CN111190156B - Radar and photoelectric based low-slow small target and sea surface small target identification method - Google Patents
Radar and photoelectric based low-slow small target and sea surface small target identification method Download PDFInfo
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Abstract
The invention relates to a radar and photoelectric based low-slow small target and sea surface small target identification method, which is mainly suitable for classification and identification of low-slow small targets and sea surface small targets under an early warning detection system. The main process is as follows: firstly, a radar guides a target to a photoelectric sensor, and the radar and the photoelectric sensor perform composite tracking on the target; after the radar and the photoelectricity stably track the target, calling the radar to perform high repetition frequency detection on the target, and calling the photoelectricity to perform infrared imaging on the target; then extracting the micro Doppler modulation characteristic and the infrared characteristic of the target respectively; and constructing a target feature matrix by combining the target micro-Doppler modulation feature and the infrared feature with the target motion feature and the motion feature, and finally performing low-slow small target and sea surface small target classification and identification by combining a linear two-classifier. The method provided by the invention can give full play to the advantages of the radar and the photoelectric equipment in the early warning detection system, make up the deficiency of the classification and identification capabilities of the respective low-speed small target and the sea surface small target, and comprehensively improve the identification capability.
Description
Technical Field
The invention relates to a target identification method.
Background
Unconventional sea-air targets such as low and slow small targets (helicopters, unmanned planes, airships, etc.), sea surface small targets (small fishing boats, etc.) and the like gradually become important objects of attention of warning detection equipment. Most of the non-traditional threat targets have small RCS, are often mixed with various civil targets, and can be hidden among large ships or even submerged in sea clutter. The targets are difficult to effectively find, track and classify and identify by means of radar or photoelectric equipment alone.
The radar has long acting distance, all-weather, all-time and long-distance target detection, positioning and tracking capabilities, and continuous tracking and track processing capabilities of multiple batches of targets. The photoelectric equipment has the capability of immunity to electronic interference, has no multipath effect in low-altitude and ultra-low-altitude environments, has sea waves only existing as a heat radiation background, and has the characteristics of high detection precision, high angular resolution and strong low-altitude and ultra-low-altitude target detection capability.
Both radar and photovoltaic devices have their limitations. Compared with photoelectric equipment, the radar has poor angular precision and low resolution, is easily interfered by clutter and electromagnetism, and has poor elevation precision of low-altitude and ultra-low-altitude targets; the photoelectric equipment has short acting distance, poor ranging capability, small field range, weak searching and multi-target detecting capability and large weather influence on the detection distance.
The advantages of radar and photoelectric equipment are obviously complemented, the advantages of respective detection equipment can be fully exerted, the defects are made up, and the classification and identification capabilities of low-speed small targets and sea surface small targets are comprehensively improved.
Disclosure of Invention
The invention aims to provide a method for classifying and identifying low-slow small targets and sea surface small targets in an early warning detection system. The technical solution for realizing the invention is as follows:
firstly, a radar guides a target to a photoelectric device, and the radar and the photoelectric device perform composite tracking on the target; after the radar and the photoelectric equipment stably track the target, calling the radar to perform high repetition frequency detection on the target, and simultaneously calling the photoelectric equipment to perform infrared imaging on the target; detecting the position of a target in high repetition frequency detection according to a radar target track and a target posture, and extracting the micro-Doppler modulation characteristic of the target by using an outlier skewness method; detecting thick edges of the target infrared image by using a variation force field conversion method of the image, and extracting structural features of the target infrared image; constructing a target characteristic matrix by combining the target radar motion characteristic, the target photoelectric equipment motion characteristic, the target micro-Doppler modulation characteristic and the target infrared image structure characteristic; designing a linear two-classifier by combining a Sigmoid function; and finally, carrying out classification and identification on the low-slow small target and the sea surface small target.
The method has high popularization and application values in the field of low-slow small target and sea surface small target classification and identification, can realize effective classification of the low-slow small target and the sea surface small target in an early warning detection system, and improves the accuracy of sea-air attribute identification of the target. The method for extracting the micro-Doppler modulation characteristics of the target by the outlier skewness method can accurately and effectively judge whether the micro-Doppler modulation characteristics exist in the target, and has the characteristics of good adaptivity and high extraction probability. The method for detecting the thick edge of the target infrared image by adopting the variation force field conversion method reduces the influence of the external environment and the self-motion of the target on the extraction of the structural characteristics of the target infrared image.
