A phased array multi-target capturing method based on adaptive filter

Abstract

The application discloses a phased array multi-target capturing method based on an adaptive filter and belongs to the technical field of phased array communication of unmanned aerial vehicles. The method comprises the following steps: setting an initial parameter combination of the adaptive filter; acquiring a mixed signal vector at a current moment, distinguishing far and near target signals, and preliminarily setting filter parameters; establishing a joint optimization function containing phased array filter parameters, target capturing probability and target capturing speed; adopting an adaptive Bregman ADMM algorithm to perform dynamic parameter updating on the joint optimization function; loading the finally optimized filter parameters to a phased array system to form a synthetic beam pointing to multiple targets, and performing differentiated gain distribution on the far and near targets to realize synchronous capturing of the multiple targets. The application can capture far and near targets, and is especially suitable for the measurement and control communication scene of unmanned aerial vehicles in a complex electromagnetic environment.

Description

Technical Field

[0001] This invention belongs to the field of UAV phased array communication technology, specifically relating to a phased array multi-target acquisition method based on adaptive filters, applicable to scenarios such as UAV swarm communication, low-altitude detection, and dynamic target tracking. Background Technology

[0002] The existing technology has the following drawbacks:

[0003] (1) Inefficient beam resource allocation: Traditional phased arrays use fixed period scanning, which cannot meet the tracking accuracy requirements in large-scale target scenarios;

[0004] (2) Severe interference between near and far targets: The current phased array system does not clearly distinguish between near and far targets, resulting in poor acquisition accuracy;

[0005] (3) Slow convergence speed of filters: Conventional algorithms are difficult to converge in UAV motion scenarios and cannot adapt to high-speed dynamic environments;

[0006] While existing multi-target acquisition algorithms can achieve a general acquisition capability, they do not solve the problem of near and far targets in dynamic tracking, and they do not integrate a closed-loop feedback mechanism. Summary of the Invention

[0007] In view of this, the present invention provides a phased array multi-target acquisition method based on adaptive filters. This invention can achieve accurate acquisition of multiple targets, and by combining near-far target differentiation and joint optimization, it solves the problems of difficulty in near-far target differentiation, low acquisition accuracy, and high computational complexity in traditional phased array systems for multi-target acquisition.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0009] A phased array multi-target acquisition method based on adaptive filters, applied to a UAV phased array system, includes the following steps:

[0010] Step a: Before the phased array system starts transmitting measurement and control data, set the initial parameter combination of the adaptive filter;

[0011] Step b: Obtain the mixed signal vector at the current moment through the phased array receiving channel, distinguish the distant and near target signals based on the mixed signal vector, and initially set the filter parameters according to the distance of the target;

[0012] Step c: Based on the signal model, establish a joint optimization function that includes phased array filter parameters, target acquisition probability, and target acquisition velocity;

[0013] Step d involves using the adaptive Bregman ADMM algorithm to dynamically update the parameters of the joint optimization function, loading the final optimized filter parameters into the phased array system to form a synthetic beam pointing to multiple targets, and performing differentiated gain allocation for near and far targets to achieve synchronous acquisition of multiple targets.

[0014] Furthermore, the initial parameter combination of the adaptive filter in step a is:

[0015]

[0016] in, This indicates the total number of subarrays in the phased array antenna. For the first The initial weight coefficients of each submatrix, i=1,2,...,N, where the superscript T denotes the transpose of the matrix.

[0017] Furthermore, in step b, the mixed signal vector at the current moment is:

[0018]

[0019] in, For the target total number, For the first The reflectance coefficient of the target For the first The relative distance between the targets This is the path loss index. Let m be the guidance vector for the m-th target. This represents the direction of arrival (DOA) of the m-th target. The distance between adjacent array elements. Represents the total number of subarrays in a phased array antenna system. For the signal wavelength, It is additive white Gaussian noise.

