A multi-target anti-jamming detection system based on holographic staring radar

Abstract

This invention discloses a multi-target anti-interference detection system based on holographic staring radar, belonging to the field of holographic staring radar technology. It constructs a technical architecture consisting of four major modules: array calibration, beamforming and optimization, adaptive filtering, and adaptive control. The system ensures beam pointing accuracy through periodic array calibration, effectively suppresses inter-beam interference by employing a minimum sidelobe beamforming algorithm, achieves dynamic suppression of strong near-angle interference by combining spatial-temporal joint filtering, and adaptively optimizes beam parameters using fusion analysis and feedback mechanisms. This significantly improves the system's multi-target detection accuracy and anti-interference capability in complex urban environments, and demonstrates strong stability and practicality.

Description

Technical Field

[0001] This invention relates to the field of holographic staring radar technology, and more specifically to a multi-target anti-jamming detection system based on holographic staring radar. Background Technology

[0002] Multi-target anti-jamming detection refers to the ability of radar or sensing systems to simultaneously identify and track multiple targets in complex environments, while possessing strong anti-jamming capabilities. It effectively suppresses the influence of natural or man-made interference sources, ensuring the accuracy and stability of detection results. This technology is commonly used in military, aviation, and autonomous driving fields to improve the reliability and practicality of systems in complex electromagnetic environments.

[0003] The existing technology has the following shortcomings:

[0004] In holographic staring radar systems, wide-beam transmission and digital beamforming (DBF) technologies are used to achieve simultaneous multi-beam reception. While this enhances the ability to detect multiple targets across the entire airspace, in complex environments such as cities, inter-beam interference and sidelobe leakage can easily occur when multiple high-speed targets fly at close angles or when there is a strong reflective background. This can lead to serious problems such as false alarms or missed alarms. For example, in urban low-altitude surveillance, the flight of multiple UAVs in formation can cause signal crosstalk, leading the system to misjudge the number of targets or miss concealed targets, exposing the technical bottlenecks of multi-beam systems in dynamic interference suppression and array accuracy management. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-target anti-jamming detection system based on holographic staring radar to address the shortcomings of the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-target anti-interference detection system based on holographic staring radar, comprising an array calibration module, a beamforming and optimization module, an adaptive filtering module, and an adaptive control module;

[0007] Array calibration module: performs periodic calibration of the radar receiving array to correct phase errors caused by environmental changes;

[0008] Beamforming and optimization module: Achieves full coverage through wide beam transmission, constructs multiple narrow beams for synchronous reception using digital beamforming technology, and employs a minimum sidelobe beamforming algorithm to control the sidelobe directivity of the received beam, thereby reducing signal leakage and crosstalk between beams.

[0009] Adaptive filtering module: Constructs a dynamic spatial model of the target and interference, and combines a spatial-temporal joint filtering algorithm to adaptively suppress interference signals from near-angle targets or strong reflection sources;

[0010] Adaptive control module: It fuses and analyzes data received from multiple beams, identifies and eliminates false targets, and dynamically adjusts beamforming parameters based on detection feedback information.

[0011] Preferably, the array calibration module includes a calibration antenna element or a coupled reference signal source, and constructs an amplitude-phase error feature vector by receiving array acquisition response data.

[0012] Preferably, the beamforming and optimization module employs a minimum sidelobe beamforming algorithm, which optimizes the beam weighting vector by constraint, thereby suppressing energy leakage in the sidelobe direction while maintaining the main lobe gain.

[0013] Preferably, the optimization includes: defining an objective function, the objective of which is to minimize the intensity of the sidelobes, constructing an objective function for the beamforming pattern, the mathematical expression of which is: Where w is the beamforming weighting vector, H is the complex conjugate transpose, A is the gain matrix with respect to the target direction, is the sidelobe control matrix, λ is the weighting factor that controls the sidelobe suppression intensity, and N is the number of sidelobe target directions.

[0014] During beamforming, the gain requirement in the main lobe direction is defined, and the weights of the array elements are adjusted by the weighting vector w to maximize the gain in the target signal direction.

[0015] To ensure sidelobes are minimized, the gain in the sidelobe direction is set to the minimum value;

[0016] The weighted vector w is solved using a convex optimization method to minimize the objective function.

