CN108562897B - Structure sparse imaging method and device of MIMO through-wall radar - Google Patents

Structure sparse imaging method and device of MIMO through-wall radar Download PDF

Info

Publication number
CN108562897B
CN108562897B CN201810079149.1A CN201810079149A CN108562897B CN 108562897 B CN108562897 B CN 108562897B CN 201810079149 A CN201810079149 A CN 201810079149A CN 108562897 B CN108562897 B CN 108562897B
Authority
CN
China
Prior art keywords
extended target
scattering coefficient
pseudo
sparse
random
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201810079149.1A
Other languages
Chinese (zh)
Other versions
CN108562897A (en
Inventor
晋良念
戴耀辉
谢辉玉
纪元法
孙希延
刘庆华
谢跃雷
蒋俊正
欧阳缮
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guilin University of Electronic Technology
Original Assignee
Guilin University of Electronic Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guilin University of Electronic Technology filed Critical Guilin University of Electronic Technology
Priority to CN201810079149.1A priority Critical patent/CN108562897B/en
Publication of CN108562897A publication Critical patent/CN108562897A/en
Application granted granted Critical
Publication of CN108562897B publication Critical patent/CN108562897B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G01S13/887—Radar or analogous systems specially adapted for specific applications for detection of concealed objects, e.g. contraband or weapons
    • G01S13/888—Radar or analogous systems specially adapted for specific applications for detection of concealed objects, e.g. contraband or weapons through wall detection
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention is suitable for the field of through-wall radar imaging, and provides a structure sparse imaging method and device of an MIMO through-wall radar. The method comprises the following steps: sparse transformation is carried out on echo signals of an extended target collected by the MIMO through-wall radar array, a pseudo-random measurement matrix based on a pseudo-random m sequence is constructed for compression sampling, and a perception matrix is further constructed; obtaining an initial value of a scattering coefficient of an extended target according to a back projection imaging algorithm, and estimating an index set for an orthogonal matching pursuit compressed sensing algorithm; fully considering the structure sparse prior information among pixels according to the one-by-one transfer process of the first-order neighborhood of the Markov random field to obtain a new index set; and then recalculating the scattering coefficient of the extended target until a convergence condition is met, and using the scattering coefficient of the extended target at the moment for two-dimensional imaging. The invention does not need to manually set the partitioning parameters, has small required storage space, reduces the computation amount and the system complexity and is easy to realize hardware.

Description

Structure sparse imaging method and device of MIMO through-wall radar
Technical Field
The invention belongs to the field of through-wall radar imaging, and particularly relates to a structure sparse imaging method and device of an MIMO through-wall radar.
Background
Multiple Input Multiple Output (MIMO) through-wall radar imaging is a novel technology that can acquire scene information behind a wall by using the propagation characteristics of electromagnetic waves, and detect, identify and image hidden targets behind the wall, and has a wide application prospect in military and civil aspects. At present, when an extended target is mostly imaged by adopting a structure recovery algorithm of an MIMO through-wall radar imaging system, some methods do not use a Compressed Sensing (CS) structure sparse imaging method, however, the required antenna aperture and bandwidth are large, the data operation amount is large, and the imaging resolution is not high. The other method is that a compressed sensing structure sparse imaging method is used, but a Gaussian random measurement matrix which is not beneficial to hardware realization and has a large storage space is adopted, so that the operation complexity is high, the imaging position deviates, the blocking parameters need to be manually set, and the blocking prior information is difficult to accurately know in practical application.
Disclosure of Invention
The invention aims to provide a structure sparse imaging method, a structure sparse imaging device and a computer readable storage medium of an MIMO through-wall radar, aiming at solving the problems that a compressed sensing structure sparse imaging method is not used, the required antenna aperture and bandwidth are large, the data operation amount is large, and the imaging resolution ratio is not high; the compressed sensing structure sparse imaging method is used, but a Gaussian random measurement matrix which is not beneficial to hardware realization and large in storage space is adopted, so that the operation complexity is high, the imaging position deviates, the blocking parameters need to be manually set, and the problem that the prior information of the blocking is difficult to accurately know in practical application is solved.