The present invention is described in further detail below with reference to the attached drawing figures.
Drawings
FIG. 1 is a data processing flow diagram of the present invention.
FIG. 2 is a diagram of the present invention of low-slow small-target high-repetition-frequency detection echo.
Figure 3 is a schematic diagram of the low-slow small-target micro-doppler modulation characteristic of the present invention.
Fig. 4 is a diagram of the sea surface small target high repetition frequency detection echo of the present invention.
Fig. 5 is a schematic diagram of the sea surface small target micro-doppler modulation characteristics of the present invention.
Fig. 6 is a schematic diagram of the extraction of structural features of the target infrared image according to the present invention.
Detailed Description
The method for identifying the low-slow small target and the sea surface small target based on the radar and the photoelectricity is implemented by the following specific steps of referring to the attached drawing 1.
(1) The radar guides the target of the photoelectric equipment.
The radar sends the detected track information of the target to be identified, including target batch number, direction, distance, elevation angle, course and navigational speed information to the photoelectric equipment, and the photoelectric equipment is guided to quickly capture the target to be identified.
(2) And the radar and the photoelectric equipment perform compound tracking on the target.
After the photoelectric equipment completes rapid capture of a target to be identified, tracking the target to be identified to form a target track of the photoelectric equipment, sending the target track of the photoelectric equipment, including azimuth, elevation angle, course and speed information to the radar, and performing track fusion on the target radar track and the photoelectric equipment track by the radar.
(3) And calling a radar to perform high repetition frequency detection on the target.
a) Radar target attitude resolution
Wherein theta is the attitude angle of the radar target, alpha is the heading of the radar target, and beta is the azimuth of the radar target;
b) resolving the radial movement speed of the target according to the resolved target attitude and the resolved target movement speed;
c) and selecting high repetition frequency detection parameters to perform high repetition frequency detection on the target according to the radial movement speed and the target distance of the target, so as to prevent the target from falling into a detection blind distance and blind speed.
(4) And calling the photoelectric equipment to carry out infrared imaging on the target.
a) The optoelectronic device captures a target image, calculates a definition evaluation index of the target image, adopts the sum of absolute values of 4 neighborhood gray scale differences as the evaluation index of the image definition, and for an image with N × N pixels, f (x, y) represents the pixel value of a certain point in the image, and the definition evaluation index of the image is P1:
b) adjusting the focal length in one direction, then calculating a definition index P2 at the moment, if P1> P2, adjusting the focal length in the opposite direction until the definition is poor, and at the moment, the state target image at the previous moment is clearest; if P1< P2, continuing to adjust the focal length in the direction until the definition is degraded, and the target image in the state at the previous moment is clearest;
c) capturing a clear target image, performing threshold transformation on the image, and performing threshold transformation on the image to obtain a binary image of the target; carrying out binarization on the image by setting a threshold value, and directly setting a gray value to be 0 for pixels with the gray value smaller than the threshold value; pixels with gray values greater than the threshold are set directly to 255. The threshold selection rule is as follows:
using f (i, j) {0 ≦ i < M,0 ≦ j < N } to represent the corresponding pixel value of the corresponding position of an M × N image, and the average value of all pixel sums of the whole image is:
let K be
The threshold R is:
(5) the method for extracting the target micro Doppler characteristics by the outlier skewness method comprises the following steps:
a) taking the target track distance as the center and taking 2 to the leftm1 distance unit, take 2 to the rightmA distance unit of 22mTaking the distance units as data samples M;
b) to 22mThe distance units are respectively subjected to FFT processing and modulus calculation;
c) to 2 after the module is solved2mRespectively accumulating the distance units, searching the distance unit L corresponding to the accumulation maximum value, and taking the distance unit L as the distance unit corresponding to the target;
d) taking out data corresponding to the distance unit L from the data sample M, carrying out FFT processing, carrying out modulus calculation, and taking a result after the modulus calculation as a target frequency spectrum K;
e) resolving a position corresponding to the maximum value in the frequency spectrum K, taking 3 units on the left and right of the position as the Doppler frequency spectrum of the target, and removing the Doppler frequency spectrum to obtain a target frequency spectrum K';
f) taking 5 distance units on the left and right of the distance unit L, respectively carrying out FFT processing and modulus calculation on data corresponding to the 5 distance units on the left and right, and obtaining a background frequency spectrum set { H }i},i＝L+1,L-1,L+2,L-2,L+3,L-3,L+4,L-4,l+5,L-5；
g) Respectively calculating the skewness O 'of the target spectrum K' and the background spectrum set { HiBias set of { O }i}
Where O (X) is sample data skewness, X is target region sample data, μ is sample mean, σ is sample standard deviation, E (X- μ)3Is the 3 rd order center-to-center distance of the sample data.
