A coherent target angle estimation method and system for polarization MIMO array radar

Abstract

The present invention relates to the technical field of radar coherent target angle estimation, and specifically to a coherent target angle estimation method and system for a polarization MIMO array radar, comprising the following steps: setting initial polarization parameters and environmental variables of the radar, continuously scanning the environment and target using the polarization MIMO array radar, collecting environmental and target reflection signals, and obtaining basic environmental and target data. In the present invention, by dynamically adjusting the polarization angle and shape of the radar beam, the signal reflection characteristics are monitored and optimized in real time, effectively improving the adaptability to target state changes. In combination with time series analysis and processing of dynamic angle data, the continuous monitoring capability of target motion trends is enhanced, making angle estimation more accurate. Multi-frequency scanning is introduced to make waveform adjustment more precise, improve the sensitivity and response speed of angle estimation, and ensure the accuracy and real-time performance of angle estimation under constantly changing environmental conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of radar coherent target angle estimation, and in particular to a coherent target angle estimation method and system for a polarization MIMO array radar. Background Art

[0002] Radar coherent target angle estimation is a field of radar technology specifically designed for detecting and tracking target objects. It focuses on leveraging the coherence of radar signals to estimate the target's angular position in space. In radar systems, coherence refers to the fact that transmitted signals maintain a certain phase relationship upon reception, a property that enhances the radar's ability to identify target features. Angle estimation is a core issue in radar technology, involving the use of algorithms and signal processing techniques to extract target direction information from received radar data. It has widespread applications in military reconnaissance, aerospace tracking, meteorological monitoring, and civilian fields.

[0003] Among them, the coherent target angle estimation method of polarization MIMO array radar utilizes a multiple-input multiple-output (MIMO) array and combines polarization information to improve the accuracy and efficiency of angle estimation. The MIMO array enhances the spatial resolution of the radar system through the configuration of multiple transmit and receive antennas, while polarization information provides additional data about the target's physical properties, thereby making the angle estimation more accurate. The main uses of this technology include but are not limited to improving the accuracy of target detection, more effective surveillance and tracking in complex environments, and better classification and identification of dynamic targets in both civilian and military fields.

[0004] Existing technologies exhibit limitations in handling target tracking in dynamic and complex environments, particularly in the flexibility and real-time nature of radar parameter adjustment. This results in an inability to react immediately to rapidly changing scenarios, impacting the accuracy of angle estimation and the system's response speed. Furthermore, existing technologies rely on traditional signal processing methods, which are ineffective in dealing with noise and often require additional processing steps to improve signal quality. This not only reduces operational efficiency but can also impact the ability to process data in real time. These deficiencies are particularly pronounced in multi-target environments, often leading to reduced accuracy in target recognition and classification, particularly in military and aerospace applications requiring highly precise monitoring, impacting overall mission execution. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a coherent target angle estimation method and system for a polarization MIMO array radar.

[0006] To achieve the above object, the present invention adopts the following technical solution: a method for estimating the angle of a coherent target of a polarization MIMO array radar, comprising the following steps:

[0007] S1: setting the initial polarization parameters and environmental variables of the radar, continuously scanning the environment and the target through the polarization MIMO array radar, collecting the environment and target reflection signals, and obtaining basic environment and target data;

[0008] S2: Using the basic environment and target data, dynamically adjusting the polarization angle and shape of the radar beam, recording polarization parameter changes in real time, analyzing signal reflection characteristics, and obtaining adjusted polarization parameters;

[0009] S3: Continuously tracking the target angle using the adjusted polarization parameters, recording angle change data caused by target movement, and analyzing target dynamic changes in combination with time series to obtain target dynamic angle data;

[0010] S4: Processing the target dynamic angle data, using adaptive filtering to correct the frequency and phase of the signal, optimizing signal reception quality, eliminating noise interference, and generating optimized angle tracking data;

[0011] S5: adjusting radar waveform design parameters based on the optimized angle tracking data, performing a multi-frequency sweep test, analyzing waveform response characteristics to angle estimation, adjusting waveform parameters, and generating fine-tuning waveform parameters;

[0012] S6: Using the fine-tuning waveform parameters to perform a final angle estimation on the target, monitoring the angle response of the target under differentiated conditions in real time, verifying the adjustment effect of the waveform parameters, and obtaining a coherent target angle estimation result.

[0013] As a further solution of the present invention, the basic environment and target data include environment feature description records and target feature description records, the adjusted polarization parameters include polarization angle values ​​and polarization shape characteristics, the target dynamic angle data include angle change sequences and angle change trend analysis results, the optimized angle tracking data include corrected frequency, corrected phase and noise reduction level, the fine-tuning waveform parameters include frequency optimization values ​​and amplitude adjustment values, and the coherent target angle estimation results include final coherent target angle estimation values ​​and angle response analysis results.

[0014] As a further solution of the present invention, the steps for obtaining the basic environment and target data are:

[0015] S101: Based on the initial polarization parameter setting of the radar, the polarization MIMO array radar performs continuous scanning operations to collect the target's reflected signals and obtain a preliminary signal data set;

[0016] S102: Using the preliminary signal data set, performing a quantitative signal strength evaluation, adjusting a signal reception threshold according to the evaluation result, performing signal screening and purifying signal data, and obtaining a selected signal data set;

[0017] S103: Comparing the time delay and frequency distribution of the difference signal with the selected signal data set, identifying the signal with target reflection characteristics, distinguishing and generating basic environment and target data.

