CN110554384A - imaging method based on microwave signal - Google Patents
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- CN110554384A CN110554384A CN201911006452.XA CN201911006452A CN110554384A CN 110554384 A CN110554384 A CN 110554384A CN 201911006452 A CN201911006452 A CN 201911006452A CN 110554384 A CN110554384 A CN 110554384A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/0507—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves using microwaves or terahertz waves
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4058—Detecting, measuring or recording for evaluating the nervous system for evaluating the central nervous system
- A61B5/4064—Evaluating the brain
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
Abstract
The invention discloses an imaging method based on microwave signals, which comprises the following steps: transmitting a microwave signal to a detected object; collecting echo signals of the microwave signals; sampling the acquired echo signals to form an echo matrix; expanding the echo matrix, sequentially carrying out signal screening, time delay compensation and superposition processing on the expanded echo matrix, and generating a two-dimensional confocal image of the detected object according to the echo matrix subjected to superposition processing; and performing height-direction three-dimensional imaging on all the two-dimensional confocal images to generate a three-dimensional image of the detected object. The imaging method based on the microwave signals provided by the embodiment of the invention is based on a method for transmitting the microwave signals to the detected object, and realizes three-dimensional rapid reconstruction imaging of the detected object by a two-dimensional confocal and group-sparse-based height-direction imaging method.
Description
Technical Field
the invention relates to the technical field of electromagnetic wave imaging, in particular to an imaging method based on microwave signals.
background
A number of imaging techniques are known, such as X-ray, laser, acoustic, microwave, etc., but the interaction of the chosen information carrier with the target is different. However, microwave imaging relies on the interaction between electromagnetic waves and the imaged object, and information of the imaged object is mined and extracted from the received scattered echo signals, so as to reconstruct the characteristics of the imaged object. Because of the large impedance difference between different media, an object with a plurality of different dielectric layers can detect the impedance change of different dielectric layers under the action of microwaves, which is very obvious. How to apply the microwave technology to object imaging is an important issue to be researched urgently.
Disclosure of Invention
it is an object of the present invention to provide a new solution for imaging based on microwave signals. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
According to an aspect of an embodiment of the present invention, there is provided a microwave signal-based imaging method, including: transmitting a microwave signal to a detected object;
Collecting echo signals of the microwave signals;
Sampling the acquired echo signals to form an echo matrix;
expanding the echo matrix, sequentially carrying out signal screening, time delay compensation and superposition processing on the expanded echo matrix, and generating a two-dimensional confocal image of the detected object according to the echo matrix subjected to superposition processing;
And performing height-direction three-dimensional imaging on all the two-dimensional confocal images to generate a three-dimensional image of the detected object.
further, the height-wise three-dimensional imaging is performed on all the two-dimensional confocal images to generate a three-dimensional image of the detected object, and the method includes:
constructing a signal measurement vector;
And performing minimum dimension reduction processing and optimal estimation of scattering signals on the signal measurement vector to complete a three-dimensional height direction reconstruction model, and comparing scattering parameters of all scattering points in the three-dimensional height direction reconstruction model to form a group of three-dimensional image data so as to realize three-dimensional image imaging of the detected object.
Further, the optimal estimation of the scattering signal comprises performing optimal estimation of the scattering signal using bidirectional regression with AIC criteria.
further, the signal measurement vector is expressed as g ═ R γ + epsilon
Where g is a signal measurement vector of length N, R is an N × L matching matrix, γ represents a matrix formed by scattering signal parameters of scattering points included in all two-dimensional confocal images, and ε represents an error.