[0020] Furthermore, in step b, the signals of distant and near targets are distinguished based on the mixed signal vector, and the filter parameters are initially set according to the distance of the targets. The specific method is as follows:

[0021] Step b01, generate using phased array antenna Each preformed beam points to a different angle. For mixed signals Spatial filtering is performed to separate the independent signal components of each target. ,in The superscript H indicates conjugate transpose;

[0022] Step b02, for each independent signal component Calculate the time-domain average power :

[0023]

[0024] in, Indicates the start time of the current signal processing moment. This indicates the length of the observation time window used for power estimation, in seconds;

[0025] Then calculate the relative distance to the target. :

[0026]

[0027] in, The nominal received power at the reference distance;

[0028] Step b03, according to Calculate dynamic distance threshold , will satisfy The target is classified as a short-range set. The rest are grouped into a remote set, and classification label vectors are generated. ,in, For the first The classification identifier of each target, if Then it means the first The first goal is a short-term goal, if... Then it means the first The target is a remote target;

[0029] Step b04: Differentiate the initial filter weights based on the classification identifier:

[0030]

[0031] in, Let be the normalization coefficient, so that , It is the first Each sub-array is paired with the first One target direction The magnitude of the guiding vector component. It characterizes the spatial response intensity of the subarray to the signal in that direction.

[0032] Furthermore, the joint optimization function in step c is:

[0033]

[0034] st

[0035]

[0036] in, For the first The probability of capturing a target. The variance of additive white Gaussian noise characterizes the noise power of the receiving channel. The capture time for the slowest target. For the first Detection range of each target For the first The speed of movement of the target These are weighting coefficients. This is the minimum overall capture probability threshold required by the system. Where W is the longest capture time allowed for the task, W is the parameter combination of the adaptive filter, and Z is an auxiliary variable.

[0037] Furthermore, in step d, the adaptive Bregman ADMM algorithm is used to dynamically update the parameters of the joint optimization function. Specifically, the method is as follows:

[0038] Step d01: Decompose Z into weighted modulus constraints. Capture probability constraints Capture time constraints Three auxiliary variables are used to decompose the constraints on the joint optimization function:

[0039]

[0040] Step d02: Select KL divergence as the Bregman distance function to match the exponential structure in the capture probability.

[0041]

[0042] Setting the augmented Lagrange function:

[0043]

[0044] definition For dual variables, corresponding to the Lagrange multipliers of the three constraints;

[0045] in, For adaptive penalty parameters;

[0046] Step d03, for the non-convex terms in the objective function Perform a first-order Taylor expansion:

[0047]

[0048] Where, gradient Calculated using the chain rule:

[0049]

[0050] The linearized objective function is obtained as follows:

[0051]

[0052] Step d04: Initialize the iteration counter Set the maximum number of iterations. and convergence threshold Alternate direction updates are performed:

[0053] (1) Update the main variable :

[0054]

[0055] Solve using the complex field conjugate gradient method;

[0056] (2) Update auxiliary variables :

[0057]

[0058]

[0059]

[0060] in, This is a projection operator used to truncate matrix elements to the unit circle.

[0061] Use the LogSumExp function to smooth the maximum value term:

[0062]

[0063] in, This is used to control the smoothness level;

[0064] (3) Update the dual variable :

[0065]

[0066] (4) Based on the original residuals and dual residuals Adjust the adaptive step size according to the rules :

[0067]

[0068] in, As a regulating factor, This is the balance threshold;

[0069] (5) Determine whether the following termination conditions are met:

[0070] Initial feasibility:

[0071] Duality feasibility:

[0072] Relative change of objective function:

[0073] If all the above termination conditions are met, the iteration ends; otherwise, the alternating direction update continues.

[0074] Furthermore, in step d, the final optimized filter parameters are loaded into the phased array system to form a synthetic beam pointing towards multiple targets. Specifically:

[0075] (1) The optimized filter weight coefficients are assigned to each subarray of the phased array antenna, and each subarray contains The direction of arrival (DOA) angles of each subarray are corrected by coordinate transformation among adjacent array elements. ;

[0076] (2) Based on the adaptive LMS algorithm, the signals between subarrays are phase aligned to eliminate the position deviation of array elements caused by the attitude change of the UAV and to meet the coherent superposition condition of the synthesized beam. ;in, This represents the maximum phase difference between adjacent subarrays, corresponding to a path difference constraint of 0.125λ;

[0077] (3) Update the beam pointing parameters according to the target dynamic position and adopt a three-level beamforming architecture: the first level uses an analog phase shifter to achieve ±45° coarse scanning in the subarray, the second level uses a digital phase shifter to perform ±5° fine adjustment, and the third level uses a digital beamformer to complete 0.1° level micro pointing.

[0078] (4) By fusing the beam status feedback information of each subarray through federated filtering, the 3dB bandwidth of the synthesized beam is dynamically adjusted. :

[0079]

[0080] in, This represents the total number of all array elements involved in the synthesis.