In a first aspect, the present invention provides a structural sparse imaging method for a MIMO through-wall radar, where the method includes:
receiving echo signals of an extended target collected by the MIMO through-wall radar array;
echo signals of the extended target by using sparse dictionary
Performing sparse transformation, constructing a pseudo-random measurement matrix based on a pseudo-random m sequence, performing compression sampling, and further constructing a sensing matrix according to the pseudo-random measurement matrix and a sparse dictionary;
obtaining an initial value of a scattering coefficient of an extended target according to a back projection imaging algorithm, and estimating an index set for an orthogonal matching pursuit compressed sensing algorithm according to a sensing matrix and the initial value of the scattering coefficient of the extended target;
fully considering the structure sparse prior information among pixels according to the one-by-one transfer process of the first-order neighborhood of the Markov random field to obtain a new index set;
and recalculating the scattering coefficient of the extended target by adopting an orthogonal matching pursuit compressed sensing algorithm according to the new index set until a convergence condition is met, and using the scattering coefficient of the extended target at the moment for two-dimensional imaging.
In a second aspect, the present invention provides a structural sparse imaging apparatus for a MIMO through-wall radar, the apparatus comprising:
the receiving module is used for receiving echo signals of the extended target collected by the MIMO through-wall radar array;
the construction module is used for carrying out sparse transformation on the echo signals of the extended target by adopting a sparse dictionary, constructing a pseudo-random measurement matrix based on a pseudo-random m sequence for compression sampling, and further constructing a perception matrix according to the pseudo-random measurement matrix and the sparse dictionary;
the estimation module is used for obtaining an initial value of a scattering coefficient of an extended target according to a back projection imaging algorithm and estimating an index set for an orthogonal matching pursuit compressed sensing algorithm according to a sensing matrix and the initial value of the scattering coefficient of the extended target;
the new index set generation module is used for fully considering the structure sparse prior information among the pixels according to the one-by-one transfer process of the first-order neighborhood of the Markov random field to obtain a new index set;
and the extended target scattering coefficient calculating module is used for recalculating the extended target scattering coefficient by adopting an orthogonal matching pursuit compressed sensing algorithm according to the new index set until a convergence condition is met, and using the extended target scattering coefficient at the moment for two-dimensional imaging.
In a third aspect, the present invention provides a computer-readable storage medium storing a computer program, which when executed by a processor, implements the steps of the structural sparse imaging method for MIMO through-the-wall radar as described above.
In the invention, a pseudo-random measuring matrix based on a pseudo-random m sequence and a one-by-one transmission process according to a Markov random field first-order neighborhood are adopted to fully consider the structure sparse prior information among pixels to obtain a new index set; and recalculating the scattering coefficient of the extended target by adopting an orthogonal matching pursuit compressed sensing algorithm according to the new index set until a convergence condition is met, and using the scattering coefficient of the extended target at the moment for two-dimensional imaging. Therefore, the invention does not need to manually set the blocking parameters, has small required storage space, reduces the operation amount and the system complexity, is easy to realize hardware, improves the sparse reconstruction performance of the sparse imaging algorithm of the extended target, and realizes the high-resolution through-the-wall extended target imaging.
Drawings
Fig. 1 is a flowchart of a structural sparse imaging method of a MIMO through-wall radar according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of an imaging scene of a MIMO through-wall radar array.
Fig. 3 is a graph comparing the Point Spread Function (PSF) and the azimuth peak amplitude at (0, 1.6) for MIMO through-wall radar arrays and equivalent virtual arrays.
FIG. 4 is a diagram of a Markov random field first order neighborhood pass-by-pass process.
Fig. 5 is a functional block diagram of a structural sparse imaging device of a MIMO through-wall radar according to a second embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly apparent, the present invention is described in further detail below with reference to the accompanying drawings and 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.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
The first embodiment is as follows:
referring to fig. 1, a structural sparse imaging method of a MIMO through-wall radar according to an embodiment of the present invention includes the following steps: it should be noted that, if the result is substantially the same, the structural sparse imaging method of the MIMO through-wall radar of the present invention is not limited to the flow sequence shown in fig. 1.