Further, the performing minimization dimension reduction processing on the signal measurement vector comprises:
Complex data reconstruction is realized through group sparseness, and an equation in a complex form of the signal measurement vector is converted into an equation in a real form;
Realizing mixed L by Group-BP algorithm1And (5) minimizing the norm and reducing the dimension to obtain the initial estimation of the position of the scattering point in the signal measurement vector.
further, the implementing complex data reconstruction by group sparseness converts an equation in a complex form of the signal measurement vector into an equation in a real form, including:
Is provided with
Rr＝real(R)，Ri＝imag(R)，
gr＝real(g),gi＝imag(g)
Then
gr＝Rr×γr-Ri×γi+εr
gi＝Ri×γr+Rr×γi+εi
Wherein R isrrepresents the real part of R; riRepresents the imaginary part of R; gamma rayrRepresents the real part of gamma; gamma rayiRepresents the imaginary part of γ; epsilonrRepresents the real part of epsilon; epsiloniRepresents the imaginary part of ε;
wherein the content of the first and second substances,
Further, the mixing L is realized by a Group-BP algorithm1and (3) minimizing the norm and reducing the dimension to obtain a preliminary estimation of the position of the scattering point in the signal measurement vector, wherein the preliminary estimation comprises the following steps:
An estimated value of gamma is
Wherein | · | purple sweetFis the Flobenius norm, λKIs a hyperparameter balancing model errors and sparsity of Γ | · | | | luminance2,1Is L of each row defined as Γ2mixed norm L of the sum of norms2,1Γ ═ γ; penalty function
further, the optimal estimation of the scatter signal comprises: and screening vector positions, and eliminating false positions in the scattering point position estimation to obtain the optimal scattering point quantity and position estimation.
Further, the echo matrix is denoted as e ═ α1,α2,...,αM]Tthe echo matrix is an M multiplied by N matrix;
Wherein alpha isi＝[ei1,ei2,...,eiN],i＝1,2,...,M
αirepresenting the strength of the echo signal received at time i.
Further, the signal screening is performed on the expanded echo matrix in sequence, and the method includes:
and setting a threshold, and screening the expanded echo matrix by using the threshold to obtain an output signal.
According to another aspect of the embodiments of the present invention, there is provided an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the microwave signal-based imaging method.
According to another aspect of embodiments of the present invention, there is provided a non-transitory computer-readable storage medium having stored thereon a computer program, which is executed by a processor, to implement the microwave signal-based imaging method.
the technical scheme provided by the embodiment of the invention has the following beneficial effects:
The imaging method based on the microwave signals provided by the embodiment of the invention is based on a method for transmitting the microwave signals to the detected object, and realizes three-dimensional rapid reconstruction imaging of the detected object by a two-dimensional confocal and group-sparse-based height-direction imaging method.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of one embodiment of the present application;
FIG. 2 is a schematic view of the layered structure of an object to be inspected;
fig. 3 is a schematic diagram of the microwave imaging geometry.
Detailed Description
in order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including 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. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As shown in fig. 1, an embodiment of the present application provides a method of imaging based on microwave signals, including:
S1, generating a microwave signal by a microwave signal generating device, and transmitting the microwave signal to the detected object by a microwave signal transmitting device;
S2, forming a scattered field around the detected object under the action of the microwave signal, collecting an echo signal of the scattered field by the receiving antenna array, and transmitting the received echo signal to the microwave signal processing and analyzing device;
And S3, the microwave signal processing and analyzing device acquires the echo signals, processes the continuously received echo signals and generates a three-dimensional image of the detected object.
The microwave signal transmitting device is a transmitting antenna array formed by a plurality of antenna units. The microwave signal acquisition device is a receiving antenna array formed by a plurality of antenna units. The microwave signal processing and analyzing device can adopt a computer.
Microwave imaging is to mine and extract information of an imaging object from a received scattered echo signal by depending on interaction between microwaves and the imaging object, and further reconstruct characteristics of the imaging object. Since different tissues have a large impedance difference and the same tissue has different impedance in different physiological states, the abnormal tissue has a significant change from the normal impedance. For example, the internal structure of the brain is imaged and its lamellar model is shown in fig. 2.
