopting the above technical solution, the functional relationship between the steering vector and the array geometry (such as the radius R of the circular array and the element angles), the signal direction of arrival (elevation angle θ, azimuth angle φ), and the wavelength λ is clarified. This model provides a theoretical basis for subsequent MUSIC algorithms and beamforming, ensuring that the spatial spectrum calculation and weight design conform to physical reality. By substituting the corrected phase information into the array receiving signal model, the array manifold matrix A can be accurately reconstructed, effectively avoiding direction-finding ambiguity or main lobe shift caused by model mismatch.

[0015] In a preferred embodiment of this application, the spatial spectrum estimation step of the multiple signal classification algorithm includes: Construct the covariance matrix R xx : For the covariance matrix R xx Eigenvalue decomposition is performed to separate the noise subspace as U. N =(q N+1 ,q N+2 ,...,q M ) and signal subspace U s And construct a MUSIC spatial spectrum function based on the orthogonality between the directional guidance vector and the eigenvectors of the noise subspace: Where, q i The eigenvectors are the eigenvectors corresponding to the smallest MN eigenvalues, where N is the number of sources and M is the number of array elements; the direction of arrival of the incident signal is the angle corresponding to the peak value.

[0016] By adopting the above technical solution, the implementation process of the MUSIC algorithm is defined: This application constructs a covariance matrix and then performs eigenvalue decomposition, followed by separation of the noise subspace, and constructs a spectral function based on the orthogonality between the steering vector and the noise subspace. This application fully utilizes the orthogonality between the signal subspace and the noise subspace, which theoretically can infinitely improve the angular resolution; combined with high-quality data after channel correction, the MUSIC spectral peaks are sharp and the sidelobes are low, which can effectively distinguish multiple signals at the same frequency, break through the Rayleigh limit of traditional beam scanning, and significantly improve the perception capability of dense signal environments.

[0017] In a preferred example of this application: let the direction of the desired signal be θ0, the steering vector be a(θ0), and the optimal weighted vector w based on the minimum variance criterion of linear constraints. opt The expression is: in, The inverse matrix is ​​used to obtain the optimal weighted vector w. opt The goal is to minimize the system output power while ensuring a constant gain in the desired signal direction. The expression for the array output power P is:

[0018] By adopting the above technical solution, the Linear Constrained Minimum Variance Criterion (LCMV criterion) is introduced for adaptive beamforming. Its core principle is to minimize the total output power while ensuring a constant gain in the desired direction. The resulting optimal weighting vector automatically forms nulls in the interference direction while maintaining the main lobe aligned with the target signal. Since the weight calculation for adaptive beamforming directly depends on the calibrated and MUSIC-directed data, the beam pointing is accurate and the anti-interference capability is strong. This application achieves a leap from "knowing the direction" to "extracting the signal," enabling the system not only to locate the signal but also to output an enhanced signal that can be used for demodulation or monitoring, thus expanding the functional boundaries of the direction-finding system.

[0019] In a preferred example, this application: before calculating the optimal weighting vector, the covariance matrix R is... xx The restructuring includes: Perform eigenvalue decomposition on the sampling covariance matrix: The eigenvalues ​​are λ1…λ N ; By calculating the correlation coefficient Determine the characteristic values ​​corresponding to the desired signal; λ1…λ is determined by the value of the correlation coefficient. P Let λ be the eigenvalue corresponding to the interference. P+1 Let λ be the eigenvalue corresponding to the desired signal. P+2 …λ N These are the small eigenvalues ​​corresponding to the noise; calculate As a convergent estimate of the eigenvalues ​​corresponding to the noise; Let x' = [0, 1, ..., 0] P ,λ'-λ P+1 ,λ'-λ P+2 ,…,λ'-λ N ] serves as a correction value for all eigenvalues; Recalculate the covariance matrix As the perturbed covariance matrix, the optimal weights for beamforming are: w opt =uR '-1 a(θ P+1 ).

[0020] By adopting the above technical solution, due to the unavoidable pointing errors, array element position errors, and array element phase errors in practical engineering applications, the sample inevitably contains the desired signal. When the intensity of the desired signal is too large, its corresponding eigenvalues ​​and eigenvectors will participate in the calculation of adaptive weights, causing the beam to form nulls in both the desired signal and interference directions, resulting in a severe decrease in the signal-to-interference-plus-noise ratio of the output signal. Based on this phenomenon, this application also provides a main lobe interference suppression method based on the reconstruction of the characteristic oblique projection covariance matrix, which can effectively suppress the main lobe interference and reduce the loss of the target signal—that is, it proposes to reconstruct the covariance matrix R before calculating the optimal weighting vector. xx Reconstruction is performed by identifying the eigenvalues ​​corresponding to the desired signal through the correlation coefficient and replacing them with the mean of the noise eigenvalues. This effectively solves the problem of strong desired signals being misjudged as interference, leading to adaptive nulls appearing in the main lobe direction, i.e., the "signal cancellation" phenomenon. The reconstructed covariance matrix more realistically reflects the statistical characteristics of interference and noise, enabling the LCMV beam to maximize interference suppression while preserving the desired signal, significantly improving the output signal-to-interference-plus-noise ratio. This is particularly suitable for critical monitoring scenarios such as aviation communications where there is strong co-channel interference.

