Morphology-based scene surveillance radar target and shadow recognition method

Abstract

The application discloses a scene monitoring radar target and shadow identification method based on morphology, which comprises the following steps: S1, acquiring a radar original video signal; S2, performing scan conversion on the radar original video signal to obtain a P-display mode graph; S3, processing the P-display mode graph to obtain a P-display mode foreground graph; S4, performing graphology processing and contour identification on the P-display mode foreground graph to obtain a P-display mode suspected target outer contour set; S5, processing the P-display mode suspected target outer contour set by adopting a morphological identification method to obtain a shadow convex hull; S6, judging whether the shadow convex hull is in a target shadow area; S7, if yes, the suspected object is a shadow, and if not, the suspected object is a target; S8, comparing echo intensities of the suspected shadow and the suspected target, and if the echo intensity is greater than a set value, the suspected object is a target, and if the echo intensity is less than the set value, the suspected object is a shadow. The method can effectively identify the target and the shadow, and can improve the identification and detection capability of the system on the target.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of radar signal processing, and in particular to a scene monitoring radar signal target and shadow identification method based on morphology. BACKGROUND

[0002] With the development of airport scale in China, more and more airports begin to layout and use the scene monitoring radar to assist the air traffic control center to carry out the deployment work of the airport. As of the end of 2021, more than 20 airports in China have installed and used the airport scene monitoring radar, mainly distributed in the southeast coast of China, Heilongjiang, Jilin, Yunnan, Sichuan and other provinces, as well as Xinjiang Uygur Autonomous Region and Chongqing. In the long run, in the planning of the domestic civil aviation airport monitoring system by the Civil Aviation Administration of China, the airport scene monitoring radar is used as an auxiliary and supplementary means of the Airport Surveillance Radar (ADS-B), but compared with other monitoring means such as the Broadcast Automatic Related Surveillance System (ADS-B) and the Multi-Base Station Positioning System based on transponder, the airport scene monitoring radar has the advantages of non-cooperation, long action distance, high data rate, and working under all-weather conditions. Since the 1960s, foreign countries have begun to use scene monitoring radars for airport ground monitoring. At present, the main manufacturers of scene monitoring radars (Surface Movement Radar, SMR) in the world are Thales Company of France, Terma Company of Denmark, Indra Company of Spain, Selex Company of the United States, etc., and each company has its own radar signal processing system. In addition to the above companies, there is also a general radar signal processing system of Cambridge Pixel in the United Kingdom to adapt to various types of radars. The prices of the scene monitoring radar signal processing (SMRVSP) systems of the above foreign products are high, the universality is low, and the scalability is limited. Customers have strong customization needs for signal processing equipment, and foreign equipment systems have the disadvantage of high cost of demand change. Due to the strong professionalism of domestic similar products, it is difficult to realize general application. At the same time, due to the use of traditional radar signal processing technology, there are too many parameters, which is not conducive to the maintenance of the equipment system.

[0003] In the morphological-based radar signal processing system, target and shadow recognition is a core function. The traditional shadow recognition mainly adopts color value channel processing. Because the shadow area is generally lower in brightness than the non-shadow area, has a clear boundary with the non-shadow area, and the color value channel proportion of the shadow area is close to that of the non-shadow area. Similar technology first appeared in the processing of synthetic aperture radar images. The geometric and radiometric characteristics of moving targets forming shadows and static backgrounds in video time sequence images are used to obtain high-precision position, speed and other motion state information of moving targets. Video moving target shadow detection and tracking processing is one of the key technologies of video application. Due to the lack of extensive and effective data sources, this research is still in its infancy. The basic idea is to draw on the existing optical video target detection and tracking results, fully consider the characteristics of video sequence images and moving targets, and study targeted processing methods. The video moving target detection method based on image sequence uses a single Gaussian model to model the background of the image sequence. The background and the current image are differentiated and binarized to obtain a foreground binary image. The target shadow is extracted by morphological processing of the binary image.

[0004] The traditional scene monitoring radar video signal processing shadow recognition has the following obvious weaknesses:

[0005] 1. The traditional one-dimensional signal extraction method is used for target detection, which usually hides the shadow signal in the clutter signal. The traditional one-dimensional signal detection is a single-sided signal detection method, which cannot extract the shadow signal.

[0006] 2. In the traditional shadow recognition technology based on digital images, light intensity analysis is mainly used, and shadow intensity information is detected by filtering. The shadow information is obtained by comparing the brightness of the target or by using two-dimensional brightness information and average filtering. Prior morphological method is used for shadow recognition.