Assuming that uniform plane waves are vertically incident, the propagation direction is + z direction, the interface of air and skin is zero point, and the electric field of incident waves is Eina transmitted wave EtExpressed as:
Wherein the content of the first and second substances,Is the transmission coefficient at the interface of the ith layer and the (i + 1) th layer,In order to be the phase position,is the intrinsic impedance of the i-th layer tissue, μ0is a vacuum permeability of epsiloniis the dielectric constant, σ, of the i-th layer structureiIs the electrical conductivity of the ith layer of tissue, ω is the angular frequency;Is the scattering coefficient of the i-th layer tissue, muiorganized as layer iMagnetic conductivity; diis a transmission path in the i-th layer organization. The phase change of the transmitted wave relative to the incident wave is:
From the above equation, the phase of the transmitted wave is related to the operating frequency of the microwave source, the propagation path of the electromagnetic wave, and the dielectric constant of the object to be detected.
Therefore, under the action of the microwave signal, the dielectric properties of various tissues in the complex medium determine the propagation and feedback of the microwave signal. In view of the above difference, the inside of the object to be detected can be imaged by using microwaves.
If multiple layers of antenna elements are arranged at multiple height levels, a three-dimensional spatial spectrum can be obtained. According to the three-dimensional space spectrum structure, amplitude and phase processing is carried out on the two-dimensional array echo signals in the time-frequency domain, and two-dimensional resolution of distance and direction can be achieved. If the height direction resolving power needs to be further improved, height direction focusing needs to be achieved by combining methods such as classical spectrum estimation or super-resolution spectrum estimation.
A signal model of three-dimensional imaging is introduced by taking dynamic synthetic aperture quasi-transient high-precision three-dimensional imaging as an example. FIG. 3 is a schematic diagram of imaging geometry, assuming that the curvilinear motion of the antenna elements at the periphery of the cavity is equivalent to a uniform linear motion along the x-direction, umIndicating the azimuth position, v, of the antenna element at the moment of the m-th pulsenRepresents the cross-heading (y-axis/range) position, H, of the nth cellnindicating the height-wise position of the nth cell.
According to the geometrical relationship, the echo of the object Q obtained by the mth pulse of the nth unit at the fast time t is represented as the superposition of all point target echo signals:
Wherein σithe scattering coefficient of the object is represented, p (t) represents the emission signal, and tau is the echo time delay:
Where c is the speed of light.
first, the echo signal is Fourier transformed with respect to t, and f is transformedtmapping to the wavenumber domain, i.e. kt＝4πftand c, then:
Wherein, P (k)t) To transmit a signal spectrum, typically a chirp signal.
Next, the pair of equations (5) is related to the azimuth direction umperforming a Fourier transform to obtain:
Then, the distance wave number domain and the azimuth wave number domain of the formula (6) are solved by using the stationary phase principle, and the following results are obtained:
Substituting formula (7) for formula (6) yields:
Finally, the above equation can be approximated as:
By substituting and simplifying formula (9) for formula (8):
Wherein the content of the first and second substances,
thus, the scene echo undergoes a two-dimensional Fourier transform in the form P0(kz,ku,kv) Multiplication with scene space spectrum, and three-dimensional imaging process only needs to be carried out on P0(kz,ku,kv) And performing matched filtering on the function, and then performing inverse Fourier transform to obtain the backscattering coefficient distribution of the scene. It should be noted that the above spatial spectrum sampling is non-uniform, interpolation processing is required in the imaging process, the spatial spectrum is sampled to uniform sampling points, and then imaging is performed by using IFFT, and the corresponding interpolation process has a large amount of calculation. Therefore, the three-dimensional reconstruction can adopt the process of firstly two-dimensional imaging and then elevation reconstruction, and the calculation burden can be greatly reduced.
the different electromagnetic scattering properties of the tissue inside complex dielectric objects are fundamental principles and prerequisites for microwave imaging. This means that the electromagnetic scattering properties will directly have a large impact on the subsequent microwave imaging performance. Electromagnetic scattering properties include media type, frequency, amplitude, angle, etc.; and the imaging performance comprises indexes such as resolution, imaging speed, reconstruction error, false target and the like. Imaging performance has some specific deterministic relationship to electromagnetic scattering properties. In the embodiment, the relationship between the multidimensional scattering characteristic and the imaging performance is analyzed by means of a computer, a semi-physical simulation platform and the like, and a quantitative relationship between the multidimensional scattering characteristic and the imaging performance is found.
since microwave imaging requires good penetration and high distance resolution, an antenna element having ultra-wideband characteristics is generally required. The units need to be uniformly arranged in a limited space position range, the mutual coupling degree of the units is increased due to the fact that the physical distance between the antenna units is short, the closer the arrangement is, the stronger the coupling is, and the corresponding technical cost is also high.
