A multi-feature combined wideband stationary target detection method

Abstract

The application discloses a kind of multi-feature joint wideband stationary target detection method, belong to radar signal processing field.Aiming at the problem such as single feature poor robustness, feature isolation and single detection easy to miss detection in existing wideband radar ground stationary target detection, the application extracts target geometric feature, energy feature and environmental feature by signal preprocessing, constant false alarm detection, motion compensation and space clustering, and carries out multi-dimensional feature synthesis;Introduce target confidence concept, match the current scanning detection result with historical detection result, improve the confidence of target matched successfully and fuse features according to power weighting, reduce the confidence of target matched unsuccessfully;Finally, according to the preset threshold, the target is screened, stored and outputted.The application fully excavates target multi-dimensional feature information, realizes the stable detection of ground stationary target under strong flicker condition, effectively suppresses false target, and improves detection probability and reliability.

Description

Technical Field

[0001] This application belongs to the field of radar signal processing, and specifically relates to a broadband stationary target detection method that combines multiple features. Background Technology

[0002] Wideband radar, with its superior range resolution, has been widely used in military and civilian fields such as ground reconnaissance and security monitoring. Compared with narrowband radar, wideband radar has smaller range resolution cells, and the target echo energy is dispersed across multiple range cells, forming a one-dimensional range image feature. The reduction in clutter area of ​​the resolution cells also improves the signal-to-clutter ratio of the target. Therefore, wideband radar can be used for the detection of stationary ground targets. However, radar detection of stationary ground targets is affected by complex terrain backgrounds, clutter fluctuations, and multipath effects, making detection relatively difficult.

[0003] Existing radar methods for detecting stationary ground targets mainly employ constant false alarm rate (CFAR) detection and single-domain feature matching, which have several shortcomings:

[0004] (1) Poor robustness of single feature detection. Existing methods rely on single scattering features such as echo energy for threshold discrimination, which makes it difficult to fully extract effective information about the target. In complex ground environments, it is easy to miss detections and have a high false alarm rate.

[0005] (2) Features are isolated and not comprehensively utilized. Existing methods mostly use various features of the target independently for detection, without associating and complementing the multi-dimensional features of the target, and without forming a constraint relationship between features, resulting in low detection accuracy.

[0006] (3) Single detection is prone to missed detection. Existing methods mostly use single detection, that is, the detection results of a single scan are directly output. When the ground target echo flicker is severe due to factors such as multipath effect, single detection is prone to missed detection, resulting in unstable detection. Summary of the Invention

[0007] To address the aforementioned problems, this application provides a multi-feature joint broadband stationary target detection method, comprising:

[0008] Step 1: Preprocess the sum-path echo data and difference-path echo data to obtain a single-frame sum-path data vector. With single-frame differential data vector ;

[0009] Step 2: Filter out single-frame and path echo data The set of detection points that reach the detection threshold ,,in It is the first One CFAR detection point;

[0010] Step 3: Put all detection points Transform to the reference point, and then, based on the nearest neighbor principle, identify detection points from the same target. Perform clustering to obtain the clustered detection points. m≤k;

[0011] Step 4: Based on the clustered detection points Calculate the target centroid;

[0012] Step 5: Calculate the target symmetry and target size based on the target centroid.

[0013] Preferably, the formula for calculating the target centroid is:

[0014] ;

[0015] ;

[0016] in( , The orientation and distance of the target's center of mass. The first step towards achieving this goal Each testing point azimuth angle, The first step towards achieving this goal Each testing point distance, The first step towards achieving this goal Each testing point The power.

[0017] Preferably, the formula for calculating the target symmetry is as follows:

[0018] ;

[0019] in, For the target symmetry, For point set The number of midpoints For point set The number of symmetrical points in the middle.

[0020] Preferably, ;

[0021] ;

[0022] in, The target azimuth width, The target distance is the width. For point set The maximum azimuth of all points, For point set The minimum azimuth angle of all points, For point set The maximum distance of all points For point set The minimum distance to all points.

[0023] Preferably, the method also includes step 6: calculating the distance between the target and historical targets; when the distance is less than a given threshold, the target is successfully matched with historical targets, and the confidence of the target is increased by a set value, and the target features are weighted and fused with historical targets according to power; for targets that fail to match, their confidence is decreased by a set value.

[0024] Preferably, the method further includes step 7: setting an output threshold and an internal storage threshold for the target features; if all features of the target meet the output threshold, the target is displayed; if all features of the target meet the internal storage threshold, the target information is saved, and the internally stored target is used as the historical target; if the features of the target do not meet the output threshold and the internal storage threshold, the target is discarded.

[0025] Compared with existing technologies, the broadband stationary target detection method proposed in this invention, which combines multiple features, has advantages such as stable target detection, strong false alarm suppression, and strong anti-flicker capability. By extracting and integrating multi-dimensional target features, the method can detect targets more accurately and effectively suppress false alarms. Through the correlation and fusion of multiple scans, the current detection results are enhanced based on historical data, which can effectively combat missed detections caused by strong ground target flicker, making the detection effect more stable. Attached Figure Description

[0026] Figure 1 This is a flowchart of a broadband stationary target detection method based on multiple features according to the present invention.

[0027] Figure 2 This is a screenshot of the results from a single scan.

[0028] Figure 3 A comparison diagram of the detection results of the cooperative target and the azimuth angle of the actual target.