[0021] Secondly, the objective of this invention is achieved through the following technical solution: A monitoring and direction-finding system based on channel-corrected spatial spectrum estimation and beamforming, the system comprising: A direction-finding antenna consists of multiple antenna elements arranged in a preset geometric structure and is used to receive incident signals in the airspace. An array receiver includes multiple parallel direction-finding channels, each connected to one of the antenna array elements, for down-converting and digitizing the signals received by each array element. The calibration source module, built into the array receiver, is used to generate a single-carrier calibration signal and feed the calibration signal synchronously into all direction finding channels through a power divider; A matrix switch, integrated in the front end of the direction-finding antenna or array receiver, controls the switching of signal paths to access the calibration signal in calibration mode and the antenna element receiving signal in direction-finding mode. A digital signal processing unit, connected to the array receiver, is used to eliminate phase errors introduced by channel inconsistencies based on the calibration signals output by each channel in calibration mode and the antenna array received signals output by each channel in direction finding mode, to obtain calibrated array received data; for the calibrated array received data, spatial spectrum estimation based on a multiple signal classification algorithm is sequentially performed to obtain the direction of arrival of the incident signal, and adaptive beamforming is performed based on the linear constraint minimum variance criterion to output the enhanced desired signal; The display and control unit is communicatively connected to the digital signal processing unit and is used to issue direction finding mission commands, configure calibration parameters, and display direction finding results and beamforming output signals.

[0022] By adopting the above technical solution, the direction-finding antenna can receive incident signals in the spatial domain, the array receiver processes the received signals of each array element, the calibration source module generates calibration signals and synchronously feeds them into each direction-finding channel, the matrix switch can switch the signal path, the digital signal processing unit can eliminate the phase error introduced by channel inconsistency, obtain the corrected array received data, perform spatial spectrum estimation to obtain the direction of arrival, perform adaptive beamforming to output the enhanced desired signal, and the display and control unit can issue commands, configure parameters and display results. Overall, the direction-finding accuracy of the system is improved, the environmental adaptability is more robust, and it can realize the enhancement of signals of interest, the suppression of interference signals, and also perform signal demodulation, monitoring and other functions.

[0023] Thirdly, the objective of this invention is achieved through the following technical solution: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described monitoring and direction finding method based on channel correction spatial spectrum estimation and beamforming.

[0024] Fourthly, the objective of this invention is achieved through the following technical solution: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned monitoring and direction-finding method based on channel correction spatial spectrum estimation and beamforming.

[0025] In summary, this application includes at least one of the following beneficial technical effects: 1. Channel correction of the direction finding system can eliminate phase errors introduced by inconsistencies between multiple direction finding channels, obtain the true phase difference information of the antenna array received signal, improve the direction finding accuracy of the system, and make the system more robust in environmental adaptability; by using the channel-corrected antenna array received signal to perform spatial spectrum estimation, the direction of arrival of one or more incident signals can be obtained. Compared with the phase method and amplitude method, which can only measure the direction of arrival of a single signal, this application can measure the direction of arrival of multiple signals, and has higher direction finding accuracy, direction finding sensitivity, direction finding resolution, and better environmental adaptability. 2. Adaptive beamforming can be performed by selecting the desired signal direction based on the direction of arrival, which can enhance the desired signal and suppress interference signals. This achieves the enhancement of the signal of interest and the suppression of interference signals. Furthermore, it can be used for direction finding and signal demodulation and monitoring, thus closely linking and complementing the two different fields of spatial spectrum estimation and beamforming, and better serving practical engineering applications. Attached Figure Description

[0026] Figure 1 This is a flowchart of a monitoring and direction finding method based on channel correction spatial spectrum estimation and beamforming in one embodiment of this application; Figure 2 This is a system block diagram of a monitoring and direction finding system based on channel correction spatial spectrum estimation and beamforming in one embodiment of this application; Figure 3 This is a block diagram of the channel correction principle in a monitoring and direction finding method based on channel correction spatial spectrum estimation and beamforming in one embodiment of this application; Figure 4 This is a comparison diagram of the spectrum before and after beam combining processing in a monitoring and direction finding method based on channel correction spatial spectrum estimation and beam combining in one embodiment of this application; Figure 5 This is a schematic diagram of the spatial spectrum direction finding algorithm in a monitoring and direction finding method based on channel correction spatial spectrum estimation and beamforming in one embodiment of this application. Detailed Implementation

[0027] The present application will be further described in detail below with reference to the accompanying drawings.

[0028] In one embodiment, such as Figure 1 As shown, this application discloses a monitoring and direction finding method based on channel correction spatial spectrum estimation and beamforming, which specifically includes the following steps: S1: Perform channel correction on the direction finding system to eliminate phase errors introduced by inconsistencies between multiple direction finding channels and obtain the true phase difference information of the antenna array received signal.

[0029] In this embodiment, as Figure 2The diagram shows the system composition of a monitoring and direction-finding system based on channel-corrected spatial spectrum estimation and beamforming. The system mainly consists of a digital signal processing unit, an array receiver, a control unit, a power supply, a direction-finding antenna, and display and control software (or display and control unit). This embodiment proposes a direction-finding system based on channel-corrected spatial spectrum estimation and beamforming, combining signal processing and spatial dimensions.