In general, the number of cells can be reduced in two ways. Firstly, the relation between the multi-directional scattering, projection and reflection characteristics in the medium and the imaging performance is researched, and the number and the positions of the units are optimized according to the relation. Secondly, the information content of the observation matrix is increased through entropy expansion, namely, a non-uniformly spaced sparse array is adopted, on the premise that the aperture is not changed, the antenna arrays are arranged sparsely, and the actual number of antenna units is reduced through the research of a unit entropy expansion algorithm. The first path is mainly completed in the imaging mechanism part; the second approach is an entropy expansion-based antenna array sparsity optimization algorithm, which is described in detail below.
The microwave signal acquisition device adopts a receiving antenna array comprising a plurality of receiving antenna units.
The step of acquiring the state information of the scattered field by a processing and analyzing module of the microwave signal processing and analyzing device comprises the following steps:
(1) The receiving antenna array collects an initial echo signal; the echo signal e of the detected object acquired by the microwave signal acquisition device at the moment t is assumed;
(2) And carrying out subtraction operation on the acquired initial signals, eliminating noise and obtaining the scattering echo signals only containing the information of the detected object.
the microwave signal processing and analyzing device acquires the echo signals, processes the continuously received echo signals and generates a three-dimensional image of the detected object, and comprises:
s31, sampling the echo signals by a microwave signal processing and analyzing device to form an echo matrix; expanding the echo matrix, sequentially carrying out signal screening, time delay compensation and superposition processing on the expanded echo matrix, and generating a two-dimensional confocal image of the detected object according to the echo matrix subjected to superposition processing;
Specifically, the method comprises the following steps:
And assuming that the number of the receiving antenna units is N, performing time sampling on the scattering echo signals collected by the receiving antenna array, wherein the number of sampling points is M.
Respectively sampling N groups of echo signals, and combining echo signal data into an M multiplied by N matrix (echo matrix) as follows:
Wherein alpha isiReceiving the strength of echo signals at the same sampling point i moment for each receiving antenna unit;
And performing L-time expansion processing on the echo signals to obtain virtual antenna units which are equivalent to L times of the number of the receiving antenna units so as to improve the imaging precision.
in the case of signal dispersion, the finite dimensional probability space can be expressed as follows:
wherein, ai(i 1, 2.. n.) denotes the individual element of the signal output with a probability P (a)i). X represents a discrete memoryless signal array. The above equation can be expressed as a mathematical model of a discrete memoryless signal array that satisfies the complete set of conditions:
the average uncertainty of the signal is represented by an information entropy, which is defined as:
For the output element sequence of the discrete memoryless signal array X, a plurality of groups of sequences with the length L can be used for representing, thereby forming a new signal; the output elements of the new signal are random sequences of length L and are statistically independent from one element to another. It is called L-time expansion signal of discrete memoryless signal array X, described by L-probability space and marked as XL
wherein, biIs a random sequence of length L composed of elements in the discrete memoryless signal X and can be recorded asP(bi) Is b isiCan be expressed as follows:
l-order spread signal X of discrete memoryless signal XLThe probability space of (a) also satisfies the complete set condition.
Expanding the signal X according to the definition of the entropy of the informationLthe entropy of (A) is:
Where the summation sign may be equivalent to L groups of summations, and each group is summing n output elements in the discrete signal X.
(4) Setting a threshold Z (i), screening the expanded echo matrix by using the threshold Z (i) to obtain an output signal as follows:
eZ(i，j)＝Z(i)·e(i，j)， (20)
Wherein the content of the first and second substances,
By the imaging configuration self-adaptive optimization theoretical algorithm, the detection positioning precision and the imaging quality can be improved, the number of antenna units under the condition of limited space area is reduced, the mutual coupling among the antenna units is reduced, and the hardware cost is also reduced for the research and development of the microwave imaging method in practical application.