[0007] The reasons for the above shortcomings are:

[0008] 1. Conventional scene monitoring radar signal processing mostly uses one-dimensional signal processing technology, which is difficult to analyze the source of information from the morphological point of view. If one-dimensional range image is used to recognize the shadow part, different wavebands will form different shadow images, which requires a large number of sample inputs for statistical learning, so it is difficult to reduce the cost;

[0009] 2. The morphological-based shadow recognition technology is mostly derived from synthetic aperture radar processing technology, but there are great differences between scene monitoring radar signal processing and synthetic aperture. The resolution is relatively low, and the radiation source is in a fixed position. The shadow formed is not a complete high-precision direct shadow;

[0010] 3. Shadows in the field surveillance radar signal processing are grayscale images, making it difficult to use conventional shadow recognition technology based on three-channel color values. Furthermore, due to the large number of diffuse reflection targets generated by ground clutter, shadows cannot be presented in a connected state, which poses a significant challenge to shadow recognition. Summary of the Invention

[0011] To address the shortcomings of existing technologies, this invention provides a morphology-based method for target and shadow recognition in scene surveillance radar signals. This method integrates image morphology, geometric illumination, and multi-level processing to achieve target and shadow recognition in radar signal processing. This method can effectively identify targets and shadows, improve the accuracy of target recognition, and has lower requirements for environmental conditions, thus reducing computational complexity.

[0012] This invention discloses a morphology-based method for target and shadow recognition in scene surveillance radar signals, comprising the following steps:

[0013] S1: Acquire the raw radar video signal;

[0014] S2: Scan and convert the original radar video signal to obtain the P-display mode diagram;

[0015] S3: Process the P display mode image to obtain the P display mode foreground image;

[0016] S4: Perform graphics processing and contour recognition on the foreground image of the P display mode to obtain a set of suspected target outer contours in the P display mode;

[0017] S5: The set of suspected target outer contours in the P display mode is processed using a morphological recognition method to obtain the shadow convex hull;

[0018] S6: Determine whether the shadow convex hull is within the target shadow area;

[0019] S7: If so, it is suspected to be a shadow; if not, it is suspected to be a target.

[0020] S8: Compare the echo intensity of suspected shadows and suspected targets. If the echo intensity is greater than the set value, it is a target; if the echo intensity is less than the set value, it is a shadow.

[0021] The beneficial effects of this invention are as follows:

[0022] The application provides a scene monitoring radar signal target and shadow identification method based on morphology, which utilizes image morphology, geometric illumination and multi-level processing integration to complete target and shadow identification in radar signal processing. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the present application or the prior art, the following will briefly introduce the drawings needed in the specific embodiments or prior art description. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual proportions.

[0024] Figure 1 It is a side view of an aircraft relative to a scene radar in the embodiment of the present application;

[0025] Figure 2 It is a top view of an aircraft relative to a scene radar in the embodiment of the present application;

[0026] Figure 3 It is a flowchart of a scene monitoring radar signal target and shadow identification method based on morphology provided by the embodiment of the present application;

[0027] Figure 4 It is a flowchart of target and shadow identification processing in the embodiment of the present application;

[0028] Figure 5 It is a target and shadow overlap calculation diagram in the embodiment of the present application;

[0029] Figure 6 It is a radar original P display mode diagram in the embodiment of the present application;

[0030] Figure 7 It is a target and shadow diagram after processing and identification in the embodiment of the present application;

[0031] Figure 8 It is a same target diagram located at 02R runway for four consecutive frames in the embodiment of the present application. DETAILED DESCRIPTION

[0032] The embodiments of the technical solutions of the present application will be described in detail below with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and therefore only serve as examples, and cannot limit the protection scope of the present application.

[0033] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0034] This invention provides a morphology-based method for identifying targets and shadows in scene surveillance radar signals. By starting from the generation mechanism of shadows in scene surveillance radar, it achieves uninterrupted processing of scene surveillance signals, real-time identification of targets and shadows, and has low hardware resource requirements, enabling real-time processing on low-performance hardware platforms.

[0035] The mechanism by which shadows are generated by surface surveillance radar is as follows: Figure 1 As shown, from a three-dimensional perspective, the shadow area formed by an aircraft under radar illumination is a wedge-shaped region. According to... Figure 1 The length of the shaded area can be calculated from the resulting planar figure using the following formula:

[0036]

[0037] Wherein: H a For the aircraft's altitude, 20 meters is usually sufficient; H r D represents the relative position of the radar radiation center point to the runway plane. t This refers to the distance between the aircraft and the radar station, i.e., the target detection range.