[0017] Preferably, the convex optimization method includes: solving for the weighted vector w using the interior point method, specifically: constructing the Lagrangian function, with the objective optimization problem being: R s θ0 is the weighted covariance matrix in the sidelobe direction, used to measure the sidelobe energy distribution, and a(θ0) is the vector in the main lobe direction, i.e. the direction of the target signal.

[0018] Define Lagrange multipliers: λ∈C: corresponding to the main lobe gain equality constraint; μ≥0: corresponding to the weight energy inequality constraint;

[0019] Construct the Lagrangian function: Where, λ * Indicates conjugate transpose; This indicates that both the objective function and the constraint function are considered simultaneously, γ * γ is the complex conjugate of γ, and μ is a Lagrange multiplier;

[0020] Introducing a logarithmic barrier term to handle inequality constraints, the inequality constraints are introduced into the objective function φ(w) through a logarithmic barrier function: φ(w) = -log(η - w) H w); Construct the objective function f with obstacle terms. barrier (w), the expression is: f barrier (w)=w H R s w+τ·φ(w); τ>0 represents the obstacle term weight parameter. Let the current iteration point be w. (k) Using Newton's method, a Taylor expansion is performed at the current point to calculate the gradient and Hessian matrix, and the direction increment Δw is solved:

[0021] gradient:

[0022] Hessian matrix:

[0023] Solution to Newton's update direction:

[0024] After obtaining Δw, update the solution: w (k+1) =w (k) +α·Δw;

[0025] α is the step size parameter, which decreases by τ after each iteration.

[0026] When the gradient norm is satisfied: Convergence is considered conditional upon condition:

[0027] The final optimized beam weight is the optimal beam weighting vector that satisfies the constraints of minimum sidelobe, main lobe gain, and energy.

[0028] Preferably, the adaptive filtering module constructs a dynamic spatial model based on the direction vectors, power spectral density, and Doppler characteristics of the target and interference, uses the spatial projection matrix method to filter out the interference direction signal, and further employs the minimum mean square error or recursive least squares filtering algorithm to dynamically track and adaptively suppress the time domain residual of the interference signal.

[0029] Preferably, in the adaptive control module, two channel data with different spatial directions but overlapping beam coverage areas or target association are selected from the multiple output received beams, denoted as X1(t) and X2(t), respectively; where X1(t) and X2(t) are complex echo data received within the time window; the beam signals of the two directions are fused by amplitude or power weighting to construct fused data and generate a target confidence index, which is compared with a preset decision threshold to determine whether there is a false target or beam inaccuracy in the current beam combination: if the target confidence index is less than the preset decision threshold, it is considered that there is interference or the target is not accurately identified; according to the determination result, the beam reconstruction mechanism is triggered.

[0030] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0031] This invention systematically solves the problems of inter-beam sidelobe leakage and signal crosstalk in multi-beam reception of holographic staring radar in complex environments by introducing an array calibration module, beamforming and optimization module, adaptive filtering module, and adaptive control module. By periodically calibrating the array channels, phase errors caused by environmental changes are accurately corrected, ensuring beam pointing stability. A minimum sidelobe beamforming algorithm is employed to effectively suppress energy diffusion in non-target directions, reducing false alarms and missed alarms in multi-target detection, and improving the spatial resolution and directional selectivity of the radar system. Furthermore, this invention constructs a dynamic spatial model of targets and interference, combining spatial projection and temporal filtering to jointly suppress strong near-angle interference signals, achieving adaptive suppression of complex electromagnetic interference in high-density environments such as urban areas. By fusing observation data from multiple beams and making decisions and feedback adjustments based on confidence thresholds, a closed-loop optimization mechanism is formed, significantly improving the system's detection capability and anti-interference stability against weak and concealed targets, demonstrating good engineering feasibility and practical application value. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0033] Figure 1 This is a system module diagram of the present invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] For examples, please refer to Figure 1 As shown in the figure, the multi-target anti-jamming detection system based on holographic staring radar described in this embodiment includes an array calibration module, a beamforming and optimization module, an adaptive filtering module, and an adaptive control module;

[0036] Array calibration module: performs periodic calibration of the radar receiving array to correct phase errors caused by environmental changes;

[0037] Beamforming and optimization module: Achieves full coverage through wide beam transmission, constructs multiple narrow beams for synchronous reception using digital beamforming technology, and employs a minimum sidelobe beamforming algorithm to control the sidelobe directivity of the received beam, thereby reducing signal leakage and crosstalk between beams.