[0081] Furthermore, in step d, differentiated gain allocation is performed for near and far targets, specifically as follows:

[0082] (1) Based on the relative distance to the target Set the nonlinear gain function:

[0083] At that time, allocation The attenuation gain;

[0084] At that time, allocation Enhanced gain;

[0085] (2) Calculate the signal-to-noise ratio :

[0086]

[0087] Based on signal-to-noise ratio Dynamically adjust beam dwell time, when At that time, the stay period will be extended to:

[0088]

[0089] Where min represents taking the minimum number; simultaneously compressing the beamwidth to:

[0090]

[0091] in, This represents the current number of active array elements.

[0092] (3) A dual-priority arbitration mechanism is adopted. The first level allocates 60% of the gain resources according to the threat level, and the second level allocates them according to the movement speed. Allocate remaining resources to satisfy ;in To be assigned to the The second-level resource quantity of an entity, This refers to the total amount of remaining resources after the first level of allocation. For the first The speed of movement of each entity For the total number of entities, It is the sum of the velocities of all moving entities;

[0093] (4) Generate a set of gain allocation schemes through Monte Carlo sampling, and select the scheme that meets the following conditions as the optimal scheme for loading and execution:

[0094] and .

[0095] The beneficial effects of this invention are as follows:

[0096] 1. Differentiated processing of near and far targets: Through dynamic distance classification and weight gradient initialization, high gain is assigned to near targets to improve accuracy, while noise interference is reduced for far targets to optimize the signal-to-noise ratio.

[0097] 2. Multi-objective collaborative optimization: The joint optimization function balances capture probability and speed, ensures the minimum performance threshold, prioritizes time-consuming objectives, and avoids task failure.

[0098] 3. Efficient parameter updates: The Bregman ADMM algorithm improves the convergence speed of non-convex problems, supports real-time processing, and meets the low latency requirements of UAVs. Detailed Implementation

[0099] The technical solution of the present invention will be further described below. Obviously, these contents are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the following embodiments without creative effort are within the protection scope of the present invention.

[0100] A phased array multi-target acquisition method based on adaptive filters, applied to a UAV phased array system, includes the following steps:

[0101] Step a, initialize filter parameters. Before the phased array system starts transmitting telemetry and control data, set the initial parameter combination of the adaptive filter.

[0102] Step b, multi-target signal acquisition and calculation. The mixed signal vector at the current moment is acquired through the phased array receiving channel. Based on the mixed signal vector, the signals of distant and near targets are distinguished, and the filter parameters are initially set according to the distance of the targets.

[0103] Step c, construct the joint optimization function. Based on the signal model, establish a joint optimization function that includes phased array filter parameters, target acquisition probability, and target acquisition velocity.

[0104] Step d: Dynamic parameter update and target acquisition. An adaptive Bregman ADMM algorithm is used to dynamically update the parameters of the joint optimization function. The final optimized filter parameters are then loaded into the phased array system to form a synthetic beam pointing towards multiple targets. This enables differentiated gain allocation for near and far targets, achieving synchronous acquisition of multiple targets.

[0105] This embodiment focuses on low-to-medium altitude UAV communication scenarios to verify the robustness of the multi-target acquisition method during both near and far flight. The system configuration environment is as follows:

[0106] Table 1 System Configuration Environment

[0107]

[0108] In step a, the initial parameter combination of the adaptive filter is:

[0109]

[0110] in: Indicates the number of phased array elements. For the first The initial weighting coefficients of the unit.

[0111] At the same time, initialize the iteration counter. Set the maximum number of iterations. and convergence threshold .

[0112] In step b, the mixed signal vector at the current moment is:

[0113]

[0114] in: For the target total number, For the first The reflectance coefficient of the target For the first The relative distance between the targets This is the path loss index (typical value 2-4). Let m be the guidance vector for the m-th target. The distance between adjacent array elements. Represents the total number of phased array antenna elements. For the signal wavelength, It is additive white Gaussian noise.

[0115] In step b, based on the mixed signal vector Distinguish between near and far target signals, and initially set filter parameters according to the distance of the target. The specific method is as follows:

[0116] (1) Multi-beam signal separation: generated by phased array antenna Each preformed beam points to a different angle. For mixed signals Spatial filtering is performed to separate the independent signal components of each target. ,in ;

[0117] (2) Delay power mapping: For each separated signal Calculate the time-domain average power Combined with path loss model To infer the relative distance of the target ,in The nominal received power at the reference distance;

[0118] (3) Dynamic threshold classification: based on Calculate dynamic distance threshold , will satisfy The target is classified as a short-range set. The rest are grouped into a remote set, and classification label vectors are generated. ( Indicates short distance, (Indicates remote)

[0119] (4) Weight gradient initialization: Assign differentiated values ​​to the initial filter weights based on the classification results:

[0120]

[0121] Where γ is the normalization coefficient, such that .