S101, receiving echo signals of the extended target collected by the MIMO through-wall radar array.
In the first embodiment of the invention, the MIMO through-wall radar array is controlled by a computer to acquire echo signals of an extended target.
S102, sparse transformation is carried out on the echo signals of the expansion target by adopting a sparse dictionary, a pseudo-random measurement matrix based on a pseudo-random m sequence is constructed for compression sampling, and then a perception matrix is constructed according to the pseudo-random measurement matrix and the sparse dictionary.
In one embodiment of the present invention, the performing sparse transformation on the echo signal of the extended target by using the sparse dictionary, and constructing the pseudorandom measurement matrix based on the pseudorandom m sequence to perform compressive sampling specifically includes:
performing sparse transformation on the echo signals of the extended target by adopting a formula X-A sigma, wherein X is the received echo signals of the extended target, sigma is a scattering coefficient of the extended target,
Figure BDA0001560482960000041
as a sparse dictionary, AMIs defined as
Figure BDA0001560482960000042
fnIs the nth frequency point, tau is time delay, NxAnd NzThe grid numbers are respectively the direction and distance division;
length D of 2 according to pseudorandom m-sequence ac-1 ≧ MN and the primitive polynomial mapping table yields the polynomial g (x) ═ xc+xh+1, wherein M is the array element number of the equivalent virtual array corresponding to the MIMO through-wall radar array, and N is the frequency point number;
according to
Figure BDA0001560482960000043
Determining polynomial coefficients vc＝(v1,v2,v3,…vc) Generating a pseudo-random m-sequence a by a c-bit linear feedback shift register, wherein vcIs 0 or 1;
randomly selecting MN elements from the pseudorandom m sequence a to obtain a sequence a' as follows: a' ═ a1,a2,…aMN]；
By subjecting the sequence a' to Q1Q2Left shift of-1 cycle to Q1Q2Constructing a pseudo-random measurement matrix based on a pseudo-random m sequence by using the row vectors as follows:
Figure BDA0001560482960000051
the measurement vector y is thus obtained as follows: y ═ Φ X, where Φ is a pseudo-random measurement matrix.
The method for constructing the perception matrix according to the pseudo-random measurement matrix and the sparse dictionary specifically comprises the following steps:
and constructing a perception matrix T by using the formula T ═ phi A.
In the first embodiment of the present invention, the first,
the MIMO through-wall radar array is configured into a mode of transmitting at two ends, receiving in the middle and multiplexing in a time-sharing mode according to configuration indexes, and an even and non-redundant equivalent virtual array is obtained to acquire data of through-wall imaging.
In one embodiment of the present invention, the configuration includes a mode of transmitting at two ends, receiving in the middle, and multiplexing in time division according to the configuration index, and the data acquisition for obtaining a uniform and non-redundant equivalent virtual array to perform through-wall imaging specifically includes:
calculating equivalent virtual aperture L of MIMO through-wall radar arrayEA：
Figure BDA0001560482960000052
Where η is the beam width factor, η is 0.886, λcIs the wavelength, ρ is the azimuthal resolution;
according to
Figure BDA0001560482960000053
And calculating the array element number M of the equivalent virtual array, wherein IGLL is the set ideal grating lobe level.
In order to prevent imaging space spectrums from overlapping and reduce side lobes and grating lobes and simultaneously consider the complexity of the MIMO through-wall radar array, the MIMO through-wall radar array is configured into a mode of transmitting at two ends, receiving in the middle and multiplexing in a time-sharing mode, so that an equivalent virtual array which is uniform and not redundant is obtained, and the structure of the MIMO through-wall radar array can be simplified. The imaging scene of the MIMO through-wall radar array is shown in fig. 2. Only one of the two-way propagation paths is shown.