(5) performing two-dimensional confocal imaging by using the output signal;
Microwave confocal imaging mainly adopts a large number of receiving antenna units to be placed on a focal plane for focusing, and imaging is carried out by means of echo signals received by the large number of receiving antenna units. This gaze-based focal plane technique enables real-time imaging.
the flow of the two-dimensional confocal imaging algorithm comprises the following steps:
And calculating the time delay from each point in the detection area to each receiving antenna unit, performing time delay compensation and superposition processing on the scattered echo signals received by each receiving antenna unit, and generating a two-dimensional confocal image according to energy distribution information obtained after signal superposition.
And S32, performing height direction three-dimensional imaging on all two-dimensional confocal images by using a height direction imaging method based on group sparsity, and generating a three-dimensional image of the detected object.
Step S32 includes:
s321, constructing a signal measurement vector:
g＝Rγ+ε (19)
Where g is a signal measurement vector of length N, R is an N × L matching matrix (partial fourier transform matrix), γ represents a matrix formed by scattering signal parameters of scattering points included in all two-dimensional confocal images, and ε represents an error. The number of scattering points is denoted as K.
S322, performing minimum dimension reduction processing and optimal estimation of scattering signals on the signal measurement vectors to complete a three-dimensional height direction reconstruction model, and forming a group of three-dimensional image data by comparing scattering parameters of K scattering points in the three-dimensional height direction reconstruction model so as to realize three-dimensional image imaging of the detected object;
Specifically, the signal measurement vector g is subjected to minimum dimensionality reduction, and mixed L1And performing minimum dimensionality reduction processing on the norm, performing optimal estimation on a scattering signal by using an AIC (an Information Criterion by Akaike) Criterion bi-directional regression, completing a three-dimensional height direction reconstruction model, and forming a group of three-dimensional image data by comparing scattering parameters of K scattering points in the three-dimensional height direction reconstruction model, thereby realizing the three-dimensional image imaging of the detected object.
Step S322 specifically includes:
(1) Complex data reconstruction is realized through group sparseness, and an equation in a complex form of the signal measurement vector is converted into an equation in a real form;
Is provided with
equation (21) then translates to:
Wherein R isrRepresents the real part of R; rirepresents the imaginary part of R; gamma rayrrepresents the real part of gamma; gamma rayiRepresents the imaginary part of γ; epsilonrRepresents the real part of epsilon; epsiloniRepresents the imaginary part of ε;
Therefore, the first and second electrodes are formed on the substrate,
Wherein the content of the first and second substances,thus, 2N sample points are obtained, again with the above equation being one N<<L, underdetermined system of equations. Each column of B is the result of the superposition of echoes from elevation to the location of the same scatter point. Therefore, the number of non-zero rows of B is also limited in terms of rows, and this characteristic of the signal is referred to as group sparseness. In this mode, the equation in complex form of the signal measurement vector is converted to an equation in real form.
Mixing L using group sparsity characteristics of signals1And solving the minimization of the norm. Assuming that the number of scattering points included in γ is K (i.e., the sparsity is K), the observation length is N. After transformation, the sparsity becomes 2K and the signal measurement vector length becomes 2N. When the data is transformed into a group structure, the estimates for the non-zero elements become estimates for the non-zero group vectors, and the number of non-zero group vectors in γ is defined as the group sparsity K. Since the nonzero elements of the real part signal and the imaginary part signal are at the same elevation position, 2K nonzero elements are divided into K groups, two-by-two highly-related nonzero element variables appear in the same group, and the group sparsity K. For the condition that the dimensionality of a signal measurement vector is increased to 2N and the group sparsity is not increased, the model has a group effect, compared with a conventional sparse model, the performance of non-zero element variable estimation is improved, and the height can be improvedrange resolution capability.
