An airport wake vortex radar wind field data enhancement method based on spectral width anomaly detection

Abstract

The present application relates to radar wind field data related technical field, specifically including an airport wake vortex radar wind field data enhancement method based on spectral width anomaly detection, the method comprises: based on the initial error matrix, the spatial error component of each detection partition, configure dynamic data enhancement matrix, under the constraint of wake vortex dissipation time iteration optimization enhancement parameter, carry out the enhancement decision of wind field data deviation. The technical problems of unable to accurately decompose the spatial error component of horizontal wind direction angle and vertical wind shear, easy to confuse the action law of different interference sources, and difficult to effectively improve the signal-to-noise ratio of wind field data are solved, the technical effects of introducing the spatial correlation mapping of the detection partition wind field direction vector and the wake vortex influence area, optimizing the enhancement coefficient, accurately splitting the horizontal and vertical direction spatial error component, extracting the environmental disturbance enhancement factor, efficiently decoupling the atmospheric turbulence, electromagnetic interference and equipment noise, improving the space-time consistency of wind field data, and reducing the wake vortex early warning false alarm rate are realized.

Description

Technical Field

[0001] This invention relates to the field of radar wind field data technology, specifically to a method for enhancing airport wake radar wind field data based on spectral width anomaly detection. Background Technology

[0002] Airport wake vortices are dangerous air currents generated during aircraft takeoff and landing. The intensity and diffusion trajectory of airport wake vortices are closely related to the wind field environment and directly affect the safety of subsequent flight takeoffs and landings. With the continuous growth of air transport volume and the increasing density of airport operations, higher requirements are placed on the spatiotemporal resolution, accuracy and real-time performance of wind field data. As the core equipment for wind field monitoring, the quality of data collected by wake vortex radar directly determines the reliability of the wake vortex early warning system. However, the complex airspace environment of airports and factors such as atmospheric turbulence, electromagnetic interference and equipment noise can easily lead to distortion of radar wind field data.

[0003] Current airport wake vortex radar wind field data augmentation methods are insufficient to meet the requirements of high-precision wake vortex early warning. Most methods rely on data from a single detection point to construct error models, resulting in inaccurate identification of spatial error components and limited data deviation correction effects. They also fail to accurately locate key areas for data augmentation through spectral width characteristics. The influences of atmospheric turbulence, electromagnetic interference, and equipment noise on wind field data are coupled, and the lack of an efficient decoupling mechanism leads to over-correction and under-correction problems during data augmentation. Furthermore, the augmentation parameters lack dynamic adaptability, and the detection cycle of the detection zone and the time interval between adjacent zones are set unreasonably, further reducing the spatiotemporal consistency and reliability of wind field data and severely restricting the accuracy of wake vortex early warning.

[0004] In summary, existing technologies suffer from several problems: they rely on data from a single detection point to construct an error model, cannot accurately decompose the spatial error components of horizontal wind direction angle and vertical wind shear, and the effects of atmospheric turbulence, electromagnetic interference, and radar noise in airport airspace on wind field data are superimposed, easily confusing the effects of different interference sources. This leads to overcompensation or undercompensation in data correction, making it difficult to effectively improve the signal-to-noise ratio of wind field data. Summary of the Invention

[0005] This application provides a method for enhancing airport wake vortex radar wind field data based on spectral width anomaly detection. It aims to solve the technical problems in existing technologies, such as relying on data from a single detection point to construct an error model, failing to accurately decompose the spatial error components of horizontal wind direction angle and vertical wind shear, and the superimposed effects of atmospheric turbulence, electromagnetic interference, and radar noise on wind field data in airport airspace, which easily confuse the effects of different interference sources, leading to over-compensation or under-compensation in data correction, and making it difficult to effectively improve the signal-to-noise ratio of wind field data.

[0006] In view of the above problems, the technical solution to achieve the present application is as follows:

[0007] This application provides a method for enhancing airport wake vortex radar wind field data based on spectral width anomaly detection. The method includes: establishing a three-dimensional wind field calibration benchmark based on airport wake vortex radar configuration information; obtaining spectral width anomaly deviation and wind field data deviation; and formulating an initial error matrix; introducing the wind field direction vector of each detection zone and spatially associating it with the potential wake vortex influence area identified by the spectral width anomaly deviation to determine the spatial error components of each detection zone; determining enhancement coefficients using weighted least squares based on the initial error matrix and the spatial error components of each detection zone; extracting environmental disturbance enhancement factors using a deep separable convolutional neural network to separate the coupling effects of atmospheric turbulence, electromagnetic interference, and equipment noise; and configuring a dynamic data enhancement matrix; deploying distributed enhancement nodes according to the dynamic data enhancement matrix and the wind field data accuracy requirements; iteratively optimizing enhancement parameters under the constraint of wake vortex dissipation time; and simultaneously making enhancement decisions regarding the wind field data deviation.

[0008] Preferably, a data detection period is set, and the initial partition data deviation is determined based on the wind field signal amplitude collected separately for each detection partition; the initial partition data deviation is verified for reliability based on the spatial continuity characteristics corresponding to the abnormal spectral width deviation, and the adjacent deviation coefficient between each detection partition is determined; the bias of each detection partition is corrected by the adjacent deviation coefficient, and fuzzy decision is used to perform adaptive data augmentation.

[0009] Preferably, a first adaptive enhancement index is configured based on each detection zone and the spatial correlation characteristics of spectral width anomaly deviation; a second adaptive enhancement index is configured based on each detection zone and the spatial correlation characteristics of wind field data deviation; and the data detection period is set based on the first adaptive enhancement index and the second adaptive enhancement index.

[0010] Preferably, the airport detection area is divided into various detection zones, each containing a preset area range, and the interference isolation between the detection zones meets the isolation limit conditions; according to the timing control logic, the detection zones are polled and detected through the radar beam phased array matrix.

[0011] Preferably, the detection interval between adjacent detection zones corresponding to each detection zone meets the time interval limitation condition; at the same time, an initial wind field compensation weight is applied to each detection zone according to the deviation between the wind field direction vector of each detection zone and the airport wake trajectory.

[0012] Preferably, a first time factor is determined based on the center frequency of the radar operating band; a second time factor is determined using an atmospheric propagation attenuation model; a third time factor corresponding to the radar beam switching delay and a fourth time factor corresponding to the signal processing pipeline delay are determined; and the time interval constraint conditions are comprehensively set by combining the first time factor and the second time factor.

[0013] Preferably, based on the atmospheric propagation attenuation model, the time required for the echo signal of the first detection zone to decay to the point where its signal-to-noise ratio impact on the echo signal of the second detection zone is lower than the interference threshold is determined, thus obtaining the second time factor; wherein, the detection zone corresponding to the echo signal of the first detection zone and the detection zone corresponding to the echo signal of the second detection zone are adjacent detection zones.

[0014] Preferably, the deviation between the wind field direction vector of each detection zone and the theoretical trajectory of the airport wake vortex is decomposed into a horizontal wind direction angle error component and a vertical wind shear error component; the spatial error component is constructed through the horizontal wind direction angle error component and the vertical wind shear error component.