[0018] As a further solution of the present invention, the step of obtaining the adjusted polarization parameters is:

[0019] S201: Based on the basic environment and target data, adjust the polarization angle of the radar beam, record the parameter adjustment process, and obtain preliminary polarization parameter records;

[0020] S202: Based on the preliminary polarization parameter record, analyze the signal reflection change in real time, adjust the beam shape to match the target dynamics, and obtain the beam shape adjustment matching result;

[0021] S203: Based on the beam shape adjustment matching result, perform signal analysis to evaluate the statistical performance of the reflection characteristics, and optimize the polarization angle and frequency settings according to the evaluation results to generate adjusted polarization parameters.

[0022] As a further solution of the present invention, the step of acquiring the target dynamic angle data is:

[0023] S301: Based on the adjusted polarization parameters, continuously track and record the target movement process, capture the target's angle change in real time, collect angle change data, and obtain angle tracking records;

[0024] S302: Based on the angle tracking record, a Kalman filter is used to perform data encoding and serialization processing, analyze the speed change and direction adjustment of the target, distinguish normal movement from abnormal movement, and obtain angle change analysis results;

[0025] S303: Based on the angle change analysis results, integrate all time point data, perform data synchronization and error correction, ensure data consistency through statistical verification, and generate target dynamic angle data.

[0026] As a further solution of the present invention, the data encoding and serialization processing is performed using a Kalman filter, according to the formula:

[0027] x k|k =x k|k-1 +αK k (z k -βH k x k|k-1 )

[0028] Calculate the target state estimate and obtain the angle change analysis results, where x k|k is the estimated value of the state after considering the measurement information at the kth moment, x k|k-1 is the predicted state before the kth moment, z k is the actual measured value at the kth moment, H k is the observation model matrix, which is used to map the state space to the measurement space, K k is the Kalman gain coefficient, which is used to minimize the estimation error, α is the dynamic factor for adjusting the gain, which is used to enhance the filter response, and β is the state adjustment coefficient, which is used to adjust the sensitivity of the state estimation.

[0029] As a further solution of the present invention, the step of obtaining the optimized angle tracking data is:

[0030] S401: Based on the target dynamic angle data, correct the signal frequency, perform frequency adjustment to match the target dynamics, and synchronously adjust the signal phase to ensure signal integrity, thereby obtaining signal optimization data;

[0031] S402: Based on the signal optimization data, applying a high-pass filter to reduce background noise, performing signal enhancement, optimizing signal reception quality, and obtaining a signal processing result;

[0032] S403: Based on the signal processing result, perform signal quality detection to evaluate signal stability and accuracy, adjust signal processing parameters according to the detection result, refine signal quality control measures, and generate optimized angle tracking data.

[0033] As a further solution of the present invention, the steps for obtaining the fine-tuning waveform parameters are as follows:

[0034] S501: Based on the optimized angle tracking data, a multi-frequency sweep test is performed to evaluate the impact of the difference frequency on the angle accuracy, and a waveform test result is obtained through frequency response analysis and angle deviation measurement;

[0035] S502: Based on the waveform test results, adjust waveform design parameters, refine the waveform through iterative processing, enhance waveform response characteristics, perform waveform tuning and amplitude optimization, and obtain waveform adjustment results;

[0036] S503: Based on the waveform adjustment result, the frequency and amplitude are adjusted to maximize the angle estimation accuracy, ensure that the waveform matches the target angle data, and generate fine-tuning waveform parameters.

[0037] As a further solution of the present invention, the steps of obtaining the coherent target angle estimation result are:

[0038] S601: Based on the fine-tuning waveform parameters, measuring the angle of the target, simulating the response of the monitoring waveform to differentiated environmental factors through condition changes, and obtaining an angle response record;

[0039] S602: Based on the angle response record, perform in-depth data analysis, verify the actual impact of the waveform parameter adjustment through statistical methods, and obtain angle estimation data through signal stability and response time analysis;

[0040] S603: Based on the angle estimation data, perform data fusion analysis to verify the accuracy of the data, optimize the angle estimation by minimizing the error, and generate a coherent target angle estimation result.

