Intermediate frequency simulation method of phased array radar

Abstract

The invention belongs to the technical field of phased array radar intermediate frequency simulation, and discloses an intermediate frequency simulation method of a phased array radar, which comprises the following steps: carrying out time sequence anti-interference stabilization processing on beam scheduling information output by the phased array radar in real time to obtain a stable scheduling signal; performing domain division signal feature analysis of a beam pointing domain, a target trajectory domain and a resource allocation domain to obtain a sum path signal parameter, an azimuth difference path signal parameter and a pitch difference path signal parameter; performing coupling relevance verification according to the sum-path signal parameters and the actually measured echo data to obtain an operation abnormity judgment result; when the operation abnormity judgment result indicates that the intermediate frequency injection signal has a fault, performing signal track replacement compensation processing based on the azimuth difference path signal parameter and the pitch difference path signal parameter to obtain a target intermediate frequency simulation signal; performing radar tracking and search mode optimization, and outputting a phased array radar beam control instruction; and the tracking precision of the phased array radar on a complex tracking scene is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of phased array radar intermediate frequency simulation, and more particularly, to a phased array radar intermediate frequency simulation method. BACKGROUND

[0002] With the rapid development and wide application of phased array radar technology, the accurate evaluation and verification of radar system performance have become a key link in the modern radar development process; in the target detection and tracking task under complex electromagnetic environment, accurately simulating the working state of the radar and verifying the system performance is of great significance to shorten the development cycle, reduce the test cost and improve the system reliability; in order to realize efficient system-level verification and in-depth analysis of radar performance characteristics, in recent years, intermediate frequency simulation technology as a core means of phased array radar system debugging and testing has been widely valued, which realizes the comprehensive verification of radar detection, tracking and data processing whole process by injecting simulated signals in the intermediate frequency stage, combining digital signal processing and closed-loop control.

[0003] However, the existing phased array radar intermediate frequency simulation technology has significant limitations in the implementation of closed-loop control under the tracking and search working mode, which is particularly prominent in the multi-target dynamic tracking scene; specifically, when the phased array radar works in TAS mode, the system needs to maintain stable tracking of the discovered target while continuing to perform the search task, at this time, the target echo signal not only depends on the spatial position and motion characteristics of the target, but also is closely related to the real-time pointing of the radar beam, the dwell time and the resource scheduling strategy; under this working mode, the traditional open-loop intermediate frequency signal simulation method cannot dynamically adjust the simulation signal parameters according to the real-time wave position information of the radar scheduler and the tracking data rate, resulting in the inability to accurately simulate the related characteristics of the three-channel signals of azimuth, elevation and range; the current mainstream intermediate frequency simulation equipment adopts the signal generation method of preset trajectory, which generates target echoes conforming to a specific motion law through direct digital frequency synthesis technology and directly injects into the receiver; this method can simulate simple target motion, but cannot respond to real-time control instructions at the system level, and cannot adapt to the complex working mechanism of the coupling between beam scheduling and target tracking under TAS mode; in addition, the existing method has obvious deficiencies in dealing with key problems such as multi-target alternate tracking, beam switching timing and dynamic allocation of system resources, and cannot truly reflect the closed-loop control characteristics of the phased array radar in actual work, which ultimately limits the application effect of intermediate frequency simulation technology in the system-level verification of phased array radar.

[0004] In view of this, the present application provides a phased array radar intermediate frequency simulation method to solve the above problems. SUMMARY

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a mid-frequency simulation method for phased array radar, comprising:

[0006] Step S1: Perform time-series anti-interference stabilization processing on the beam scheduling information output in real time by the phased array radar to obtain a stable scheduling signal;

[0007] Step S2: Based on the stable scheduling signal, perform domain-specific signal feature analysis in the beam pointing domain, target trajectory domain, and resource allocation domain to obtain the sum path signal parameters, azimuth difference path signal parameters, and elevation difference path signal parameters;

[0008] Step S3: Perform coupling correlation verification based on the sum and path signal parameters and the measured echo data of the phased array radar receiver to obtain the result of the abnormal operation of the intermediate frequency injection signal;

[0009] Step S4: When the operation anomaly determination result indicates an intermediate frequency injection signal fault, perform signal trajectory substitution compensation processing based on the azimuth difference path signal parameters and the pitch difference path signal parameters to obtain the target intermediate frequency simulation signal;

[0010] Step S5: Optimize radar tracking and search modes based on the target intermediate frequency simulation signal, and output phased array radar beam control commands.

[0011] The technical effects and advantages of the intermediate frequency simulation method for phased array radar of the present invention are as follows:

[0012] This invention eliminates timing jitter and noise interference during beam switching through timing anti-interference stabilization processing, providing a reliable foundation for subsequent domain-specific signal feature analysis. Through domain-specific analysis of the beam pointing domain, target trajectory domain, and resource allocation domain, it accurately extracts the signal parameters of the sum path, azimuth difference path, and elevation difference path, achieving comprehensive capture of complex signal characteristics in TAS mode. The coupling correlation verification between the sum path signal parameters and measured echo data establishes a dynamic mapping relationship between the intermediate frequency simulated signal and the actual radar response. By monitoring the correlation deviation in real time, abnormal states of the intermediate frequency injected signal can be detected promptly. When a signal fault is detected, a trajectory substitution compensation mechanism based on the azimuth difference path and elevation difference path signal parameters ensures the continuity and reliability of the simulation system, avoiding simulation interruptions caused by signal anomalies in traditional methods. In TAS mode, the radar needs to simultaneously handle the tracking tasks of multiple targets and perform search scans. Traditional open-loop simulation methods cannot adapt to this dynamically changing working mode. This invention establishes a closed-loop control mechanism, enabling the intermediate frequency simulation signal to adaptively adjust according to the radar's real-time beam scheduling information and resource allocation strategy. It accurately captures the target motion characteristics through pulse stream tracking and trajectory projection technology, and combines an intelligent fusion strategy with adaptive compensation weight coefficients to achieve accurate tracking of complex scenes. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of an intermediate frequency simulation method for a phased array radar according to the present invention;

[0014] Figure 2 This is a schematic diagram of an intermediate frequency simulation system for a phased array radar according to the present invention. Detailed Implementation

[0015] 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, and 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.

[0016] Example 1

[0017] Please see Figure 1 As shown in this embodiment, an intermediate frequency simulation method for a phased array radar includes:

[0018] Step S1: Perform time-series anti-interference stabilization processing on the beam scheduling information output in real time by the phased array radar to obtain a stable scheduling signal;

[0019] It is understood that the executing entity of this invention can be a phased array radar intermediate frequency simulation system, or it can be a terminal or a server; no specific limitation is made here. This embodiment of the invention uses a radar signal processing unit as an example for illustration.

