CN110554367A - target scattering characteristic measurement interference removing method based on compressed sensing - Google Patents
target scattering characteristic measurement interference removing method based on compressed sensing Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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Abstract
The application provides a target scattering characteristic measurement interference removing method based on compressed sensing, which comprises the following steps: acquiring a two-dimensional imaging signal of a target to be detected, performing imaging processing, and marking an interference area; the interference processing based on the compressed sensing comprises the steps of carrying out redundancy on a marked interference area, improving the sparsity in the area where the interference is located, carrying out compressed sensing radar imaging on data in the redundant area, and only keeping the result in the marked interference area after imaging; processing the retained result to obtain an interference image; and canceling the interference image and the original image to obtain an imaging result after the interference is removed. By the method, certain interference components in the radar image can be removed more efficiently, the more accurate reverse RCS performance of the stealth equipment is obtained, and targeted guidance is conducted in the stages of research and development, production, service and maintenance of the stealth equipment.
Description
Technical Field
the application relates to a target scattering characteristic measuring method, in particular to a target scattering characteristic measuring interference removing method based on compressed sensing.
Background
In recent years, the stealth technology research in China is mature day by day, a large number of stealth equipment (aircrafts, ships, tanks and the like) gradually enters the stages of commissioning and train army from research and development, and the stealth performance becomes a necessary index for equipment in research. The Radar Cross Section (RCS) is a physical quantity for measuring the scattering characteristics of a Radar target, reflects the area of a detected target in a Radar field of view, and is an important index for evaluating the stealth performance of a stealth target. Whether in the development stage of stealth equipment or in the production, service and maintenance stages, the RCS level of the stealth equipment needs to be measured and evaluated. The RCS value of the equipment is lower and lower, higher requirements are put forward for the stealth performance measurement technology and the evaluation method of the stealth equipment, and the RCS measurement precision is urgently required to be further improved, and the RCS measurement error is reduced.
The RCS of the target can be directly measured under far-field conditions or compact-field conditions, but if the measurement field is not ideal, the direct measurement method can also calculate interference factors in the environment, and the accuracy of the measurement result is affected.
Another method for measuring and calculating the target RCS is as follows: firstly, a radar imaging technology is used for carrying out two-dimensional imaging on a target, and then the RCS of the target is inversely calculated through a specific algorithm according to an imaging result. The method is characterized in that the radar two-dimensional imaging result not only contains the target per se, but also contains an interference source in the environment. At this time, the imaging result can be processed, and the RCS inverse calculation is carried out after the influence of the interference source is reduced. In the process of processing the interference factors of the imaging result, the most intuitive method is to define an image processing area around the interference target, wherein the area at least comprises a main lobe of the interference target, and the window function is used for carrying out amplitude suppression on the image in the area, so that the purpose of reducing the amplitude of the interference source is achieved. However, due to the influence of the side lobe of the radar image, in a defined processing area, besides the main lobe part of the interference source, the side lobe part of the target or other effective scattering sources is also included, and meanwhile, the side lobe part of the interference source is generally difficult to be included in the processing area, so that when the RCS is finally calculated reversely, a certain error still exists between the calculated value and the RCS of the target.
the compressive sensing theory was proposed at the earliest in 2006, but the compressive sensing theory has high requirements on the memory capacity of a computer during resolving, so that the compressive sensing theory is not suitable for large scene imaging and cannot be directly used for measuring and calculating the target RCS.
Disclosure of Invention
In order to overcome the problems, the invention aims to provide a method for measuring interference removal based on compressed sensing of scattering characteristics of a target. In order to achieve the purpose, the invention adopts the following technical scheme:
Specifically, a method for removing interference based on compressed sensing target scattering characteristic measurement comprises the following steps:
S101, collecting a two-dimensional imaging signal of a target to be detected, carrying out imaging processing, and marking an interference area;
S102, interference processing based on compressed sensing, including redundancy of the marked interference region, improvement of sparsity of interference in the region, compressed sensing radar imaging of data in the redundant region, and only retaining results in the interference region marked in the step S101 after imaging;
S103, processing the reserved result in the S102 to obtain an interference image;
And S104, interference removal, namely canceling the interference image and the original image obtained in the S101 to obtain an imaging result after the interference is removed, and finishing the interference removal of the imaging result.