[0041] A coherent target angle estimation system for a polarization MIMO array radar, comprising:

[0042] The signal acquisition and evaluation module uses the polarization MIMO array radar to perform continuous scanning operations, collect the target's reflected signals, perform quantitative signal strength evaluation, filter and purify the signal data, identify signals with target reflection characteristics, and generate basic environment and target data;

[0043] The beam polarization adjustment module adjusts the polarization angle of the radar beam based on the basic environment and target data, analyzes the signal reflection changes in real time, evaluates the statistical performance of the reflection characteristics, optimizes the polarization angle and frequency settings, and generates adjusted polarization parameters;

[0044] The real-time angle capture module captures the target's angle changes in real time based on the adjusted polarization parameters, collects angle change data, uses a Kalman filter to encode and serialize the data, distinguishes normal movement from abnormal movement, and obtains angle change analysis results;

[0045] The data synchronization and correction module performs data synchronization and error correction based on the angle change analysis results, adjusts the frequency to match the target dynamics, and synchronously adjusts the signal phase to ensure signal integrity to obtain signal optimization data;

[0046] The signal enhancement and stabilization module applies a high-pass filter to reduce background noise based on the signal optimization data, performs signal enhancement, detects and evaluates signal stability and accuracy, adjusts signal processing parameters to refine signal quality control measures, and generates optimized angle tracking data;

[0047] The waveform optimization test module performs multi-frequency sweep testing based on the optimized angle tracking data, adjusts waveform design parameters, performs waveform tuning and amplitude optimization, ensures that the waveform matches the target angle data by adjusting the frequency and amplitude, and generates fine-tuning waveform parameters;

[0048] The final angle estimation module monitors the waveform response to differentiated environmental factors based on the fine-tuning of the waveform parameters, verifies the actual impact of the waveform parameter adjustment through statistical methods, optimizes the angle estimation by minimizing the error, and generates a coherent target angle estimation result.

[0049] Compared with the prior art, the advantages and positive effects of the present invention are:

[0050] In the present invention, by adjusting the dynamic mechanism of the polarization angle and shape of the radar beam, the signal reflection characteristics are monitored and optimized in real time, effectively improving the adaptability to changes in target state, combining time series analysis to process dynamic angle data, enhancing the continuous monitoring capability of target motion trends, making angle estimation more accurate, optimizing signal processing through adaptive filtering technology, significantly reducing the impact of environmental noise, ensuring the clarity and stability of signal reception, introducing multi-frequency scanning, making waveform adjustment more precise, improving the sensitivity and response speed of angle estimation, and ensuring the accuracy and real-time performance of angle estimation under constantly changing environmental conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0052] Figure 2 This is a flowchart for obtaining the basic environment and target data of the present invention;

[0053] Figure 3 This is a flow chart for obtaining the adjusted polarization parameters of the present invention;

[0054] Figure 4 This is a flow chart for obtaining the target dynamic angle data of the present invention;

[0055] Figure 5 A flowchart for obtaining angle tracking data optimized for the present invention;

[0056] Figure 6 This is a flow chart for obtaining fine-tuning waveform parameters of the present invention;

[0057] Figure 7 This is a flow chart for obtaining the coherent target angle estimation result of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0059] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0060] See also Figure 1 The present invention provides a technical solution: a method for estimating the angle of a coherent target of a polarization MIMO array radar, comprising the following steps:

[0061] S1: Set the initial polarization parameters and environmental variables of the radar, continuously scan the environment and target through the polarization MIMO array radar, collect the environment and target reflection signals, and obtain basic environment and target data;

[0062] S2: Using basic environment and target data, the polarization angle and shape of the radar beam are dynamically adjusted, polarization parameter changes are recorded in real time, and signal reflection characteristics are analyzed to obtain the adjusted polarization parameters.

[0063] S3: Continuously track the target angle using the adjusted polarization parameters, record the angle change data caused by target movement, and analyze the target dynamic changes in combination with the time series to obtain the target dynamic angle data;

[0064] S4: Process the target dynamic angle data, use adaptive filtering to correct the signal frequency and phase, optimize the signal reception quality, eliminate noise interference, and generate optimized angle tracking data;

[0065] S5: Based on the optimized angle tracking data, adjust the radar waveform design parameters, perform multi-frequency sweep testing, analyze the waveform response characteristics to angle estimation, adjust the waveform parameters, and generate fine-tuning waveform parameters;

[0066] S6: Use fine-tuning waveform parameters to make the final target angle estimate, monitor the target angle response under differentiated conditions in real time, verify the adjustment effect of the waveform parameters, and obtain the coherent target angle estimation result.

[0067] The basic environment and target data include environment feature description records and target feature description records, the adjusted polarization parameters include polarization angle values ​​and polarization shape characteristics, the target dynamic angle data include angle change sequence and angle change trend analysis results, the optimized angle tracking data include corrected frequency, corrected phase and noise reduction level, the fine-tuning waveform parameters include frequency optimization value and amplitude adjustment value, and the coherent target angle estimation results include the final coherent target angle estimation value and angle response analysis results.

[0068] See also Figure 2 , the steps for obtaining the basic environment and target data are:

[0069] S101: Based on the initial polarization parameter setting of the radar, the polarization MIMO array radar performs continuous scanning operations to collect the target's reflected signals and obtain a preliminary signal data set;

[0070] Based on the radar's initial polarization parameter settings, the polarimetric MIMO array radar performs continuous scanning operations. First, the initial polarization parameters are determined based on the radar system's hardware specifications, setting the polarization angles θ and φ to optimize the radar beam's directivity and coverage. Next, the scanning process is initiated, and the radar gradually adjusts the preset angles to ensure full coverage. After each scanning cycle, the received reflected signals are initially analyzed to form a preliminary signal dataset. Next, a real-time monitoring system assesses data quality during the scanning process. If any abnormal data fluctuations are detected, the polarization parameters are automatically fine-tuned to ensure signal stability and reliability. After these steps are completed, the target's reflected signals are collected to form a preliminary signal dataset.