[0038] Adaptive filtering module: Constructs a dynamic spatial model of the target and interference, and combines a spatial-temporal joint filtering algorithm to adaptively suppress interference signals from near-angle targets or strong reflection sources;

[0039] Adaptive control module: It fuses and analyzes data received from multiple beams, identifies and eliminates false targets, and dynamically adjusts beamforming parameters based on detection feedback information.

[0040] To ensure the accuracy of beam pointing and the overall detection performance of the system during digital beamforming, it is necessary to correct for phase errors and amplitude inconsistencies caused by environmental changes (such as temperature fluctuations, humidity, and device aging). Specifically, this includes:

[0041] The system pre-sets several calibration antenna elements or coupled reference signal sources at the receiver end. By transmitting a known reference signal and acquiring the response of each channel at the array receiver end, it constructs an amplitude-phase error feature vector in the current state. Using the minimum mean square error (MMSE) algorithm or the least squares method, it compares the signal with a pre-set reference template to extract the relative error between channels and generate an error compensation matrix.

[0042] The error compensation matrix is ​​updated in real time or periodically and applied to the DBF weighting coefficients to correct beam pointing offset and sidelobe rise caused by channel mismatch, effectively reducing the risk of inter-beam interference and crosstalk. This calibration process can adaptively adjust the calibration frequency according to environmental conditions, achieving the system's high stability detection requirements in complex dynamic environments.

[0043] In this invention, the system can continuously maintain high-precision beam control capabilities, providing stable and reliable basic data support for subsequent multi-target anti-interference processing.

[0044] To achieve full airspace coverage and high-precision multi-target detection, this invention employs a combination of wide-beam transmission and digital beamforming (DBF), and introduces the minimum sidelobe beamforming (MSL-BF) algorithm at the receiver to reduce inter-beam signal leakage and crosstalk, thereby improving the system's anti-interference performance and detection accuracy. The specific steps are as follows:

[0045] The radar transmitting array employs a uniform excitation method with equal amplitude to form a wide-coverage main beam, enabling continuous illumination of a large airspace and avoiding missed target areas. This beam features low directionality and high coverage, making it suitable for simultaneous monitoring of multiple targets.

[0046] At the receiving array end, the echo signals received by each array element are weighted and synthesized using digital beamforming (DBF) technology to construct multiple narrow beams in real time, pointing in different directions simultaneously, thereby enabling synchronous detection of multiple space targets.

[0047] During beamforming, a weighted vector design method with minimum sidelobe constraints is adopted. Under the premise of ensuring that the main lobe gain meets the target signal reception requirements, the beam weight distribution is optimized, sidelobe energy is significantly suppressed, and sensitivity to interference sources from adjacent beams or other directions is reduced.

[0048] The system monitors the received signal strength and target characteristics in all directions in real time, identifies the strong interference direction through the interference direction estimation module, and dynamically reconstructs the sidelobe weights of the affected beams to form an interference-avoiding low sidelobe reception mode.

[0049] The signals received from multiple narrow beams are extracted and initially classified separately, and then spatial correlation and fusion matching are performed to eliminate overlapping detection or false alarm results caused by beam crosstalk.

[0050] In minimum sidelobe beamforming (MSL-BF), the goal of optimizing beam weight distribution is to minimize sidelobe leakage while ensuring that the main lobe gain is large enough to receive the target signal. Optimizing beam weights is typically achieved by designing weighting vectors, specifically including:

[0051] Define an objective function that aims to minimize the sidelobe intensity while ensuring the main lobe gain meets requirements. To achieve this, construct an objective function f(w), typically representing the beam pattern (i.e., the signal gain distribution), whose mathematical expression is: Where w is the beamforming weighting vector, H is the complex conjugate transpose, A is the gain matrix with respect to the target direction, and B...i λ is the sidelobe control matrix, λ is the weighting factor that controls the sidelobe suppression intensity, and N is the number of sidelobe target directions.