[0030] Specifically, step S1 includes: S11: Receives radio frequency signals propagating in space and receives them through multiple antenna elements arranged in a set geometric structure in the direction-finding antenna array.

[0031] S12: Perform down-conversion, filtering, and analog-to-digital conversion on the signals received by each antenna element to obtain a digital intermediate frequency signal.

[0032] In this embodiment, the antenna elements are arranged according to a specific geometric structure, such as a uniform circular array or a uniform linear array, to achieve effective reception of signals from different directions. In this example, an 8-element uniform circular array (UCA) is used, with the element spacing satisfying the half-wavelength criterion to avoid grating lobes. The array radius R = 0.5m, suitable for monitoring in the VHF / UHF band (30–1000MHz). Specifically, eight broadband dipole antenna elements are uniformly distributed on the circumference, with adjacent elements at an angle of 45°. When an amplitude-modulated broadcast signal from an azimuth angle of 120° exists in the airspace, each element receives a radio frequency signal with a specific phase relationship due to the different path differences from the signal's incident direction. For example, at a frequency of 100MHz (λ=3m), the theoretical phase difference between the first array element (located at 0°) and the second array element (located at 45°) is approximately β=(2πR / λ)·cos(120°-0°)-(2πR / λ)·cos(120°-45°)≈-45.5° S13: Generate a single-carrier calibration signal based on the built-in calibration source module, distribute the single-carrier calibration signal to each direction finding channel through a power divider, and collect the calibration response signal of each direction finding channel; calculate the phase difference between any two direction finding channels, including the error introduced by the channel hardware inconsistency.

[0033] Specifically, downconversion refers to mixing the radio frequency signal with the local oscillator signal through a mixer and shifting it to a lower intermediate frequency (IF) or baseband; analog-to-digital conversion refers to converting the analog intermediate frequency signal into a digital IQ data stream that can be processed by a digital signal processor (DSP) or FPGA through a high-speed ADC.

[0034] S14: According to the formula Calculate the original phase difference between direction-finding channel i and direction-finding channel j, where Let φ be the true phase of the signals received by direction-finding channel i and direction-finding channel j, respectively;i φ j These represent the phase errors introduced by the inconsistency between direction finding channels i and j, respectively.

[0035] In this embodiment, each antenna element is followed by an independent direction-finding channel, which includes a low-noise amplifier (LNA), a bandpass filter, a mixer, a digitally controlled attenuator, and a 14-bit, 100MSPS ADC. The system operates at a center frequency of 100MHz and a bandwidth of 200kHz. The local oscillator frequency is set to 99.9MHz, and after mixing, a 100kHz intermediate frequency signal is output, which is then digitally down-converted (DDC) to generate baseband IQ data. The eight IQ data channels are synchronously sent to the FPGA via the JESD204B interface.

[0036] S15: Using one of the direction-finding channels as the reference channel, the true relative phase difference after eliminating channel inconsistency errors is obtained through differential calculation: This is to achieve effective correction of phase errors between direction-finding channels.

[0037] In this embodiment, the power divider is a passive microwave device that can distribute a single input signal to multiple output ports with equal amplitude and phase. The built-in calibration source module is a signal generator integrated within the calibration unit, capable of generating continuous wave (CW) signals with controllable frequency and power. The calibration source is implemented using a DDS (Direct Digital Frequency Synthesis) chip, which can programmably output a single-tone signal with a frequency range of 30–1000MHz and a power of -10dBm. At the start of calibration, the display and control software issues a command to switch the matrix switch to the calibration path and simultaneously activates the calibration source to output a 100MHz single-carrier signal. This signal is then divided into eight by a 1-to-8 power divider and fed into the RF input terminals of eight direction-finding channels (bypassing the antenna). After processing by each channel according to the S12 procedure, the FPGA acquires the IQ data of the eight calibration responses. The phase difference between any two channels is calculated using FFT or complex correlation methods.

[0038] Specifically, the reference channel is typically selected as a physically centrally located and stable channel, such as channel 1, and all other channels are calibrated based on it. Differential operation refers to subtracting the phase difference in calibration mode from the phase difference in direction-finding mode, thereby canceling out common channel error terms.

[0039] For example, the most direct way to reduce phase error caused by phase inconsistencies between receiver channels is to improve the consistency of the receiver channels. For instance... Figure 3The diagram shown is a block diagram of the channel correction principle of the direction finding system in this embodiment. The direction finding antenna integrates a power divider and a set of matrix switches. The calibration source module is built into the array receiver to generate a single-carrier calibration signal. The display and control software sets parameters such as calibration frequency, output power, and signal type (single-tone signal or comb spectrum signal) and sends instructions to the digital signal processing unit. After passing through the control unit, the array receiver generates the required single-carrier calibration signal. The single-carrier calibration signal is then divided by the power divider to output multiple in-phase signals of the same frequency and in phase, which are transmitted to the direction finding antenna.