[0038] according to Figure 2 The formula for calculating the length of the shadowed area farther from the radar end based on the formed planar shape is as follows:

[0039]

[0040] Where: L a Let be the length of the aircraft, usually taken as 90 meters. From this, the formula for calculating the area of ​​the shaded region can be obtained as follows:

[0041]

[0042] in, The area of ​​the shaded region is calculated using this formula, thus enabling shadow filtering.

[0043] like Figure 3 The diagram shows a flowchart of a morphology-based target and shadow recognition method for scene surveillance radar according to a first embodiment of the present invention. The method includes the following steps:

[0044] S1: Acquire the raw radar video signal;

[0045] S2: The original radar video signal is displayed in P display mode to obtain a P display mode diagram;

[0046] S3: Process the P display mode image to obtain the P display mode foreground image;

[0047] S4: Perform graphics processing and contour recognition on the foreground image of the P display mode to obtain a set of suspected target outer contours in the P display mode;

[0048] S5: The set of suspected target outer contours in the P display mode is processed using a morphological recognition method to obtain the shadow convex hull;

[0049] S6: Determine whether the shadow convex hull is within the target shadow area;

[0050] S7: If so, it is suspected to be a shadow; if not, it is suspected to be a target.

[0051] S8: Compare the echo intensity of suspected shadows and suspected targets. If the echo intensity is greater than the set value, it is a target; if the echo intensity is less than the set value, it is a shadow.

[0052] In this embodiment, step S3 specifically includes:

[0053] Noncoherent clutter maps are obtained by applying noncoherent cumulative sliding window processing to the P-display mode map;

[0054] The P-mode foreground image is obtained by binarizing the P-mode image and the non-coherent clutter image.

[0055] In the above steps, the noncoherent clutter map is generated using a noncoherent cumulative sliding window method. This technique uses 10 sliding windows to generate the clutter map, which can suppress short-period clutter targets. To improve the detection probability, such as under good weather conditions, the number of sliding windows can be increased; to achieve a stronger suppression level for dynamic targets, such as under adverse weather conditions, the number of sliding windows can be reduced. Morphological Gaussian two-dimensional filtering is used to suppress small-scale clutter, and the sliding window technique is used to obtain the radar signal background map. Finally, ensemble operations are used to obtain the radar signal foreground map.

[0056] In this embodiment, step S4 specifically includes:

[0057] Binarize the foreground image of the P display mode to obtain the processed foreground binary image;

[0058] The outer contour set of suspected targets is obtained by detecting the outer contour of the processed foreground binary image while retaining the endpoint coordinates.

[0059] like Figure 4 As shown, step S5 specifically includes:

[0060] Perform contour recognition on the set of suspected targets to obtain a set of suspected targets;

[0061] Determine whether the size of the suspected target set meets the target size range threshold;

[0062] If the conditions are not met, then delete the suspected target set;

[0063] If the size is satisfied, then determine whether the area of ​​the suspected target set meets the target area range threshold.

[0064] If the area requirement is not met, then delete the suspected target set;

[0065] If the area satisfies the condition, then obtain the convex hull of the suspected target set;

[0066] Obtain the convex hull of the suspected target set;

[0067] Convert the coordinates of the suspected target's convex hull to polar coordinates centered on the radar.

[0068] Traverse the set of fixed points of the convex hull, obtain the target endpoints in the direction connecting the radar point and the target center point, and calculate the maximum and minimum azimuth angles;

[0069] The coordinates of the target endpoint shadow are calculated based on the maximum and minimum azimuth angles.

[0070] Obtain the shadow convex hull based on the coordinates of the target endpoint shadow.

[0071] like Figure 5 The diagram illustrates the target and shadow overlap calculation in this embodiment. The coordinates of the outer envelope set obtained from the suspected target set using a morphological processing algorithm are: Ev1 = (x... i ,y i (i = 1, 2, ..., M), converting the outer envelope convergence point to polar coordinates centered on the radar is: Ev2 = (ρ i ,θ i If (i = 1, 2, ..., M), then traverse the location set to obtain the target endpoint, i.e., θ. min =argmin(θ) i ), θ max =argmax(θ) i (i = 1, 2, ..., M), let θ min With θ max With the corresponding outer envelope set indices k1 and k2, the coordinates of the endpoint shaded area can be calculated as follows: and If the target crosses due north, a north calibration is required, limited to the range (-π, π].