[0020] Specifically, beam scheduling information is acquired in real time by a high-speed digital acquisition device connected to the output of the phased array radar control system, continuously obtaining the beam scheduling digital signal stream at a constant sampling rate (e.g., 200MHz). This signal stream undergoes time-series segmented sampling analysis to separate periodic disturbance components caused by electromagnetic interference in the signal sequence, thereby identifying the dominant beam switching frequency component in the current radar operating environment. This frequency range is typically between 1kHz and 100kHz. After identifying the dominant beam switching frequency, key parameters required by the timing prediction module are dynamically calculated based on its frequency characteristics, including the transfer interval coefficient and the interference noise amplitude matrix. These parameters reflect the timing discontinuities and signal deviation characteristics caused by electromagnetic interference. By mapping the dominant beam switching frequency to the structural parameters of the timing filter, a set of filter control factors with beam switching adaptability is generated, allowing the filtering algorithm to dynamically adjust according to the actual radar operating conditions instead of relying on static or preset models. After completing the above parameter calculations, the system introduces the signal processing module configured with the adaptive filtering parameters into the deep temporal network. Specifically, a temporal anti-interference encoder-decoder network is used as the core framework. In this network structure, the front-end encoder performs multi-scale compression extraction of the input signal features, and the back-end decoder restores and reconstructs the signal features after anti-interference processing. An updated adaptive filtering control unit is embedded in the intermediate layer to filter out periodic signal disturbances caused by the main interference frequency. The final output stable scheduling signal has time stability and sequence continuity.

[0021] Step S2: Based on the stable scheduling signal, perform domain-specific signal feature analysis in the beam pointing domain, target trajectory domain, and resource allocation domain to obtain the sum path signal parameters, azimuth difference path signal parameters, and elevation difference path signal parameters.

[0022] Specifically, based on the digital signal that has undergone anti-interference stabilization processing, a signal semantic analysis process is constructed, and different functional domains in the signal are identified and segmented. This process uses a temporal convolutional neural network or a feature attribution mechanism based on a fusion of traditional signal processing methods and deep learning to perform temporal clustering and semantic labeling of signal pulses, identifying the sequence categories of beam pointing pulses, target trajectory pulses, and resource allocation pulses. Based on this, the entire scheduling signal is divided into three sub-domains: the beam pointing domain, the target trajectory domain, and the resource allocation domain. Within the beam pointing domain, the temporal contour of the beam pointing is extracted using a pulse edge detection algorithm. The beam pointing offset is quantified using morphological operations and pulse fitting algorithms. Simultaneously, the position of the pointing intersection point is identified and its offset change in a continuous sequence is tracked to measure the degree of state deformation caused by the beam pointing. The pulse width change rate and intersection point offset are extracted and recorded as core physical indicators of the path state, constituting the path signal parameters. Simultaneously, within the target trajectory domain, a target tracking module based on pulse flow tracing is deployed to dynamically capture the target's trajectory between signal sequences. Amplitude gradient analysis and trajectory projection methods are used to perform three-dimensional reconstruction of the target's motion, thereby restoring the target's motion path, altitude hierarchy, and velocity distribution. Based on this, the trajectory velocity distribution function and direction vector field are calculated, extracting azimuth difference path signal parameters containing the target's velocity, direction, and distribution parameters. Within the resource allocation domain, inter-sequence background pulse subtraction technology is used to separate the micro-displacement information of resource allocation caused by interference. Sub-pulse level position tracking and structural feature alignment algorithms are used to quantify the displacement vector of resource allocation feature points in time, thereby obtaining quantitative data on resource allocation in amplitude, direction, and frequency, forming pitch difference path signal parameters.

[0023] Step S3: Perform coupling correlation verification based on the sum-path signal parameters and the measured echo data of the phased array radar receiver to obtain the result of the abnormal operation judgment of the intermediate frequency injection signal;

[0024] Specifically, a coupling model reflecting the physical correlation between the sum-path signal and the radar echo is established. This model uses the sum-path signal parameters as input variables and the measured echo data as response variables. By constructing the sum-path-echo coupling function relationship, interactive fitting or surface fitting methods are introduced during the modeling process to describe the influence trend of sum-path signal changes on echo amplitude and frequency changes, forming a physical response model with time continuity and state interpretability. The output signals of various components such as the receiver, digital processor, and controller, acquired in real time, are input into the coupling model as actual observation data. These signals are compared hourly with the echo response values ​​predicted by the model based on the sum-path signal, and the correlation deviation values ​​of various receiving components are calculated. These deviation values ​​reflect the degree of agreement between the actual component signals and the theoretical values ​​derived from the sum-path state. Each deviation value is compared with a preset tolerance threshold range (e.g., ±10%). If the deviation exceeds this range, the receiving component is judged to have an operational anomaly, and a corresponding anomaly judgment label is generated. Dynamic analysis of the time-series characteristics of the deviation is performed to identify specific patterns of anomalies. When the deviation exhibits a linear increasing trend, it corresponds to component drift faults. If it manifests as intermittent jumps, it is caused by poor connection. If it is accompanied by high-frequency random fluctuations, it is often related to electromagnetic interference or external signal conflicts. By jointly inputting the deviation value and anomaly identifier into the fault identification module, the current operating status of each receiving component is qualitatively classified and quantitatively evaluated, thereby obtaining an operational anomaly determination result that includes anomaly type, severity, and time stamp.

[0025] Step S4: When the operation anomaly judgment result indicates an intermediate frequency injection signal fault, perform signal trajectory substitution compensation processing based on the azimuth difference path signal parameters and the pitch difference path signal parameters to obtain the target intermediate frequency simulation signal;

[0026] Specifically, after detecting an anomaly in the intermediate frequency injection signal, parameters such as trajectory offset thickness, velocity coverage range, and directional distribution are extracted from the azimuth difference path signal parameters. These parameters are high-precision trajectory information obtained through pulse flow tracing and signal gradient reconstruction. Based on these parameters, the trajectory load is calculated inversely using a preset density constant and trajectory geometry model to obtain the perceived trajectory load value, which is the equivalent reflection mapping of the current target acting on the radar receiving surface. This perceived trajectory load value is used as an input variable and substituted into the trajectory load-echo response transfer function model of the phased array radar. This model is a system-preset empirical-physical hybrid form, which is a delayed first-order response system, including gain adjustment values, response interval constants, and delay parameters, to simulate the typical response behavior of the radar under different loads. By solving the output response of this transfer function, the theoretical echo parameter reference values, including echo amplitude and dominant frequency position, are obtained. Based on the fault determination results from the previous stage, the availability of the current intermediate frequency (IF) injected signal is assessed. If the signal is completely ineffective or the output is severely abnormal, the system automatically switches to a pure trajectory substitution mode, generating the target IF simulation signal solely based on trajectory data. If the signal exhibits slight deviation or partial distortion, a hybrid trajectory and signal compensation mode is adopted. In this mode, the dynamic weighting coefficient between the trajectory and signal data is further calculated. This weighting is determined by multiple dimensions, including signal deviation, noise level, and historical data stability, to achieve optimal information fusion. The theoretical echo parameter baseline value and the measured values ​​of the currently available receiving components are weighted and fused according to the adaptive weighting coefficient. Intelligent reconstruction is then performed using linear combination or time-series filtering to obtain the target IF simulation signal.

[0027] Step S5: Optimize radar tracking and search modes based on the target intermediate frequency simulation signal, and output phased array radar beam control commands.