[0071] S102: Using the preliminary signal data set, perform a quantitative evaluation of signal strength, adjust the signal reception threshold based on the evaluation result, perform signal screening and purify the signal data, and obtain a selected signal data set;

[0072] A preliminary signal dataset is used to quantitatively assess signal strength. First, the strength of each signal is calculated and normalized according to pre-set signal processing rules to generate quantified signal strength data. Next, a signal strength reception threshold, T, is set. This threshold is the median signal strength plus a multiple of the standard deviation, adjusted based on the ambient noise level. The quantified data is then filtered, retaining only signals above threshold T to eliminate interference from background noise and non-target signals. Finally, the filtered signals are further processed and analyzed to purify the signal data and obtain a selected signal dataset.

[0073] S103: By selecting the signal data set, comparing the time delay and frequency distribution of the difference signals, identifying the signals with target reflection characteristics, distinguishing and generating basic environment and target data;

[0074] By carefully selecting signal datasets and comparing the time delay and frequency distribution of the different signals, the team first uses time-frequency analysis tools to calculate the time delay and frequency characteristics of each signal, then converts the time domain signals into frequency domain signals using a Fourier transform. Next, they perform cluster analysis on the frequency characteristics to distinguish signals from different sources. Next, using statistical analysis methods, they compare the time delay and frequency distribution of different signals to identify signals with typical target reflection characteristics. Finally, based on the signal sources and characteristics, the dataset is divided into baseline environmental data and target data to ensure the accuracy and efficiency of subsequent processing.

[0075] See also Figure 3 , the steps to obtain the adjusted polarization parameters are:

[0076] S201: Based on the basic environment and target data, adjust the polarization angle of the radar beam, record the parameter adjustment process, and obtain preliminary polarization parameter records;

[0077] Based on the basic environment and target data, the polarization angle of the radar beam is adjusted. First, the polarization angle of the radar beam is initially set according to the basic environment and target data provided in the previous stage to ensure that the beam can effectively cover the target area. Next, a simulation model is used to simulate the impact of different polarization angles on signal reflection to evaluate the signal coverage quality and reflection efficiency at each angle. Subsequently, the polarization angle of the radar beam is adjusted based on the simulation results and gradually optimized to the optimal state to achieve the best signal reception effect. After each adjustment, the system automatically records the current polarization parameters, including the polarization angle and its corresponding signal quality indicators, to form a preliminary polarization parameter record. After completing these steps, the parameter adjustment process is recorded to obtain a preliminary polarization parameter record.

[0078] S202: Based on the preliminary polarization parameter records, analyze the signal reflection changes in real time, adjust the beam shape to match the target dynamics, and obtain the beam shape adjustment matching result;

[0079] Based on preliminary polarization parameter records, signal reflection changes are analyzed in real time. First, a real-time data monitoring system is used to track the interaction between the radar beam and the target, promptly capturing the target's dynamic changes. Next, based on the monitored signal reflection changes, a dynamic adjustment algorithm is used to adjust the radar beam shape in real time to better match the target's current state and motion. Furthermore, the effectiveness of each adjustment is evaluated, and the adjustment parameters and results are recorded to ensure accuracy and responsiveness. Finally, based on feedback from the adjustment process, the beam shape adjustment strategy is optimized to improve matching efficiency and accuracy, and the beam shape adjustment matching results are obtained.

[0080] S203: Based on the beam shape adjustment matching result, perform signal analysis to evaluate the statistical performance of the reflection characteristics, and optimize the polarization angle and frequency settings according to the evaluation results to generate adjusted polarization parameters;

[0081] Based on the beam shape adjustment matching results, signal analysis is performed. First, advanced signal processing tools are used to analyze the signal reflection data from the adjusted beam shape to identify key signal features and reflection patterns. Next, statistical analysis methods are used to evaluate the signal reflection characteristics, such as signal strength and signal consistency, under different polarization angles and frequency settings. Furthermore, based on the statistical results, strengths and weaknesses in the signal reflection characteristics are identified, and polarization angle and frequency are further optimized. Finally, the radar's polarization angle and frequency settings are adjusted based on the optimization results, generating adjusted polarization parameters to ensure optimal performance of the radar system in different environments and conditions.

[0082] See also Figure 4 , the steps for obtaining target dynamic angle data are:

[0083] S301: Based on the adjusted polarization parameters, the target movement process is continuously tracked and recorded, the angle change of the target is captured in real time, and the angle change data is collected to obtain the angle tracking record;

[0084] Based on the adjusted polarization parameters, continuous tracking is used to record the target's movement. First, the radar system is configured according to the adjusted polarization parameters to ensure that the system's sensitivity and response speed meet the current monitoring requirements. Next, target tracking mode is activated, and the radar continuously monitors the target area, capturing the target's angular changes in real time. Furthermore, a high-precision angle sensor is used to record the angular changes caused by each target movement, ensuring data accuracy and real-time availability. Simultaneously, the system automatically records the angular data at each time point, forming a continuous record of angular changes. After completing these steps, the angular change data is collected to form an angular tracking record.

