Fixed double-runway FOD detection method based on multi-source circumferential scanning

Abstract

The invention discloses a fixed dual-runway FOD detection method based on multi-source circumferential scanning, and belongs to the technical field of data fusion detection, and the method specifically comprises the steps: synchronously obtaining dual-runway multi-polarization echo data through a fixed circumferential scanning rada; performing time-varying clutter suppression by combining an adaptive clutter cancellation method matched with a dynamic background template and a runway material feature library, and generating a time-frequency domain three-dimensional feature matrix through time-frequency analysis; synchronously collecting and preprocessing image data, and marking a target area through a constant false alarm rate algorithm and image detection; a multi-source data geographic coordinate mapping model is established, data registration and cross validation are completed under a runway physical coordinate system, and real-time tracking of a real FOD target is realized based on radar and image fusion data through multi-dimensional confirmation of radar polarization / micro-motion features and image visual features; according to the method, the FOD detection accuracy and the position identification precision are improved through multi-source data fusion, the false alarm rate is effectively reduced, and a guarantee is provided for safe operation of an airport.

Description

Technical Field

[0001] The present invention belongs to the technical field of data fusion detection, and in particular to a fixed dual-runway FOD detection method based on multi-source circumferential scanning. Background Art

[0002] Foreign object debris (FOD) on airport runways poses a threat to the safe takeoff and landing of aircraft. Millimeter-wave radar, due to its high range resolution, immunity to light, and excellent all-weather operation, has become a key technology for FOD detection. However, in practical applications, millimeter-wave radar faces numerous challenges in FOD detection.

[0003] Clutter interference is a major issue. Airport environments are complex, with buildings, vehicles, people, and natural vegetation surrounding the runways. These reflect millimeter-wave radar signals, creating clutter. Even with conventional clutter cancellation, it's difficult to completely eliminate interference reflections from strong scattering targets or non-uniform clutter, resulting in a high false alarm rate and impacting FOD detection accuracy.

[0004] Furthermore, non-metallic FOD has a weak reflectivity to millimeter waves. Many common non-metallic FOD, such as plastic fragments, rubber particles, and paper, have small radar cross-sections, resulting in weak signals reflected back to the radar. These signals, masked by strong background clutter, make it difficult for traditional FOD detection methods to detect and distinguish them, reducing the probability of detecting these types of FOD.

[0005] Existing FOD detection methods have shortcomings in addressing these difficulties and cannot meet the airport's requirements for high-precision and high-reliability FOD detection. Therefore, developing a FOD detection method that can effectively overcome the above problems is of great practical significance. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention proposes a fixed dual-runway FOD detection method based on multi-source circular scanning. The dual-runway multi-polarization echo data are synchronously acquired by a fixed circular scanning radar, and time-varying clutter suppression is performed in combination with an adaptive clutter cancellation method based on dynamic background template matching and a runway material feature library. A three-dimensional feature matrix in the time-frequency domain is generated through time-frequency analysis. Image data is synchronously collected and preprocessed, and the target area is marked using a constant false alarm rate algorithm and image detection respectively. A multi-source data geographic coordinate mapping model is established, and data registration and cross-validation are completed in the runway physical coordinate system. Through multi-dimensional confirmation of radar polarization / micro-motion features and image visual features, real FOD targets are tracked in real time based on radar and image fusion data. The present invention improves the FOD detection accuracy and position recognition accuracy through multi-source data fusion, effectively reduces the false alarm rate, and provides a guarantee for the safe operation of the airport.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A fixed dual-runway FOD detection method based on multi-source circumferential scanning includes:

[0009] S1: A fixed circular scanning radar is used to synchronously acquire multi-polarization echo data from both runways. An adaptive clutter cancellation method based on dynamic background template matching is used, combined with a pre-established runway material feature library, to suppress time-varying clutter in the multi-polarization echo data. Time-frequency analysis is then performed on the clutter-suppressed multi-polarization echo data to generate a three-dimensional feature matrix in the time-frequency domain containing amplitude-frequency-polarization characteristics. This matrix is ​​then used to synchronously trigger the runway monitoring camera to capture image data in the corresponding area, completing image preprocessing.

[0010] S2: Input the three-dimensional feature matrix in the time-frequency domain into the constant false alarm rate detection algorithm to preliminarily detect areas with FOD targets across the entire dual-runway area and mark the target points in the radar coordinate system. At the same time, target detection is performed on the preprocessed image data, marking the suspected foreign object areas in the image coordinate system.

[0011] S3: Establish a geographic coordinate mapping model for multi-source data, and perform data registration and cross-validation based on the runway physical coordinate system;

[0012] S4: For the candidate targets that are preliminarily matched, multi-dimensional feature confirmation is introduced, and the confirmed real FOD targets are tracked in real time based on fusion data; the fusion data is multi-source fusion information formed by alignment and association of radar echo data and image data.

[0013] Specifically, the synchronous acquisition of dual-runway multi-polarization echo data by a fixed circumferential scanning radar includes:

[0014] Set the radar scanning period to T seconds based on the length, width, and detection accuracy requirements of the dual runways;

[0015] Configure the radar's multi-polarization transmission and reception mode, using horizontal polarization and vertical polarization to alternately transmit and receive signals and obtain echo data in different polarization modes;

[0016] The acquired multi-polarization echo data is preliminarily digitally processed to obtain multi-polarization echo data.

[0017] Specifically, the adaptive clutter cancellation method using dynamic background template matching, combined with a pre-established runway material feature library, performs time-varying clutter suppression on multi-polarization echo data, including:

[0018] Collect radar surrounding environment data in real time to build an initial dynamic background template; the radar surrounding environment data includes background echo data when there is no target;

[0019] Using a sliding window approach, statistical analysis is performed on the background echo data within the window, its mean is calculated, and the parameters of the background template are adjusted according to the mean;

[0020] Acquire multi-polarization echo data and correct it based on the pre-established runway material feature library;

[0021] The minimum mean square error criterion is used to calculate the error between the corrected echo data and the dynamic background template point by point. Clutter cancellation is performed according to the error size to obtain multi-polarization echo data after clutter suppression.