[0029] Figure 4 A comparison chart showing the detection results of the cooperative target and the distance to the actual target. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are only some, not all, of the embodiments of this application. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0031] The algorithm flow proposed in this invention is as follows: Figure 1 As shown, the specific steps are as follows:

[0032] Signal preprocessing. Preprocessing is performed on the echo data of each frame and the echo data of the difference path; this includes channel calibration, pulse compression, distance travel compensation, coherent accumulation, AGC compensation, and angular error calculation, to obtain the single-frame and path data vector. ,in For the first The sum of the power at each sampling point, and the single-frame difference data vector ,in For the first Differential power at each sampling point.

[0033] Constant false alarm rate (CFAR) detection. First, perform CFAR detection on the preprocessed single-frame and path echo data. A sliding window is used to calculate the signal-to-noise ratio (SNR) for each sampling point, and the set of detection points that meet the detection threshold is selected. ,in It is the first CFAR detection points.

[0034] Motion compensation and spatial clustering. Using the coordinates of the carrier platform at the beginning of the first frame of each scan as a reference point, all detection points for that scan are collected. They are transformed to the reference point, and then clustered based on the nearest neighbor principle to obtain the clustered detection points. ,m≤k.

[0035] Target multi-dimensional feature extraction and synthesis.

[0036] The target centroid is calculated using the following formula:

[0037] ,

[0038] in( , The orientation and distance of the target's center of mass. The first step towards achieving this goal The azimuth angle of each detection point The first step towards achieving this goal The distance between each detection point The first step towards achieving this goal The power of each detection point.

[0039] Calculate the target symmetry in the detection point set. Search for the centroid of the target ( , A temporary point set is obtained by considering all points within a certain range around the given location. traversal All points , to pass Line symmetry, point Symmetric points in the point set Within the neighborhood of a given point, that point is considered symmetrical. The formula for calculating the symmetry of the target is as follows:

[0040]

[0041] in, For the target symmetry, For point set The number of midpoints For point set The number of symmetrical points in the middle.

[0042] Calculate the target size using the following formula:

[0043] ,

[0044] in, The target azimuth width, The target distance is the width. For point set The maximum azimuth of all points, For point set The minimum azimuth angle of all points, For point set The maximum distance of all points For point set The minimum distance to all points.

[0045] Historical information association and fusion. All internally stored targets are paired with all currently detected targets. If the distance between two targets is less than a given threshold, the match is considered successful; otherwise, it fails. For successfully matched targets, the confidence level of that target is increased, and the target features are weighted and fused according to power. For targets that fail to match, their confidence level is decreased. Target selection, storage, and output.

[0046] For the target's features, including energy features, geometric features, and target confidence, output thresholds and internal storage thresholds are set. If all the target's features meet the output thresholds, the target is displayed; if all the target's features meet the internal storage thresholds, the target information is saved, and the internally stored target is used as historical data in the association fusion in step (5); if the target's features do not meet the output thresholds and internal storage thresholds, the target is discarded.

[0047] The experimental procedure and results are as follows:

[0048] Analyzing test data from a certain type of radar, the targets were three vans. The target output results of a certain scan are as follows: Figure 2 As shown, the comparison between the detected target traces and the actual target information is as follows: Figure 3 and Figure 4 As shown in the figure, the number of scans in one flight was 175, covering a total of 3 targets, which means 525 detections were performed, with 489 targets detected, resulting in a detection probability of 93.1%, essentially achieving stable detection throughout the entire flight.

[0049] The above experiments have confirmed that the method proposed in this invention can accurately estimate the multi-dimensional features of the target, achieve stable detection of stationary ground targets, effectively suppress false targets, and can be applied to radar detection of stationary ground targets.

[0050] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A broadband stationary target detection method combining multiple features, characterized in that, include: Step 1: Preprocess the sum-path echo data and difference-path echo data to obtain a single-frame sum-path data vector. With single-frame differential data vector ; Step 2: Filter out single-frame and path echo data The set of detection points that reach the detection threshold ,,in It is the first One CFAR detection point; Step 3: Put all detection points Transform to the reference point, and then, based on the nearest neighbor principle, identify detection points from the same target. Perform clustering to obtain the clustered detection points. m≤k; Step 4: Based on the clustered detection points Calculate the target centroid; Step 5: Calculate the target symmetry and target size based on the target centroid.

2. The broadband stationary target detection method based on multi-feature combination as described in claim 1, characterized in that, include: The formula for calculating the target centroid is: ； ； in( , The orientation and distance of the target's center of mass. The first step towards achieving this goal Each testing point azimuth angle, The first step towards achieving this goal Each testing point distance, The first step towards achieving this goal Each testing point The power.

3. The broadband stationary target detection method based on multi-feature combination as described in claim 1, characterized in that, include: The formula for calculating the symmetry of the target is as follows: ； in, For the target symmetry, For point set The number of midpoints For point set The number of symmetrical points in the middle.

4. The broadband stationary target detection method based on multi-feature combination as described in claim 1, characterized in that, include: ； ； in, The target azimuth width, The target distance is the width. For point set The maximum azimuth of all points, For point set The minimum azimuth angle of all points, For point set The maximum distance of all points For point set The minimum distance to all points.

5. The broadband stationary target detection method based on multi-feature combination as described in claim 1, characterized in that, include: It also includes step 6: calculate the distance between the target and historical targets. When the distance is less than a given threshold, the target is successfully matched with historical targets. The confidence of the target is increased by a set value, and the target features are weighted and fused with historical targets according to power. For targets that fail to match, their confidence is decreased by a set value.

6. The broadband stationary target detection method based on multi-feature combination as described in claim 1, characterized in that, It also includes step 7: setting the output threshold and internal storage threshold for the target features. When all features of the target meet the output threshold, the target is displayed. If all features of the target meet the internal storage threshold, the target information is saved and the internally saved target is used as the historical target. If the features of the target do not meet the output threshold and internal storage threshold, the target is discarded.

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