[0122] The joint optimization objective function mentioned in step c is:

[0123]

[0124] st

[0125]

[0126] in: For the first Target capture probability, The variance of additive white Gaussian noise characterizes the noise power of the receiving channel. The capture time for the slowest target. For the first Target detection range For its speed of movement, These are weighting coefficients. This is the minimum overall capture probability threshold required by the system. The maximum capture time allowed for the mission is preset based on the drone mission requirements.

[0127] In step d, the adaptive Bregman ADMM algorithm is used to dynamically update the parameters of the joint optimization function, including:

[0128] (1) Introducing auxiliary variables , , Decompose the constraints of the joint optimization function:

[0129]

[0130] (2) The Kullback-Leibler divergence is chosen as the Bregman distance function to match the exponential structure in the capture probability:

[0131]

[0132] Augmented Lagrangian function:

[0133]

[0134] Parameter definition: For dual variables, corresponding to the Lagrange multipliers of the three constraints; This is an adaptive penalty parameter.

[0135] (3) For non-convex terms in the objective function Perform a first-order Taylor expansion (linearization):

[0136]

[0137] Where, gradient Calculated using the chain rule:

[0138]

[0139] The linearized objective function:

[0140]

[0141] (4) Alternating direction update steps

[0142] Step 1: Main Variable renew:

[0143]

[0144] Step 2: Auxiliary Variables renew:

[0145] Amplitude constraint :

[0146]

[0147] in This is a projection operator that truncates matrix elements to the unit circle.

[0148] Capture probability term :

[0149]

[0150] Time constraints :

[0151]

[0152] Use the LogSumExp function to smooth the maximum value term:

[0153]

[0154] in, To control the smoothness.

[0155] Step 3: Dual Variables renew:

[0156]

[0157] Step 4: Adaptive step size adjustment:

[0158] Based on the original residual and dual residuals Adjust according to the rules :

[0159]

[0160] in, As a regulating factor, This is the balance threshold.

[0161] (5) Convergence guarantee and termination condition

[0162] The objective function must satisfy the Kurdyka-Łojasiewicz (KL) property to ensure that the algorithm converges globally to a stable point in non-convex scenarios. The termination criterion is as follows:

[0163] ① Initial feasibility:

[0164] ② Duality feasibility:

[0165] ③ Relative changes in the objective function:

[0166] In step d, the final optimized filter parameters are loaded into the phased array system to form a synthetic beam pointing towards multiple targets, including:

[0167] (1) The optimized filter weight coefficients ( (For iterative counters) are allocated to each subarray module of the phased array, and each subarray contains Adjacent array elements ( , This represents the total number of elements in the phased array. (Total number of targets), the direction of arrival angle of each subarray is corrected through coordinate transformation. ;

[0168] (2) Based on the adaptive LMS algorithm, the signals between subarrays are phase aligned to eliminate the position deviation of array elements caused by the attitude change of the UAV and to meet the coherent superposition condition of the synthesized beam. ( This represents the maximum phase difference between adjacent subarrays, corresponding to a path difference constraint of 0.125λ.

[0169] (3) Update the beam pointing parameters according to the target dynamic position and adopt a three-level beamforming architecture: the first level achieves ±45° coarse scanning in the subarray (using analog phase shifters), the second level performs ±5° fine adjustment through digital phase shifters, and the third level completes 0.1° level micro pointing using digital beamformers;

[0170] (4) The beam state feedback information of each subarray is fused by federated filtering (federated filtering is defined as a data fusion structure of distributed Kalman filtering and main filter) to dynamically adjust the 3dB bandwidth of the synthesized beam. ,in To activate the total number of array elements ( (This represents the number of active elements in the s-th subarray).

[0171] In step d, differentiated gain allocation is applied to near and far targets to complete multi-target synchronous acquisition, including:

[0172] (1) Based on the relative distance to the target Set the nonlinear gain function:

[0173] Close-range targets ( )distribute attenuation gain

[0174] distant targets ( )distribute Enhanced gain ( Indicates the first (Target gain coefficient)

[0175] (2) Calculate the signal-to-noise ratio:

[0176]

[0177] Based on signal-to-noise ratio ( , Dynamically adjust beam dwell time (for noise power): when At that time, the stay period will be extended to ( (to meet the maximum acquisition time constraint), while compressing the beamwidth to ( (Number of currently active array elements);

[0178] (3) A dual-priority arbitration mechanism is adopted: the first level allocates 60% of the gain resources according to the threat level (the threat level is input by the external tactical system), and the second level allocates resources according to the movement speed. Allocate remaining resources;

[0179] (4) Generate a set of gain allocation schemes through Monte Carlo sampling, and select the schemes that satisfy the requirements. and The optimal solution is loaded and executed. It has a low capture probability.