[0022] Specifically, the time-frequency analysis is performed on the multi-polarization echo data after clutter suppression to generate a three-dimensional feature matrix in the time-frequency domain containing amplitude-frequency-polarization features, including:

[0023] The short-time Fourier transform method is used to perform time-frequency analysis on the multi-polarization echo data after clutter suppression. During the time-frequency analysis process, the echo data after clutter suppression is segmented according to the preset window function type and window length parameters;

[0024] For each segment of echo data, calculate its amplitude value at different frequency points, and combine it with multi-polarization information to obtain the corresponding amplitude-frequency characteristics under different polarization modes;

[0025] Integrate the amplitude-frequency characteristics under different polarization modes to form a three-dimensional data set containing amplitude-frequency-polarization characteristics;

[0026] The three-dimensional data set is normalized to generate the final three-dimensional feature matrix in the time-frequency domain.

[0027] Specifically, the specific steps of S2 include:

[0028] Obtain a three-dimensional feature matrix in the time-frequency domain, preprocess the three-dimensional feature matrix in the time-frequency domain, and normalize each element in the matrix;

[0029] In the entire dual-runway area, a sliding window method is used to calculate the local statistics of each data point in the normalized three-dimensional feature matrix in the time-frequency domain; the local statistics include the mean and variance;

[0030] According to the preset false alarm probability, the detection threshold is calculated using local statistics;

[0031] Compare the amplitude value of each data point in the window with the detection threshold value. If the amplitude value exceeds the threshold value, it is marked as a target point in the radar coordinate system.

[0032] The preprocessed image data is obtained, and a deep learning-based target detection algorithm is used to identify foreign objects. The bounding box coordinates of the suspected foreign object area in the preprocessed image are extracted and marked as the suspected foreign object area in the image coordinate system. At the same time, the confidence score of each suspected foreign object area is recorded.

[0033] Specifically, the establishment of a multi-source data geographic coordinate mapping model includes:

[0034] Establishing a conversion relationship between the radar coordinate system and the geographic coordinate system based on the latitude and longitude coordinates of the radar installation location and the scanning parameters; the conversion relationship is described by a coordinate transformation matrix;

[0035] Based on the camera intrinsic calibration results and extrinsic parameters, the mapping relationship between the image coordinate system and the geographic coordinate system is established;

[0036] A runway physical coordinate system is established with any end point of the runway as the origin and the runway direction as the coordinate axis, and the radar-physical coordinate and image-physical coordinate mapping models are constructed respectively;

[0037] The radar target point and the suspected foreign object area are converted to the runway physical coordinate system, and the spatial position deviation value is calculated.

[0038] Specifically, the data registration and cross-validation based on the runway physical coordinate system includes:

[0039] Calculate the Euclidean distance between the radar target point and the centroid of the suspected foreign object area. When the Euclidean distance is less than or equal to the preset threshold, it is determined to be a candidate matching target;

[0040] Cluster analysis is performed on candidate matching targets based on prior information about runway pavement partitions, preliminarily distinguishing between real and false targets; the prior information about runway pavement partitions includes centerline areas, edge areas, and taxiway areas;

[0041] Radar target points without image matching are marked as pending verification, and suspected foreign object areas with no radar response are marked as low-confidence targets.

[0042] Specifically, the candidate targets for preliminary matching are confirmed by introducing multi-dimensional features, including:

[0043] At the radar data level, the target polarization scattering matrix characteristics are extracted through multi-polarization processing and compared with the typical FOD polarization feature library to calculate the radar feature matching degree;

[0044] At the image data level, the visual features of the suspected foreign object area are extracted and matched with the typical FOD visual feature library for similarity, and the image feature matching degree is calculated;

[0045] Fusion decision: When the radar feature matching degree is greater than or equal to the preset radar feature matching degree threshold and the image feature matching degree is greater than or equal to the preset image feature matching degree threshold, the real FOD target is confirmed.

[0046] Specifically, target characteristics are confirmed through multi-polarization processing and micro-motion feature analysis, including:

[0047] For the initially identified real FOD targets, the corresponding multi-polarization echo data is obtained, including amplitude and phase information under horizontal and vertical polarization modes;

[0048] Performing polarization decomposition on the multi-polarization echo data, calculating the polarization scattering matrix characteristics of the target, and analyzing the polarization characteristics of the target based on the element values ​​of the polarization scattering matrix; the polarization characteristics of the target include polarization degree and polarization angle;

[0049] The target's polarization characteristics are compared with a pre-established library of polarization characteristics of typical FOD targets and false targets, and the target is judged to be a real FOD target based on the similarity.

[0050] At the same time, for the real FOD target that is initially distinguished, its echo data is acquired in multiple consecutive scanning cycles to obtain the target echo data;

[0051] The target's echo data is processed using short-time Fourier transform to extract the target's micro-motion frequency and micro-motion amplitude characteristics;

[0052] Based on the difference between the typical micro-motion pattern of the FOD target and the micro-motion characteristics of the false target, it is judged whether the target is a real FOD target.