(2) Realizing mixed L by Group-BP algorithm1and (5) minimizing the norm and reducing the dimension to obtain a preliminary estimation of the positions and the quantity of scattering points in the signal measurement vector.
for the solution of γ, since it is sparsely distributed in elevation, the concept of using compressed sensing can be used to solve γ by using L1Norm regularization is used for minimizing residual quantity, prior information of the number of scattering points is not needed, and the algorithm is called a basis pursuit algorithm. After dimension reduction, a more robust and reliable estimate of the position of the scattering point is obtained, although some outliers of the estimated position due to noise may exist.
to solve for, the estimated value of γ can be estimated using the group-BP algorithm:
Wherein | · | purple sweetFIs the Flobenius norm, λKIs a hyperparameter balancing model errors and sparsity of Γ | · | | | luminance2,1Is L of each row defined as Γ2mixed norm L of the sum of norms2,1Wherein G is a matrix formed by G, Γ ═ γ, and a penalty function responsible for improving group sparsity is:
By selecting the corresponding nonzero row vector in the gamma, the measurement matrix R can obtain obvious dimensionality reduction, so that an underdetermined equation of the original equation is converted into an overdetermined equation, and operability is provided for accurate estimation of the position of the scattering point.
(3) And screening vector positions, and eliminating false positions in the scattering point position estimation to obtain the optimal scattering point quantity and position estimation.
Mixing L1norm minimization shrinks the dimension of R significantly, resulting in a preliminary sparse estimate of the scattering point location. However, this estimate may still existThe sparsity K is often overestimated with respect to outliers of a location. And eliminating false and unimportant vector positions in the scattering point position estimation, and finally obtaining the most possible sparsity K estimation and scattering point position result in an azimuth distance unit.
a model that adapts better to the data requires more complexity, which in turn requires more parameters. Therefore, a trade-off needs to be made between model complexity and model adaptability. To achieve balance, define the AIC (an information criterion by Akaike) statistic:
Wherein constant represents a constant; a model with the smallest AIC is more preferred. In the variable screening process, two-way regression is adopted. Assuming that there is a set of candidate variables, starting from a model containing fewer variables or a model with more variables, in each step one of the following three operations is performed:
a. when there is some variable in the candidate variable set that is not in the current model and is added to the current model to reduce the AIC, the variable that minimizes the AIC is added.
b. if a certain variable exists in the current model, the current model is removed to reduce the AIC, and the variable which enables the AIC to be minimum is removed; otherwise, stopping.
After the series of processing, the optimal scattering point number and position estimation are obtained, the dimension of the matching matrix R is reduced to the least optimal combined matrix, and the optimal combined matrix is expressed as a measurement matrix R'.
(4) parameter estimation, height dimension focusing.
The measurement matrix R' is obtained by establishing, which is an N × K matrixFinally, the reconstruction of the scattering signal parameter matrix gamma of K scattering points relies on solving the following overdetermined system of equations:
Here, the LARS algorithm (Least Angle Regression) is introduced, which is more friendly, fast and concise in linear Regression path, can remove possible outliers, optimize the result of compressive sensing, and provide more accurate amplitude and phase estimation.
The imaging method based on the microwave signal provided by the embodiment of the invention can be applied to detection imaging of various objects, such as human head detection imaging, breast detection imaging and the like, so as to detect the change of internal tissue structures of human bodies.
according to another aspect of the embodiments of the present invention, there is provided an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the microwave signal-based imaging method.
According to another aspect of embodiments of the present invention, there is provided a non-transitory computer-readable storage medium having stored thereon a computer program, which is executed by a processor, to implement the microwave signal-based imaging method.
The imaging method based on the microwave signals provided by the embodiment of the invention is based on a method for transmitting the microwave signals to the detected object, and realizes three-dimensional rapid reconstruction imaging of the detected object by a two-dimensional confocal and group-sparse-based height-direction imaging method.