[0040] In this embodiment, the channel correction step further includes: selecting either a calibration mode or a direction-finding mode via display and control software. In calibration mode, the calibration signal response is collected to update the antenna sample database; in direction-finding mode, sample data of the corresponding frequency and angle are called to correct the measured phase difference. That is, the switch input signal is a single-carrier calibration signal and an antenna element signal. During operation, the switch is switched as needed to select whether the signal source is a single-carrier calibration signal or an antenna element signal. In calibration mode, the single-carrier calibration signal is selected; in direction-finding mode, the antenna element signal is selected.

[0041] Figure 3 In this embodiment, the signal receives a direction-finding channel, generating a phase difference φ, which is the phase difference caused by the channel's inconsistency. When the received antenna signal passes through two direction-finding channels and the phase difference between the two channels is calculated, the phase difference already includes the phase error introduced by the channel inconsistency. In this embodiment, channel 1 is used as the reference channel (other direction-finding channels can also be used as reference channels or mutually referenced): When a single-carrier calibration signal passes through two direction-finding channels and the phase difference between the two channels is calculated, the phase difference also includes the phase error introduced by channel inconsistency: Subtracting the two will eliminate the error introduced by the channel inconsistency, and yield the true phase difference information of the direction-finding antenna array signal: The above operations eliminate both channel errors and direction-finding channel errors in some cables after the direction-finding switch, as well as some random errors. The phase difference Δ is then divided according to different frequencies and angles. 1,2 ,Δ 1,3 ...Δ 1,n Save it as an antenna sample database. Subsequently, the sample data of the corresponding frequency point is retrieved by looking up the table for the direction finding azimuth algorithm calculation. S2: Receive the signal using the antenna array after channel correction, and perform spatial spectrum estimation through a multi-signal classification algorithm to obtain the direction of arrival of one or more incident signals.

[0042] In this embodiment, the Multiple Signal Classification (MUSIC) algorithm is a super-resolution Direction of Arrival (DOA) estimation algorithm based on subspace decomposition. DOA refers to the azimuth angle of the signal incident on the antenna array (in this example, the elevation angle is assumed to be 0°, i.e., a plane wave). The basic idea of ​​the MUSIC algorithm is to perform eigenvalue decomposition on the covariance matrix of any array output data to obtain the signal subspace corresponding to the signal components and the noise subspace orthogonal to the signal components. Then, the orthogonality of these two subspaces is used to estimate the signal parameters.

[0043] Specifically, consider a uniform circular array consisting of N elements, with an element spacing of d, and all elements are isotropic. Assume there is a desired signal and P narrowband interferences, arriving at angles θ0 and θ1 respectively. k (k = 1, 2, ..., P). The array received signal model for the antenna array in this embodiment is: X(t) = AS(t) + n(t), where X(t) is an N×1 array data vector, X(t) = [x1(t), x2(t), ..., x...]. N (t)] T n(t) is the array noise vector, n(t) = [n1(t), n2(t), ..., n N (t)] T S(t) is the complex envelope vector of the signal, S(t) = [s0(t), s1(t), ..., s P (t)] T S k (t) represents the complex envelope of the k-th source; A is the array manifold matrix. in This is the steering vector of the k-th source; R is the array radius, and θ is the source elevation angle. λ is the azimuth angle, and λ is the wavelength. The angle between the nth element and the x-axis

[0044] Furthermore, from the array received signal model, it can be seen that the constructed covariance matrix R xx for: Assume the received signal is a narrowband far-field signal, and neglect time delay spread and spatial spread. The relationship between noise and signal is assumed to be uncorrelated between the input signal and noise, and independent of each other. Therefore: Therefore, by the assumptions, the covariance matrix is ​​a non-singular positive definite matrix. For the covariance matrix R... xxEigenvalue decomposition yields M eigenvalues ​​{λ1, λ2, λ3, ..., λ4}. M Furthermore, according to matrix theory, we know that: Since the received signal comes from N independent signal sources, therefore, according to R xx rank{R xx Given R = N, the number of incoming signal sources N can be estimated using a signal source number estimation method. xx By performing eigenvalue decomposition, we can obtain N relatively large eigenvalues ​​and MN eigenvalues ​​equal to 1. The eigenvalues ​​of N larger eigenvalues ​​are expanded into a signal subspace U. s The eigenvectors with MN small eigenvalues ​​can be expanded into a noise subspace U. N .

[0045] Assume the eigenvalue λ i The corresponding eigenvector is q i And satisfy |(R) xx -λ i q i If I = 0, and the eigenvectors corresponding to MN small eigenvalues ​​are given, then the noise subspace is U. N =(q N+1 ,q N+2 ,...,q M ),have: Since matrix A is full rank, and the covariance matrix R xx It is also nonsingular, that is, A exists. H q i =0.

[0046] The above equation shows that the eigenvectors with the MN smallest eigenvalues ​​are orthogonal to the N directional guidance vectors constituting matrix A. The basic idea of ​​the MUSIC algorithm is to utilize the characteristic that the directional guidance vectors of the signal components are orthogonal to the eigenvectors of the noise subspace, and to obtain the corresponding incident angle DOA value by constructing the MUSIC spatial spectrum function and searching for its peak value.

[0047] S3: Select the desired signal direction based on the direction of arrival, and use adaptive beamforming based on the minimum variance criterion of linear constraints to weight the received signal of the antenna array in order to enhance the desired signal and suppress interference signals.