h) Calculating the outlier skewness O' -max { OiIf O' -max { O }i}>And 0, the micro Doppler modulation characteristics exist in the target frequency spectrum, otherwise, the micro Doppler modulation characteristics are not detected in the target frequency spectrum.
(6) And extracting structural features of the target infrared image by a variable force field conversion method.
a) Calculating the magnitude and direction of variation resultant force suffered by pixel points in the image, wherein the position in the image is riThe pixel position of is rjThe variable gravitation F of the pixel pointsi(rj) The magnitude of the force of variation and riThe gray value of a point is proportional to the point rjAnd point riThe square of the distance between them is inversely proportional, the direction of the variation attraction, i.e. the direction of the line connecting two points, is represented by the following vector:
wherein I (r)i) The representation position is a pixel point riPhi is the target attitude angle, ri-rjThe direction of the connecting line of (A) represents Fi(rj) In the vector direction, | ri-rjAnd | represents the distance between two pixels. Pixel point rjThe resultant of variation force of all pixels can be expressed as
Wherein N represents riNumber of pixels in a certain neighborhood, Fi(rj) In the direction of Fi(rj) The resultant force direction, the magnitude and direction of the resultant force of variation suffered by the pixel point are calculated as follows:
i. calculating the variation gravitation of the other point in the neighborhood of the point, and decomposing the variation gravitation along the horizontal axis and the vertical axis respectively according to the direction of the variation gravitation;
summing all other variant attractions to the point on the horizontal and vertical axes;
performing vector synthesis on the components obtained on the horizontal axis and the vertical axis to obtain the magnitude and the direction of the variation resultant force finally received by the point;
b) calculating the magnitude of the variation resultant force borne by each pixel point according to the magnitude normalization of the variation resultant force borne by all the pixel points in the image;
c) carrying out binarization processing on the normalized image to obtain an edge pixel point and a region image of a neighborhood thereof;
d) obtaining final coarse edge points according to the direction and the size of the variation resultant force, and extracting the infrared structural characteristics of the target;
e) and performing library comparison on the extracted target infrared structural features by a template matching method to obtain a target infrared structural feature comparison result.
(7) And constructing an object feature matrix.
Combining the motion characteristics of the target radar, the motion characteristics of target photoelectric equipment, the micro-Doppler modulation characteristics of the target and the structural characteristics of the target infrared image to construct a target characteristic matrix F epsilon R4×1
x1 represents the target attitude, x2 represents the target height calculated according to the radar track and the photoelectric equipment track, x3 represents the target micro-Doppler modulation characteristic, and x4 represents the target infrared structural characteristic comparison result.
(8) And (4) combining a Sigmoid function to design a linear two-classifier.
The invention adopts a linear two-classifier as a classifier to judge the multi-target attribute of the radar, and the linear two-classifier consists of an input layer, an excitation function and an output node. The input layer is composed of 4 nodes, the input layer respectively corresponds to 4 characteristics of a target one-dimensional range profile energy gathering area characteristic matrix, a Sigmoid function is used as an excitation function, and the output node y is
Wherein F ∈ R4×1Is a characteristic matrix of a one-dimensional range profile energy gathering area of the target, W belongs to R4×1Weight matrix as input layer
(9) And (4) classifying and identifying the low-slow small target and the sea surface small target.