Further, in step S101, a matched filtering type imaging algorithm is applied to the acquired signal to obtain a target two-dimensional image, which is recorded as an original image, and an interference existence region is found and defined and recorded as area.
further, the interference existence region is directly marked by a human.
Further, in step S103, the imaging result of S102 is inverted according to a conventional matched filtering type imaging method, so that an interference image is inverted from the interference point.
Further, in step S102, the marked interference region is made redundant, specifically, the periphery of the marked interference region is appropriately enlarged.
further, the step S102 of performing redundancy on the marked interference region specifically includes selecting more valley regions around the marked interference region, avoiding the peak point of the target imaging result, and thus forming a redundant region.
According to the scheme provided by the invention, the compressed sensing radar imaging is carried out on the redundant interference existing area, the result in the defined area is reserved, the points outside the defined area are removed, the processed imaging result is inverted according to the traditional matched filtering type imaging method, the interference image obtained by inverting the interference points is obtained, the interference image and the original image are cancelled, the imaging result after the interference is removed is obtained, and the interference removal of the imaging result is finished, namely, certain interference components in the radar image can be more effectively removed after the method is adopted. Compared with the traditional method for inhibiting the influence of interference factors, in the concerned RCS interval, after the method is adopted to remove the target scattering characteristic measurement interference, the error of the RCS test result can be reduced to less than 0.5dB from the original 1-2dB, a more accurate result is obtained in the process of reverse calculation of the RCS, and targeted guidance is performed in the stages of research and development, production, service and maintenance of stealth equipment.
Drawings
fig. 1 is a flowchart of a method for removing interference based on compressive sensing for measuring scattering characteristics of a target according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of far field radar two-dimensional imaging.
Fig. 3 is an original image containing disturbance obtained in step S101.
Fig. 4 is a result of performing compressed sensing radar imaging on the redundant area' and retaining only the area in step S102.
Fig. 5 is an interference image obtained by inverting the interference points in the area region in fig. 4.
Fig. 6 is a result of removing the interference image of fig. 5 from the original image of fig. 3.
Detailed Description
The invention is further illustrated by the following figures and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Fig. 1 shows a flowchart of a method for removing interference based on compressive sensing for measuring scattering characteristics of a target, which includes the following steps:
s101, collecting a two-dimensional imaging signal of a target to be detected, carrying out imaging processing, and marking an interference area. And completing signal acquisition of two-dimensional imaging on the target to be detected, obtaining a target two-dimensional image by adopting a matched filtering type imaging algorithm, recording the target two-dimensional image as an original image, finding and defining an interference existence area, and recording the interference existence area as area. There are various methods for defining the interference existence region, and the simple method can be directly marked by manual work.
And S102, interference processing based on compressed sensing. Specifically, area of a region is subjected to redundancy, the sparsity of interference in the region is improved, and the redundant region is marked as area'; and carrying out compressed sensing radar imaging on the redundant area', only keeping the result in the area of the defined area after imaging, and removing points except the area.
And S103, obtaining an interference image. And (4) inverting the imaging result of the S102 according to a traditional matched filtering type imaging method to obtain an interference image obtained by inverting the interference point.
and S104, removing interference. And canceling the interference image and the original image obtained in the step S101 to obtain an imaging result after the interference is removed, and finishing the interference removal of the imaging result.