[0180] The following quantitative results were obtained through a verification method combining microwave anechoic chamber and field measurements:

[0181] Table 2 Performance Test Results

[0182]

[0183] This embodiment verifies the engineering applicability of the method in complex electromagnetic environments, and its core innovative advantages are reflected in:

[0184] (1) The dynamic weighted gradient mechanism reduces the difference in the probability of capturing near and far targets to 8.7%;

[0185] (2) The Bregman ADMM optimization algorithm achieves fast iterative convergence, which is 40% faster than the standard ADMM;

[0186] (3) The joint optimization function design maintains a high capture probability under low signal-to-noise ratio;

[0187] (4) Differentiated beam gain allocation increases the gain of near-range targets by 4.3 dB, effectively suppressing the blocking effect of strong targets.

[0188] This invention can be used on vehicle-mounted phased array platforms to achieve multi-target acquisition. By combining a joint optimization function with an improved ADMM algorithm, it solves the problems of channel inconsistency, low angular resolution, and high computational complexity in traditional phased array systems when acquiring multiple targets. This invention employs time delay compensation based on initial angle estimation, signal weighting processing with an adaptive weighting factor, beamforming optimized by the MVDR criterion, and a closed-loop iterative update mechanism.

[0189] In summary, the technical solution of this invention significantly improves multi-target angle tracking, signal stability, and anti-interference capability, and solves problems such as interference between near and far targets and slow filter convergence in conventional phased array systems. It is applicable to the fields of UAV communication, radar detection, and satellite communication.

[0190] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An adaptive filter based phased array multi-target acquisition method applied to a UAV phased array system, characterized in that, The method comprises the following steps: Step a, before the phased array system starts the transmission of the measurement and control data, setting an initial parameter combination of the adaptive filter; The initial parameter combination of the adaptive filter is: in, This indicates the total number of subarrays in the phased array antenna. For the first The initial weight coefficients of each submatrix, i=1,2,...,N, where the superscript T denotes the transpose of the matrix; Step b, obtaining a mixed signal vector at the current time through the phased array receiving channel, distinguishing the far and near target signals based on the mixed signal vector, and preliminarily setting the filter parameters according to the far and near degrees of the targets; the mixed signal vector at the current time is: wherein, is the total number of targets, is the reflection coefficient of the th target, is the relative distance of the th target, is the path loss exponent, is the steering vector of the mth target, denotes the direction of arrival angle of the mth target, is the adjacent element spacing, represents the total number of subarrays of the phased array antenna, is the signal wavelength, is the complex additive white Gaussian noise; Distinguishing the far and near target signals based on the mixed signal vector, and preliminarily setting the filter parameters according to the far and near degrees of the targets, and the specific mode is: Step b01, generating a pre-beam by a phased array antenna, respectively pointing to different angles , spatially filtering the mixed signal to separate independent signal components of each target , where the superscript H represents conjugate transpose; Step b02, for each individual signal component , calculate the time domain average power : wherein denotes the start time of the current signal processing instant, denotes the observation time window length for power estimation in seconds; Then the target relative distance is calculated : wherein, is the nominal received power at the reference distance; Step b03, according to computing dynamic distance threshold , the target meeting is classified as a near-range set, and the rest is classified as a far-range set, generating a classification identification vector , wherein, is the classification identification of the th target, if , it indicates that the th target is a near-range target, if , it indicates that the th target is a far-range target; Step b04, differentially assigning the initial filter weight according to the classification identifier: wherein is a normalization coefficient such that , is the th subarray's steering vector component for the th target direction , whose modulus characterizes the subarray's spatial response strength for that direction signal; Step c, based on the signal model, establishing a joint optimization function containing the phased array filter parameters, the target capture probability and the target capture speed; Step d, using an adaptive Bregman ADMM algorithm to dynamically update the joint optimization function, loading the finally optimized filter parameters to the phased array system to form a synthetic beam pointing to multiple targets, and differentially assigning the gain to the far and near targets to realize the synchronous capture of multiple targets.