[0052] During beamforming, the gain requirement in the main lobe direction is first defined. The weights of the array elements are adjusted using a weighting vector w to maximize the gain in the target signal direction (i.e., the main lobe direction).

[0053] To ensure sidelobes are minimized, the gain in the sidelobe direction is set to a minimum. This can be controlled by introducing a minimum gain constraint on the sidelobes (e.g., the sidelobe gain must be below -20dB).

[0054] Convex optimization methods (such as least squares, gradient descent, interior point method, etc.) are used to solve for the weighted vector w, so as to minimize the objective function.

[0055] The steps of the convex optimization algorithm are as follows: Initialize the beam weight w; update the weight through the optimization algorithm, iterate the calculation until the sidelobe minimization requirement is met and the main lobe gain is greater than or equal to the preset value.

[0056] For example, in this invention, the interior-point method is used to solve for the weighted vector w. Specifically, the Lagrangian function is constructed, and the objective optimization problem is: R s θ0 is the weighted covariance matrix in the sidelobe direction, used to measure the sidelobe energy distribution, and a(θ0) is the vector in the main lobe direction, i.e. the direction of the target signal.

[0057] Define the Lagrange multipliers: λ∈C: corresponding to the main lobe gain equality constraint; μ≥0: corresponding to the weight energy inequality constraint.

[0058] Construct the Lagrangian function: Where, λ * This represents the conjugate transpose, ensuring the differentiability of complex numbers in optimization. This indicates that both the objective function and the constraint function are considered simultaneously, γ * γ is the complex conjugate of γ, and μ is a Lagrange multiplier;

[0059] To address inequality constraints, a logarithmic barrier term is introduced to ensure that the system remains within the feasible region (i.e., w) throughout the interior-point method iteration. H If w < η, the inequality constraint is introduced into the objective function φ(w) through a logarithmic barrier function: φ(w) = -log(η - w) H w); Construct the objective function f with obstacle terms. barrier (w), the expression is: f barrier (w)=w H R sw+τ·φ(w); τ>0: represents the weight parameter of the obstacle term, which is initially large and gradually decreases with iteration; when the obstacle term tends to infinity, it approaches the infeasible boundary, thus restricting w to fall within the feasible set.

[0060] Let the current iteration point be w. (k) Using Newton's method, a Taylor expansion is performed at the current point to approximate the gradient and Hessian matrix, and the direction increment Δw is solved:

[0061] gradient:

[0062] Hessian matrix:

[0063] Solution to Newton's update direction:

[0064] After obtaining Δw, update the solution: w (k+1) =w (k) +α·Δw;

[0065] α is the step size parameter, which satisfies the Armijo condition (guaranteeing descent) through line search; the equality constraints can be solved by jointly solving λ and w using the KKT conditions (Karush-Kuhn-Tucker), or by applying a projection during the update.

[0066] Decrease τ after each iteration, for example: τ (k+1) =β·τ (k) β∈(0,1) represents the adjustment weight, which gradually reduces the influence of the obstacle term;

[0067] Convergence is considered complete when any of the following conditions are met:

[0068] Gradient norm:

[0069] Relative update amount: ∥Δw∥ / ∥w∥≤δ;

[0070] The objective function changes very little.

[0071] The final optimized beam weight is the optimal beam weighting vector that satisfies the constraints of minimum sidelobe, main lobe gain, and energy.

[0072] During the iteration process, the weighting coefficients of each array element are continuously adjusted to ensure that the updated radiation pattern not only maintains the main lobe gain but also significantly reduces the gain in the side lobe directions. The gain of the entire system is optimized by adjusting the constraints of the weighting vector (such as maximum side lobe gain limits and dynamic gain constraints).

[0073] Further adjustments using nonlinear constraints optimize and correct any abrupt changes in sidelobe gain caused by array imperfections (such as array element offsets, coupling effects, etc.). Nonlinear programming optimization techniques are then used to further refine minute errors occurring during beamforming, ensuring optimal sidelobe suppression in actual operation.