[0048] In this embodiment, the Linear Constrained Minimum Variance Criterion (LCMV) is an adaptive beamforming algorithm. Its goal is to minimize the total output power (including interference and noise) of the array while ensuring that the signal in the desired direction passes through without distortion (linear constraint). The desired signal direction is manually selected by the operator from multiple DOAs output by the MUSIC according to task requirements, or it is automatically determined by the system based on signal characteristics such as bandwidth and modulation type.

[0049] Specifically, for the covariance matrix R xx Eigenvalue decomposition is performed to separate the noise subspace as U. N =(q N+1 ,q N+2 ,...,q M ) and signal subspace U s And construct a MUSIC spatial spectrum function based on the orthogonality between the directional guidance vector and the eigenvectors of the noise subspace: Where, q i Let N be the eigenvector corresponding to the smallest MN eigenvalues, where N is the number of sources and M is the number of array elements; the angle corresponding to the peak value is the direction of arrival of the incident signal. i ) and U N The orthogonality of the denominator minimizes the value, thus obtaining the peak value of the MUSIC spectrum as defined in the above formula. The N maximum peak values ​​in the MUSIC spectrum correspond to the directions of arrival of the N signals incident on the array.

[0050] In this embodiment, the direction-finding system algorithm employs a hybrid design scheme combining FPGA and general-purpose DSP. The FPGA is used as a coprocessor to handle large amounts of regular computations, while the DSP's flexibility handles complex, irregular computations, thus optimizing the overall algorithm's execution efficiency. The FPGA primarily handles the fixed-point regular computation portion, employing parallel processing to construct the covariance matrix, resulting in good real-time performance. The DSP mainly handles the floating-point irregular computation portion, including solving eigenvalue decomposition, source estimation, and spectral peak search.

[0051] In this embodiment, since the angle information of the signal or interference in practical applications may be unknown, spatial spectrum estimation is typically used first to obtain the direction of the signal or interference. Then, beamforming is used to adjust the weighting of the array elements, enabling the sensor array to output a strong signal in the set direction, thereby improving the signal-to-noise ratio of the incident signals in these directions and enhancing system performance. Therefore, spatial spectrum estimation and beamforming are closely related in practical applications.

[0052] The array receives data and performs beamforming. The output signal Y(t) of the array after beamforming is: in, The Hermitian transpose of the optimal weighted vector is used to perform complex inner product operations; X(t) is the array received signal vector with dimension N×1, representing the baseband IQ data received by each array element at time t; assuming the direction of arrival (DOA) of the desired signal is known, in order to allow the desired signal to pass through without power loss and to suppress interference and noise, adaptive beamforming based on the linearly constrained minimum variance LCMV criterion is adopted, and its cost function is: stw H a(θ0)=1 a(θ0) is the assumed desired signal steering vector, and the nth element is... Where d n Let λ be the distance from the nth array element to the reference point, and λ be the signal wavelength; constraint condition w H a(θ0) = 1 indicates that the beam gain is 1 (no power loss) in the desired direction, i.e., a "linear constraint". The optimal array weighting vector (complex vector) calculated using the Lagrange operator is: In the formula: for The inverse matrix, The estimated value of the received signal covariance matrix is ​​N×N, and the calculation method is as follows: Where K is the number of snapshots, X(t) k ( ) represents the k-th sampled data; the obtained optimal weight vector minimizes the system output power while ensuring a constant gain in the desired signal direction, thereby effectively suppressing interference and noise. (Molecular part) This represents the projection along the direction of minimum variance.

[0053] This leads to the array output power P, where P represents the output power after LCMV beamforming in the desired direction: In practical engineering applications, due to unavoidable pointing errors, array element position errors, and array element phase errors, the sample inevitably contains the desired signal. When the intensity of the desired signal is too large, its corresponding eigenvalues ​​and eigenvectors will participate in the calculation of the adaptive weights, causing the beam to form nulls in both the desired signal and interference directions, resulting in a severe decrease in the signal-to-interference-plus-noise ratio of the output signal.

[0054] To address the aforementioned issues, a main lobe interference suppression method based on characteristic oblique projection covariance matrix reconstruction can effectively suppress main lobe interference and reduce the loss of the target signal. First, the eigenvalues ​​of the desired signal are determined, and then the average value of the noise eigenvalues ​​is used to replace the eigenvalues ​​of the desired signal. Next, a new covariance matrix is ​​reconstructed based on the new eigenvalues. Finally, the weights for beamforming are calculated using the new covariance matrix.

[0055] Specifically, before calculating the optimal weighting vector, the covariance matrix R is... xx The reconstruction process includes the following algorithm steps: ① Perform eigenvalue decomposition on the sampling covariance matrix: The eigenvalues ​​are λ1…λ N Where R is the sampling covariance matrix with dimensions N×N; λ i For the i-th eigenvalue, sorted in descending order: λ1≥λ2≥…≥λ N ;v i Is with λ i The corresponding eigenvectors form an orthogonal basis; Let be the outer product, representing the energy component projected onto the i-th feature direction.