[0015] Preferably, based on the detection range, accuracy requirements, and resolution in the airport wake vortex radar configuration information, a spherical sampling grid with a radius of V times the working wavelength is constructed within the airport airspace mapped by the airport detection area; based on the spherical sampling grid, a Doppler radar receiver and a reference wind profiler are configured to collect full-airspace wind field data, including full-airspace wind field spectral width and radial velocity data, along a preset spiral scanning path; with the runway starting line midpoint as the coordinate origin, the geographical coordinates of each detection zone are associated with the full-airspace wind field data to construct a wind field calibration benchmark database.

[0016] Preferably, environmental parameters, spectral width anomaly deviation, and wind field data deviation in historical wind field enhancement records are used as training samples; atmospheric turbulence branch, electromagnetic interference branch, and equipment noise branch are set in the output layer of the deep separable convolutional neural network to decouple the coupling effect and set the dynamic data enhancement matrix.

[0017] In summary, one or more technical solutions provided in this application achieve the following: introducing a spatial correlation mapping between the wind field direction vector of the detection zone and the wake vortex influence area; accurately separating the spatial error components in the horizontal and vertical directions; optimizing the enhancement coefficient using the weighted least squares method; accurately locating the wake vortex influence area; extracting environmental disturbance enhancement factors; efficiently decoupling atmospheric turbulence, electromagnetic interference, and equipment noise; iteratively optimizing the enhancement parameters under the constraint of wake vortex dissipation time; and setting the detection cycle based on dual adaptive enhancement indices and combining multiple time factors to set the zone detection interval, thereby improving the spatiotemporal consistency of wind field data and reducing the false alarm rate of wake vortex warnings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This application provides a flowchart illustrating a method for enhancing airport wake radar wind field data based on spectral width anomaly detection. Detailed Implementation

[0020] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0021] The embodiments are described in detail below with reference to the accompanying drawings, such as... Figure 1 As shown, this application provides a method for enhancing airport wake radar wind field data based on spectral width anomaly detection, wherein the method includes:

[0022] S1: Based on the airport wake vortex radar configuration information, establish a three-dimensional wind field calibration benchmark, obtain spectral width anomaly deviation and wind field data deviation, and formulate an initial error matrix; S2: Introduce the wind field direction vector of each detection zone, and spatially correlate and map it with the spectral width anomaly deviation to identify the potential wake vortex influence area, and determine the spatial error components of each detection zone.

[0023] Specifically, airport wake vortex radar configuration information refers to parameters such as radar detection range, accuracy requirements, resolution, and operating wavelength, used to determine the radar's detection capability and data acquisition accuracy in airport airspace; three-dimensional wind field calibration benchmark refers to a wind field data reference model constructed using radar configuration information, used to calibrate actual measurement data and ensure data accuracy and consistency; spectral width anomaly deviation refers to the deviation of the spectral width in the radar echo signal from the normal value. Spectral width anomalies are usually related to special meteorological phenomena such as wake vortices and are a manifestation of the wake vortex's influence.

[0024] Wind field data deviation refers to the difference between the actual measured wind field data and the calibration benchmark, reflecting the degree of data error. The initial error matrix is ​​a matrix formed by combining the spectral width anomaly deviation and the wind field data deviation, used for subsequent error analysis and calibration. The wind field direction vector of the detection zone refers to the directional information of the wind field in each detection zone, represented in vector form, used to analyze the spatial distribution characteristics of the wind field. Spatial correlation mapping refers to the correlation between the spectral width anomaly deviation and the wind field direction vector through a mathematical model to identify the areas that may be affected by the wake vortex. Spatial error components refer to the error parts decomposed in the horizontal and vertical directions, used for more refined analysis and calibration of wind field data.

[0025] Execution steps: In the airport detection area, a three-dimensional wind field calibration benchmark is established based on the radar configuration information, including detection range, accuracy requirements, and resolution. Furthermore, the spectral width and radial velocity data of the wind field are collected to construct full-airspace wind field data. On this basis, the spectral width anomaly deviation and wind field data deviation are obtained to form an initial error matrix, ensuring the comprehensiveness and accuracy of error calibration.

[0026] The wind field direction vectors of each detection zone are introduced and spatially correlated with the spectral width anomaly deviation. Specifically, the airport detection area is divided into multiple detection zones, and the wind field direction vector of each zone is obtained by polling the radar beam phased array matrix. The spectral width anomaly deviation is correlated with the wind field direction vector to identify potential wake vortex influence areas. Furthermore, the deviation between the wind field direction vector of the detection zone and the wake vortex trajectory is determined by spatial correlation mapping to determine the spatial error component of the zone. By combining the spectral width anomaly deviation with the wind field direction vector, the wake vortex influence area can be accurately located, thereby improving the pertinence and effectiveness of data augmentation.

[0027] S3: Based on the initial error matrix and the spatial error components of each detection zone, the enhancement coefficient is determined using the weighted least squares method. Environmental disturbance enhancement factors are extracted using a deep separable convolutional neural network to separate the coupling effects of atmospheric turbulence, electromagnetic interference, and equipment noise, and a dynamic data enhancement matrix is ​​configured. S4: Based on the dynamic data enhancement matrix and combined with the wind field data accuracy requirements, distributed enhancement nodes are deployed. The enhancement parameters are iteratively optimized under the constraint of wake dissipation time. At the same time, enhancement decisions are made for the wind field data deviation.

[0028] Specifically, the weighted least squares method is used to calculate the enhancement coefficients based on the initial error matrix and spatial error components. By assigning different weights to different data points, the weighted sum of squared errors is minimized, thereby determining the optimal parameters. The deep separable convolutional neural network is used to extract environmental disturbance enhancement factors and separate coupling effects. By separating convolution operations, the computational load and number of parameters are reduced while maintaining network performance. Furthermore, the separating convolution operations include spatial convolution and channel-wise convolution. The environmental disturbance enhancement factor is a parameter that reflects the impact of environmental changes on wind field data and is used to adjust the data enhancement strategy. Environmental changes include atmospheric turbulence and electromagnetic interference.

[0029] Coupling effect separation refers to decomposing the combined effects of atmospheric turbulence, electromagnetic interference, and equipment noise on wind field data to address each type of interference individually; the dynamic data augmentation matrix is ​​used to optimize the enhancement effect of wind field data, and is a data augmentation model that is dynamically adjusted based on real-time data and environmental conditions; distributed augmentation nodes are used to locally optimize wind field data quality, consisting of multiple data augmentation processing units distributed within the airport detection area; the wake dissipation time constraint refers to the time limit set based on the natural dissipation characteristics of the wake, ensuring that data augmentation and optimization are completed before the wake dissipates; and augmentation decision refers to determining whether to perform data augmentation and the degree of augmentation based on augmentation parameters and wind field data deviations.

[0030] Execution steps: Based on the initial error matrix and the spatial error components of each detection zone, the enhancement coefficient is calculated using the weighted least squares method. Specifically, the initial error matrix includes spectral width anomaly deviation and wind field data deviation, while the spatial error components reflect the horizontal and vertical error distribution of the wake vortex influence area. Through the weighted least squares method, the optimal enhancement coefficient can be calculated based on these error information for subsequent data enhancement processing. Furthermore, the objective function is configured as the mean square error between the enhanced wind field data and the high-confidence reference wind field, and the constraint condition of the objective function is the physically reasonable range of the enhancement coefficient. Based on the objective function, the overdetermined equation system is solved using the weighted least squares method, wherein the weight coefficients are dynamically allocated according to the attenuation characteristics of each detection zone at its distance from the ground true value monitoring station.