[0028] Specifically, the velocity distribution characteristics and echo flow rate of the target are extracted from the target's intermediate frequency simulation signal. Combined with the total signal flow rate, the real-time tracking efficiency is calculated, reflecting the proportion of targets effectively captured during the current tracking process. The real-time tracking efficiency is then compared with the preset mission target value to obtain the current efficiency difference. Simultaneously, the difference between the currently detected target velocity value and the mission standard velocity value is calculated to obtain the velocity deviation and flow rate deviation, respectively. These two indicators, as the main variables affecting tracking performance, are directly input into the control model to drive beam parameter adjustments. Considering that phased array radar is a typical dynamic system, its response characteristics are affected by multiple factors such as beam force, pointing frequency, and radar load status. In the control strategy, an intelligent control optimization model is constructed based on the modeled dynamic response characteristics of the radar. This model adopts a fuzzy PID control structure, using velocity deviation and flow rate deviation as input variables. It utilizes 32 preset control logic rules in the rule base for fuzzy inference processing, outputting beam force adjustment and pointing frequency adjustment. Each control item in the rule base is set based on mission experience and dynamic behavior mapping relationships. This rule structure ensures that the control strategy has good responsiveness to different combinations of deviations. The adjustment parameters output by fuzzy inference are quantitatively defuzzified, and the control range of the beamforming device is limited by the physical constraints of the radar. For example, the beam force is controlled within 65% to 135% of the rated value, and the frequency is adjusted within 80kHz to 120kHz to prevent over-control or structural impact. The calculated beam adjustment parameters are converted into specific control command formats and sent to the beam control unit of the phased array radar. These commands include phase adjustment commands, frequency adjustment commands, or energy allocation parameters. The specific format is determined according to the structure of the equipment's beamforming system. For example, in a fully digital phased array system, the output is sent to a digital signal processing unit; in an analog hybrid phased array system, the adjustment values ​​are converted into phase control pressure commands. The command output frequency is set to a fixed control cycle, such as updating every 10 microseconds, to ensure sufficient real-time performance and response accuracy. The system corrects the tracking efficiency in real time within each control cycle, thereby continuously optimizing the beam state.

[0029] In one specific embodiment, the process of performing step S1 may specifically include the following steps:

[0030] Step S11: Perform time-series segmented sampling of the beam scheduling information output in real time by the phased array radar, and identify the dominant beam switching frequency component segment by segment.

[0031] Step S12: Use the dominant beam switching frequency component to dynamically adjust the transfer interval coefficient and interference noise amplitude matrix of the timing prediction module, and form beam switching adaptive filtering parameters based on the transfer interval coefficient and interference noise amplitude matrix.

[0032] Step S13: Input the beam scheduling information into the timing anti-interference encoder-decoder network configured with beam switching adaptive filtering parameters for signal stabilization and reconstruction processing, and output a stable scheduling signal.

[0033] Specifically, the continuous scheduling signals output by the phased array radar beam control unit are acquired in real time and continuously recorded at a fixed sampling rate (e.g., 200MHz). The resulting signal data constitutes a time-series signal sequence, which includes beam pointing information, target trajectory information, and resource allocation status. Time-domain analysis is performed on this signal sequence. A time-series segmented sampling algorithm is used to perform spectral transformation on the changes in continuous pulse values ​​over time. By analyzing the dominant frequency peak of the spectral amplitude curve in the 1kHz to 100kHz range, the dominant beam switching frequency component of the current radar is accurately identified. Based on the dominant beam switching frequency component, the parameters of the prediction and filtering modules in signal processing are updated, with a focus on adjusting two core components of the time-series filter: the transfer interval coefficient and the interference noise amplitude matrix. The transfer interval coefficient is used to model the dynamic behavior of pulse position or signal block changes over time, and its parameters determine the degree of response of the prediction model to the continuous sequence switching trend. The interference noise amplitude matrix reflects the observation uncertainty caused by electromagnetic interference during the current signal acquisition process. When the dominant beam switching frequency is low, signal disturbance changes relatively slowly. Therefore, the weights of the rate and acceleration terms in the transfer interval coefficient are appropriately reduced, while simultaneously improving the suppression capability of the interference noise amplitude matrix. However, in high-frequency switching scenarios, to ensure prediction accuracy, the dynamic response capability is enhanced, increasing sensitivity to abrupt changes. This adaptive parameter adjustment process is based on a predefined mapping function between frequency and control coefficients, or it is learned and adjusted in real-time during system operation, forming a set of filtering parameters for different switching frequencies. The configured beam switching adaptive filtering parameters are embedded into a deep learning network structure for signal processing, specifically using a temporal anti-interference encoder-decoder network as the core processing framework. The front-end encoder of this network compresses the input signal into a high-dimensional feature representation at multiple scales, extracting edge, structural, and target state features. The decoder then reconstructs features based on the encoding results, restoring the disturbance-free signal information into a highly temporally stable output signal. An intermediate dynamic filtering module is constructed between the encoder and the decoder. The transition interval prediction mechanism and the filter update mechanism are embedded in this module, and a temporal filtering algorithm is introduced as the basic engine for temporal feature interference suppression processing. This enables the network to dynamically adjust the filtering behavior based on the temporal domain analysis results while processing signals sequence by sequence.

[0034] In one specific embodiment, the process of performing step S13 may specifically include the following steps:

[0035] Step S131: Configure the transfer interval coefficient in the beam switching adaptive filtering parameters to the encoder part of the temporal anti-interference encoder-decoder network. In the encoder part, perform switching state prediction on the scheduling features to obtain a multi-level temporal feature sequence. The encoder part includes 5 temporal convolutional layers, and each temporal convolutional layer is followed by a temporal prediction module based on the transfer interval coefficient.

[0036] Step S132: Configure the interference noise amplitude matrix in the beam switching adaptive filtering parameters into the intermediate layer of the time-series anti-interference encoder-decoder network, and perform time-series update filtering on the multi-level time-series feature sequences through the interference noise amplitude matrix to obtain the filtered and purified time-series feature sequences;

[0037] Step S133: Input the filtered and purified temporal feature sequence into the decoder part of the temporal anti-interference encoder-decoder network for sequence expansion and reconstruction processing to obtain the initial stable scheduling signal. The decoder part includes 5 inverse temporal convolutional layers. Each inverse temporal convolutional layer combines the optimal estimation result of temporal filtering to perform pulse-level reconstruction.

[0038] Step S134: Perform pulse offset compensation processing based on time-series filtering state estimation on the initial stable scheduling signal, and use the switching offset predicted by the transfer interval coefficient to perform reverse correction on the signal pulse to obtain a stable scheduling signal.

[0039] Specifically, the dominant beam switching frequency value extracted through time-series segmented sampling analysis is converted into transfer interval coefficients and interference noise amplitude matrices in the time-series filter, and injected into the key modules of the network structure as dynamic parameters. In the encoder part, the system uses a 5-layer time-series convolutional neural network as the signal feature compression channel. Each convolutional layer extracts low-level to high-level representations of the signal, such as edge, texture, and shape information. A transfer interval prediction module based on the current dominant frequency configuration is connected to each convolutional operation. This module uses the transfer interval coefficients to extrapolate the feature representation of the previous time step, that is, predicts the feature change trend of the signal at the current time step before observing the next sequence signal. After all 5 convolutional layers are completed, the system performs unified processing on the multi-level time-series feature sequence output by the encoder in the intermediate connection layer, and applies the interference noise amplitude matrix to complete the time-series filtering update step. In this process, based on the results of the previous state prediction, the residual covariance between the prediction and the current observation is jointly analyzed to calculate the time-series gain, thereby performing the optimal state update operation to obtain the filtered and purified time-series feature sequence. The system inputs the filtered and purified temporal feature sequence into the decoder section, which is symmetrical to the encoder structure and also consists of five inverse temporal convolutional layers. Each inverse temporal convolutional layer not only performs conventional sequence expansion and temporal domain reconstruction operations, but also combines the optimal estimation results from the previous stage of temporal filtering to reconstruct and correct the position and intensity of each pulse during the reconstruction process. This ensures that the signal avoids offset ambiguity or structural distortion due to residual interference errors when reconstructing from the compressed state, resulting in an initial stable scheduling signal. Pulse offset compensation processing is then applied to the initial stable scheduling signal. Based on the current sequence pulse offset prediction stored in the temporal state estimator, the system inversely shifts each pulse in the initial stable scheduling signal to its theoretical stable position. This is achieved by using the offset increment predicted by the transfer interval coefficient to perform global offset field correction on the signal, forming an inverse transformation matrix for the signal pulses. The position of each pulse is then remapped and interpolated, ultimately outputting a stable scheduling signal.