[0056] ② By calculating the correlation coefficient Where j is the index of the eigenvectors of the covariance matrix, used to traverse all M eigenvectors; M represents the number of elements in the antenna array, i.e., the number of receiving channels; the eigenvalues ​​corresponding to the desired signal are determined; when e d The correlation coefficient reaches its maximum value when the eigenvector corresponding to the steering vector 'a' in the desired signal direction is e. d The index of the eigenvector corresponding to the desired signal, i.e. This is the eigenvector with the highest correlation to 'a'; the value of this correlation coefficient can be used to determine the eigenvector corresponding to the desired signal, thereby determining the eigenvalues ​​of the desired signal. The correlation coefficient is the eigenvector a(θ0) and the eigenvector v of the covariance matrix. j The normalized complex inner product (i.e., cosine similarity) between them; where the molecule The core concept is the "correlation measure," and the entire fraction is the normalized correlation coefficient. ③ Through the correlation coefficient e d The value of λ1…λ is determined P Let λ1…λ be the eigenvalues ​​corresponding to the disturbance conditions. P Let λ be the first P largest eigenvalues, corresponding to all incident signals, including the desired signal and interference. P+1 Let λ be the eigenvalue corresponding to the desired signal. P+2 …λ N These are the small eigenvalues ​​corresponding to the noise; ④ Calculation λ' serves as the convergent estimate of the noise's corresponding eigenvalues, and λ' serves as the average estimate of the noise's eigenvalues; it is the set of noise eigenvalues ​​after excluding one possible interfering eigenvalue; if the desired signal power is very strong, then its eigenvalues... It may be much larger than other signals, even becoming λ1, which is easily mistaken for interference and suppressed. Therefore, it is identified through step ②. The desired feature value is selected, not the interference, and one possible interference feature value is removed from the noise feature value set in step ④.

[0057] ⑤ Let x' = [0, 1, ..., 0 P ,λ'-λ P+1 ,λ'-λ P+2 ,…,λ'-λ N The correction vector, λ'-λ, serves as the correction value for all eigenvalues. It is a correction vector of length N, with the first P elements being 0 to preserve the signal subspace, and the last NP elements being λ'-λ. i λ represents the correction amount for the noise eigenvalues. P+1 It is the first characteristic value suspected of being interference (it may actually be the desired signal), so it is not included in the correction. ⑥ Recalculate the covariance matrix As the perturbation-induced covariance matrix, where λ i +x' i R' is the corrected eigenvalue, and R' is the reconstructed covariance matrix used for subsequent LCMV weight calculation; the optimal weights for beamforming are: w opt =uR '-1 a(θ P+1 ).

[0058] In practical applications, adaptive beamforming adjusts weighting coefficients to align the main lobe of the beam with the desired signal direction and the null with the interference direction, thereby enhancing the desired signal and suppressing interference. This effectively improves system performance. This paper uses a single-tone signal received by the antenna and an airport amplitude-modulated dual signal as an example to perform beamforming processing. The null single-tone signal and the desired signal are shown in the following figures. The actual effect is as follows: Figure 4 As shown.

[0059] like Figure 5 The diagram shown is a schematic of the spatial spectrum direction finding algorithm. Figure 5In this context, AD sampling refers to high-speed AD sampling of intermediate frequency signals. Orthogonal transformation refers to orthogonally transforming the AD signal into an IQ signal; correction coefficients refer to calculating the channel correction coefficients; covariance matrix refers to constructing the spatial spectrum covariance matrix; eigenvalue decomposition indicates the eigenvalue decomposition of the matrix; source estimation is the estimation of the number of signal sources in the subspace, including the signal subspace and the noise subspace; steering vector is the antenna steering vector, i.e., the sample library data; correlation spectrum refers to constructing the correlation operation spectrum; and peak search refers to searching for peaks to obtain the signal azimuth direction finding results for direction finding result processing and display.

[0060] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0061] In one embodiment, a monitoring and direction finding system based on channel-corrected spatial spectrum estimation and beamforming is provided, which corresponds to the monitoring and direction finding method based on channel-corrected spatial spectrum estimation and beamforming in the above embodiment.

[0062] A monitoring and direction-finding system based on channel-corrected spatial spectrum estimation and beamforming. Detailed descriptions of each functional module are as follows: A direction-finding antenna consists of multiple antenna elements arranged in a preset geometric structure and is used to receive incident signals in the airspace. An array receiver contains multiple parallel direction-finding channels, each connected to an antenna array element, and is used to down-convert and digitize the signals received by each array element. The calibration source module, built into the array receiver, is used to generate a single-carrier calibration signal and feeds the calibration signal synchronously into all direction finding channels through a power divider. A matrix switch, integrated into the front end of a direction-finding antenna or array receiver, controls the switching of signal paths to receive calibration signals in calibration mode and antenna element received signals in direction-finding mode. The digital signal processing unit, connected to the array receiver, is used to eliminate phase errors introduced by channel inconsistencies based on the calibration signals output by each channel in calibration mode and the antenna array received signals output by each channel in direction finding mode, to obtain the corrected array received data; for the corrected array received data, spatial spectrum estimation based on a multiple signal classification algorithm is sequentially performed to obtain the direction of arrival of the incident signal, and adaptive beamforming is performed based on the linear constraint minimum variance criterion to output the enhanced desired signal; The display and control unit communicates with the digital signal processing unit and is used to issue direction finding mission commands, configure calibration parameters, and display direction finding results and beamforming output signals.