[0031] A deep separable convolutional neural network (DNN) is used to extract environmental disturbance enhancement factors and separate the coupling effects of atmospheric turbulence, electromagnetic interference (EMI), and equipment noise. By learning features from historical wind field data, the DNN efficiently extracts these factors and further identifies the impact of atmospheric turbulence on the wind field data. After separation through coupling effects, these disturbances are addressed specifically and separated from EMI and equipment noise. Based on the separated coupling effects and calculated enhancement coefficients, a dynamic data enhancement matrix is ​​configured. This matrix dynamically adjusts the enhancement strategy according to real-time wind field data and environmental conditions. For example, when the wake vortex intensity is high, the enhancement matrix increases compensation for atmospheric turbulence; when EMI is strong, the matrix adjusts the correction parameters for EMI. This dynamic adjustment ensures the reliability and accuracy of wind field data under different environmental conditions.

[0032] Based on the dynamic data augmentation matrix and combined with the accuracy requirements of wind field data, distributed augmentation nodes are deployed. Under the constraint of wake vortex dissipation time, the augmentation parameters are iteratively optimized, and augmentation decisions are made based on wind field data deviations. The distributed augmentation nodes continuously adjust the augmentation parameters according to the dynamic data augmentation matrix. Furthermore, if the wind field data deviation exceeds a preset threshold, an augmentation decision is triggered to enhance the data. Through this dynamic optimization and augmentation decision mechanism, the spatiotemporal consistency of wind field data is effectively improved, the false alarm rate of wake vortex warnings is reduced, and the high accuracy and reliability of airport wake vortex radar wind field data are ensured.

[0033] A weighting function is set for each detection zone to determine the initial wind field compensation weight for each detection zone. Specifically, the initial wind field compensation weight for each detection zone is set based on the degree of proximity of that zone to the typical trajectory of the wake vortex. Furthermore, if a detection zone is located on the path where the wake vortex is most likely to appear after aircraft takeoff or landing, i.e., close to the theoretical wake vortex trajectory, the data in that area is considered more reliable and is given a higher compensation weight. If the detection zone deviates far from the wake vortex trajectory, such as being far to the side of the runway, its data is less affected by the wake vortex and may even mainly reflect background wind or interference, so it is given a lower weight. In specific settings, the system will combine the airport runway direction, the wake vortex intensity level corresponding to the current takeoff and landing aircraft type, and historical wake vortex observation patterns to pre-generate a high-impact area map of the wake vortex. Each detection zone will automatically match its initial compensation weight according to the coverage of this map. This weight is configured before data processing begins and can be fine-tuned during operation based on the real-time wake vortex status.

[0034] In real-world scenarios, the preset threshold is dynamically determined. Specifically, the preset threshold is not a fixed value but is dynamically adjusted based on the current flight phase, the life cycle of the wake vortex, and environmental conditions. In the short period immediately after the wake vortex is generated, such as the first 30 seconds, the accuracy requirement for the wind field is the highest. At this time, the preset threshold is set relatively low, and even slight deviations will trigger enhancement processing. As time goes on and the wake vortex naturally decays, the allowable deviation range of the system gradually widens, and the preset threshold is increased accordingly to avoid over-processing of areas where the wake vortex has dissipated. At the same time, if there is heavy rainfall, low visibility, or poor radar signal-to-noise ratio, the preset threshold is appropriately widened to prevent frequent enhancement triggers due to environmental noise being misjudged as real wind field anomalies. Furthermore, the specific conditions for triggering enhancement decisions are: when the wind field data deviation of a certain detection zone exceeds the current dynamic threshold and continues for a certain period of time, such as two consecutive detection cycles, it is determined that data enhancement needs to be initiated, and the corresponding distributed enhancement node is called for correction.

[0035] Specifically, the training data includes features under different environmental conditions. Historical data used to train the deep separable convolutional neural network includes wind field data and spectral width data collected by the radar. It also records various environmental parameters, such as the weather conditions at the time, atmospheric stability information provided by the airport meteorological station, whether there is interference from other equipment in the radar's operating frequency band, and the radar's own operating status. This environmental information is used as additional input features and fed into the neural network along with the wind field data for joint learning. The weather conditions at the time include sunny, rainy, foggy, wind speed and direction, and the radar's own operating status includes receiver gain and temperature. Furthermore, in this way, it is possible to distinguish which signal changes are caused by real wake vortices, rainfall attenuation, electromagnetic interference, or equipment noise. Therefore, even if the environment changes, the corresponding type of disturbance factor (turbulence, interference, noise) can be accurately extracted to achieve targeted data augmentation.

[0036] Furthermore, prior to making enhanced decisions regarding the wind field data bias, the method of this application further includes:

[0037] Set a data detection cycle, determine the initial partition data deviation based on the wind field signal amplitude collected separately for each detection partition; verify the reliability of the initial partition data deviation based on the spatial continuity characteristics corresponding to the abnormal spectral width deviation, and determine the adjacent deviation coefficient between each detection partition; correct the bias of each detection partition through the adjacent deviation coefficient, and perform adaptive data augmentation using fuzzy decision-making.

[0038] Specifically, the data detection cycle refers to the time interval between data acquisition and processing by the radar system for each detection zone, used to control the frequency of data updates; the wind field signal amplitude refers to the intensity of the wind field signal detected by the radar, which is usually related to parameters such as wind speed and wind direction, and is used to evaluate the quality of wind field data; the initial zone data deviation reflects the difference between the detection zone data and the benchmark, and is the preliminary data deviation of each detection zone calculated based on the wind field signal amplitude.

[0039] Spatial continuity characteristics refer to the continuous spatial distribution of spectral width anomalies, used to judge the rationality of data deviations; the adjacent deviation coefficient represents the correlation coefficient of data deviations between adjacent detection zones, used to assess the propagation and impact of zone data deviations; the bias amount refers to the actual deviation value of the detection zone data, which needs to be verified and corrected to improve data accuracy; fuzzy decision-making is used for adaptive data augmentation, a decision-making method based on fuzzy logic, which handles uncertainty and fuzziness through fuzzy rules and membership functions; adaptive data augmentation refers to dynamically adjusting the data augmentation strategy according to real-time data and environmental conditions to optimize the quality of wind field data.

[0040] Execution steps: Set a reasonable data detection cycle, which directly affects the spatiotemporal resolution and real-time performance of wind field data. Collect and process data for each detection zone separately to ensure the spatiotemporal resolution of the data. Determine the initial zone data deviation based on the wind field signal amplitude collected separately for each detection zone. Compare the wind field signal amplitude of the detection zone with the reference signal amplitude to obtain the initial zone data deviation. The initial zone data deviation reflects the preliminary difference between the zone data and the reference.

[0041] Based on the spatial continuity characteristics corresponding to the spectral width anomaly deviation, the reliability of the initial partition data deviation is verified. The spatial continuity characteristics of the spectral width anomaly deviation indicate that the spectral width anomaly in the wake vortex influence area is usually spatially continuous. For example, if the spectral width anomaly deviation of the detection partition is ±0.5 m / s, and the spectral width anomaly deviation of the adjacent partition is also ±0.5 m / s, then the initial data deviation of that partition can be considered to have high reliability. Through this verification, errors caused by random noise or local interference are eliminated.