[0040] In one specific embodiment, the process of performing step S2 may specifically include the following steps:

[0041] Step S21: Identify the sequence assignment of beam pointing pulses, target trajectory pulses and resource allocation pulses in the stable scheduling signal, and divide the stable scheduling signal into beam pointing domain, target trajectory domain and resource allocation domain based on the sequence assignment;

[0042] Step S22: Perform pulse edge detection and pointing offset calculation based on the beam pointing domain, extract the beam pointing profile, and calculate the pointing intersection offset and pulse width change rate based on the beam pointing profile to obtain the path signal parameters;

[0043] Step S23: Perform pulse flow tracking and trajectory projection based on the target trajectory domain to obtain the target motion pulse sequence, and extract the trajectory velocity distribution and direction vector based on the target motion pulse sequence to obtain the azimuth difference path signal parameters;

[0044] Step S24: Perform background pulse subtraction and allocation displacement detection based on the resource allocation domain to obtain the pitch difference signal parameters.

[0045] Specifically, the system utilizes a deep temporal segmentation model or a multi-channel semantic extraction network based on an improved TCN structure to perform feature mapping on each pulse in the input signal. Combining amplitude gradient, edge response, and local pulse vectors, it determines the temporal attribution of three types of objects: beam pointing pulses, target trajectory pulses, and resource allocation pulses. Beam pointing pulses appear as regularly dense straight lines or intersecting pulses in the signal, exhibiting periodic shape structures and directional edge features. Target trajectory pulses, on the other hand, appear as clustered structures with varying sizes, blurred edges, and active changes. Resource allocation pulses are distributed at the signal edges, exhibiting static morphology and rigid contours. The system uses regional morphology factors, boundary contrast, and inter-sequence motion consistency for fusion discrimination, dividing the signal into three label domains: beam pointing domain, target trajectory domain, and resource allocation domain. After region segmentation, in the beam pointing domain, the system uses a pulse edge detection algorithm or an edge network based on directional gradients to extract the beam pointing contour. Hough transform is used to accurately identify the pulse segment coordinates and their direction information, and the beam pointing offset change is calculated. To quantify the pointing state, the system statistically analyzes the rate of change of pulse width relative to the standard beam geometry reference, and simultaneously identifies the positions of beam pointing intersections. It tracks the offset changes of these nodes in a continuous signal sequence, thereby obtaining the dynamic offset vector and width deformation factor of the pointing intersection. The rate of change of pulse width and the offset amplitude of the intersection can be directly mapped to pointing change modes such as local beam offset and stretching, forming the key physical basis for the path signal parameters. In the target trajectory domain, the system performs target tracking analysis using a pulse flow tracing method. It selects dense or sparse pulse flows to obtain continuous sequence motion trajectories between targets, and maps these trajectories to the trajectory projection module. The system estimates the target motion velocity and direction distribution using signal amplitude gradient and phase depth models, thus completing the three-dimensional trajectory morphology reconstruction. Based on the target motion pulse sequence data, the system statistically analyzes the offset paths of multiple targets per unit time, and constructs a velocity vector field after normalizing their length and direction. Then, it uses a distribution statistical function to cluster and classify the trajectory velocities, forming velocity distribution data. The aforementioned velocity vectors and direction distributions together constitute the azimuth difference path signal parameters, reflecting the current target distribution density, dynamic motion direction, and potential trends in tracking efficiency. Meanwhile, within the resource allocation domain, since most allocation components remain relatively stationary, their minute displacements can be identified using the inter-sequence background difference method. The system constructs an interference-free reference background signal for the resource allocation domain, and then performs pulse differential operations on the real-time monitoring signal to extract the boundary regions where minute displacements occur under high-frequency interference. A sub-pulse block matching method (such as phase correlation) is used to calculate the pulse displacement, and its trajectory is tracked in the time dimension to obtain the local change amplitude and frequency information of resource allocation. This information is used to reflect the stability of the support structure and the overall dynamic health status of the radar system, constituting the elevation differential signal parameters.

[0046] In one specific embodiment, the process of performing step S23 may specifically include the following steps:

[0047] Step S231: Perform segmented optical flow correlation on the continuous pulse sequence in the target trajectory domain, and construct inter-pulse connection paths segment by segment;

[0048] Step S232: Expand the inter-pulse connection path into a two-dimensional projection plane, and mark the velocity gradient changes point by point on the projection plane;

[0049] Step S233: Perform directional vector layer separation on the projection plane according to the velocity gradient change, and extract the vector components of the low-velocity layer and the high-velocity layer;

[0050] Step S234: Combine the vector components of the low-speed layer and the high-speed layer into azimuth difference path signal parameters.

[0051] Specifically, the system performs in-depth analysis of the separated pulse sequences within the target trajectory domain. First, a segmented optical flow correlation algorithm is applied to the continuous pulse sequence, dividing the entire sequence into multiple time windows (5 to 10 ms in length). Within each window, the correlation metric between adjacent pulses is calculated. An improved optical flow estimation method (such as the extended Farneback algorithm or the hierarchical Lucas-Kanade method) is used to identify the motion consistency between pulses, establishing a pulse-to-pulse connection mapping. This process calculates the temporal changes in pulse brightness gradient, contrast variation, and texture features within local regions, resulting in a dense set of connection vector fields. Each vector describes the displacement direction and amplitude of the pulse from time t to time t+1. The system integrates these vector fields into inter-pulse connection paths, forming a preliminary trajectory representation of the target motion.

[0052] Next, the system extends the inter-pulse connection paths from the temporal domain to the spatial domain, constructing a two-dimensional projection plane. On this plane, the horizontal axis represents the time dimension, and the vertical axis represents relative displacement or signal intensity changes. The system maps the previously calculated connection paths onto this two-dimensional plane, forming a spatiotemporal trajectory diagram. To accurately characterize the target's motion characteristics, the system calculates the velocity gradient change at each node on the projection plane. Specifically, it extracts the first and second derivatives of the displacement vector within a local 5×5 window, representing velocity and acceleration features respectively, and visually labels these feature values ​​on the projection plane in the form of color gradients or scalar fields. This visualization can intuitively reflect the time-varying characteristics of the target's motion state, including acceleration regions, deceleration regions, and velocity stabilization regions.