Fig. 3 is a graph comparing the Point Spread Function (PSF) and the azimuth peak amplitude at (0, 1.6) for MIMO through-wall radar arrays and equivalent virtual arrays. It can be seen from fig. 3 that the point spread function and the azimuth peak amplitude diagram of the two are very close, and the MIMO through-wall radar array and the equivalent virtual array have equivalence.
S103, obtaining an initial value of the scattering coefficient of the extended target according to a back projection imaging algorithm, and predicting an index set for an orthogonal matching pursuit compressed sensing algorithm according to the sensing matrix and the initial value of the scattering coefficient of the extended target.
In one embodiment of the present invention, S103 specifically includes:
setting an initial value sigma of the scattering coefficient0＝THw0Initial residual error w0Y, index set
Figure BDA0001560482960000061
The sparsity is K, and the iteration number is l;
solving the scattering coefficient of the extended target according to a back projection imaging algorithm as follows: sigmal＝THwlWhere T is the perceptual matrix;
finding out the index value corresponding to the largest K pixel values from the sigma to form an index set which is estimated and used for the orthogonal matching pursuit compressed sensing algorithm, wherein the index set comprises the following steps: omegal＝max_ind(σ,K)。
And S104, fully considering the structure sparse prior information among the pixels according to the one-by-one transfer process of the first-order neighborhood of the Markov random field to obtain a new index set.
In one embodiment of the present invention, S104 specifically includes:
the support vector is denoted as λ, NiIs the set of the adjacent pixels of the ith pixel point,
Figure BDA0001560482960000062
is a complement of omega, an
Figure BDA0001560482960000068
For the extended target, besides that the pixel points of the imaging region meet sparsity, a dependency relationship still exists between a non-zero target pixel point and an adjacent pixel point, and the ith pixel of the one-dimensional signal sigma can be transmitted one by one through the first-order neighborhood of the Markov random field shown in FIG. 4 to acquire the structure sparse prior information among the pixels;
according to the one-by-one transfer process of the first-order neighborhood of the Markov random field, the weight of the interaction between the ith pixel point and the pixel in the neighborhood is expressed as:
Figure BDA0001560482960000064
wherein
Figure BDA0001560482960000065
Is a normalized conditional probability density function;
λiwhen 1, the ith pixel value σiIs non-zero, λiWhen-1, it corresponds to the i-th pixel value σiIs zero, σiAnd
Figure BDA0001560482960000066
the joint probability distribution of (c) is:
Figure BDA0001560482960000067
further taken from the natural logarithm, two functions are defined, respectively:
Figure BDA0001560482960000071
note the book
Figure BDA0001560482960000072
λiThe estimation of (d) is:
Figure BDA0001560482960000073
study is lung2-Ρ1And ε is the adaptive relaxation parameter for separating pixel values, when |. σi|≥ε，I(σi) 1, whereas, I (σ)i) γ is 0:
Figure BDA0001560482960000074
wherein rho is a weight parameter satisfying
Figure BDA0001560482960000075
Thereby obtaining
Figure BDA0001560482960000076
The solution of (a) is:
Figure BDA0001560482960000077
by
Figure BDA0001560482960000078
New index set with corresponding index position reconstruction
Figure BDA0001560482960000079
And S105, recalculating the scattering coefficient of the extended target by adopting an orthogonal matching pursuit compressed sensing algorithm according to the new index set until a convergence condition is met, and using the scattering coefficient of the extended target at the moment for two-dimensional imaging.
In one embodiment of the present invention, S105 specifically includes:
and obtaining the update of the scattering coefficient of the extended target by a least square method according to the new index set as follows:
Figure BDA00015604829600000710
the corresponding residuals are updated as:
Figure BDA00015604829600000711
if it is
Figure BDA00015604829600000712
Or l is more than K, the loop iteration is stopped, and the scattering coefficient of the extended target at the moment is measured
Figure BDA00015604829600000713
For two-dimensional imaging, where ξ is the convergence threshold.