[0053] Specifically, the real-time tracking of the confirmed FOD target based on the fused data includes:

[0054] The Kalman filter algorithm is used to track the confirmed real FOD target in real time, and the state equation and observation equation of the target are established; the state equation describes the change of the position and velocity state of the target over time; the observation equation describes the radar's observation process of the target state;

[0055] In each scanning cycle, the Kalman filter algorithm is used to update the state of the target based on the current state estimate of the target and the radar observation value, and the state of the target at the next moment is predicted;

[0056] According to the prediction results, the target's trajectory information is updated in real time, and the tracking results are fed back.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1. The present invention proposes a fixed dual-runway FOD detection method based on multi-source circular scanning. This method uses a fixed circular scanning radar to synchronously acquire dual-runway multi-polarization echo data, and uses an adaptive clutter cancellation method based on dynamic background template matching combined with a runway material feature library to suppress time-varying clutter. A three-dimensional feature matrix in the time-frequency domain containing amplitude-frequency-polarization features is then generated through time-frequency analysis. This series of operations effectively improves the quality of the echo data, can more accurately extract feature information related to FOD targets, provides a reliable data basis for subsequent detection, enhances detection accuracy and sensitivity, and reduces false detections and missed detections due to clutter interference.

[0059] 2. The present invention proposes a fixed dual-runway FOD detection method based on multi-source circumferential scanning. After the three-dimensional feature matrix in the time-frequency domain is input into the constant false alarm rate detection algorithm to preliminarily mark the target points, a geographic coordinate mapping model is established for coordinate conversion. The target points are clustered and analyzed in combination with the runway pavement partitioning prior information to preliminarily distinguish between real and false targets. Finally, the real FOD target is confirmed and tracked in real time through multi-polarization processing and micro-motion feature analysis. This process can accurately determine the target position, effectively eliminate the interference of false targets, and realize the precise identification and real-time monitoring of real FOD targets, providing a strong guarantee for the safe operation of the airport, helping to timely detect and deal with FOD on the runway, and reducing flight safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 Schematic diagram of a fixed dual-runway FOD detection method based on multi-source circumferential scanning according to the present invention;

[0061] Figure 2 This is a principle flow chart of a fixed dual-runway FOD detection method based on multi-source circumferential scanning according to the present invention;

[0062] Figure 3 This is a flow chart of radar target point marking of the present invention. DETAILED DESCRIPTION

[0063] Example 1

[0064] See also Figure 1 and Figure 2 The present invention provides an embodiment of a fixed dual-runway FOD detection method based on multi-source cyclic scanning, comprising the following steps:

[0065] S1: A fixed circular scanning radar is used to synchronously acquire multi-polarization echo data from both runways. An adaptive clutter cancellation method based on dynamic background template matching is used, combined with a pre-established runway material feature library, to suppress time-varying clutter in the multi-polarization echo data. Time-frequency analysis is then performed on the clutter-suppressed multi-polarization echo data to generate a three-dimensional feature matrix in the time-frequency domain containing amplitude-frequency-polarization characteristics. This matrix is ​​then used to synchronously trigger the runway monitoring camera to capture image data in the corresponding area, completing image preprocessing.

[0066] Among them, image preprocessing includes denoising, distortion correction and region of interest cropping.

[0067] S2: Input the three-dimensional feature matrix in the time-frequency domain into the constant false alarm rate detection algorithm to preliminarily detect areas with FOD targets across the entire dual-runway area and mark the target points in the radar coordinate system. At the same time, target detection is performed on the preprocessed image data, marking the suspected foreign object areas in the image coordinate system.

[0068] It should be noted that the CFAR detection algorithm processes multidimensional data, including time, frequency, and amplitude information. For a three-dimensional feature matrix in the time-frequency domain containing amplitude-frequency-polarization characteristics, the CFAR detection algorithm can input and process it in the following ways:

[0069] (1) The constant false alarm rate detection algorithm treats each layer of the three-dimensional feature matrix, for example, each pair of frequency and polarization combination, as a two-dimensional slice for processing;

[0070] (2) For each 2D slice, the constant false alarm rate detection algorithm applies a sliding window on the slice, calculates the statistical characteristics of the local background noise, and determines the detection threshold based on these characteristics;

[0071] (3) For each pixel’s position in the three-dimensional feature matrix in the time-frequency domain, the constant false alarm rate detection algorithm compares its amplitude value with the threshold value obtained by estimating the background noise;

[0072] (4) If the amplitude value of any pixel exceeds the threshold value, it is considered that there may be a target signal at the corresponding position;

[0073] (5) The constant false alarm rate detection algorithm outputs a binary matrix with the same shape as the input matrix, marking all pixels exceeding the threshold value as the detected target area.

[0074] S3: Establish a geographic coordinate mapping model for multi-source data, and perform data registration and cross-validation based on the runway physical coordinate system;

[0075] S4: For the candidate targets that are preliminarily matched, multi-dimensional feature confirmation is introduced, and the confirmed real FOD targets are tracked in real time based on fusion data; the fusion data is multi-source fusion information formed by alignment and association of radar echo data and image data.

[0076] It needs to be explained that FOD detection, or foreign object debris detection, refers to the process of identifying, locating and warning of foreign objects that may threaten aviation safety through technical means. Its core goal is to promptly detect foreign objects in areas such as airport runways, taxiways, and aprons to prevent these objects from being sucked into aircraft engines or colliding with aircraft landing gear and other components, thereby preventing mechanical failures, flight delays and even flight accidents.

[0077] Example 2

[0078] See also Figure 3 In this embodiment, the method of synchronously acquiring dual-runway multi-polarization echo data by using a fixed circumferential scanning radar includes:

[0079] A1: Set the radar scanning period. Based on the length, width, and detection accuracy requirements of the dual runways, determine the scanning period to be T seconds.

[0080] It should be emphasized that when setting the radar scanning cycle, the airport's flight traffic and weather conditions should be further considered. When the flight traffic is large or the weather conditions are poor, the scanning cycle T seconds needs to be shortened to improve the timeliness of detection.