[0053] Based on the labeled velocity gradient change information, the system further performs directional vector hierarchical separation on the projection plane. Clustering algorithms (such as K-means or DBSCAN) are used to group the velocity vector field, classifying regions with similar velocity characteristics into the same category. According to the statistical distribution of velocity amplitude, the system typically divides the vector field into two main levels: a low-velocity layer (velocity values ​​below 50% of the overall average) and a high-velocity layer (velocity values ​​above 50% of the overall average). For the low-velocity layer, the system extracts its directional consistency and spatial distribution characteristics, which typically correspond to the target's slow maneuvering or stationary state. For the high-velocity layer, the system focuses on its acceleration change rate and direction change frequency, which reflect the target's rapid movement and high maneuverability.

[0054] Finally, the system fuses the vector components of the low-speed and high-speed layers to generate complete azimuth difference path signal parameters. The specific fusion process includes: 1. Weighting the two layers of vector components according to a 2:3 weight ratio, with a slightly higher weight for the high-speed layer to highlight its high dynamic characteristics; 2. Calculating the principal direction distribution histogram of the fused vector field, recording the direction distribution probability at 15° intervals within the range of 0° to 360°; 3. Extracting statistical features of the vector field, including average velocity, standard deviation, peak velocity, and direction consistency index; 4. Constructing temporal features to describe the changes in velocity and direction over time. The resulting azimuth difference path signal parameters are a set of parameters containing multi-dimensional features, capable of comprehensively describing the target's motion state and changing trends in the azimuth dimension, providing accurate azimuth feature basis for subsequent mid-frequency simulations.

[0055] In one specific embodiment, the process of performing step S3 may specifically include the following steps:

[0056] Step S31: Based on the sum-path signal parameters and the measured echo data of the phased array radar receiver, perform sum-path-echo coupling relationship modeling to obtain the sum-path-echo coupling relationship model;

[0057] Step S32: Predict and analyze the correlation deviation of the real-time output signal input of the phased array radar receiver, digital processor and controller and the path-echo coupling relationship model to obtain the correlation deviation values ​​of each receiving component;

[0058] Step S33: Perform threshold comparison and determination on the correlation deviation values ​​to obtain the anomaly determination identifier of each receiving component;

[0059] Step S34: Based on the correlation deviation value and the anomaly judgment identifier, the fault type is identified to obtain the operation anomaly judgment result of the intermediate frequency injection signal.

[0060] Specifically, the modeling of the sum-path signal parameters and measured echo data from the phased array radar receiver is performed. This framework uses the sum-path signal parameters as the dependent variable input source and the original signals (frequency, amplitude, or phase) acquired by the phased array radar receiver as the response target. A mapping model between the sum-path signal and echo data is constructed through multiple sampling and time alignment. During the modeling process, interactive fitting and nonlinear least squares methods are used to fit the coupling relationship. For example, an interactive nonlinear model is used to reflect the modulation effect of sum-path changes on the echo output. The model structure includes interactive terms, basic terms, and offset terms, and the modeling result is the sum-path-echo coupling relationship model. After modeling, the system collects multiple types of receiver component data in real time during radar operation, including the receiver's echo signal, the digital processor's processed data, and the controller's state response values. This real-time data is input into the previously established coupling model. Using the sum-path signal detection results as a reference, the theoretical response values ​​that each receiver component should output under the current sum-path state are predicted. The system compares the predicted values ​​output by the model with the real-time measured values ​​of the corresponding components, calculating the deviation between the two. These deviation values ​​are the correlation deviation values ​​of each receiving component, and their units are divided into echo difference, amplitude difference, and phase difference according to the component type, and are uniformly normalized to a percentage deviation. For the deviation value corresponding to each component, the system introduces a fixed threshold or dynamic threshold comparison mechanism for anomaly identification. In the setting, ±10% is used as the deviation tolerance limit. When the deviation value of a component exceeds this threshold, it is marked as an abnormal state. When performing this judgment, the system tracks and fits the changing trend of the deviation value to identify whether it belongs to sudden anomaly, long-term drift, or intermittent distortion. If the deviation value fluctuates sharply in a short period of time but the mean does not change much, it belongs to occasional disturbance; if the deviation value shows a linear growth trend, it indicates that the component itself is gradually degrading; if the deviation value shows a jump change between different time periods and lacks stability, it is caused by loose component connection, unstable line, or data communication interruption. Based on the joint analysis of deviation values ​​and anomaly indicators, the system constructs a fault type identification module. This module classifies and judges different types of anomalies by comparing their performance patterns in the time and frequency domains using pattern recognition algorithms. For example, linearly increasing deviations often correspond to component stability degradation caused by internal thermal drift; abrupt signal anomalies reflect poor contact between components and the controller; and high-frequency noise or random mismatches indicate environmental faults such as electromagnetic interference or external signal conflicts on the component surface. The system combines the identified deviation amplitude, time location, frequency distribution, and error pattern for feature matching, ultimately outputting an operational anomaly judgment result that includes the fault type, impact level, and recommended handling measures.

[0061] In one specific embodiment, the process of performing step S31 may specifically include the following steps:

[0062] Step S311: Pulse synchronization is performed between the sum-path signal parameters and the measured echo data from the phased array radar receiver to obtain the sum-path-echo pulse sequence;

[0063] Step S312: Construct an interaction function between the path signal and the echo output based on the physical characteristics of the phased array radar echo response, where the echo data is used as the response variable and the path signal is used as the excitation variable. The interaction function includes an interaction term, a basic term, an offset term, and a random fluctuation term.

[0064] Step S313: Based on the path-echo pulse sequence, the parameters of the interaction function are adjusted by iterative fitting method, and the weights of the interaction terms, the weights of the basic terms, and the values ​​of the offset terms are calculated successively.

[0065] Step S314: Establish a path-echo coupling relationship model based on the interaction term weights, basic term weights, and offset term values.

[0066] Specifically, pulse synchronization is performed between the sum-path signal parameters and the measured echo data from the phased array radar receiver. The sum-path signal parameters, derived from sum-path indices calculated based on signal identification, exhibit pulse rate characteristics, outputting at a rate of 5000 to 10000 pulses per second. The receiver, however, records physical response signals such as echo amplitude, phase, or frequency at a higher sampling frequency (e.g., 25MHz to 50MHz). Therefore, in their original state, there are inconsistencies in sampling intervals and timestamp synchronization. The system uses a unified reference clock, employing the radar control system's built-in master timer as a unified time base. The timestamps of the sum-path signal data are aligned with the receiver's data acquisition time. Linear interpolation or spline interpolation is used to interpolate and extend the low-frequency sum-path data, establishing a temporal correspondence with the high-frequency echo data, thereby generating a sum-path-echo pulse sequence. Based on the response characteristics of the echo system, a functional relationship between the sum-path signal and the echo output is constructed. Considering the dual influence of target echo changes and sum-path signal alterations on the radar receiving system, an interactive function is used to model its coupling mechanism. This function uses the sum-path signal value as the excitation variable and the echo receiver output (such as echo amplitude or main frequency amplitude) as the response variable. Specifically, it is defined as follows: ,in This indicates the current output value of the echo receiver. Indicates the signal value of the path. The electromagnetic interference factors, such as clutter interference (e.g., ground clutter, sea clutter) and man-made interference (e.g., deception interference, suppression interference), are represented by a normalized value based on the actual measured interference power. Weights for interaction items, As the weight of the basic items of the road, As the weight of environmental fundamentals, For offset items, This represents the random fluctuation term; all the above calculations normalize the terms to simplify the calculations, focusing on characterizing the nonlinear dynamic behavior of the echo response. To identify the parameters in the above model... , , and An iterative fitting algorithm is introduced based on the data sequence to perform model fitting. This process arranges the signal values ​​in time sequence to construct a model containing... , , Design matrix of constant terms Simultaneously, the echo response values ​​at the corresponding time points are constructed into observation vectors. Establish a system of fitting equations ,in Let be the parameter vector to be determined, i.e., containing , , and The system obtains the optimal model coefficients by iteratively minimizing the fitting loss function using gradient descent or its regularized variation. The interaction term weights are then identified based on the above methods. Weights of basic road items Environmental foundation item weights and offset terms Establish a path-echo coupling relationship model.