[0063] In this embodiment, the array receiver mainly consists of an RF channel and a frequency synthesizer. The RF channel performs frequency conversion, wide and narrow band filtering, and level control, providing the digital processing section with a signal to be processed that has a fixed frequency, selectable bandwidth, and a compressed level variation range. It also implements attenuation control to meet the needs of receiving input signals with a large dynamic range. The frequency synthesizer provides the local oscillator signal to the mixer of each channel, ensuring frequency consistency across all channels. Its phase noise performance directly affects the receiver's dynamic range and demodulation performance.

[0064] The digital signal processing unit adopts an FPGA+DSP architecture, with a gigabit Ethernet interface. The spatial spectrum estimation direction-finding algorithm uses a hybrid design of FPGA and general-purpose DSP. The FPGA is used as a coprocessor to handle large amounts of regular computations, while the DSP's flexibility handles complex and irregular computations, thus optimizing the overall algorithm's execution efficiency. The FPGA primarily handles the fixed-point regular computations, employing parallel processing to construct the covariance matrix, resulting in good real-time performance. The DSP mainly handles the floating-point irregular computations, including eigenvalue decomposition, source estimation, subspace computation, spectral peak search, and direction-finding result calculation.

[0065] For specific limitations regarding the monitoring and direction finding system based on channel correction spatial spectrum estimation and beamforming, please refer to the limitations of the monitoring and direction finding method based on channel correction spatial spectrum estimation and beamforming above, which will not be repeated here. Each module in the above-mentioned monitoring and direction finding system based on channel correction spatial spectrum estimation and beamforming can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in the processor of the computer device in hardware form or independent of the processor, or it can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0066] In one embodiment, a computer device is provided, which may be a server. The computer device includes a processor, memory, a network interface, and a database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores real phase difference information and direction of arrival, etc. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a monitoring and direction-finding method based on channel correction spatial spectrum estimation and beamforming.

[0067] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: S1: Perform channel correction on the direction finding system to eliminate phase errors introduced by inconsistencies between multiple direction finding channels and obtain the true phase difference information of the antenna array received signal; S2: Receive signals using an antenna array that has been channel-corrected, and perform spatial spectrum estimation using a multiple signal classification algorithm to obtain the direction of arrival of one or more incident signals; S3: Select the desired signal direction based on the direction of arrival, and use adaptive beamforming based on the minimum variance criterion of linear constraints to weight the received signal of the antenna array in order to enhance the desired signal and suppress interference signals.

[0068] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: S1: Perform channel correction on the direction finding system to eliminate phase errors introduced by inconsistencies between multiple direction finding channels and obtain the true phase difference information of the antenna array received signal; S2: Receive signals using an antenna array that has been channel-corrected, and perform spatial spectrum estimation using a multiple signal classification algorithm to obtain the direction of arrival of one or more incident signals; S3: Select the desired signal direction based on the direction of arrival, and use adaptive beamforming based on the minimum variance criterion of linear constraints to weight the received signal of the antenna array in order to enhance the desired signal and suppress interference signals.

[0069] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Furthermore, any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory.

[0070] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0071] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. 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 features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A monitoring and direction finding method based on spatial spectrum estimation and beamforming with channel correction, characterized in that, include: Channel correction is performed on the direction finding system to eliminate phase errors introduced by inconsistencies between multiple direction finding channels and to obtain the true phase difference information of the antenna array received signal; By using a channel-corrected antenna array to receive signals, spatial spectrum estimation is performed through a multiple signal classification algorithm to obtain the direction of arrival of one or more incident signals. The desired signal direction is selected based on the direction of arrival, and adaptive beamforming is performed using a linear constraint minimum variance criterion. The received signal from the antenna array is weighted to enhance the desired signal and suppress interference signals.

2. The monitoring and direction finding method based on channel correction spatial spectrum estimation and beamforming according to claim 1, characterized in that, The direction-finding antenna of the direction-finding system includes a direction-finding antenna array and a power divider; the channel correction of the direction-finding system includes: It receives radio frequency signals propagating in space and receives them through multiple antenna elements arranged in a set geometric structure in a direction-finding antenna array; The signals received by each antenna element are down-converted, filtered, and analog-to-digital converted to obtain a digital intermediate frequency signal. A single-carrier calibration signal is generated based on the built-in calibration source module, and the single-carrier calibration signal is distributed to each direction finding channel through a power divider. The calibration response signal of each direction finding channel is collected. The phase difference between any two direction finding channels is calculated, and the phase difference includes the error introduced by the channel hardware inconsistency. According to the formula Calculate the original phase difference between direction-finding channel i and direction-finding channel j, where Let φ be the true phase of the signals received by direction-finding channel i and direction-finding channel j, respectively; i φ j These represent the phase errors introduced by the inconsistency between direction finding channels i and j, respectively. Using one of the direction-finding channels as the reference channel, the true relative phase difference after eliminating channel inconsistency errors is obtained through differential calculation: This is to achieve effective correction of phase errors between direction-finding channels.