[0081] A2: Configure the radar in multi-polarization transmission and reception mode, alternately transmitting and receiving signals in horizontal and vertical polarization modes to obtain echo data in different polarization modes;

[0082] Furthermore, the specific steps of A2 include:

[0083] (1) Two polarization modes are used: horizontal polarization and vertical polarization. Horizontal polarization means that when the radar transmits and receives signals, the electric field vector direction of the electromagnetic wave is horizontal; vertical polarization means that the electric field vector direction of the electromagnetic wave is vertical. The selection of these two polarization modes is based on the different reflection characteristics of different ground objects to different polarization signals. By alternately transmitting and receiving these two polarization signals, more abundant target information can be obtained.

[0084] (2) Determine the order of alternating transmission and reception of horizontally polarized and vertically polarized signals. For example, first transmit the horizontally polarized signal and receive its echo, then transmit the vertically polarized signal and receive its echo, and so on. This alternating method ensures that echo data in both polarization modes can be obtained in each scanning cycle.

[0085] (3) Antenna configuration. The radar antenna needs to be able to transmit and receive horizontally polarized and vertically polarized signals. This is achieved by setting different polarizers in the antenna system. For example, an orthogonal mode coupler is used to separate and combine horizontally polarized and vertically polarized signals to ensure that the antenna can accurately transmit and receive signals in the corresponding polarization mode.

[0086] (4) Debug the radar transmitter and receiver to enable them to stably transmit and receive horizontally polarized and vertically polarized signals. At the same time, optimize the receiver's sensitivity, bandwidth, and dynamic range to improve its ability to receive weak echo signals.

[0087] (5) According to the set alternating transmission strategy, the radar transmitter transmits horizontally polarized and vertically polarized signals in sequence. During the transmission process, it is necessary to precisely control the signal parameters such as transmission time, pulse width and repetition frequency to ensure signal stability and consistency;

[0088] (6) The radar antenna receives the echo signal reflected by the target and transmits it to the receiver, where the receiver amplifies, filters and digitizes the echo signal and converts it into a digital signal suitable for subsequent processing. During the reception process, it is necessary to distinguish between echo signals in different polarization modes to avoid signal confusion.

[0089] (7) Storing the acquired echo data under different polarization modes, wherein a high-speed data storage device such as a solid-state hard drive or a disk array can be used to ensure the security and fast storage of the data;

[0090] (8) Establish a data management system to classify, label and index the stored echo data. For example, classify and store the data according to information such as scanning time, polarization mode, target area, etc. to facilitate subsequent query and processing.

[0091] A3: Perform preliminary digital processing on the acquired multi-polarization echo data to obtain multi-polarization echo data.

[0092] Among them, the preliminary digital processing of the acquired multi-polarization echo data mainly involves analog-to-digital conversion.

[0093] The adaptive clutter cancellation method using dynamic background template matching, combined with a pre-established runway material feature library, performs time-varying clutter suppression on multi-polarization echo data, including:

[0094] B1: Real-time acquisition of radar surrounding environment data to construct an initial dynamic background template; the radar surrounding environment data includes background echo data when there is no target;

[0095] Furthermore, constructing the initial dynamic background template includes: using a statistical method to construct the initial dynamic background template, for example, using a mean method to calculate an average value of image pixels within a period of time as the background template.

[0096] B2: Using a sliding window approach, perform statistical analysis on the background echo data within the window, calculate its mean, and adjust the parameters of the background template based on the mean;

[0097] It should be noted that when updating the dynamic background template, the exponentially weighted sliding average method is used to update the statistical parameters of the background echo data, giving greater weight to recent data to better adapt to environmental changes. Among them, the exponentially weighted sliding average method is the existing technology content in this field and is not the creative solution of this application, so it will not be elaborated here.

[0098] B3: Acquire multi-polarization echo data and correct it based on the pre-established runway material feature library;

[0099] Furthermore, the specific steps of B3 include:

[0100] (1) Acquire multi-polarization echo data;

[0101] (2) Collect material samples from different areas of the runway, such as the runway surface, runway edges, and joints. The sampling should ensure that the samples are representative and cover various materials and conditions of the runway;

[0102] (3) Bring the collected samples to the laboratory and use professional instruments and equipment, such as a vector network analyzer, to analyze the electromagnetic properties of the samples, including dielectric constant and conductivity parameters;

[0103] (4) Organize the data obtained from laboratory analysis and classify them according to different areas and material types of the runway. Use the database management system MySQL to establish a runway material feature library, store the organized data in the runway material feature library, and add corresponding tags to each data record.

[0104] (5) Based on the position information of the radar scan, the collected multi-polarization echo data is associated with the data in the runway material feature library. For example, if the radar scans any specified position on the runway, the material characteristic data corresponding to the position is searched from the runway material feature library;

[0105] Because the scattering characteristics of runway materials for electromagnetic waves may vary under different polarization modes, it is necessary to ensure that the polarization mode of the echo data matches the polarization mode corresponding to the material characteristic data recorded in the feature library;

[0106] (6) Select a scattering model based on the runway material and the radar operating principle. For example, for a surface with a certain degree of roughness such as a runway, the Kirchhoff approximation model is used to describe the scattering process of electromagnetic waves. The Kirchhoff approximation model is a prior art in this field and is not an inventive solution of this application, so it will not be described in detail here.

[0107] (7) Using the parameters in the runway material feature library and the selected scattering model, the multi-polarization echo data is corrected. The purpose of the correction is to eliminate the influence of the runway material on the echo signal so that the echo data can better reflect the characteristics of the target itself.

[0108] B4: Use the minimum mean square error criterion to calculate the error between the corrected echo data and the dynamic background template point by point, perform clutter cancellation based on the error size, and obtain multi-polarization echo data after clutter suppression.

[0109] Furthermore, the specific steps of performing clutter cancellation according to the error size include:

[0110] (1) Set the error threshold according to the actual application scenario and requirements;

[0111] (2) Obtain the error result between the corrected echo data and the dynamic background template;

[0112] When the absolute value of the error result is less than the set error threshold, the corresponding point is considered to belong to background clutter, and the corresponding background template value is subtracted from the corrected echo data to perform clutter cancellation.