[0042] The adjacent deviation coefficient between each detection partition is determined. The adjacent deviation coefficient reflects the correlation of the data deviation between adjacent partitions. For example, if the spectral width anomaly deviations of two adjacent partitions are ±0.5m / s and ±0.4m / s, respectively, the calculated adjacent deviation coefficient is 0.8, indicating that the data deviations of these two partitions have a high correlation. The adjacent deviation coefficient is used to further correct the bias of each detection partition and improve the accuracy of the data.

[0043] The bias of each detection zone is corrected by the adjacent deviation coefficient, and adaptive data augmentation is performed by fuzzy decision-making. The data augmentation strategy is dynamically adjusted according to real-time data and environmental conditions, including atmospheric turbulence intensity and electromagnetic interference level. When the atmospheric turbulence intensity is high, fuzzy decision-making will increase the augmentation degree of the data in that zone, effectively improving the spatiotemporal consistency and reliability of wind field data, reducing the false alarm rate of wake vortex warning, and ensuring the reliability and accuracy of data.

[0044] Furthermore, regarding the setting of data detection cycles, the method in this application includes:

[0045] Based on each detection zone and the spatial correlation characteristics of spectral width anomaly deviation, a first adaptive enhancement index is configured; based on each detection zone and the spatial correlation characteristics of wind field data deviation, a second adaptive enhancement index is configured; based on the first adaptive enhancement index and the second adaptive enhancement index, the data detection period is set.

[0046] Specifically, the spatial correlation characteristics of spectral width anomaly deviation refer to the correlation between spectral width anomaly deviations in different detection zones, reflecting the spatial propagation and distribution patterns of wake vortex effects; the spatial correlation characteristics of wind field data deviation refer to the correlation between wind field data deviations in different detection zones, reflecting the spatial distribution patterns of wind field data errors; the first adaptive enhancement index is an enhancement index configured based on the spatial correlation characteristics of spectral width anomaly deviation, used to evaluate and guide data enhancement strategies targeting wake vortex effects; the second adaptive enhancement index is an enhancement index configured based on the spatial correlation characteristics of wind field data deviation, used to evaluate and guide data enhancement strategies targeting wind field data errors; the data detection cycle refers to the radar data acquisition time interval dynamically adjusted according to the first and second adaptive enhancement indices, ensuring the real-time performance and effectiveness of the data enhancement strategy.

[0047] Execution steps: First, an adaptive enhancement index is configured based on the spatial correlation characteristics of spectral width anomaly deviations in each detection zone. Specifically, by analyzing the correlation between spectral width anomaly deviations in different detection zones, the propagation path and intensity of the wake vortex effect are determined. The first adaptive enhancement index is derived by calculating the correlation coefficient of the spectral width anomaly deviations between two adjacent detection zones. This first adaptive enhancement index reflects the spatial continuity and intensity of the wake vortex effect, providing targeted guidance for data enhancement. Second, a second adaptive enhancement index is configured based on the spatial correlation characteristics of wind field data deviations in each detection zone. The spatial correlation characteristics of wind field data deviations reflect the spatial distribution pattern of errors. The second adaptive enhancement index is derived by calculating the correlation coefficient of the wind field data deviations between two adjacent detection zones. This second adaptive enhancement index reflects the spatial distribution characteristics of wind field data errors, providing a basis for error calibration for data enhancement.

[0048] The data detection period is set based on the first and second adaptive enhancement indices. The setting of the data detection period needs to comprehensively consider the propagation speed of the wake vortex effect and the rate of change of wind field data errors. Furthermore, if the first adaptive enhancement index indicates a relatively fast propagation speed of the wake vortex effect, and the second adaptive enhancement index indicates a relatively high rate of change of wind field data errors, then the data detection period can be set to a shorter time interval. Through this dynamic adjustment, the radar system can promptly capture the latest wind field data even when the wake vortex effect is rapidly changing, and perform effective data enhancement processing. This enables dynamic adjustment of the data enhancement strategy, improves the spatiotemporal consistency and reliability of wind field data, reduces the false alarm rate of wake vortex warnings, and ensures the high accuracy and real-time performance of airport wake vortex radar wind field data.

[0049] Furthermore, by incorporating the wind field direction vectors of each detection zone, the method of this application includes:

[0050] The airport detection area is divided into various detection zones, each containing a preset area range. The interference isolation between each detection zone meets the isolation limit conditions. According to the timing control logic, each detection zone is polled and detected through a radar beam phased array matrix.

[0051] Specifically, the airport detection area refers to the entire spatial range covered by the airport wake vortex radar, typically including the runway, taxiway, and surrounding airspace; detection zones refer to dividing the airport detection area into multiple smaller zones, each with independent detection and data processing capabilities; preset area range refers to the specific spatial range of each detection zone, usually pre-set based on the radar's detection capabilities and the airport's actual needs; interference isolation refers to the degree of mutual interference between different detection zones, typically controlled through electromagnetic isolation measures; isolation limit conditions refer to the maximum allowable interference level between detection zones to ensure detection accuracy; timing control logic refers to the time sequence and control rules for switching the radar beam phased array matrix between different detection zones; radar beam phased array matrix refers to achieving rapid switching and multi-target detection through electronic control of beam direction; polling detection refers to the process of sequentially collecting data from each detection zone according to a preset time sequence.

[0052] Execution steps: The airport detection area is divided into multiple detection zones, each containing a preset area. For example, if the airport detection area is a circular area with a radius of 10km, it is divided into 100 detection zones, each with an area of ​​approximately 3.14km². This division method can improve the radar's detection accuracy in local areas and facilitate subsequent data processing and enhancement. To ensure that the detection data between different detection zones are not mutually interfered with, the interference isolation between detection zones needs to meet the isolation limit conditions. By designing a reasonable radar antenna beamwidth and pattern, the interference isolation between adjacent detection zones is ensured to reach a certain limit, meaning that the signal interference intensity between adjacent zones is only at the interference suppression threshold of the main signal, thereby ensuring the accuracy and reliability of the detection data.

[0053] According to the timing control logic, the radar beam phased array matrix polls and detects each detection zone. The radar beam phased array matrix can quickly switch beam directions according to a preset time sequence to collect data from each detection zone in turn. For example, if the detection time for each detection zone is 0.01 seconds, the radar beam phased array matrix can complete one round of polling and detection of 100 detection zones per second. This polling and detection method can ensure that the radar system can efficiently cover the entire airport detection area, while avoiding data aging or omissions caused by focusing on a certain area for a long time.

[0054] This method of zoned detection and polling detection can significantly improve the detection efficiency and data quality of radar systems. Specifically, on the one hand, zoned detection allows radar to monitor wind field changes in local areas more precisely, especially in areas affected by wake vortices; on the other hand, polling detection ensures that the radar system can update data across the entire detection area in real time, avoiding outdated local data. In addition, by controlling the interference isolation between detection zones, data errors can be effectively reduced, improving the reliability and accuracy of wind field data, and providing a foundation for wind field data enhancement and wake vortex early warning.

[0055] Furthermore, the method of this application includes:

[0056] The detection intervals of adjacent detection zones corresponding to each detection zone meet the time interval limit condition; at the same time, an initial wind field compensation weight is applied to each detection zone according to the deviation of the wind field direction vector of each detection zone from the airport wake trajectory.