Classifying and identifying the low-slow small target and the sea surface small target according to the value of the output contact y in the step (8)
Claims (2)
1. The method for identifying the low-slow small target and the sea surface small target based on the radar and the photoelectricity is characterized by comprising the following steps of:
(1) the target track information is shared by the photoelectric equipment, and the radar guides the photoelectric equipment to finish rapid capture on the target to be identified;
(2) then, performing track fusion on the radar track of the target and the photoelectric equipment track;
(3) resolving a target attitude according to the target radar track, resolving a radial movement speed of the target by using the resolved target attitude and the target movement speed, and selecting high repetition frequency detection parameters to perform high repetition frequency detection on the target according to the radial movement speed and the target distance of the target;
(4) calling photoelectric equipment to perform infrared imaging on a target, calculating a definition evaluation index of a target image, wherein the image definition adopts the sum of absolute values of 4 neighborhood gray differences as the evaluation index, performing focal length adjustment by using the definition evaluation index of the target image, capturing the clear target image, and performing threshold value transformation on the image to obtain a binary image of the target;
(5) combining with a target radar track, detecting the micro-Doppler modulation characteristics in a target frequency spectrum by using an outlier skewness method, and taking the target track distance as the center and 2 to the leftm1 distance unit, take 2 to the rightmA distance unit of 22mOne distance unit as data sample M, respectively for 22mPerforming FF T and modulo processing on each distance unit, and performing modulo processing on the distance unit 22mRespectively accumulating the distance units, searching the distance unit L corresponding to the accumulated maximum value, taking the distance unit L as the distance unit corresponding to the target, obtaining the frequency spectrum K of the distance unit corresponding to the target, calculating the position corresponding to the maximum value in the frequency spectrum K, taking 3 units on the left and right of the position as the Doppler frequency spectrum of the target, removing the Doppler frequency spectrum of the target to obtain a target frequency spectrum K', taking 5 distance units on the left and right of the distance unit L, respectively carrying out FFT (fast Fourier transform) processing and modulus calculation on data corresponding to the 5 distance units on the left and right, and obtaining a background frequency spectrum set { H }iH, where i is L +1, L-1, L +2, L-2, L +3, L-3, L +4, L-4, L +5, L-5, by calculating the skewness O 'of the target spectrum K' and the set of background spectra { H }iBias set of { O }iUsing the outlier bias O' -max { O }iDetecting the micro-Doppler modulation characteristics in the target frequency spectrum;
(6) calculating variation resultant force borne by pixels in an image by using a variation force field conversion method, calculating the magnitude of the variation resultant force borne by each pixel according to the magnitude normalization of the variation resultant force borne by all the pixels in the image, obtaining a final thick edge point according to the direction and the magnitude of the variation resultant force, extracting infrared structural features of a target, and performing library comparison on the extracted infrared structural features of the target by using a template matching method to obtain a comparison result of the infrared structural features of the target;
(7) target radar motion characteristic, target photoelectric equipment motion characteristic and target are combinedConstructing a target characteristic matrix F e R by using the micro Doppler modulation characteristic and the target infrared image structure characteristic4×1；
(8) Designing a linear two-classifier by using a Sigmoid function;
(9) and (4) performing low-slow small target and sea surface small target classification identification through the output result of the second classifier.
2. The radar and photoelectric based low-slow small target and sea surface small target identification method according to claim 1, characterized in that: calculating the variation resultant force borne by the pixel points in the image by using a variation force field conversion method, and calculating the position r in the imageiThe pixel position of is rjThe variable gravitation F of the pixel pointsi(rj) Wherein
I(ri) The representation position is a pixel point riPhi is the target attitude angle, ri-rjThe direction of the connecting line of (A) represents Fi(rj) In the vector direction, | ri-rjAnd l represents the distance between two pixel points, the variation attraction is decomposed along the horizontal axis and the vertical axis respectively according to the direction of the variation attraction, and all other components of the variation attraction received by the point on the horizontal axis and the vertical axis are summed to obtain the magnitude and the direction of the variation resultant received by the point finally.
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