[0113] The time-frequency analysis is performed on the multi-polarization echo data after clutter suppression to generate a three-dimensional feature matrix in the time-frequency domain containing amplitude-frequency-polarization features, including:

[0114] C1: Using a short-time Fourier transform method to perform time-frequency analysis on the multi-polarization echo data after clutter suppression. During the time-frequency analysis process, the echo data after clutter suppression is segmented according to a preset window function type and window length parameter. The short-time Fourier transform method is a prior art content in this field and does not constitute an inventive solution of the present application, and is not described in detail here.

[0115] Furthermore, the preset window function type and window length parameters are determined according to the following method:

[0116] (1) Analyze the echo signal characteristics in a dual-runway environment, including the signal bandwidth, time-varying characteristics, and Doppler shift range;

[0117] (2) Through simulation experiments, the effects of different window function types and window length parameters on the time-frequency analysis results are compared. In the present invention, the window function types are selected as Hanning window, Hamming window, and rectangular window;

[0118] (3) According to the simulation results, the window function type and window length parameters that can achieve the optimal time-frequency resolution are selected, where the time-frequency resolution is evaluated by the time-frequency concentration index.

[0119] C2: For each echo data segment, calculate its amplitude value at different frequency points and combine it with multi-polarization information to obtain the corresponding amplitude-frequency characteristics under different polarization modes.

[0120] Furthermore, the specific steps of C2 include:

[0121] (1) Obtain each segment of echo data, and perform denoising and normalization processing on each echo data segment to obtain the preprocessed echo data segment;

[0122] (2) performing a fast Fourier transform on each preprocessed echo data segment to convert the time domain signal into a frequency domain signal to obtain a frequency spectrum of each data segment, and extracting an amplitude spectrum from the frequency spectrum of each preprocessed echo data segment. The fast Fourier transform is a prior art in this field and is not an inventive solution of the present application, and is not described in detail here.

[0123] (3) Select the frequency point. For each pre-processed echo data segment, record its amplitude value at the selected frequency point. For multi-polarization data, it is necessary to record the amplitude values ​​under different polarization modes separately.

[0124] (4) The amplitude-frequency characteristics under different polarization modes are organized into a characteristic matrix. The rows of the characteristic matrix represent different frequency points, the columns represent different polarization modes, and the matrix elements represent the amplitude values ​​under the corresponding frequency points and polarization modes.

[0125] C3: Integrate the amplitude-frequency characteristics under different polarization modes to form a three-dimensional data set containing amplitude-frequency-polarization characteristics;

[0126] C4: Normalize the three-dimensional data set to generate the final three-dimensional feature matrix in the time-frequency domain.

[0127] The specific steps of S2 include:

[0128] D1: Obtain the three-dimensional feature matrix in the time-frequency domain, preprocess the three-dimensional feature matrix in the time-frequency domain, and normalize each element in the matrix;

[0129] D2: Within the entire dual-runway domain, a sliding window approach is used to calculate the local statistics of each data point in the normalized three-dimensional feature matrix in the time-frequency domain. The local statistics include the mean and variance.

[0130] D3: Calculate the detection threshold using local statistics based on the preset false alarm probability;

[0131] Among them, when calculating the detection threshold, the noise characteristics of the radar system are taken into account, and the false alarm probability is adjusted in an adaptive manner. The detection threshold is dynamically optimized according to the actual detection situation to improve the detection accuracy.

[0132] Furthermore, the specific steps of D3 include:

[0133] (1) Set the false alarm probability, that is, the probability of misjudging the background signal as the target signal;

[0134] (2) Obtaining a local statistic, and calculating a detection threshold based on the distribution characteristics of the local statistic and the false alarm probability, wherein the detection threshold is equal to the sum of the product of the standard deviation of the local statistic and the first component plus the mean; the first component is the inverse cumulative distribution function of the standard normal distribution; the independent variable of the inverse cumulative distribution function of the standard normal distribution is the difference between 1 and the false alarm probability.

[0135] D4: Compare the amplitude value of each data point in the window with the detection threshold value. If the amplitude value exceeds the threshold value, it is marked as a target point in the radar coordinate system;

[0136] The preprocessed image data is obtained, and a deep learning-based target detection algorithm is used to identify foreign objects. The bounding box coordinates of the suspected foreign object area in the preprocessed image are extracted and marked as the suspected foreign object area in the image coordinate system. At the same time, the confidence score of each suspected foreign object area is recorded.

[0137] The establishment of a multi-source data geographic coordinate mapping model specifically includes:

[0138] E1: Establish a conversion relationship between the radar coordinate system and the geographic coordinate system based on the latitude and longitude coordinates of the radar installation location and the scanning parameters; the conversion relationship is described by a coordinate transformation matrix;

[0139] Among them, the installation location of the radar is obtained through the global positioning system.

[0140] E2: Based on the camera intrinsic calibration results and extrinsic parameters, establish the mapping relationship between the image coordinate system and the geographic coordinate system;

[0141] E3: Establish a runway physical coordinate system with any end point of the runway as the origin and the runway direction as the coordinate axis, and construct the radar-physical coordinate and image-physical coordinate mapping models respectively;

[0142] E4: Convert the radar target point and the suspected foreign object area to the runway physical coordinate system and calculate the spatial position deviation value.

[0143] Furthermore, the radar target point is converted from the radar coordinate system to the runway physical coordinate system, including:

[0144] (1) For each target point marked in the radar coordinate system, obtain its coordinate value;

[0145] (2) According to the dual-runway geographic coordinate mapping model, the coordinate transformation matrix is ​​used to convert the target point coordinates in the radar coordinate system into the coordinates in the geographic coordinate system. Then, according to the mapping relationship between the runway physical coordinate system and the geographic coordinate system, the target point coordinates in the geographic coordinate system are converted into the coordinates in the runway physical coordinate system.