The above steps will be described in more detail below. Fig. 2 shows a schematic diagram of far-field radar two-dimensional imaging. The coordinate axis xOy represents a target imaging area, the axis v represents an antenna sight line direction, the observation angle θ is defined as an included angle (counterclockwise is positive) formed by rotating the axis v along the origin O, and when θ is 0 degree, the axis v coincides with the axis y. Assuming that there is a scattering intensity in the target region ofWhen the scattering point P is at an observation angle ofThe projection of the point P on the line of sight of the antennaExpressed as:
(1)
At a scanning frequency ofThe echo generated by this point P can be expressed as:
(2)
In the above formula, c represents the speed of light. Equation (2) is the case when there is only a single scattering source in the imaging volume, and if there are multiple scattering sources in the imaging volume, the echo of a single measurement should be the integral of equation (2) on the xOy plane, i.e.:
(3)
imaging a target to a spaceQuantization of m xOyThe integral, per lattice point, can be in the form of a summation:
(4)
when in use hasThe frequency of the step frequency signal of the number of the sweep frequency points is sweptA series of echoes can be obtained by measuring at different antenna positions, and equation (4) can be further written as a matrix multiplication form, and linear noise is also considered, and is expressed as:
(5)
In the above formula, vectorRepresenting echo signals at different frequencies, sampling angles, and dimensions thereof(ii) a Matrix arrayRepresenting a signal acquisition process; vector quantityrepresents a scattering distribution of the target region having a dimension of。
in step S101, signal acquisition directly obtains a signal vector。
for the radar imaging problem, namely in the known formula (5)AndUnder the condition of (1), solvingThe process of (1). In the process of obtaining the original image by matched filtering, least square model can be used to find the image in equation (5)The terms are estimated, and the expression is:
(6)
in the above formula, the first and second carbon atoms are,Which represents an estimate of the value of g,representing the conjugate transpose of matrix a. In fact, for the radar imaging problem, in equation (6)the moiety is usually irreversible or the inverse is unstable. To eliminate the effect of the irreversible part, the product with the sinc response form is usually left-multiplied simultaneously on both sides of equation (6)This is also the root cause of sidelobes in the imaging result when solving the radar imaging problem in the conventional matched filtering method. The final solution of the conventional matched filter-based imaging method is:
(7)
the compressed sensing theory shows that when a target signal has sparsity or potential sparsity, the original signal can be recovered at a high probability by combining a specific algorithm through a small amount of incoherent sampling. Therefore, in step S102, the selected area is first made redundant to increase the degree of sparseness of interference in the area, and the larger area after redundancy is referred to as area'. During redundancy, the periphery of the marked interference area can be directly and properly expanded to obtain the target interference area, and a digital image processing technology can be utilized to avoid peak points of a target imaging result and select more valley areas to finish the target interference area.
And carrying out compressed sensing radar imaging on the redundant area', only keeping the result in the area of the defined area after imaging, and removing points except the area.
a common processing method of the compressed sensing theory is to define a signal as a signal with sparse characteristics, or as a sparse signal, when only a few non-zero values or only a few large values of the signal play a major role in the domain where the signal exists. For a certain length ofNof (2) a signalIn other words, if only K non-zero values or only K larger values play a major role, thenIs a K order sparse signal. In practical measurements, most of the obtained signals are non-sparse, but generally the signals can satisfy the sparsity characteristic in a certain transformation domain. For such signalsCan be generally expressed as a sparse signalIn a certain radicalthe following transformations, namely:
(8)
While the signalthe process of sampling via a linear system can be represented as:
(9)
Substituting formula (8) for formula (9) yields:
(10)
In the formulae (9) and (10),Representing signals with sparse characteristics, matricesIs a sampling matrixAnd sparse transform matrixThe product of (a), called the perceptual matrix,is the sampled signal.
The compressed sensing theory shows that when the signal isSatisfy sparse characteristics, perceptual matrixwhen certain specific conditions are met, the system can be used,Can be measured byBy solving for optimalityThe norm is accurately reconstructed and the norm is accurately reconstructed,The solution model of norm optimization is as follows:
(11)
Under the indoor three-dimensional ISAR imaging measurement condition, an ISAR image is usually formed by only gathering a few scattering points, has sparsity, and can be used for collecting target signalsThe signal is directly used as a sparse signal to be processed without sparse transformation. I.e. the signal in equation (8)The sparse transformation matrix in the formula (10) is a sparse signalIs an identity matrix, and thus a matrixBoth sampling and sensing matrices, echo signalsAre still measured values. The solution model of equation (11) may be changed to:
(12)
Equation (12) is used for compressed sensing radar imagingand (3) a norm optimization model is used for solving the problem of the formula (5). Because the orthogonal matching pursuit algorithm has the characteristics of high algorithm convergence speed, less iteration times, good reconstruction quality and the like, the method adopts the orthogonal matching pursuit algorithm to solve the model of the formula (12).