[0085] S302: Based on the angle tracking records, a Kalman filter is used to perform data encoding and serialization processing, analyze the target's speed changes and direction adjustments, distinguish normal movement from abnormal movement, and obtain angle change analysis results;

[0086] The Kalman filter is used to perform data encoding and serialization processing according to the formula:

[0087] x k|k =x k|k-1 +αK k (z k -βH k x k|k-1 )

[0088] Calculate the target state estimate and obtain the angle change analysis results, where x k|k is the estimated value of the state after considering the measurement information at the kth moment, x k|k-1 is the predicted state before the kth moment, z k is the actual measured value at the kth moment, H k is the observation model matrix, which is used to map the state space to the measurement space, K k is the Kalman gain coefficient, which is used to minimize the estimation error, α is the dynamic factor for adjusting the gain, which is used to enhance the filter response, and β is the state adjustment coefficient, which is used to adjust the sensitivity of the state estimation.

[0089] The execution process is as follows:

[0090] First, parameters α and β are introduced to improve the flexibility and responsiveness of the filter. α dynamically adjusts the influence of the Kalman gain to adapt to changes in different dynamic environments and optimize the accuracy of target tracking. β improves the ability to detect abnormal motion by adjusting the sensitivity of the observation model. The values ​​of these two parameters can be optimized through experimental data or based on real-time performance feedback. For example, the values ​​of these coefficients are adjusted by minimizing the sum of squares of prediction errors. The integrated formula updates the state estimate at each time point. By optimizing the parameter values, it is ensured that the system can more accurately track and analyze the dynamic changes of the target.

[0091] S303: Based on the angle change analysis results, integrate all time point data, perform data synchronization and error correction, ensure data consistency through statistical verification, and generate target dynamic angle data;

[0092] Based on the results of the angle change analysis, data from all time points is integrated. First, data synchronization technology is used to ensure temporal consistency of data collected from different time points, allowing for data merging. Next, an error correction algorithm is applied to correct potential errors in the data, improving its accuracy. Furthermore, statistical analysis is performed on the integrated data to verify its consistency and reliability. Finally, dynamic angle data for the target is generated based on this corrected and verified data, ensuring its validity and practicality, and supporting subsequent applications and analysis.

[0093] See also Figure 5 , the steps to obtain the optimized angle tracking data are:

[0094] S401: Based on the target dynamic angle data, the signal frequency is corrected, the frequency is adjusted to match the target dynamics, and the signal phase is synchronously adjusted to ensure signal integrity, thereby obtaining signal optimization data;

[0095] The signal frequency is corrected based on the target's dynamic angle data. First, the target's motion trends and speed changes are analyzed using this data. Based on these changes, the required signal frequency adjustment is calculated. Next, based on the specific changes in the motion data, the radar's transmit frequency is dynamically adjusted to match the target's speed and direction. Furthermore, to ensure signal integrity, the signal phase is synchronously adjusted. By carefully adjusting the phase parameters, the phase consistency between the transmitted and received signals is ensured. Finally, the system automatically records the adjusted frequency and phase parameters for final signal optimization, generating optimized signal data.

[0096] S402: Based on the signal optimization data, a high-pass filter is applied to reduce background noise, and signal enhancement is performed to optimize signal reception quality and obtain a signal processing result;

[0097] Based on the signal optimization data, a high-pass filter is applied to reduce background noise. First, the high-pass filter's cutoff frequency is set to ensure that only signals above this frequency are allowed to pass, effectively removing low-frequency background noise. Next, the optimized signal data is processed through a high-pass filter to further remove non-target-related low-frequency noise components. Furthermore, signal enhancement technology is used to improve the clarity and strength of the filtered signal. By adjusting the gain parameter, the target signal's discernibility is enhanced. Finally, the signal data after high-pass filtering and enhancement processing is comprehensively analyzed to optimize signal reception quality and obtain the signal processing results.

[0098] S403: Based on the signal processing results, perform signal quality detection to evaluate signal stability and accuracy, adjust signal processing parameters according to the detection results, refine signal quality control measures, and generate optimized angle tracking data;

[0099] Based on the signal processing results, signal quality testing is performed to assess signal stability and accuracy. First, signal analysis tools are used to perform detailed quality checks on the processed signal, including assessments of metrics such as signal stability, noise levels, and error rates. Next, based on these test results, potential issues in the signal processing process, such as signal interference or distortion, are identified. Furthermore, based on these evaluation results, signal processing parameters, such as filter configuration and signal enhancement, are adjusted to further optimize signal processing. Finally, signal quality control measures are refined to ensure optimal signal processing at each stage, generating optimized angle tracking data and providing more accurate signal support for subsequent applications.

[0100] See also Figure 6 , the steps to obtain fine-tuning waveform parameters are:

[0101] S501: Based on the optimized angle tracking data, a multi-frequency sweep test is performed to evaluate the impact of the difference frequency on the angle accuracy. The waveform test results are obtained through frequency response analysis and angle deviation measurement.