[0146] Among them, the geographic information of the dual runways is obtained from the airport's geographic information system database, and the obtained geographic information is cleaned and preprocessed to ensure the accuracy and completeness of the data.

[0147] The data registration and cross-validation based on the runway physical coordinate system includes:

[0148] F1: Calculate the Euclidean distance between the radar target point and the centroid of the suspected foreign object area. When the Euclidean distance is less than or equal to the preset threshold, it is determined to be a candidate matching target;

[0149] F2: Cluster analysis of candidate matching targets is performed based on prior information about runway pavement partitions, preliminarily distinguishing between real and false targets; the prior information about runway pavement partitions includes centerline areas, edge areas, and taxiway areas;

[0150] Furthermore, the initial distinction between real FOD targets and false targets includes:

[0151] (1) Read the runway pavement partitioning prior information, divide the runway into different areas according to the runway pavement partitioning prior information, including the runway centerline area, runway edge area, and taxiway area, and record the characteristics and functions of each area;

[0152] Among them, runway pavement partition prior information refers to the knowledge about the division of different areas of the runway pavement and related characteristics that is pre-acquired based on the runway design planning, actual usage, functional requirements, and historical experience before performing target detection, status monitoring, and foreign object identification on the runway pavement. Using this runway pavement partition prior information, key areas of concern can be quickly located. For example, in target detection, knowing that the takeoff and landing area is the area where foreign objects are most likely to appear and have the greatest impact on flight safety, detailed inspection of this area can be prioritized to reduce unnecessary calculations and resource waste.

[0153] (2): For each region, different clustering parameters are set; the clustering parameters include cluster radius and minimum number of cluster points;

[0154] Furthermore, the setting of different clustering parameters includes:

[0155] (1) Analyze the historical FOD target distribution data in different runway areas and collect statistics on the average size, distribution density and other information of real FOD targets in different areas;

[0156] (2) Based on the statistical results and the prior information of runway pavement partitioning, an appropriate clustering radius and minimum number of clustering points are set for each area to ensure that the real FOD targets can be accurately clustered while avoiding misjudging false targets as real targets.

[0157] (3) Using a density-based clustering algorithm to perform cluster analysis on the target points in the physical coordinate system of the runway, wherein the density-based clustering algorithm is the prior art content in this field and is not an inventive solution of this application, and is not described in detail here;

[0158] (4) Based on the clustering results and the prior information of runway pavement partitioning, determine whether each cluster conforms to the distribution characteristics of the real FOD target. If so, the target point in the cluster is determined to be a real FOD target; otherwise, it is determined to be a false target.

[0159] F3: Radar target points with no image match are marked as pending verification, and suspected foreign object areas with no radar response are marked as low-confidence targets.

[0160] The candidate targets for preliminary matching are confirmed using multi-dimensional features, including:

[0161] G1: At the radar data level, the target polarization scattering matrix characteristics are extracted through multi-polarization processing and compared with the typical FOD polarization feature library to calculate the radar feature matching degree;

[0162] G2: At the image data level, the visual features of the suspected foreign object area are extracted and matched with the typical FOD visual feature library for similarity, and the image feature matching degree is calculated;

[0163] G3: Fusion decision: When the radar feature matching degree is greater than or equal to the preset radar feature matching degree threshold and the image feature matching degree is greater than or equal to the preset image feature matching degree threshold, the real FOD target is confirmed.

[0164] The target characteristic confirmation by multi-polarization processing and micro-motion feature analysis includes:

[0165] H1: For the initially identified real FOD target, obtain its corresponding multi-polarization echo data, including amplitude and phase information under horizontal and vertical polarization modes;

[0166] H2: Perform polarization decomposition on the multi-polarization echo data, calculate the target's polarization scattering matrix characteristics, and analyze the target's polarization characteristics based on the element values ​​of the polarization scattering matrix. The target's polarization characteristics include polarization degree and polarization angle.

[0167] Furthermore, the process of performing polarization decomposition on the multi-polarization echo data and calculating the polarization scattering matrix characteristics of the target includes:

[0168] (1) Decomposing the multi-polarization echo data using the Pauli decomposition method. The Pauli decomposition method is a prior art in this field and is not an inventive solution of the present application, so it will not be described in detail here.

[0169] (2) Based on the decomposition results, calculate the target's characteristic parameters such as polarization amplitude, polarization phase, and polarization correlation coefficient;

[0170] (3) The calculated characteristic parameters are normalized to eliminate the differences between different polarization channels and improve the comparability of the characteristics.

[0171] H3: Compare the target's polarization characteristics with a pre-established library of polarization characteristics of typical FOD targets and false targets, and determine whether the target is a real FOD target based on the similarity;

[0172] Furthermore, the specific steps of H3 include:

[0173] (1) Obtaining the target's echo data in different polarization modes. The target's echo data in different polarization modes contains the target's polarization characteristic information, such as amplitude and phase information in different polarization channels;

[0174] (2) performing wavelet denoising on the collected raw echo data to remove background noise and interference signals. Wavelet denoising is a prior art in this field and is not an inventive solution of this application, so it will not be described in detail here.

[0175] (3) extracting polarization features from the de-noised polarization echo data; the polarization features include polarization scattering matrix, polarization degree, and polarization ratio;

[0176] (4) Collect common FOD targets on the runway, such as metal fragments, plastic bottles, and stones, and use a multi-polarization radar system to measure these targets, obtain their echo data in different polarization modes, and extract polarization features to build a polarization feature library of typical FOD targets;

[0177] (5) For each FOD target, record its characteristic parameters such as polarization scattering matrix and polarization degree, and classify and store them, for example, by material such as metal or non-metal;

[0178] (6) False targets include echoes generated by uneven runway surfaces, vegetation, and building edges. A multi-polarization radar system is also used to measure these false targets, extract polarization features, and construct a polarization feature library of false targets.