input and output parameters of the orthogonal matching tracking algorithm and the algorithm flow are as follows:
Inputting parameters: perception matrixobservation vector (echo)Sparsity of signalm。
Outputting parameters: sparse signal estimation。
The algorithm flow is as follows:
(1) initialization: residual errorIndex set of 0 th iterationNumber of iterations；
(2) The column number of the t iteration is calculated:；
(3) order to，（representation matrixTo (1) aColumns);
(4) solving space；
(5) And (3) residual error updating:；
(6)If, ifReturning to the step (2), otherwise, entering the step (7);
(7) Will be provided withAccording to the position extension corresponding to the sequence number asand return to whereinIs t × 1 dimension.
In the above flow, step (6) as a basis for judging the end of iteration may also be replaced by other conditions according to actual use needs, such as: when the residual error is updated in step (5)Ending the iteration when the energy is smaller than the threshold value; when step (4) is carried out to solve the spaceAnd ending the iteration when the ratio of the medium maximum absolute value to the minimum absolute value reaches a certain threshold value, and the like.
Fig. 3-6 show schematic diagrams of the results of the processing performed by the method according to the invention. Fig. 3 shows an original image containing interference obtained when the target to be measured is interfered in step S101, and defines an area where the interference exists and an area' after the area is redundant to the area. Fig. 4 shows the result of compressed sensing radar imaging of the redundant area' in step S102, and retaining only the area. Fig. 5 shows an interference image obtained by inverting the interference points in the area region in fig. 4. Fig. 6 shows the result of removing the interference image in fig. 5 from the original image in fig. 3. As can be seen from fig. 6, it forms some "ripples" after the interference is removed, which have some effect on the target side lobe shape, but have less effect on its amplitude; meanwhile, the side lobe of the interference source is also suppressed, and the method more closely restores the original appearance of the two-dimensional image.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. A target scattering characteristic measurement interference removal method based on compressed sensing comprises the following steps:
s101, collecting a two-dimensional imaging signal of a target to be detected, carrying out imaging processing, and marking an interference area;
S102, interference processing based on compressed sensing, including carrying out redundancy on the marked interference region to improve the sparsity of interference in the region, carrying out compressed sensing radar imaging on data in the redundant region, and only keeping the result in the interference region marked in the step S101 after imaging;
S103, processing the reserved result in the S102 to obtain an interference image;
and S104, interference removal, namely canceling the interference image and the original image obtained in the S101 to obtain an imaging result after the interference is removed, and finishing the interference removal of the imaging result.
2. The method for removing the interference based on the target scattering characteristic measurement of the compressed sensing as claimed in claim 1, wherein: in step S101, a target two-dimensional image is obtained from the acquired signal by using a matched filtering imaging algorithm, and is recorded as an original image, and an interference existence region is found and defined and recorded as area.
3. the method for removing the interference based on the target scattering characteristic measurement of the compressed sensing as claimed in claim 2, wherein: the interference existence region is directly marked by a human.
4. The method for removing the interference based on the target scattering characteristic measurement of the compressed sensing as claimed in claim 1, wherein: in step S103, the imaging result of S102 is inverted according to a conventional matched filtering imaging method, so that an interference image is obtained by inverting the interference point.
5. The method for removing the interference based on the target scattering characteristic measurement of the compressed sensing as claimed in claim 1, wherein: in step S102, the marked interference region is made redundant, specifically, the periphery of the marked interference region is appropriately enlarged.
6. the method for removing the interference based on the target scattering characteristic measurement of the compressed sensing as claimed in claim 1, wherein: in step S102, the marked interference region is subjected to redundancy, specifically, around the marked interference region, a peak point of the target imaging result is avoided, and more valley regions are selected, so as to form a redundant region.
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