[0102] Based on the optimized angle tracking data, a multi-frequency sweep test was performed to evaluate the impact of the difference frequency on the angle accuracy. First, multiple different test frequencies were set to cover a wide range from low frequency to high frequency. Then, a frequency response analysis was performed, and a signal was transmitted at each test frequency and the angular deviation of the reflected signal was recorded. Using the formula Δθ = θ 实测 -θ 理论 , where θ 实测 Indicates the actual measured angle, θ 理论 In addition, the angle deviation data of each frequency is analyzed and the standard deviation is used to Where μ is the mean value of the deviation and n is the number of samples to evaluate the specific impact of frequency on angle accuracy. Finally, a comprehensive analysis is performed to obtain waveform test results, showing the impact of different frequencies on angle tracking accuracy.

[0103] S502: Based on the waveform test results, adjust the waveform design parameters, refine the waveform through iterative processing, enhance the waveform response characteristics, perform waveform tuning and amplitude optimization, and obtain the waveform adjustment result;

[0104] Based on the waveform test results, the waveform design parameters are adjusted. First, the key frequency points that affect the waveform performance are identified based on the waveform test results. The waveform is iterated, and the frequency distribution f and phase delay φ are adjusted in each iteration to optimize the waveform response. The adjustment formula f is used. 新 =f 旧 +gΔf, where Δf is the frequency adjustment value and g is the learning rate, which is used to control the adjustment step size. Similarly, the phase delay φ is adjusted 新 =φ 旧 +hΔφ, where h is the adjustment coefficient, is used to enhance the waveform's response characteristics and accuracy. Through iterative optimization, each adjusted waveform is ensured to better meet the target angle detection requirements, resulting in waveform adjustment results.

[0105] S503: Based on the waveform adjustment result, the frequency and amplitude are adjusted to maximize the angle estimation accuracy, ensure that the waveform matches the target angle data, and generate fine-tuning waveform parameters;

[0106] Based on the waveform adjustment results, the waveform is precisely adjusted to maximize the angle estimation accuracy by adjusting the frequency f and amplitude A. Using the optimization formula A 新 =A |旧(1 + γΔA), where ΔA is the amplitude adjustment amount and γ is the adjustment factor used to refine the impact of the amplitude adjustment. Furthermore, the frequency adjustment effect is continuously monitored to ensure that the parameter adjustments are effectively improving the accuracy of the angle estimation. The resulting fine-tuned waveform parameters improve the system's measurement accuracy through these adjustments, ensuring optimal consistency between the waveform and the target angle data.

[0107] See also Figure 7 , the steps to obtain the coherent target angle estimation result are:

[0108] S601: Based on the fine-tuning of waveform parameters, the target angle is measured, and the response of the monitoring waveform to differentiated environmental factors is simulated by condition changes to obtain an angle response record;

[0109] Based on fine-tuning waveform parameters, angle measurements of targets are performed. First, the radar system is set to use optimized waveform parameters, including adjusted frequency and amplitude. Next, through condition change simulation, the system simulates the impact of different environmental factors (such as temperature, humidity, and wind speed) on the waveform to ensure that the radar can adapt to various environmental conditions. The formula R(θ) = Acos(2πfθ + φ) is used to calculate the reflected signal intensity at different angles, where R(θ) is the response intensity at angle θ, and A, f, and φ are the amplitude, frequency, and phase, respectively. In addition, the angular response under each condition is recorded to analyze the waveform's sensitivity and adaptability to environmental changes. After completing these steps, a detailed angular response record is obtained.

[0110] S602: Based on the angle response records, perform in-depth data analysis, verify the actual impact of waveform parameter adjustment through statistical methods, and obtain angle estimation data through signal stability and response time analysis;

[0111] Based on the angle response records, in-depth data analysis is performed. First, statistical methods are used to analyze the recorded data to evaluate the actual impact of waveform parameter adjustments on angle measurement accuracy. Calculate the standard deviation where x i is a single measurement, b is the average measurement, and N is the number of samples. Next, we analyze the signal stability and response time. By calculating the mean and coefficient of variation of the response time, we verify the improved system performance achieved by waveform adjustment. Finally, by combining all the data analysis results, we obtain comprehensive angle estimation data, which reflects the performance and reliability of the adjusted waveform.

[0112] S603: Perform data fusion analysis based on the angle estimation data to verify the accuracy of the data, optimize the angle estimation by minimizing the error, and generate a coherent target angle estimation result;

[0113] Based on the angle estimation data, data fusion analysis is performed. First, the results of multiple data sources and measurement cycles are integrated, and the data fusion technology is used to enhance the overall accuracy of the data. The error minimization method is used

[0114] To optimize the angle estimation, where y i is the measured angle value, is the predicted angle value, and N is the number of samples. By adjusting the weight parameters and model configuration in the data fusion algorithm, prediction errors can be reduced and estimation accuracy improved. Furthermore, systematic validation of the data fusion results ensures that the resulting angle estimates maintain high accuracy and reliability under various environments and conditions.