[0067] In one specific embodiment, the process of performing step S4 may specifically include the following steps:

[0068] Step S41: When the abnormal operation judgment result indicates that the intermediate frequency injection signal is faulty, the trajectory load is calculated in reverse based on the trajectory offset thickness, velocity coverage range and direction distribution parameters in the azimuth difference signal parameters to obtain the sensing trajectory load value.

[0069] Step S42: Substitute the sensing trajectory load value as an input variable into the phased array radar trajectory load-echo response transfer function to solve for the theoretical echo output value and obtain the theoretical echo parameter reference value.

[0070] Step S43: Determine the signal compensation strategy and weight allocation scheme based on the abnormal operation judgment result of the intermediate frequency injected signal. When the signal is completely unreliable, the pure trajectory replacement mode is adopted. When the signal is partially available, the trajectory-signal hybrid compensation mode is adopted. Calculate the adaptive compensation weight coefficients of trajectory data and signal data.

[0071] Step S44: Use adaptive compensation weighting coefficients to intelligently fuse and reconstruct the theoretical echo parameter reference value and the measured data of the available receiving components to obtain the target intermediate frequency simulation signal.

[0072] Specifically, using the anomaly detection result as the trigger signal, when it is determined that the intermediate frequency injection signal exhibits continuous drift, abnormally fixed output, or significant deviation, the signal compensation path is activated. This involves calling the azimuth difference path signal parameters updated in real-time in the current signal analysis module. These parameters include thickness data reflecting vertical trajectory offset, coverage range data indicating the degree of target coverage per unit beam, and direction distribution data extracted from the target direction contour in the signal. Through the fusion processing of these three data points, the system can perform a reverse estimation of the total trajectory density borne by the current radar beam based on spatial geometry and structural distribution patterns, obtaining a perceived trajectory load value representing the current actual load level. This perceived trajectory load value is then substituted into the phased array radar trajectory load-echo response transfer function. This function is calibrated at the factory under standard trajectory, standard pointing, and typical beam parameters, and is used to characterize the echo response behavior of the equipment under specific loads. By inputting the current trajectory load level into this model, the corresponding theoretical echo parameter benchmark value is derived, serving as a reference for the echo characteristics the radar should achieve under ideal, fault-free conditions. The system selects a compensation mode based on the judgment results of the anomaly identification module in the previous stage: if the intermediate frequency (IF) injection signal is completely unusable, the system directly enters the pure trajectory substitution mode, using the theoretical echo value derived above as the current operating parameter input; if the signal still has partial availability, such as only slight drift or periodic fluctuations, the system activates the fusion mode, establishing a weight allocation mechanism between trajectory data and signal data. The weight allocation mechanism is calculated in real-time by the system's adaptive adjustment module, based on the abnormal amplitude of the current IF injection signal, its historical stability, and the degree of deviation from the trajectory reference, determined using a weighted summation method. The system integrates this information and outputs a set of weight coefficients to control the data fusion ratio. After completing the weight calculation, the system fuses the theoretical echo value with the original output of the receiving component to finally obtain the target IF simulation signal under the current operating conditions.

[0073] In one specific embodiment, the process of executing step S42 may specifically include the following steps:

[0074] Step S421: Determine the gain adjustment value, delay interval constant, and response interval constant in the phased array radar trajectory load-echo response transfer function according to the phased array radar equipment specifications and current mission conditions, and obtain the set of transfer parameters;

[0075] Step S422: Adjust the sequence format and convert the units of the sensing trajectory load values ​​to obtain the trajectory load variable;

[0076] Step S423: Based on the set of transfer parameters, input the trajectory load variables into the phased array radar trajectory load-echo response transfer function to perform sequence response analysis and obtain the dynamic response sequence;

[0077] Step S424: Map the echo characteristics in the dynamic response sequence according to the output standard of the receiving component and normalize the amplitude to obtain the theoretical echo parameter reference value.

[0078] Specifically, the system calls upon a matching transfer function template library based on parameters such as the phased array radar model, beam pattern, installation type, and target characteristics. It then dynamically selects appropriate gain adjustment values, delay interval constants, and response interval constants based on the current operational task (including target type, velocity range, range, beam range, and radar frequency). These parameters are obtained from factory calibration experiments, simulation tests, or on-site identification and are stored in the system in a database. They are automatically retrieved through equipment coding, forming a targeted set of transfer parameters. The system also performs sequence format adjustment and unit conversion on the sensing trajectory load values. The trajectory load data is derived from the offset thickness, velocity coverage range, and directional distribution parameters in the signal. The original output is represented in floating-point form, and the unit dimension is inconsistent with the expected excitation variable format of the transfer function input. Therefore, sequence format adjustment and unit conversion operations are performed. This process includes rounding, amplitude limiting, outlier removal, and dimension conversion of the original trajectory load data to ensure that the obtained trajectory load variables meet the input requirements of the phased array radar response model in terms of numerical range. Simultaneously, the unit system is standardized, such as converting the density representation from target count / cubic meter to the nominal unit defined by the system, or normalizing the trajectory estimation results to a percentage input range, making the load variables effective excitation quantities in the transfer function. The system inputs the processed trajectory load variables into the trajectory load-echo response transfer function of the phased array radar and performs sequence response analysis based on the aforementioned parameter set. This analysis uses numerical integration, differential iteration, or convolutional superposition to simulate the system's response process under the influence of load changes, generating a set of dynamic response sequences containing information such as timestamps, response amplitudes, and echo change trends, used to describe the change process of echo amplitude under load disturbances. The generated dynamic response sequences are arranged in time sequence, from which the system extracts echo characteristic quantities such as steady-state echo amplitude, response time, maximum jump variable, or frequency principal component. The response characteristics are formatted and standardized, and a sequence mapping and amplitude normalization mechanism is introduced to ensure a one-to-one correspondence between the echo characteristics and the standard output format of the receiving component. If the receiving component outputs a millivolt value, the system maps the response characteristics to an equivalent voltage range and performs a linear transformation based on the component's range, output accuracy, and calibration coefficients. If the component output is frequency or echo energy density, the system converts the response result into a standard range of the corresponding frequency distribution amplitude or energy unit, and standardizes indicators such as the time integration window and filter bandwidth to ensure that both have the same evaluation dimension. The final converted response data is defined as the theoretical echo parameter benchmark value.

[0079] In one specific embodiment, the process of performing step S5 may specifically include the following steps:

[0080] Step S51: Extract the target velocity distribution and echo flow rate from the target intermediate frequency simulation signal, calculate the real-time tracking efficiency by the ratio of echo flow rate to total flow rate, and perform a difference calculation between the real-time tracking efficiency and the set target value to obtain the velocity deviation and flow rate deviation.