3. The monitoring and direction finding method based on channel correction spatial spectrum estimation and beamforming according to claim 2, characterized in that, The calculated real relative phase difference at each frequency and angle is associated with the corresponding frequency and azimuth parameters to construct and store an antenna sample correction database, so as to correct the measured phase data in real time during the actual direction finding process. The channel correction step further includes: selecting a calibration mode or a direction finding mode through the display and control software; in calibration mode, collecting calibration signal responses to update the antenna sample database; and in direction finding mode, calling sample data of the corresponding frequency and angle to correct the measured phase difference.

4. The monitoring and direction finding method based on channel correction spatial spectrum estimation and beamforming according to claim 1, characterized in that, The array receiving signal model of the antenna array is: X(t) = AS(t) + n(t) Where X(t) is an N×1 array data vector, X(t) = [x1(t), x2(t), ..., x N (t)] T n(t) is the array noise vector, n(t) = [n1(t), n2(t), ..., n N (t)] T S(t) is the complex envelope vector of the signal, S(t) = [s0(t), s1(t), ..., s P (t)] T S k (t) represents the complex envelope of the k-th source; A is the array manifold matrix. in This is the steering vector of the k-th source; R is the array radius, and θ is the source elevation angle. λ is the azimuth angle, and λ is the wavelength. The angle between the nth element and the x-axis 5. The monitoring and direction finding method based on channel correction spatial spectrum estimation and beamforming according to claim 4, characterized in that, The spatial spectrum estimation step of the multiple signal classification algorithm includes: Construct the covariance matrix R xx : For the covariance matrix R xx Eigenvalue decomposition is performed to separate the noise subspace as U. N =(q N+1 ,q N+2 ,...,q M ) and signal subspace U s And construct a MUSIC spatial spectrum function based on the orthogonality between the directional guidance vector and the eigenvectors of the noise subspace: Where, q i The eigenvectors are the eigenvectors corresponding to the smallest MN eigenvalues, where N is the number of sources and M is the number of array elements; the direction of arrival of the incident signal is the angle corresponding to the peak value.

6. The monitoring and direction finding method based on channel correction spatial spectrum estimation and beamforming according to claim 1, characterized in that, Let the direction of the desired signal be θ0, and the steering vector be a(θ0). The optimal weighted vector w based on the linear constraint minimum variance criterion... opt The expression is: in, for The inverse matrix is ​​used to obtain the optimal weighted vector w. opt The goal is to minimize the system output power while ensuring a constant gain in the desired signal direction. The expression for the array output power P is:

7. The monitoring and direction finding method based on channel correction spatial spectrum estimation and beamforming according to claim 6, characterized in that, Before calculating the optimal weighting vector, the covariance matrix R... xx The restructuring includes: Perform eigenvalue decomposition on the sampling covariance matrix: The eigenvalues ​​are λ1…λ N ; By calculating the correlation coefficient Determine the characteristic values ​​corresponding to the desired signal; λ1…λ is determined by the value of the correlation coefficient. P Let λ be the eigenvalue corresponding to the interference. P+1 Let λ be the eigenvalue corresponding to the desired signal. P+2 …λ N These are the small eigenvalues ​​corresponding to the noise; calculate As a convergent estimate of the eigenvalues ​​corresponding to the noise; Let x' = [0, 1, ..., 0] P ,λ'-λ P+1 ,λ'-λ P+2 ,…,λ'-λ N ] serves as a correction value for all eigenvalues; Recalculate the covariance matrix As the perturbed covariance matrix, the optimal weights for beamforming are: w opt =uR '-1 a(θ P+1 ).

8. A monitoring and direction-finding system based on channel correction spatial spectrum estimation and beamforming, characterized in that, The system includes: A direction-finding antenna consists of multiple antenna elements arranged in a preset geometric structure and is used to receive incident signals in the airspace. An array receiver includes multiple parallel direction-finding channels, each connected to one of the antenna array elements, for down-converting and digitizing the signals received by each array element. The calibration source module, built into the array receiver, is used to generate a single-carrier calibration signal and feed the calibration signal synchronously into all direction finding channels through a power divider; A matrix switch, integrated in the front end of the direction-finding antenna or array receiver, controls the switching of signal paths to access the calibration signal in calibration mode and the antenna element receiving signal in direction-finding mode. A digital signal processing unit, connected to the array receiver, is used to eliminate phase errors introduced by channel inconsistencies based on the calibration signals output by each channel in calibration mode and the antenna array received signals output by each channel in direction finding mode, to obtain calibrated array received data; for the calibrated array received data, spatial spectrum estimation based on a multiple signal classification algorithm is sequentially performed to obtain the direction of arrival of the incident signal, and adaptive beamforming is performed based on the linear constraint minimum variance criterion to output the enhanced desired signal; The display and control unit is communicatively connected to the digital signal processing unit and is used to issue direction finding mission commands, configure calibration parameters, and display direction finding results and beamforming output signals.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the monitoring and direction finding method based on channel correction spatial spectrum estimation and beamforming as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the monitoring and direction finding method based on channel correction spatial spectrum estimation and beamforming as described in any one of claims 1 to 7.

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