[0057] Specifically, the detection interval between adjacent detection zones refers to the time difference between data acquisition between adjacent detection zones; the time interval constraint is the minimum time interval requirement set to avoid data interference between adjacent detection zones; the wind field direction vector represents the direction of the wind field in the detection zone, expressed in vector form, including horizontal and vertical components; the airport wake trajectory refers to the propagation path of the wake in the airport airspace, usually determined by historical data and theoretical models; the deviation is the angle or distance between the wind field direction vector and the airport wake trajectory, used to assess the degree of matching between the wind field direction and the wake trajectory; the initial wind field compensation weight refers to the weight applied according to the deviation, used to adjust the wind field data of the detection zone and compensate for the error caused by deviation from the wake trajectory.

[0058] Execution steps: It is necessary to ensure that the detection interval between adjacent detection zones meets the time interval limit condition. Specifically, the center frequency of the radar's operating frequency band is set, and the second time factor obtained from the atmospheric propagation attenuation model is combined with the radar beam switching delay and signal processing pipeline delay to comprehensively set the time interval limit condition. This means that the detection time interval between adjacent detection zones must not be less than the set time interval limit condition to avoid mutual interference during data acquisition. By setting this time interval, electromagnetic interference and data overlap between adjacent zones are effectively reduced, and the accuracy and reliability of the data are improved.

[0059] Simultaneously, based on the deviation of the wind field direction vector of each detection zone from the airport wake trajectory, an initial wind field compensation weight is applied to each detection zone. For example, the deviation of the wind field direction vector of the detection zone from the wake trajectory is determined, and an initial wind field compensation weight is applied to the corresponding detection zone through a pre-set weight function. Furthermore, the greater the deviation, the smaller the weight. If the initial wind field compensation weight applied to the detection zone is 0.8, it means that during the data processing, the wind field data of that detection zone will be multiplied by a weight of 0.8 to compensate for the error caused by the deviation from the wake trajectory. This compensation mechanism can effectively adjust the wind field data, thereby getting closer to the actual impact of the wake, improving the accuracy of the data and the reliability of the early warning system.

[0060] Preferably, the above steps significantly improve the quality and spatiotemporal consistency of airport wake vortex radar wind field data. On the one hand, the time interval constraint ensures that data acquisition between adjacent detection zones will not interfere with each other, thus improving data reliability. On the other hand, the application of initial wind field compensation weights can effectively adjust the wind field data, compensate for errors caused by deviation from the wake vortex trajectory, further improve data accuracy, help reduce false alarm rate, provide data support for wind field data enhancement and wake vortex early warning, and improve the safety and efficiency of airport operations.

[0061] Furthermore, the detection interval between adjacent detection partitions corresponding to each detection partition satisfies the time interval limitation condition. The method of this application includes:

[0062] Based on the center frequency of the radar operating band, a first time factor is determined; a second time factor is determined using an atmospheric propagation attenuation model; a third time factor corresponding to the radar beam switching delay and a fourth time factor corresponding to the signal processing pipeline delay are determined; and the time interval constraint conditions are comprehensively set by combining the first and second time factors.

[0063] Specifically, the center frequency of the radar operating band refers to the center value of the electromagnetic wave frequency range used by the radar system, usually measured in GHz, which determines the radar's detection capability and signal characteristics. The first time factor refers to the time parameter calculated based on the center frequency of the radar operating band, which is usually related to the signal propagation speed and frequency band characteristics. Furthermore, the atmospheric propagation attenuation model describes the mathematical model of energy attenuation when electromagnetic waves propagate in the atmosphere, taking into account the influence of atmospheric composition, humidity, temperature and other factors on the signal.

[0064] The second time factor refers to the time parameter calculated based on the atmospheric propagation attenuation model, reflecting the energy attenuation characteristics of the signal propagating in the atmosphere. Furthermore, the radar beam switching delay is usually related to the radar's electronic system and antenna characteristics, representing the time required for the radar beam to switch from one detection zone to another. The third time factor corresponds to the radar beam switching delay time parameter. Furthermore, the signal processing pipeline delay reflects the efficiency of the signal processing system, representing the time required for the radar system to process signals from signal reception and processing to output results. The fourth time factor corresponds to the signal processing pipeline delay time parameter. Furthermore, the time interval constraint is used to avoid mutual interference during data acquisition, and is the time interval requirement between adjacent detection zones set after comprehensively considering the above time factors.

[0065] Execution steps: Determine the first time factor based on the center frequency of the radar's operating frequency band, set the center frequency of the radar's operating frequency band, and calculate the signal propagation time over a specific distance based on the speed of electromagnetic wave propagation in the atmosphere. For example, if the radius of the radar detection area is 10km, the signal propagation time t1 can be calculated using the formula t1=(2×10000) / (3×10 8 The first factor reflects the basic propagation characteristics of the signal in the radar operating frequency band. Among them, the propagation speed of electromagnetic waves in the atmosphere is equal to the speed of light 3 × 10⁻⁶. 8 .

[0066] The second time factor is determined using an atmospheric propagation attenuation model. This model considers the influence of atmospheric composition, humidity, temperature, and other factors on signal energy. For example, under specific meteorological conditions, the energy attenuation time t2 of a signal propagating from one detection zone to an adjacent zone is 0.2 seconds. The second time factor reflects the energy attenuation characteristics of the signal propagating in the atmosphere, ensuring that the signals of adjacent zones do not interfere with each other due to excessive energy.

[0067] The third time factor corresponding to the radar beam switching delay and the fourth time factor corresponding to the signal processing pipeline delay are determined. The third and fourth time factors reflect the delay characteristics of the radar system in the beam switching and signal processing processes. Taking into account the first, second, third, and fourth time factors, time interval constraints are set. The above time factors are added together, and the time interval constraints between adjacent detection zones are set accordingly.

[0068] Preferably, the above steps ensure that the radar system avoids signal interference between adjacent zones during the detection process, improves the accuracy and reliability of data acquisition, sets time interval limits, and combines the physical characteristics of the radar, atmospheric propagation characteristics, and system delay characteristics to provide a basis for the efficient operation of the radar system. At the same time, a reasonable detection interval can effectively reduce errors during data acquisition, improve the quality of wind field data, and provide more reliable support for data enhancement and wake vortex early warning.

[0069] Furthermore, by utilizing an atmospheric propagation attenuation model to determine the second time factor, the method of this application includes:

[0070] Based on the atmospheric propagation attenuation model, the time required for the echo signal of the first detection zone to decay to the point where its signal-to-noise ratio impact on the echo signal of the second detection zone is lower than the interference threshold is determined, thus obtaining the second time factor; wherein, the detection zone corresponding to the echo signal of the first detection zone and the detection zone corresponding to the echo signal of the second detection zone are adjacent detection zones.

[0071] Specifically, the atmospheric propagation attenuation model is a mathematical model used to describe the energy attenuation of electromagnetic waves propagating in the atmosphere, considering the influence of atmospheric composition, humidity, temperature, and other factors on signal propagation; the echo signal of the first detection zone refers to the signal reflected back to the radar from the first detection zone, and the intensity and characteristics of the echo signal of the first detection zone reflect the wind field information of that area; the echo signal of the second detection zone refers to the signal reflected back to the radar from the second detection zone, which also reflects the wind field information of that area; the signal-to-noise ratio (SNR) is the ratio of signal strength to background noise intensity, used to evaluate the signal quality and detectability; when the signal-to-noise ratio of the signal is lower than the interference threshold, the interference of the signal to adjacent zones is considered negligible; the second time factor refers to the time parameter calculated based on the atmospheric propagation attenuation model, representing the time required for the energy of the echo signal of the first detection zone to attenuate to the point where its influence on the SNR of the second detection zone is lower than the interference threshold.