[0072] Calculations are performed within the target's shadow region. Through the above process, the coordinates of the convex hull of the quadrilateral vertices within the target's shadow region can be obtained as follows: Recorded as: This represents the shadow area formed by the k-th target under radar illumination. Since surface surveillance radars often employ discretization sampling techniques, the target and the shadow area can be discretized for subsequent intersection calculations. The calculation formula for "within the target's shadow area" in the above equation is:

[0073]

[0074] in, S i,k Let the area of ​​the intersecting portion be greater than the area of ​​the intersection. Among them: G k The kernel size for using morphological filtering (3,3 in this case), Let P1 be the Euclidean distance between points P1 and P4 in the Cartesian coordinate system.

[0075] Verification tests were conducted using data from Chengdu Shuangliu Airport. Taking runway 02R at Shuangliu Airport as an example, the relative altitude of the radar to the airport runway is: H. r = 24.6m; Taking the common Airbus A321 as an example, its height is: H a =11.76m; Since the aircraft's heading is unknown, and the A321 is known to be 44.51m long and 35.8m wingspan, we take its maximum length as L. a =70.6m. Therefore, the approximate area of ​​the shaded region is S. s =59.43D t m 2 .like Figure 6 As shown, the original P-mode radar diagram is illustrated, and the target obtained through the identification method of this embodiment is as follows: Figure 7 The location marked with a number 1, and the corresponding shaded area, such as Figure 7 The location marked with number 2, and the identified shadow, such as Figure 7 The area labeled 3 and the portion labeled 4 represent the bounding ellipse of the shaded area. For example... Figure 8 As shown, four consecutive frames of the same target are displayed on runway 02R.

[0076] The application provides a scene monitoring radar target and shadow identification method based on morphology, which utilizes image morphology, geometric illumination and multi-level processing integration to complete target and shadow identification in radar signal processing, more effectively identifies target and shadow, improves target identification precision, adopts geometric illumination, all parameters can be obtained through geometric calculation, all parameters can be explained, integrates shadow identification function in scene radar signal processing, improves target detection capability, has lower requirement on environmental conditions, reduces calculation complexity, improves shadow and target identification technology, enhances target and shadow identification capability, and improves system target detection capability.

[0077] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the description of the present application.

Claims

1. A method for morphological based scene surveillance radar target and shadow identification, characterized in that, The method comprises the following steps: S1: obtaining a radar original video signal; S2: performing scan conversion on the radar original video signal to obtain a P-mode pattern image; S3: performing processing on the P-mode pattern image to obtain a P-mode foreground image; S4: performing graphic processing and contour recognition on the P-mode foreground image to obtain a P-mode suspected target contour set; S5: performing processing on the P-mode suspected target contour set by using a morphological recognition method to obtain a shadow convex hull; S6: judging whether the shadow convex hull is in a target shadow region; S7: if yes, it is a suspected shadow, and if not, it is a suspected target; S8: comparing echo intensities of the suspected shadow and the suspected target, and if the echo intensity is greater than a set value, it is a target, and if the echo intensity is less than the set value, it is a shadow.

2. The method of claim 1, wherein, The step S3 specifically comprises: performing non-coherent cumulative sliding window processing on the P-mode pattern image to obtain a non-coherent clutter image; and performing binary image processing on the P-mode pattern image and the non-coherent clutter image to obtain the P-mode foreground image.

3. The method of claim 2, wherein, The step S4 specifically comprises: performing binary image processing on the P-mode foreground image to obtain a processed foreground binary image; performing external contour detection by retaining end point coordinates to obtain a suspected target contour set.

4. The method of claim 1, wherein, The step S5 specifically comprises: performing contour recognition on the suspected target contour set to obtain a suspected target set; judging whether a size of the suspected target set meets a target size interval threshold value; if yes, performing area judgment; if not, deleting the suspected target set.

5. The method of claim 4, wherein, The specific method of performing area judgment comprises: judging whether an area of the suspected target set meets a target area interval threshold value; if yes, obtaining a suspected target set convex hull; if not, deleting the suspected target set.

6. The method of claim 5, wherein, After the step of obtaining the suspected target set convex hull, the method further comprises: obtaining a suspected target convex hull from the suspected target set convex hull; converting coordinates of the suspected target convex hull into polar coordinates with the radar as a center; traversing a convex hull fixed point set, obtaining a target endpoint connected with a radar point and a target center point in a direction, calculating a maximum azimuth angle and a minimum azimuth angle; calculating coordinates of a target endpoint shadow according to the maximum azimuth angle and the minimum azimuth angle; obtaining a shadow convex hull according to the coordinates of the target endpoint shadow.

7. The method of claim 6, wherein, In the step S6, the calculation method of the target shadow region is as follows: calculating convex hull coordinates of four vertices of a target shadow region quadrilateral, and obtaining a target shadow region formed under radar irradiation according to the convex hull coordinates.

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