2. The adaptive filter based phased array multi-target acquisition method of claim 1, wherein, The joint optimization function in step c is: s.t. wherein, is the capture probability of the first target, is the capture probability of the nth target, is the variance of the additive white Gaussian noise, representing the noise power of the receiving channel, is the capture time of the slowest target, is the detection distance of the first target, is the detection distance of the nth target, is the motion speed of the first target, is the motion speed of the nth target, is the weighting coefficient, is the minimum overall capture probability threshold required by the system, is the maximum capture time allowed by the task, W is the parameter combination of the adaptive filter, and Z is the auxiliary variable.

3. The adaptive filter based phased array multi-target acquisition method of claim 2, wherein, In step d, the adaptive Bregman ADMM algorithm is used to dynamically update the joint optimization function, and the specific mode is: Step d01, decompose Z into weight modulus constraints , capture probability constraints , capture time constraints three auxiliary variables, decompose constraints on joint optimization function: Step d02, selecting the KL divergence as the Bregman distance function to match the exponential structure in the capture probability: Setting the augmented Lagrangian function: Definitions Lagrange multipliers corresponding to the three constraints for the dual variables; wherein is an adaptive penalty parameter; Step d03, first order Taylor expansion of non-convex terms in the objective function Perform a first order Taylor expansion: where the gradient is calculated from the chain rule The linearized target function is obtained as: Step d04, initialize iteration counter , set maximum number of iterations and convergence threshold , perform alternating direction update: (1) Update master variables : Solving by using the complex field conjugate gradient method; (2) update the auxiliary variables : wherein is a projection operator that truncates the matrix elements to lie within the unit circle; Using the LogSumExp function to smooth the maximum value term: wherein , for controlling the degree of smoothing; (3) updating dual variables : (4) Adjusting the adaptive penalty parameter according to the original residual and the dual residual , according to the rule : wherein, is a modulating factor, is a balancing threshold; (5) judging whether the following termination conditions are met: Original feasibility: dual feasibility: Relative change in objective function: If all the above termination conditions are met, the iteration is ended, otherwise the alternating direction update is continued.

4. The adaptive filter based phased array multi-target acquisition method of claim 3, wherein, In step d, the finally optimized filter parameters are loaded to the phased array system to form a synthetic beam pointing to multiple targets, and the specific mode is: (1) The optimized filter weight coefficients are assigned to each subarray of the phased array antenna, each subarray containing adjacent elements, and the direction of arrival angle of each subarray is corrected by coordinate transformation ; (2) The phase alignment of the signals between the sub-arrays is based on the adaptive LMS algorithm, the position deviation of the array elements caused by the change of the attitude of the unmanned aerial vehicle is eliminated, and the coherent superposition condition of the synthesized beam is met ; wherein, represents the maximum phase difference between adjacent sub-arrays, and corresponds to the path difference constraint of 0.125λ. (3) updating the beam pointing parameters according to the dynamic position of the target, using a three-level beam forming architecture: the first level uses an analog phase shifter to realize ±45° coarse scanning in the subarray, the second level uses a digital phase shifter to realize ±5° fine adjustment, and the third level uses a digital beam former to complete 0.1° level micro pointing; (4) The beam state feedback information of each subarray is fused by federal filtering to dynamically adjust the 3dB bandwidth of the synthesized beam : wherein, is the total number of all participating elements.

5. The adaptive filter based phased array multi-target acquisition method of claim 4, wherein, In step d, the far and near targets are differentially assigned the gain, and the specific mode is: (1) According to target relative distance Setting a non-linear gain function: at the time of allocation of the attenuation gain; at the time of allocation of the enhanced gain; (2) Calculate the signal-to-noise ratio : Based on signal-to-noise ratio Dynamic adjustment of beam dwell time, when the dwell time is extended to: Wherein, min represents the minimum number; at the same time, the beam width is compressed to: wherein is the current number of active elements; (3) A dual-priority arbitration mechanism is adopted. The first level allocates 60% of the gain resources according to the threat level, and the second level allocates them according to the movement speed. Allocate remaining resources to satisfy ;in To be assigned to the The second-level resource quantity of an entity, This refers to the total amount of remaining resources after the first level of allocation. For the first The speed of movement of each entity For the total number of entities, It is the sum of the velocities of all moving entities; (4) generating a gain assignment scheme set through Monte Carlo sampling, and selecting a scheme meeting the following conditions as the optimal scheme for loading and execution: and .

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