[0179] (7) Constructing the polarization characteristics of the target to be detected into a feature vector of the target to be detected;

[0180] (8) Select a typical target polarization feature from the typical FOD target polarization feature library and construct a typical target feature vector;

[0181] (9) Select a false target polarization feature from the false target polarization feature library and construct a false target feature vector;

[0182] (10) Using cosine similarity to measure the similarity between the feature vector of the target to be detected and all typical target feature vectors in the polarization feature library of typical FOD targets, a plurality of first-class similarity values ​​are obtained. Cosine similarity is a prior art in this field and is not an inventive solution of the present application, and is not described in detail here.

[0183] (11) Using cosine similarity to measure the similarity between the feature vector of the target to be detected and all the feature vectors of the false targets in the polarization feature library of the false targets, a plurality of second-class similarity values ​​are obtained;

[0184] (12) Set a similarity threshold. If the first-category similarity value is greater than the similarity threshold and the second-category similarity value is less than the similarity threshold, the target is judged to be a real FOD target; otherwise, it is judged to be a false target.

[0185] H4: At the same time, for the real FOD target that has been initially distinguished, its echo data is acquired in multiple consecutive scanning cycles to obtain the target's echo data;

[0186] H5: Use short-time Fourier transform to process the target's echo data and extract the target's micro-motion frequency and amplitude characteristics;

[0187] Furthermore, the specific steps of H5 include:

[0188] (1) Perform short-time Fourier transform on the target echo signal to obtain the time-frequency distribution of the signal;

[0189] (2) In the time-frequency distribution, analyze the frequency variation of the signal and extract the micro-Doppler frequency shift information;

[0190] (3) Calculating the target's micro-motion frequency and micro-motion amplitude based on the micro-Doppler frequency shift information; the micro-motion frequency is determined by the periodic change of the micro-Doppler frequency shift; the micro-motion amplitude is determined by the difference between the maximum and minimum values ​​of the micro-Doppler frequency shift.

[0191] H6: Based on the differences in the typical micro-motion patterns of FOD targets and the micro-motion characteristics of false targets, we can determine whether the target is a real FOD target.

[0192] The real-time tracking of the confirmed FOD target based on the fused data includes:

[0193] Q1: A Kalman filter algorithm is used to track a confirmed real FOD target in real time, and a state equation and an observation equation for the target are established. The state equation describes how the position and velocity of the target change over time. The observation equation describes the radar's observation process of the target state. The Kalman filter algorithm is a prior art in this field and does not constitute an inventive solution of the present application, and is not described in detail here.

[0194] Q2: In each scanning cycle, the Kalman filter algorithm is used to update the state of the target based on the current state estimate and the radar observation value, and the state of the target at the next moment is predicted;

[0195] Q3: Based on the prediction results, the target trajectory information is updated in real time, and the tracking results are fed back.

[0196] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention. These are all protected by the present invention.

Claims

1. A fixed dual-runway FOD detection method based on multi-source circumferential scanning, characterized in that: include: S1: A fixed circular scanning radar is used to synchronously acquire multi-polarization echo data from both runways. An adaptive clutter cancellation method based on dynamic background template matching is used, combined with a pre-established runway material feature library, to suppress time-varying clutter in the multi-polarization echo data. Time-frequency analysis is then performed on the clutter-suppressed multi-polarization echo data to generate a three-dimensional feature matrix in the time-frequency domain containing amplitude-frequency-polarization characteristics. This matrix is ​​then used to synchronously trigger the runway monitoring camera to capture image data in the corresponding area, completing image preprocessing. S2: Input the three-dimensional feature matrix in the time-frequency domain into the constant false alarm rate detection algorithm to preliminarily detect areas with FOD targets across the entire dual-runway area and mark the target points in the radar coordinate system. At the same time, target detection is performed on the preprocessed image data, marking the suspected foreign object areas in the image coordinate system. S3: Establish a geographic coordinate mapping model for multi-source data, and perform data registration and cross-validation based on the runway physical coordinate system; S4: For the candidate targets that are preliminarily matched, multi-dimensional feature confirmation is introduced, and the confirmed real FOD targets are tracked in real time based on fusion data; the fusion data is multi-source fusion information formed by alignment and association of radar echo data and image data.

2. The method for detecting FOD on a fixed dual-runway based on multi-source circumferential scanning according to claim 1, characterized in that: The method of synchronously acquiring dual-runway multi-polarization echo data by using a fixed circumferential scanning radar includes: Set the radar scanning period to T seconds based on the length, width, and detection accuracy requirements of the dual runways; Configure the radar's multi-polarization transmission and reception mode, using horizontal polarization and vertical polarization to alternately transmit and receive signals and obtain echo data in different polarization modes; The acquired multi-polarization echo data is preliminarily digitally processed to obtain multi-polarization echo data.

3. The method for detecting FOD on a fixed dual-runway based on multi-source circumferential scanning according to claim 2, characterized in that: The adaptive clutter cancellation method using dynamic background template matching, combined with a pre-established runway material feature library, performs time-varying clutter suppression on multi-polarization echo data, including: Collect radar surrounding environment data in real time to build an initial dynamic background template; the radar surrounding environment data includes background echo data when there is no target; Using a sliding window approach, statistical analysis is performed on the background echo data within the window, its mean is calculated, and the parameters of the background template are adjusted according to the mean; Acquire multi-polarization echo data and correct it based on the pre-established runway material feature library; The minimum mean square error criterion is used to calculate the error between the corrected echo data and the dynamic background template point by point. Clutter cancellation is performed according to the error size to obtain multi-polarization echo data after clutter suppression.