[0115] A coherent target angle estimation system for a polarization MIMO array radar, comprising:

[0116] The signal acquisition and evaluation module uses the polarization MIMO array radar to perform continuous scanning operations, collect the target's reflected signals, perform quantitative signal strength evaluation, filter and purify the signal data, identify signals with target reflection characteristics, and generate basic environment and target data;

[0117] The beam polarization adjustment module adjusts the polarization angle of the radar beam based on basic environment and target data, analyzes signal reflection changes in real time, evaluates the statistical performance of reflection characteristics, optimizes polarization angle and frequency settings, and generates adjusted polarization parameters;

[0118] The real-time angle capture module captures the target's angle changes in real time based on the adjusted polarization parameters, collects angle change data, and uses the Kalman filter to encode and serialize the data, distinguishing between normal and abnormal movement, and obtaining angle change analysis results.

[0119] The data synchronization and correction module performs data synchronization and error correction based on the angle change analysis results, adjusts the frequency to match the target dynamics, and synchronously adjusts the signal phase to ensure signal integrity, thereby obtaining signal optimization data.

[0120] The signal enhancement and stabilization module applies a high-pass filter based on signal optimization data to reduce background noise and perform signal enhancement. It also detects and evaluates signal stability and accuracy, adjusts signal processing parameters to refine signal quality control measures, and generates optimized angle tracking data.

[0121] The waveform optimization test module performs multi-frequency sweep tests based on the optimized angle tracking data, adjusts waveform design parameters, performs waveform tuning and amplitude optimization, and ensures that the waveform matches the target angle data by adjusting the frequency and amplitude to generate fine-tuned waveform parameters.

[0122] The final angle estimation module is based on fine-tuning waveform parameters, monitoring the waveform's response to differentiated environmental factors, verifying the actual impact of waveform parameter adjustment through statistical methods, optimizing angle estimation by minimizing errors, and generating coherent target angle estimation results.

[0123] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for estimating the angle of a coherent target of a polarization MIMO array radar, characterized in that: The following steps are involved: Setting the initial polarization parameters and environmental variables of the radar, continuously scanning the environment and the target through the polarization MIMO array radar, collecting the environment and target reflection signals, and obtaining basic environment and target data; Using the basic environment and target data, dynamically adjusting the polarization angle and shape of the radar beam, recording polarization parameter changes in real time, analyzing signal reflection characteristics, and obtaining adjusted polarization parameters; Continuously tracking the target angle using the adjusted polarization parameters, recording angle change data caused by target movement, and analyzing target dynamic changes in combination with time series to obtain target dynamic angle data; Processing the target dynamic angle data, using adaptive filtering to correct the frequency and phase of the signal, optimizing the signal reception quality, eliminating noise interference, and generating optimized angle tracking data; Adjusting radar waveform design parameters based on the optimized angle tracking data, performing multi-frequency sweep testing, analyzing waveform response characteristics to angle estimation, adjusting waveform parameters, and generating fine-tuning waveform parameters; The final angle estimation of the target is performed using the fine-tuned waveform parameters, the angle response of the target under differentiated conditions is monitored in real time, the adjustment effect of the waveform parameters is verified, and the coherent target angle estimation result is obtained.

2. The method for estimating coherent target angles of a polarization MIMO array radar according to claim 1, wherein: The basic environment and target data include environment feature description records and target feature description records, the adjusted polarization parameters include polarization angle values ​​and polarization shape features, the target dynamic angle data include angle change sequences and angle change trend analysis results, the optimized angle tracking data include corrected frequency, corrected phase and noise reduction level, the fine-tuning waveform parameters include frequency optimization values ​​and amplitude adjustment values, and the coherent target angle estimation results include final coherent target angle estimation values ​​and angle response analysis results.

3. The method for estimating coherent target angles of a polarization MIMO array radar according to claim 1, wherein: The steps for obtaining the basic environment and target data are as follows: Based on the initial polarization parameter settings of the radar, the polarization MIMO array radar performs continuous scanning operations to collect the target's reflected signals and obtain a preliminary signal data set; Using the preliminary signal data set, performing a quantitative signal strength assessment, adjusting a signal reception threshold according to the assessment result, performing signal screening and purifying signal data, and obtaining a selected signal data set; By comparing the time delay and frequency distribution of the difference signals through the selected signal data set, the signals with target reflection characteristics are identified, and the basic environment and target data are distinguished and generated.

4. The method for estimating coherent target angles of a polarization MIMO array radar according to claim 1, wherein: The steps for obtaining the adjusted polarization parameters are: Based on the basic environment and target data, adjust the polarization angle of the radar beam, record the parameter adjustment process, and obtain preliminary polarization parameter records; Based on the preliminary polarization parameter records, analyzing signal reflection changes in real time, adjusting the beam shape to match the target dynamics, and obtaining a beam shape adjustment matching result; Based on the beam shape adjustment matching result, signal analysis is performed to evaluate the statistical performance of the reflection characteristics, and the polarization angle and frequency settings are optimized according to the evaluation results to generate adjusted polarization parameters.