[0074] After beam weight optimization, signal simulation or actual testing is performed to verify the effectiveness of the beam pattern, especially the sidelobe suppression effect. Based on the test results, the λ factor or optimization algorithm is further adjusted to ensure that the system's anti-interference capability and target detection accuracy meet the requirements in practical applications.

[0075] Using radar echo signals, the direction information of each signal source in the current scene is extracted based on direction arrival estimation techniques (such as MUSIC and ESPRIT). A dynamic spatial model is established by taking the position, motion state, reflection intensity and other parameters of multiple detected targets and suspected interference sources, including main lobe targets and near-angle strong reflection interference sources. The model records the direction vector, power spectral density and Doppler characteristics of each signal source for subsequent differentiation between targets and interference.

[0076] In the spatial domain, spatial projection matrix methods (such as interference subspace projection) are used to identify and filter signals in the interference direction;

[0077] Construct a subspace J for the interference signal, and then project the received data X onto the interference orthogonal subspace: X filtered =(I-JJ) Z X; where X filtered For the signal after interference projection filtering, the target component is retained and the interference component is removed. J Z Let J be the conjugate transpose (Hermitian matrix) and I be the identity matrix (dimension: M×M), used to construct the projection operator; this step can effectively suppress strong interference signals from near angles while maintaining the integrity of the main lobe target information.

[0078] The filtered signal is analyzed in the time domain to extract the instantaneous amplitude change, Doppler frequency and phase characteristics of the interference signal; the minimum mean square error (LMS) adaptive filter or RLS (recursive least squares) filter is applied to dynamically track the residual interference in the time domain; the filter continuously adjusts the coefficients according to the error between the received signal and the reference template to achieve adaptive interference removal.

[0079] From the multiple output received beams, select two channel data with different spatial directions but overlapping beam coverage areas or target association, and denot them as X1(t) and X2(t), respectively; where X1(t) and X2(t) are the complex echo data received within the time window.

[0080] Amplitude or power weighted fusion of the two beam signals is performed to construct fused data; the fused data is used to improve the confidence of identifying the same target, while reducing the deviation caused by beam crosstalk.

[0081] Energy detection, coherence feature extraction, or waveform matching are performed on the fused data to generate a target confidence index Ptarget.

[0082] It is compared with a preset decision threshold θ to determine whether the current beam combination contains false targets or is misaligned:

[0083] If the target confidence index Ptarget is less than the preset decision threshold θ, it is considered that there is interference or the target is not accurately identified.

[0084] Based on the judgment results, if the confidence level is too low or the target is offset, the beam reconstruction mechanism is triggered, and the following parameters are adjusted:

[0085] Main lobe direction correction (fine-tuning beam pointing); beamwidth adjustment (narrowing / widening, controlling side lobes); side lobe weight redistribution (e.g., adjusting constraint vectors in MSL-BF); the adjusted parameters are used for beamforming calculation in the next cycle to achieve adaptive closed-loop adjustment.