[0072] Execution steps: In order to ensure that the signal interference between adjacent detection zones is minimized, it is necessary to determine the second time factor based on the atmospheric propagation attenuation model. Specifically, the first detection zone and the second detection zone are two adjacent zones. The signal emitted by the radar generates an echo signal in the first detection zone. According to the atmospheric propagation attenuation model, the time required for the echo signal to decay from emission to energy attenuation to the point that its impact on the signal-to-noise ratio of the echo signal in the second detection zone is lower than the interference threshold is determined.

[0073] Using an atmospheric propagation attenuation model, the signal-to-noise ratio (SNR) of the initial echo signal is set. For example, atmospheric conditions cause a signal attenuation rate of 0.1 dB / km. Further, atmospheric conditions include humidity and temperature. The distance between the first and second detection zones is 1 km. For every 1 km of atmospheric propagation, the SNR decreases by 0.1 dB. To ensure that the impact of the echo signal from the first detection zone on the SNR of the second detection zone is below the interference threshold, the time required for the echo signal from the first detection zone to attenuate to the point where its impact on the SNR of the echo signal from the second detection zone is below the interference threshold is determined. However, since the SNR attenuation needs to take into account the atmospheric attenuation rate, the actual time will be longer. Therefore, the second time factor is used as the minimum time interval between adjacent zone detections, indicating that after the attenuation delay represented by the second time factor, the impact of the echo signal from the first detection zone on the SNR of the second detection zone will be below the interference threshold.

[0074] Preferably, this method ensures that signal interference between adjacent detection zones is minimized, thereby improving the detection accuracy and data quality of the radar system. Based on the atmospheric propagation attenuation model, the influence of actual atmospheric conditions on signal propagation is considered, which can effectively avoid misjudgment and data error caused by signal interference. At the same time, a reasonable time interval setting can improve the spatiotemporal resolution of the radar system.

[0075] Furthermore, to determine the spatial error components of each detection zone, the method of this application includes:

[0076] Based on the deviation between the wind field direction vector of each detection zone and the theoretical trajectory of the airport wake vortex, the error is decomposed into a horizontal wind direction angle error component and a vertical wind shear error component; the spatial error component is constructed through the horizontal wind direction angle error component and the vertical wind shear error component.

[0077] Specifically, the wind field direction vector represents the direction of the wind field in the detection zone, typically including horizontal and vertical components; the airport wake vortex theoretical trajectory refers to the propagation path of the wake vortex in the airport airspace calculated based on a theoretical model, usually used to assess the potential impact area of ​​the wake vortex; the deviation value refers to the difference between the wind field direction vector and the airport wake vortex theoretical trajectory, reflecting the degree of deviation between the actual wind field and the theoretical wake vortex trajectory; the horizontal wind direction angle error component refers to the deviation of the wind field direction vector from the wake vortex theoretical trajectory in the horizontal direction, usually expressed as an angle; the vertical wind shear error component refers to the deviation of the wind field direction vector from the wake vortex theoretical trajectory in the vertical direction, usually expressed as the rate of change of wind speed; the spatial error component is used to describe the error distribution of the detection zone in three-dimensional space, and the spatial error is constructed by combining the horizontal wind direction angle error component and the vertical wind shear error component.

[0078] Execution steps: Error decomposition is performed based on the deviation between the wind field direction vector of each detection zone and the theoretical trajectory of the airport wake vortex. Specifically, the wind field direction vector of each detection zone... , for The corresponding horizontal wind speed component, for The corresponding vertical wind speed component; the wind field direction vector corresponding to the theoretical trajectory of the airport wake vortex. , for The corresponding horizontal wind speed component, for Corresponding vertical wind speed components; deviation value It is obtained by calculating the difference between the two vectors: ,in, Indicates the deviation in the horizontal direction. This indicates the deviation in the vertical direction.

[0079] The deviation is decomposed into horizontal wind direction angle error components and vertical wind shear error components. The horizontal wind direction angle error component can be calculated by measuring the angle deviation between the horizontal wind speed components. To determine, Vertical wind shear error components It is determined by calculating the rate of change of the vertical wind speed component. ,in, The vertical height difference is represented by spatial error components, which are constructed using horizontal wind direction angle error components and vertical wind shear error components. These spatial error components can be represented as three-dimensional vectors. It is used to describe the error distribution of the detection partition in three-dimensional space.

[0080] For the theoretical trajectory of an airport wake vortex, it is preferable not to explicitly solve the complete wake vortex dynamics equations, but to simplify the theoretical trajectory of the airport wake vortex into a directional reference vector, which is used to correlate with the wind field direction vectors of each detection zone. Deviation calculations are performed; the positioning accuracy of the theoretical trajectory model can reach within ±20 meters in the horizontal direction and ±10 meters in the vertical direction under typical airport meteorological conditions (neutral atmosphere, wind speed <10m / s), which is sufficient to meet the spatial matching requirements of radar voxel level (usually ≥30m) and ensure the effectiveness of error decomposition.

[0081] Preferably, through this error decomposition and construction process, the wind field error characteristics of the detection zone can be described more accurately. Furthermore, the horizontal wind direction angle error component can reflect the degree of deviation between the wind field direction and the wake trajectory, while the vertical wind shear error component can reflect the variation law of the wind field in the vertical direction. The refined error model provides support for data enhancement, which can significantly improve the accuracy and reliability of wind field data, thereby improving the performance of the wake vortex early warning system.

[0082] Furthermore, based on the airport wake radar configuration information, a three-dimensional wind field calibration benchmark is established. The method in this application also includes:

[0083] Based on the detection range, accuracy requirements, and resolution in the airport wake vortex radar configuration information, a spherical sampling grid with a radius of V times the working wavelength is constructed within the airport airspace mapped by the airport detection area. Based on the spherical sampling grid, a Doppler radar receiver and a reference wind profiler are configured to collect full-airspace wind field data, including full-airspace wind field spectral width and radial velocity data, along a preset spiral scanning path. Using the runway starting line midpoint as the coordinate origin, the geographical coordinates of each detection zone are associated with the full-airspace wind field data to construct a wind field calibration benchmark database.

[0084] Specifically, the configuration information of airport wake vortex radar includes parameters such as radar detection range, accuracy requirements, and resolution, which determine the performance and data acquisition capabilities of the radar system; the operating wavelength refers to the wavelength of the electromagnetic waves emitted by the radar, which is usually related to the radar's operating frequency and determines the radar's detection accuracy and resolution; the spherical sampling grid refers to a three-dimensional grid constructed within the airport detection area, used to uniformly distribute sampling points to ensure the comprehensiveness and uniformity of data acquisition; the Doppler radar receiver is used to receive reflected echo signals and measure the Doppler frequency shift of the reflected echo signals, thereby obtaining the radial velocity information of the wind field.

[0085] The reference wind profiler is used to measure the vertical distribution of atmospheric wind fields, providing high-precision reference wind field data; the spiral scan path refers to the path along which the radar beam scans, covering the entire detection area and acquiring full-space wind field data; the full-space wind field data includes a complete dataset of parameters such as wind field spectral width and radial velocity, used to describe the wind field characteristics throughout the entire detection area; the wind field calibration benchmark database is a reference database built based on full-space wind field data, used to calibrate and verify the accuracy of actual detection data.