[0081] Step S52: Based on the dynamic response characteristics of the phased array radar, the control optimization model is obtained by taking the velocity deviation and flow deviation as inputs and the beam force adjustment and pointing frequency adjustment as outputs.

[0082] Step S53: Use the control optimization model to perform the current deviation state analysis, obtain the beam adjustment parameters, and convert the beam adjustment parameters into phased array radar beam control commands.

[0083] Specifically, the intermediate frequency (IF) simulation signal of the target is analyzed to extract velocity distribution information and echo flow data directly related to radar tracking. The velocity distribution is derived from the spatial distribution signal of the target in its azimuth, including parameters such as median velocity, maximum velocity, and distribution dispersion. The echo flow is calculated from the echo velocity and density formed by the target passing through the beam and entering the receiving channel. The system establishes a mapping relationship between the signal change rate and target density calibration to estimate the actual received echo in real time. Simultaneously, the total signal flow is extracted from the target parameters, including all signal flow including unreceived and received echoes within the beam, and the ratio between the two is calculated. This ratio represents the real-time tracking efficiency, reflecting the overall level of the current target tracking effect. This real-time tracking efficiency is compared with the target value set for the task, yielding a difference result. This difference reflects the degree of deviation of the current operating state from the target indicator from the perspective of the tracking task. Simultaneously, the median or representative characteristic value of the target velocity distribution is extracted and compared with the target velocity required for the tracking mission to form a velocity deviation, revealing the impact of beam pointing state, target distance, or beam parameters on velocity tracking performance. The flow rate deviation, on the other hand, reflects the real-time difference between the echo flow rate and the target's set receiving capability, indicating operational problems such as insufficient beam force, target blockage, or uneven distribution. Based on these two types of deviations, the system feeds them as input variables into a control optimization model. This model is constructed based on the structural dynamic response characteristics of phased array radar, mapping the input velocity and flow rate deviations to the adjustment dimensions of the beam control strategy and outputting executable beam force and pointing frequency adjustments. In the model design, the system introduces fuzzy control logic, an empirical rule base, and dynamic gain parameters to ensure the model has response flexibility and adjustment diversity when facing different combinations of deviations. For example, when the velocity deviation is large and the flow rate deviation is small, the system appropriately increases the frequency to optimize the target tracking trajectory; when the flow rate deviation is large and the velocity matching is good, the beam force is increased to enhance the target acquisition capability. The model optimizes parameters and updates weights through continuous operation and data accumulation, enabling it to adaptively evolve and adapt to different beam specifications, target types, and mission rhythms. After completing deviation analysis, the system generates a set of beam adjustment parameters based on the beam force and frequency adjustment values ​​output by the control optimization model, and converts them into the control command format required by the phased array radar beam control unit. These control commands include adjustment signals, electrical drive quantities, frequency adjustment codes, or phase control parameters, the specific format of which is determined by the structure of the equipment's beam system. For example, in a fully digital phased array system, the output is sent to a digital signal processing unit; in an analog hybrid phased array system, the adjustment values ​​are converted into phase controller commands. The command output frequency is set to a fixed control cycle, such as updating every 1 millisecond, to ensure sufficient real-time performance and response accuracy. The system performs real-time correction of tracking efficiency within each control cycle, thereby continuously optimizing the beam state.

[0084] This embodiment eliminates timing jitter and noise interference during beam switching through timing anti-interference stabilization processing, providing a reliable foundation for subsequent domain-specific signal feature analysis. Through domain-specific analysis of the beam pointing domain, target trajectory domain, and resource allocation domain, the signal parameters of the sum path, azimuth difference path, and elevation difference path are accurately extracted, achieving comprehensive capture of complex signal characteristics in TAS mode. The coupling correlation verification between the sum path signal parameters and measured echo data establishes a dynamic mapping relationship between the intermediate frequency simulated signal and the actual radar response. By monitoring the correlation deviation in real time, abnormal states of the intermediate frequency injected signal can be detected promptly. When a signal fault is detected, a trajectory substitution compensation mechanism based on the azimuth difference path and elevation difference path signal parameters ensures the continuity and reliability of the simulation system, avoiding simulation interruptions caused by signal anomalies in traditional methods. In TAS mode, the radar needs to simultaneously handle the tracking tasks of multiple targets and perform search scans. Traditional open-loop simulation methods cannot adapt to this dynamically changing working mode. This invention establishes a closed-loop control mechanism, enabling the intermediate frequency simulation signal to adaptively adjust according to the radar's real-time beam scheduling information and resource allocation strategy. It accurately captures the target motion characteristics through pulse stream tracking and trajectory projection technology, and combines an intelligent fusion strategy with adaptive compensation weight coefficients to achieve accurate tracking of complex scenes.

[0085] Example 2

[0086] Please see Figure 2 As shown, parts not described in detail in this embodiment are described in Embodiment 1. A mid-frequency simulation system for a phased array radar is provided, comprising:

[0087] Interference processing module: Performs time-series anti-interference stabilization processing on the beam scheduling information output in real time by the phased array radar to obtain a stable scheduling signal;

[0088] Analysis module: Based on the stable scheduling signal, perform domain-specific signal feature analysis in the beam pointing domain, target trajectory domain, and resource allocation domain to obtain the sum path signal parameters, azimuth difference path signal parameters, and elevation difference path signal parameters;

[0089] Judgment module: Based on the sum and path signal parameters and the measured echo data of the phased array radar receiver, the coupling correlation is verified to obtain the judgment result of the intermediate frequency injection signal operation abnormality;

[0090] Compensation module: When the operation anomaly determination result indicates an intermediate frequency injection signal fault, signal trajectory substitution compensation processing is performed based on the azimuth difference path signal parameters and the pitch difference path signal parameters to obtain the target intermediate frequency simulation signal;

[0091] Optimization module: Based on the target intermediate frequency simulation signal, optimize the radar tracking and search mode, and output phased array radar beam control commands.

[0092] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0093] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0094] In the description of this invention, it should be understood that the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0095] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0096] In the description of this invention, "several" means one or more, and "a large number" means two or more.

[0097] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0098] All formulas in this manual are dimensionless and calculated numerically. 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 and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0099] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A mid-frequency simulation method for phased array radar, characterized in that, include: Step S1: Perform time-series anti-interference stabilization processing on the beam scheduling information output in real time by the phased array radar to obtain a stable scheduling signal; Step S2: Based on the stable scheduling signal, perform domain-specific signal feature analysis in the beam pointing domain, target trajectory domain, and resource allocation domain to obtain the sum path signal parameters, azimuth difference path signal parameters, and elevation difference path signal parameters; Step S3: Perform coupling correlation verification based on the sum and path signal parameters and the measured echo data of the phased array radar receiver to obtain the result of the abnormal operation of the intermediate frequency injection signal; Step S4: When the operation anomaly determination result indicates an intermediate frequency injection signal fault, perform signal trajectory substitution compensation processing based on the azimuth difference path signal parameters and the pitch difference path signal parameters to obtain the target intermediate frequency simulation signal; Step S5: Optimize radar tracking and search modes based on the target intermediate frequency simulation signal, and output phased array radar beam control commands.