[0086] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0087] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0088] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A multi-target anti-jamming detection system based on holographic staring radar, characterized in that: It includes an array calibration module, a beamforming and optimization module, an adaptive filtering module, and an adaptive control module; Array calibration module: performs periodic calibration of the radar receiving array to correct phase errors caused by environmental changes. The array calibration module includes calibration antenna elements or coupled reference signal sources, collects response data from the receiving array, constructs an amplitude-phase error feature vector, generates an error compensation matrix based on the amplitude-phase error feature vector, and loads the error compensation matrix into the weighting coefficients of the digital beamforming in real time or periodically to compensate for the amplitude and phase errors of the array channels caused by temperature, humidity or device aging, and adaptively adjusts the array calibration frequency according to changes in environmental conditions. Beamforming and optimization module: Achieves full coverage through wide beam transmission, constructs multiple narrow beams for synchronous reception using digital beamforming technology, and employs a minimum sidelobe beamforming algorithm to control the sidelobe directivity of the received beam, thereby reducing signal leakage and crosstalk between beams. Specifically, this includes: under the condition of wide beam transmission coverage of the detection area, based on the receiving array data compensated by the error compensation matrix, weighting and synthesizing the echo signals received by each array element through digital beamforming to form multiple narrow receiving beams pointing in different spatial directions, so as to simultaneously receive the echo signals of multiple targets; and the beamforming and optimization module adopts the minimum sidelobe beamforming algorithm to constrain and optimize the beam weighting vector under the condition of meeting the target receiving gain requirements in the main lobe direction, so as to suppress energy leakage in the sidelobe direction and signal crosstalk between different receiving beams; Adaptive filtering module: Constructs a dynamic spatial model of the target and interference, and adaptively suppresses interference signals from near-angle targets or strong reflection sources by combining spatial-temporal joint filtering algorithms. Specifically, it includes: A dynamic spatial model is constructed based on the direction vectors, power spectral density, and Doppler characteristics of the target and interference. An interference signal subspace J is constructed according to the signal source direction information. The received data X is projected onto the interference orthogonal subspace to obtain a spatial filtering signal that suppresses strong near-angle interference while maintaining the main lobe target component. Based on the spatial filtering signal, the minimum mean square error (LMS) or recursive least squares (RLS) adaptive filtering algorithm is used to dynamically track and adaptively suppress the residual interference in the time domain, thereby realizing joint spatial-time filtering. Adaptive control module: It fuses and analyzes data received from multiple beams, identifies and eliminates false targets, and dynamically adjusts beamforming parameters based on detection feedback information; In the adaptive control module, two channel data with different spatial directions but overlapping beam coverage areas or target association are selected from multiple output received beams, denoted as X1(t) and X2(t), respectively; where X1(t) and X2(t) are complex echo data received within the time window; the beam signals of the two directions are fused by amplitude or power weighting to construct fused data and generate a target confidence index, which is compared with a preset decision threshold to determine whether there is a false target or beam inaccuracy in the current beam combination: if the target confidence index is less than the preset decision threshold, it is considered that there is interference or the target is not accurately identified; according to the determination result, the beam reconstruction mechanism is triggered.

2. The multi-target anti-jamming detection system based on holographic staring radar according to claim 1, characterized in that: Optimizations include: Define an objective function that minimizes the intensity of the sidelobes. Construct an objective function for the beamforming pattern, and its mathematical expression is: Where w is the beamforming weighting vector, H is the complex conjugate transpose, A is the gain matrix with respect to the target direction, and B... i is the sidelobe control matrix, Q is the weighting factor that controls the sidelobe suppression intensity, and N is the number of sidelobe target directions; During beamforming, the gain requirement in the main lobe direction is defined, and the weights of the array elements are adjusted by the weighting vector w to maximize the gain in the target signal direction. To ensure sidelobes are minimized, the gain in the sidelobe direction is set to the minimum value; The weighted vector w is solved using a convex optimization method to minimize the objective function.

3. A multi-target anti-jamming detection system based on holographic staring radar according to claim 2, characterized in that: Convex optimization methods include: using the interior-point method to solve for the weighted vector w, specifically: constructing the Lagrangian function, with the objective optimization problem being: ; This is the weighted covariance matrix along the sidelobe direction, used to measure the energy distribution of the sidelobes. It is the vector in the direction of the main lobe, i.e., the direction of the target signal; Define Lagrange multipliers: λ∈C: corresponding to the main lobe gain equality constraint; μ≥0: corresponding to the weight energy inequality constraint; Construct the Lagrangian function: ;in, Indicates conjugate transpose; This indicates that both the objective function and the constraint functions were considered simultaneously. It is the complex conjugate of γ. For Lagrange multipliers; Introducing a logarithmic barrier term to handle inequality constraints, the inequality constraints are introduced into the objective function through a logarithmic barrier function. middle: Construct the objective function with obstacle terms. The expression is: τ>0 indicates the weight parameter of the obstacle term. Let the current iteration point be... Using Newton's method, a Taylor expansion is performed at the current point to calculate the gradient and Hessian matrix, and the direction increment Δw is solved: gradient: ; Hessian matrix: ; Solution to Newton's update direction: ; Seeking Updated solution: ; α is the step size parameter, which decreases by τ after each iteration. When the gradient norm is satisfied: Convergence is considered conditional upon condition: The final optimized beam weight is the optimal beam weighting vector that satisfies the constraints of minimum sidelobe, main lobe gain, and energy.

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