[0086] Execution steps: Based on the detection range, accuracy requirements, and resolution in the airport wake vortex radar configuration information, construct a spherical sampling grid within the airport airspace mapped from the airport detection area. The airport wake vortex radar configuration information includes the radar's operating wavelength, detection range, accuracy requirements, and resolution, including spatial resolution and velocity resolution. Based on the airport wake vortex radar configuration information, construct a spherical sampling grid with a radius of V times the operating wavelength within the airport airspace mapped from the airport detection area, where V is a magnification factor. If the radar's operating wavelength is 0.1m, V=100, then the radius of the spherical sampling grid is 100×0.1m=10m. By uniformly distributing sampling points on this grid, ensure the comprehensiveness and uniformity of data acquisition.

[0087] Based on a spherical sampling grid, a Doppler radar receiver and a reference wind profiler are configured. The Doppler radar receiver is used to measure the radial velocity of the wind field, while the reference wind profiler provides high-precision vertical distribution data of the wind field. The radar scans along a preset spiral scanning path. The design of the spiral scanning path ensures that the radar beam can cover the entire detection area. For example, the starting point of the spiral scanning path is located at the center of the airport, and the scanning radius gradually increases while rising layer by layer along the elevation direction, forming a three-dimensional spiral trajectory covering the entire spherical sampling grid. Combined with the scanning step size and the spiral scanning path, the radar system acquires a complete dataset including the wind field spectral width and radial velocity data across the entire airspace.

[0088] Using the runway starting line midpoint as the coordinate origin, the geographical coordinates of each detection zone are correlated with the full-airspace wind field data to construct a wind field calibration benchmark database. Furthermore, the geographical coordinates of the runway starting line midpoint are determined, and the geographical coordinates of the detection zones are obtained through the radar system's positioning system. These geographical coordinates are correlated with the full-airspace wind field data to form a calibration benchmark database containing parameters such as wind field spectral width and radial velocity. The calibration benchmark database serves as a reference model for calibrating and verifying the accuracy of actual detection data, ensuring that the radar system can still provide high-precision wind field data under complex meteorological conditions.

[0089] Preferably, the above steps ensure that the radar system acquires high-quality, high-resolution wind field data within the airport detection area, and calibrates and verifies the actual detection data using a wind field calibration benchmark database. This data acquisition method based on spherical sampling grids and spiral scanning paths, combined with a high-precision reference wind profiler, can significantly improve the accuracy and reliability of wind field data, providing a solid foundation for high-precision enhancement of airport wake vortex radar wind field data, thereby improving the performance and safety of the wake vortex early warning system.

[0090] Furthermore, by extracting environmental disturbance enhancement factors through a deep separable convolutional neural network, separating the coupling effects of atmospheric turbulence, electromagnetic interference, and equipment noise, and configuring a dynamic data augmentation matrix, this application's method also includes:

[0091] The environmental parameters, spectral width anomaly deviation, and wind field data deviation in the historical wind field enhancement records are used as training samples; atmospheric turbulence branch, electromagnetic interference branch, and equipment noise branch are set in the output layer of the deep separable convolutional neural network to decouple the coupling effect and set the dynamic data enhancement matrix.

[0092] Specifically, historical wind field enhancement records refer to data accumulated during past wind field data enhancement processes, including information such as environmental parameters, spectral width anomaly deviations, and wind field data deviations; training samples are datasets used to train deep learning models, containing input features and target outputs. Further, the input features include environmental parameters, spectral width anomaly deviations, and wind field data deviations; deep separable convolutional neural networks refer to optimized convolutional neural network architectures that reduce computational cost and the number of parameters by separating convolution operations while maintaining network performance. Separable convolution operations include spatial convolution and channel-wise convolution.

[0093] The atmospheric turbulence branch is the branch in the network output layer that processes the effects of atmospheric turbulence, used to extract features related to atmospheric turbulence; the electromagnetic interference branch is the branch in the network output layer that processes the effects of electromagnetic interference, used to extract features related to electromagnetic interference; the equipment noise branch is the branch in the network output layer that processes the effects of equipment noise, used to extract features related to equipment noise; decoupling of coupling effects refers to separating the combined effects of atmospheric turbulence, electromagnetic interference, and equipment noise on wind field data, so as to process each type of interference separately; the dynamic data augmentation matrix is ​​used to optimize the augmentation effect of wind field data. The elements of the dynamic data augmentation matrix are generated by weighted fusion of the decoupled outputs of the atmospheric turbulence branch, electromagnetic interference branch, and equipment noise branch, in order to specifically suppress various interference components and improve the accuracy of wind field reconstruction in the wake region.

[0094] Execution steps: Environmental parameters, spectral width anomaly deviation, and wind field data deviation from historical wind field enhancement records are used as training samples to reflect the characteristics and error patterns of wind field data under different environmental conditions. Furthermore, historical wind field enhancement records include environmental parameters, spectral width anomaly deviation, and wind field data deviation. Environmental parameters include temperature, humidity, and atmospheric pressure. The training samples are used as input features to train a deep separable convolutional neural network. The output is the enhanced wind field data, i.e., the calibrated and corrected wind field spectral width and radial velocity data.

[0095] In the output layer of a deep separable convolutional neural network, atmospheric turbulence, electromagnetic interference, and equipment noise branches are set up. Each branch is used to process a specific interference source. By learning features from historical data, the influence of these interference sources on wind field data is identified and separated. Specifically, the following features are learned through training: atmospheric turbulence features are characterized by rapid changes and local fluctuations in wind field spectral width; electromagnetic interference features are characterized by periodic or sudden signal anomalies; and equipment noise features are characterized by low-frequency, stable background noise.

[0096] By decoupling the coupling effects of atmospheric turbulence, electromagnetic interference, and equipment noise into branches, the input data can be decoupled. Furthermore, for input data containing atmospheric turbulence, electromagnetic interference, and equipment noise, the impact of each interference source is determined. This approach allows for more precise handling of each type of interference, avoiding over-correction or under-correction. Based on the decoupled coupling effects, a dynamic data augmentation matrix is ​​set. This matrix dynamically adjusts the augmentation strategy according to real-time data and environmental conditions, optimizing the enhancement effect on wind field data. Furthermore, if strong atmospheric turbulence is detected, the augmentation matrix increases compensation for atmospheric turbulence; if electromagnetic interference is weak, the correction for electromagnetic interference is reduced.

[0097] Preferably, the above steps achieve precise enhancement of wind field data, improving data reliability and accuracy; the multi-branch structure of the deep separable convolutional neural network can effectively separate the influence of different interference sources, and the dynamic data enhancement matrix can adjust the enhancement strategy according to real-time conditions, ensuring high quality and high precision of wind field data under different environments, providing support for high-precision enhancement of airport wake vortex radar wind field data, and significantly improving the performance and reliability of the wake vortex early warning system.