2. The phased array radar intermediate frequency simulation method according to claim 1, characterized in that, Step S1 includes: Step S11: Perform time-series segmented sampling of the beam scheduling information output in real time by the phased array radar, and identify the dominant beam switching frequency component segment by segment. Step S12: Use the dominant beam switching frequency component to dynamically adjust the transfer interval coefficient and interference noise amplitude matrix of the timing prediction module, and form beam switching adaptive filtering parameters based on the transfer interval coefficient and the interference noise amplitude matrix; Step S13: Input the beam scheduling information into a timing anti-interference encoder-decoder network configured with the beam switching adaptive filtering parameters for signal stabilization and reconstruction processing, and output a stable scheduling signal.

3. The phased array radar intermediate frequency simulation method according to claim 2, characterized in that, Step S13 includes: Step S131: Configure the transfer interval coefficient in the beam switching adaptive filtering parameters to the encoder part of the temporal anti-interference encoder-decoder network, and perform switching state prediction on the scheduling features in the encoder part to obtain a multi-level temporal feature sequence; Step S132: Configure the interference noise amplitude matrix in the beam switching adaptive filtering parameters into the intermediate layer of the temporal anti-interference encoder-decoder network, and perform temporal update filtering on the multi-level temporal feature sequence through the interference noise amplitude matrix to obtain the filtered and purified temporal feature sequence; Step S133: Input the filtered and purified temporal feature sequence into the decoder part of the temporal anti-interference encoder-decoder network for sequence expansion and reconstruction processing to obtain the initial stable scheduling signal; Step S134: Perform pulse offset compensation processing based on time-series filtering state estimation on the initial stabilized scheduling signal, and use the switching offset predicted by the transfer interval coefficient to perform reverse correction on the signal pulse to obtain a stable scheduling signal.

4. The phased array radar intermediate frequency simulation method according to claim 3, characterized in that, Step S2 includes: Step S21: Identify the sequence assignment of the beam pointing pulse, target trajectory pulse and resource allocation pulse in the stable scheduling signal, and divide the stable scheduling signal into beam pointing domain, target trajectory domain and resource allocation domain based on the sequence assignment; Step S22: Based on the beam pointing domain, perform pulse edge detection and pointing offset calculation, extract the beam pointing profile, and calculate the pointing intersection offset and pulse width change rate based on the beam pointing profile to obtain the path signal parameters; Step S23: Perform pulse flow tracking and trajectory projection based on the target trajectory domain to obtain the target motion pulse sequence, and extract the trajectory velocity distribution and direction vector based on the target motion pulse sequence to obtain the azimuth difference path signal parameters; Step S24: Based on the resource allocation domain, perform background pulse subtraction and allocation displacement detection to obtain pitch difference signal parameters.

5. The phased array radar intermediate frequency simulation method according to claim 4, characterized in that, Step S23 includes: Step S231: Perform segmented optical flow correlation on the continuous pulse sequence in the target trajectory domain, and construct inter-pulse connection paths segment by segment; Step S232: Expand the inter-pulse connection path into a two-dimensional projection plane, and mark the velocity gradient changes point by point on the projection plane; Step S233: Perform directional vector layer separation on the projection plane according to the velocity gradient change, and extract the vector components of the low-speed layer and the high-speed layer; Step S234: Combine the vector components of the low-speed layer and the high-speed layer into azimuth difference path signal parameters.

6. The phased array radar intermediate frequency simulation method according to claim 5, characterized in that, Step S3 includes: Step S31: Based on the sum-path signal parameters and the measured echo data of the phased array radar receiver, perform sum-path-echo coupling relationship modeling to obtain the sum-path-echo coupling relationship model; Step S32: Input the real-time output signals of the phased array radar receiver, digital processor and controller into the path-echo coupling relationship model for prediction and correlation deviation analysis, and obtain the correlation deviation values ​​of each receiving component; Step S33: Perform threshold comparison and determination on the correlation deviation values ​​to obtain the anomaly determination identifier of each receiving component; Step S34: Based on the correlation deviation value and the anomaly determination identifier, the fault type is identified to obtain the operation anomaly determination result of the intermediate frequency injection signal.

7. The phased array radar intermediate frequency simulation method according to claim 6, characterized in that, Step S31 includes: Step S311: Pulse synchronization is performed between the sum-path signal parameters and the measured echo data of the phased array radar receiver to obtain the sum-path-echo pulse sequence; Step S312: Construct an interaction function between the echo signal and the echo output based on the physical characteristics of the phased array radar echo response, where the echo data is used as the response variable and the echo signal is used as the excitation variable. The interaction function includes an interaction term, a basic term, an offset term, and a random fluctuation term. Step S313: Based on the sum-echo pulse sequence, the parameters of the interaction function are adjusted by iterative fitting method, and the weights of the interaction terms, the weights of the basic terms, and the values ​​of the offset terms are calculated successively. Step S314: Establish a path-echo coupling relationship model based on the interaction term weights, the basic term weights, and the offset term values.

8. The phased array radar intermediate frequency simulation method according to claim 7, characterized in that, Step S4 includes: Step S41: When the operation anomaly determination result indicates a fault in the intermediate frequency injection signal, the trajectory load is calculated in reverse based on the trajectory offset thickness, velocity coverage range and direction distribution parameters in the azimuth difference signal parameters to obtain the sensing trajectory load value. Step S42: Substitute the sensing trajectory load value as an input variable into the phased array radar trajectory load-echo response transfer function to solve for the theoretical echo output value, and obtain the theoretical echo parameter reference value. Step S43: Determine the signal compensation strategy and weight allocation scheme based on the abnormal operation judgment result of the intermediate frequency injected signal. When the signal is completely unreliable, a pure trajectory replacement mode is adopted. When the signal is partially available, a trajectory-signal hybrid compensation mode is adopted. Calculate the adaptive compensation weight coefficients of trajectory data and signal data. Step S44: Use the adaptive compensation weighting coefficient to intelligently fuse and reconstruct the theoretical echo parameter reference value and the measured data of the available receiving components to obtain the target intermediate frequency simulation signal.

9. The phased array radar intermediate frequency simulation method according to claim 8, characterized in that, Step S42 includes: Step S421: Determine the gain adjustment value, delay interval constant, and response interval constant in the phased array radar trajectory load-echo response transfer function according to the phased array radar equipment specifications and current mission conditions, and obtain the set of transfer parameters; Step S422: Adjust the sequence format and convert the unit of the perceived trajectory load value to obtain the trajectory load variable; Step S423: Based on the set of transfer parameters, input the trajectory load variable into the phased array radar trajectory load-echo response transfer function to perform sequence response analysis and obtain the dynamic response sequence; Step S424: Map the echo characteristics in the dynamic response sequence according to the output standard of the receiving component and normalize the amplitude to obtain the theoretical echo parameter reference value.

10. The phased array radar intermediate frequency simulation method according to claim 9, characterized in that, Step S5 includes: Step S51: Extract the target velocity distribution and echo flow rate from the target intermediate frequency simulation signal, calculate the real-time tracking efficiency by the ratio of echo flow rate to total flow rate, and perform a difference calculation between the real-time tracking efficiency and the set target value to obtain the velocity deviation and flow rate deviation. Step S52: Based on the dynamic response characteristics of the phased array radar, using the velocity deviation and flow rate deviation as inputs and the beam force adjustment and pointing frequency adjustment as outputs, a control optimization model is obtained. Step S53: Perform current deviation state analysis using the control optimization model to obtain beam adjustment parameters, and convert the beam adjustment parameters into phased array radar beam control commands.

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