5. The method for estimating coherent target angles of a polarization MIMO array radar according to claim 1, wherein: The steps for acquiring the target dynamic angle data are as follows: Based on the adjusted polarization parameters, the target movement process is continuously tracked and recorded, the angle change of the target is captured in real time, the angle change data is collected, and the angle tracking record is obtained; Based on the angle tracking record, a Kalman filter is used to perform data encoding and serialization processing, analyze the speed change and direction adjustment of the target, distinguish normal movement from abnormal movement, and obtain angle change analysis results; Based on the angle change analysis results, all time point data are integrated, data synchronization and error correction are performed, data consistency is ensured through statistical verification, and target dynamic angle data is generated.

6. The method for estimating coherent target angles of a polarization MIMO array radar according to claim 5, wherein: The Kalman filter is used to perform data encoding and serialization processing according to the formula: x k|k =x k|k-1 +αK k (z k -βH k x k|k-1 ) Calculate the target state estimate and obtain the angle change analysis results, where x k|k is the estimated value of the state after considering the measurement information at the kth moment, x k|k-1 is the predicted state before the kth moment, z k is the actual measured value at the kth moment, H k is the observation model matrix, which is used to map the state space to the measurement space, K k is the Kalman gain coefficient, which is used to minimize the estimation error, α is the dynamic factor for adjusting the gain, which is used to enhance the filter response, and β is the state adjustment coefficient, which is used to adjust the sensitivity of the state estimation.

7. The method for estimating coherent target angles of a polarization MIMO array radar according to claim 1, wherein: The steps for obtaining the optimized angle tracking data are as follows: Based on the target dynamic angle data, the signal frequency is corrected, the frequency is adjusted to match the target dynamics, and the signal phase is synchronously adjusted to ensure signal integrity, thereby obtaining signal optimization data; Based on the signal optimization data, a high-pass filter is applied to reduce background noise, and signal enhancement is performed to optimize signal reception quality and obtain a signal processing result; Based on the signal processing results, signal quality detection is performed to evaluate signal stability and accuracy, and signal processing parameters are adjusted according to the detection results to refine signal quality control measures and generate optimized angle tracking data.

8. The method for estimating coherent target angles of a polarization MIMO array radar according to claim 1, wherein: The steps for obtaining the fine-tuning waveform parameters are as follows: Based on the optimized angle tracking data, a multi-frequency sweep test is performed to evaluate the impact of the difference frequency on the angle accuracy, and waveform test results are obtained through frequency response analysis and angle deviation measurement; Based on the waveform test results, adjusting waveform design parameters, refining the waveform through iterative processing, enhancing waveform response characteristics, performing waveform tuning and amplitude optimization, and obtaining waveform adjustment results; Based on the waveform adjustment result, the frequency and amplitude are adjusted to maximize the angle estimation accuracy, ensure that the waveform matches the target angle data, and generate fine-tuning waveform parameters.

9. The method for estimating coherent target angles of a polarization MIMO array radar according to claim 1, wherein: The steps for obtaining the coherent target angle estimation result are: Based on the fine-tuning waveform parameters, the angle of the target is measured, and the response of the monitoring waveform to the differentiated environmental factors is simulated by the condition change to obtain the angle response record; Based on the angle response records, in-depth data analysis is performed to verify the actual impact of waveform parameter adjustments through statistical methods, and angle estimation data is obtained through signal stability and response time analysis; Based on the angle estimation data, data fusion analysis is performed to verify the accuracy of the data, and the angle estimation is optimized by minimizing the error to generate a coherent target angle estimation result.

10. A coherent target angle estimation system for a polarization MIMO array radar, characterized in that: A method for estimating coherent target angles for a polarization MIMO array radar according to any one of claims 1 to 9, wherein the system comprises: The signal acquisition and evaluation module uses the polarization MIMO array radar to perform continuous scanning operations, collect the target's reflected signals, perform quantitative signal strength evaluation, filter and purify the signal data, identify signals with target reflection characteristics, and generate basic environment and target data; The beam polarization adjustment module adjusts the polarization angle of the radar beam based on the basic environment and target data, analyzes the signal reflection changes in real time, evaluates the statistical performance of the reflection characteristics, optimizes the polarization angle and frequency settings, and generates adjusted polarization parameters; The real-time angle capture module captures the target's angle changes in real time based on the adjusted polarization parameters, collects angle change data, uses a Kalman filter to encode and serialize the data, distinguishes normal movement from abnormal movement, and obtains angle change analysis results; The data synchronization and correction module performs data synchronization and error correction based on the angle change analysis results, adjusts the frequency to match the target dynamics, and synchronously adjusts the signal phase to ensure signal integrity to obtain signal optimization data; The signal enhancement and stabilization module applies a high-pass filter to reduce background noise based on the signal optimization data, performs signal enhancement, detects and evaluates signal stability and accuracy, adjusts signal processing parameters to refine signal quality control measures, and generates optimized angle tracking data; The waveform optimization test module performs multi-frequency sweep testing based on the optimized angle tracking data, adjusts waveform design parameters, performs waveform tuning and amplitude optimization, ensures that the waveform matches the target angle data by adjusting the frequency and amplitude, and generates fine-tuning waveform parameters; The final angle estimation module monitors the waveform response to differentiated environmental factors based on the fine-tuning of the waveform parameters, verifies the actual impact of the waveform parameter adjustment through statistical methods, optimizes the angle estimation by minimizing the error, and generates a coherent target angle estimation result.

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