[0098] In summary, the beneficial effects of the embodiments of this application are:

[0099] By employing airport wake vortex radar configuration information to establish a three-dimensional wind field calibration benchmark, acquiring spectral width anomaly deviation and wind field data deviation, and formulating an initial error matrix; introducing the wind field direction vector of each detection zone and spatially mapping it with the spectral width anomaly deviation to identify potential wake vortex influence areas, thus determining the spatial error components of each detection zone; based on the initial error matrix and the spatial error components of each detection zone, using the weighted least squares method to determine the enhancement coefficients, extracting environmental disturbance enhancement factors through a deep separable convolutional neural network, separating the coupling effects of atmospheric turbulence, electromagnetic interference, and equipment noise, and configuring a dynamic data enhancement matrix; and deploying distributed enhancement nodes according to the dynamic data enhancement matrix and the wind field data accuracy requirements, iteratively optimizing the enhancement parameters under the constraint of wake vortex dissipation time, while simultaneously making enhancement decisions regarding wind field data deviation. This application provides a method for enhancing airport wake vortex radar wind field data based on spectral width anomaly detection. It achieves spatial correlation mapping between the wind field direction vector of the detection zone and the wake vortex-affected area, accurately separates the horizontal and vertical spatial error components, optimizes the enhancement coefficient using weighted least squares, precisely locates the wake vortex-affected area, extracts environmental disturbance enhancement factors, and efficiently decouples atmospheric turbulence, electromagnetic interference, and equipment noise. It iteratively optimizes enhancement parameters under the constraint of wake vortex dissipation time. Furthermore, it sets the detection cycle based on dual adaptive enhancement indices and combines multiple time factors to set the zone detection interval, thereby improving the spatiotemporal consistency of wind field data and reducing the false alarm rate of wake vortex warnings.

[0100] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0101] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for enhancing airport wake radar wind field data based on spectral width anomaly detection, characterized in that, The method includes: Based on the airport wake vortex radar configuration information, a three-dimensional wind field calibration benchmark is established, the spectral width anomaly deviation and wind field data deviation are obtained, and an initial error matrix is ​​proposed. The wind field direction vector of each detection zone is introduced and spatially correlated with the potential wake vortex influence area identified by the spectral width anomaly deviation to determine the spatial error components of each detection zone. Based on the initial error matrix and the spatial error components of each detection zone, the weighted least squares method is used to determine the enhancement coefficients, and the environmental disturbance enhancement factor is extracted by a deep separable convolutional neural network to separate the coupling effects of atmospheric turbulence, electromagnetic interference and equipment noise, and to configure a dynamic data enhancement matrix. Based on the dynamic data augmentation matrix, distributed augmentation nodes are deployed in conjunction with the wind field data accuracy requirements. The augmentation parameters are iteratively optimized under the constraint of wake dissipation time, and augmentation decisions are made for the wind field data deviation.

2. The airport wake radar wind field data enhancement method based on spectral width anomaly detection as described in claim 1, characterized in that, Before making enhanced decisions regarding the wind field data bias, the method further includes: Set a data detection cycle, determine the initial partition data deviation based on the wind field signal amplitude collected separately for each detection partition; verify the reliability of the initial partition data deviation based on the spatial continuity characteristics corresponding to the abnormal spectral width deviation, and determine the adjacent deviation coefficient between each detection partition; correct the bias of each detection partition through the adjacent deviation coefficient, and perform adaptive data augmentation using fuzzy decision-making.

3. The airport wake radar wind field data enhancement method based on spectral width anomaly detection as described in claim 2, characterized in that, Setting a data detection cycle, the method includes: Based on each detection partition, and combined with the spatial correlation characteristics of spectral width anomaly deviation, a first adaptive enhancement index is configured; Based on each detection zone, and combined with the spatial correlation characteristics of wind field data deviation, a second adaptive enhancement index is configured; The data detection period is set based on the first adaptive enhancement index and the second adaptive enhancement index.

4. The airport wake radar wind field data enhancement method based on spectral width anomaly detection as described in claim 3, characterized in that, The method of introducing wind field direction vectors for each detection zone includes: The airport detection area is divided into various detection zones, each containing a preset area range, and the interference isolation between the various detection zones meets the isolation limit conditions. Based on the timing control logic, the radar beam phased array is used to perform polling detection of each detection zone.

5. The airport wake radar wind field data enhancement method based on spectral width anomaly detection as described in claim 4, characterized in that, The detection interval between adjacent detection zones corresponding to each detection zone satisfies the time interval constraint condition; Meanwhile, based on the deviation of the wind field direction vector of each detection zone from the airport wake trajectory, an initial wind field compensation weight is applied to each detection zone.

6. The airport wake radar wind field data enhancement method based on spectral width anomaly detection as described in claim 5, characterized in that, The detection interval between adjacent detection partitions corresponding to each detection partition satisfies the time interval limitation condition, and the method includes: The first time factor is determined based on the center frequency of the radar's operating frequency band; The second time factor was determined using an atmospheric propagation attenuation model. The third time factor corresponding to the radar beam switching delay and the fourth time factor corresponding to the signal processing pipeline delay are determined, and the time interval limitation conditions are comprehensively set by combining the first time factor and the second time factor.

7. The airport wake radar wind field data enhancement method based on spectral width anomaly detection as described in claim 6, characterized in that, The method for determining the second time factor using an atmospheric propagation attenuation model includes: Based on the atmospheric propagation attenuation model, the time required for the echo signal of the first detection zone to decay from transmission to energy attenuation to the point where its impact on the signal-to-noise ratio of the echo signal of the second detection zone is lower than the interference threshold is determined, thus obtaining the second time factor; Among them, the detection partition corresponding to the echo signal of the first detection partition and the detection partition corresponding to the echo signal of the second detection partition are adjacent detection partitions.

8. The airport wake radar wind field data enhancement method based on spectral width anomaly detection as described in claim 5, characterized in that, The method for determining the spatial error components of each detection zone includes: Based on the deviation between the wind field direction vector of each detection zone and the theoretical trajectory of the airport wake vortex, it is decomposed into horizontal wind direction angle error component and vertical wind shear error component. The spatial error component is constructed by combining the horizontal wind direction angle error component and the vertical wind shear error component.

9. The airport wake radar wind field data enhancement method based on spectral width anomaly detection as described in claim 1, characterized in that, Based on airport wake vortex radar configuration information, a three-dimensional wind field calibration benchmark is established. The method also includes: Based on the detection range, accuracy requirements, and resolution in the airport wake vortex radar configuration information, a spherical sampling grid with a radius of V times the working wavelength is constructed within the airport airspace mapped from the airport detection area. Based on the spherical sampling grid, a Doppler radar receiver and a reference wind profiler are configured to collect full-space wind field data, including full-space wind field spectral width and radial velocity data, along a preset spiral scanning path. Using the midpoint of the runway starting line as the origin, the geographical coordinates of each detection zone are associated with the full-airspace wind field data to construct a wind field calibration benchmark database.

10. The airport wake radar wind field data enhancement method based on spectral width anomaly detection as described in claim 9, characterized in that, The method further includes extracting environmental disturbance enhancement factors using a deep separable convolutional neural network, separating the coupling effects of atmospheric turbulence, electromagnetic interference, and equipment noise, and configuring a dynamic data augmentation matrix. Environmental parameters, spectral width anomaly deviation, and wind field data deviation from historical wind field enhancement records were used as training samples. In the output layer of a deep separable convolutional neural network, atmospheric turbulence branches, electromagnetic interference branches, and equipment noise branches are set to decouple the coupling effect and set the dynamic data augmentation matrix.

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