4. The method for detecting FOD on a fixed dual-runway based on multi-source circumferential scanning according to claim 3, wherein: The time-frequency analysis is performed on the multi-polarization echo data after clutter suppression to generate a three-dimensional feature matrix in the time-frequency domain containing amplitude-frequency-polarization features, including: The short-time Fourier transform method is used to perform time-frequency analysis on the multi-polarization echo data after clutter suppression. During the time-frequency analysis process, the echo data after clutter suppression is segmented according to the preset window function type and window length parameters; For each echo data segment, calculate its amplitude value at different frequency points, and combine it with multi-polarization information to obtain the corresponding amplitude-frequency characteristics under different polarization modes; Integrate the amplitude-frequency characteristics under different polarization modes to form a three-dimensional data set containing amplitude-frequency-polarization characteristics; The three-dimensional data set is normalized to generate the final three-dimensional feature matrix in the time-frequency domain.

5. The method for detecting FOD on a fixed dual-runway based on multi-source circumferential scanning according to claim 4, characterized in that: The specific steps of S2 include: Obtain a three-dimensional feature matrix in the time-frequency domain, preprocess the three-dimensional feature matrix in the time-frequency domain, and normalize each element in the matrix; In the entire dual-runway area, a sliding window method is used to calculate the local statistics of each data point in the normalized three-dimensional feature matrix in the time-frequency domain; the local statistics include the mean and variance; According to the preset false alarm probability, the detection threshold is calculated using local statistics; Compare the amplitude value of each data point in the window with the detection threshold value. If the amplitude value exceeds the threshold value, it is marked as a target point in the radar coordinate system. The preprocessed image data is obtained, and a deep learning-based target detection algorithm is used to identify foreign objects. The bounding box coordinates of the suspected foreign object area in the preprocessed image are extracted and marked as the suspected foreign object area in the image coordinate system. At the same time, the confidence score of each suspected foreign object area is recorded.

6. The method for detecting FOD on a fixed dual-runway based on multi-source circumferential scanning according to claim 5, characterized in that: The establishment of a multi-source data geographic coordinate mapping model specifically includes: Establishing a conversion relationship between the radar coordinate system and the geographic coordinate system based on the latitude and longitude coordinates of the radar installation location and the scanning parameters; the conversion relationship is described by a coordinate transformation matrix; Based on the camera intrinsic calibration results and extrinsic parameters, the mapping relationship between the image coordinate system and the geographic coordinate system is established; A runway physical coordinate system is established with any end point of the runway as the origin and the runway direction as the coordinate axis, and the radar-physical coordinate and image-physical coordinate mapping models are constructed respectively; The radar target point and the suspected foreign object area are converted to the runway physical coordinate system, and the spatial position deviation value is calculated.

7. The method for detecting FOD on a fixed dual-runway based on multi-source circumferential scanning according to claim 6, characterized in that: The data registration and cross-validation based on the runway physical coordinate system includes: Calculate the Euclidean distance between the radar target point and the centroid of the suspected foreign object area. When the Euclidean distance is less than or equal to the preset threshold, it is determined to be a candidate matching target; Cluster analysis is performed on candidate matching targets based on prior information about runway pavement partitions, preliminarily distinguishing between real and false targets; the prior information about runway pavement partitions includes centerline areas, edge areas, and taxiway areas; Radar target points without image matching are marked as pending verification, and suspected foreign object areas with no radar response are marked as low-confidence targets.

8. The method for detecting FOD on a fixed dual-runway based on multi-source circumferential scanning according to claim 7, characterized in that: The candidate targets for preliminary matching are confirmed using multi-dimensional features, including: At the radar data level, the target polarization scattering matrix characteristics are extracted through multi-polarization processing and compared with the typical FOD polarization feature library to calculate the radar feature matching degree; At the image data level, the visual features of the suspected foreign object area are extracted and matched with the typical FOD visual feature library for similarity, and the image feature matching degree is calculated; Fusion decision: When the radar feature matching degree is greater than or equal to the preset radar feature matching degree threshold and the image feature matching degree is greater than or equal to the preset image feature matching degree threshold, the real FOD target is confirmed.

9. The method for detecting FOD on a fixed dual-runway based on multi-source circumferential scanning according to claim 8, characterized in that: The target characteristic confirmation by multi-polarization processing and micro-motion feature analysis includes: For the initially identified real FOD targets, the corresponding multi-polarization echo data is obtained, including amplitude and phase information under horizontal and vertical polarization modes; Performing polarization decomposition on the multi-polarization echo data, calculating the polarization scattering matrix characteristics of the target, and analyzing the polarization characteristics of the target based on the element values ​​of the polarization scattering matrix; the polarization characteristics of the target include polarization degree and polarization angle; The target's polarization characteristics are compared with a pre-established library of polarization characteristics of typical FOD targets and false targets, and the target is judged to be a real FOD target based on the similarity. At the same time, for the real FOD target that is initially distinguished, its echo data is acquired in multiple consecutive scanning cycles to obtain the target echo data; The target's echo data is processed using short-time Fourier transform to extract the target's micro-motion frequency and micro-motion amplitude characteristics; Based on the difference between the typical micro-motion pattern of the FOD target and the micro-motion characteristics of the false target, it is judged whether the target is a real FOD target.

10. The method for detecting FOD on a fixed dual-runway based on multi-source circumferential scanning according to claim 9, characterized in that: The real-time tracking of the confirmed FOD target based on the fused data includes: The Kalman filter algorithm is used to track the confirmed real FOD target in real time, and the state equation and observation equation of the target are established; the state equation describes the change of the position and velocity state of the target over time; the observation equation describes the radar's observation process of the target state; In each scanning cycle, the Kalman filter algorithm is used to update the state of the target based on the current state estimate of the target and the radar observation value, and the state of the target at the next moment is predicted; According to the prediction results, the target's trajectory information is updated in real time, and the tracking results are fed back.

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