It should be noted that:
The term "module" is not intended to be limited to a particular physical form. Depending on the particular application, a module may be implemented as hardware, firmware, software, and/or combinations thereof. Furthermore, different modules may share common components or even be implemented by the same component. There may or may not be clear boundaries between the various modules.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
the various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the creation apparatus of a virtual machine according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
it should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The above-mentioned embodiments only express the embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (12)
1. a method of imaging based on microwave signals, comprising:
transmitting a microwave signal to a detected object;
Collecting echo signals of the microwave signals;
Sampling the acquired echo signals to form an echo matrix;
expanding the echo matrix, sequentially carrying out signal screening, time delay compensation and superposition processing on the expanded echo matrix, and generating a two-dimensional confocal image of the detected object according to the echo matrix subjected to superposition processing;
And performing height-direction three-dimensional imaging on all the two-dimensional confocal images to generate a three-dimensional image of the detected object.
2. The method of claim 1, wherein the height-wise three-dimensional imaging of all the two-dimensional confocal images to generate a three-dimensional image of the detected object comprises:
constructing a signal measurement vector;
and performing minimum dimension reduction processing and optimal estimation of scattering signals on the signal measurement vector to complete a three-dimensional height direction reconstruction model, and comparing scattering parameters of all scattering points in the three-dimensional height direction reconstruction model to form a group of three-dimensional image data so as to realize three-dimensional image imaging of the detected object.
3. the method of claim 2, wherein the optimal estimation of the scatter signal comprises performing an optimal estimation of the scatter signal using AIC criterion bi-directional regression.
4. The method of claim 2, wherein the signal measurement vector is represented as
g＝Rγ+ε
where g is a signal measurement vector of length N, R is an N × L matching matrix, γ represents a matrix formed by scattering signal parameters of scattering points included in all two-dimensional confocal images, and ε represents an error.
5. The method of claim 4, wherein the minimizing dimensionality reduction processing of the signal measurement vector comprises:
Complex data reconstruction is realized through group sparseness, and an equation in a complex form of the signal measurement vector is converted into an equation in a real form;
Realizing mixed L by Group-BP algorithm1Norm minimization and dimension reduction to obtainTo a preliminary estimate of the location of the scattering point in the signal measurement vector.
6. The method of claim 5, wherein the performing complex data reconstruction by sparse set of data converts an equation in complex form of the signal measurement vector to an equation in real form, comprising:
Is provided with
Rr＝real(R)，Ri＝imag(R)，
gr＝real(g),gi＝imag(g)
Then
gr＝Rr×γr-Ri×γi+εr
gi＝Ri×γr+Rr×γi+εi
Wherein R isrRepresents the real part of R; riRepresents the imaginary part of R; gamma rayrrepresents the real part of gamma; gamma rayiRepresents the imaginary part of γ; epsilonrRepresents the real part of epsilon; epsiloniRepresents the imaginary part of ε;
G＝R'B+E
Wherein the content of the first and second substances,
7. The method of claim 5, wherein the implementing of the mixed L is performed by a Group-BP algorithm1And (3) minimizing the norm and reducing the dimension to obtain a preliminary estimation of the position of the scattering point in the signal measurement vector, wherein the preliminary estimation comprises the following steps:
An estimated value of gamma is
Wherein | · | purple sweetFIs Flobeniluse norm, λKIs a hyperparameter balancing model errors and sparsity of Γ | · | | | luminance2,1Is L of each row defined as Γ2Mixed norm L of the sum of norms2,1Γ ═ γ; penalty function
8. The method of claim 2, wherein the optimal estimation of the scatter signal comprises: and screening vector positions, and eliminating false positions in the scattering point position estimation to obtain the optimal scattering point quantity and position estimation.
9. The method of claim 1, wherein the echo matrix is represented as
e＝[α1,α2,...,αM]TThe echo matrix is an M multiplied by N matrix;
Wherein alpha isi＝[ei1,ei2,...,eiN],i＝1,2,...,M
αiRepresenting the strength of the echo signal received at time i.
10. The method of claim 1, wherein the sequential signal screening of the expanded echo matrix comprises:
And setting a threshold, and screening the expanded echo matrix by using the threshold to obtain an output signal.
11. an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of any one of claims 1-10.
12. a non-transitory computer readable storage medium having stored thereon a computer program, characterized in that the program is executed by a processor to implement the method according to any one of claims 1-10.
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