Example two:
referring to fig. 5, a structural sparse imaging apparatus of a MIMO through-wall radar according to a second embodiment of the present invention includes:
the receiving module 11 is configured to receive an echo signal of an extended target acquired by the MIMO through-the-wall radar array;
the construction module 12 is configured to perform sparse transformation on the echo signal of the extended target by using a sparse dictionary, construct a pseudo-random measurement matrix based on a pseudo-random m sequence, perform compression sampling, and further construct a sensing matrix according to the pseudo-random measurement matrix and the sparse dictionary;
the estimation module 13 is used for obtaining an initial value of the scattering coefficient of the extended target according to a back projection imaging algorithm, and estimating an index set for an orthogonal matching pursuit compressed sensing algorithm according to the sensing matrix and the initial value of the scattering coefficient of the extended target;
a new index set generation module 14, configured to obtain a new index set by fully considering inter-pixel structure sparse prior information according to a markov random field first-order neighborhood one-by-one transfer process;
and the extended target scattering coefficient calculating module 15 is configured to recalculate the extended target scattering coefficient according to the new index set by using an orthogonal matching pursuit compressed sensing algorithm until a convergence condition is satisfied, and use the extended target scattering coefficient at this time for two-dimensional imaging.
The structure sparse imaging device of the MIMO through-wall radar and the structure sparse imaging method of the MIMO through-wall radar provided by the second embodiment of the invention belong to the same concept, and specific implementation processes are detailed throughout the specification and are not repeated herein.
Example three:
a third embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the structural sparse imaging method for the MIMO through-the-wall radar according to the first embodiment of the present invention are implemented.
In the invention, a pseudo-random measurement matrix based on a pseudo-random m sequence and a structure sparse prior information among pixels are fully considered according to a Markov random field first-order neighborhood one-by-one transfer process, and an index set of an orthogonal matching pursuit algorithm estimated by a back projection method is updated; and recalculating the scattering coefficient of the extended target by adopting an orthogonal matching pursuit compressed sensing algorithm according to the new index set until a convergence condition is met, and using the scattering coefficient of the extended target at the moment for two-dimensional imaging. Therefore, the invention does not need to manually set the blocking parameters, has small required storage space, reduces the operation amount and the system complexity, is easy to realize hardware, improves the sparse reconstruction performance of the sparse imaging algorithm of the extended target, and realizes the high-resolution through-the-wall extended target imaging.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
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 and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (10)

1. A structural sparse imaging method of a MIMO through-wall radar is characterized by comprising the following steps:
receiving echo signals of an extended target collected by the MIMO through-wall radar array;
sparse dictionary is adopted to carry out sparse transformation on the echo signals of the extended target, a pseudo-random measurement matrix based on a pseudo-random m sequence is constructed to carry out compression sampling, and then a perception matrix is constructed according to the pseudo-random measurement matrix and the sparse dictionary, wherein the pseudo-random m sequence is generated through a c-bit linear feedback shift register;
obtaining an initial value of a scattering coefficient of an extended target according to a back projection imaging algorithm, and estimating an index set for an orthogonal matching pursuit compressed sensing algorithm according to a sensing matrix and the initial value of the scattering coefficient of the extended target;
according to the one-by-one transfer process of the first-order neighborhoods of the Markov random field, fully considering the structure sparse prior information among the pixels, obtaining the weight of the interaction between each pixel point and the pixel among the neighborhoods, obtaining the joint probability distribution of each pixel value and the support vector, and further obtaining a new reconstructed index set;
and recalculating the scattering coefficient of the extended target by adopting an orthogonal matching pursuit compressed sensing algorithm according to the new index set until a convergence condition is met, and using the scattering coefficient of the extended target at the moment for two-dimensional imaging.
2. The method according to claim 1, wherein the sparse dictionary is used for performing sparse transformation on the echo signal of the extended target, and the pseudo-random measurement matrix based on the pseudo-random m-sequence is constructed for performing compression sampling specifically as follows:
performing sparse transformation on the echo signals of the extended target by adopting a formula X-A sigma, wherein X is the received echo signals of the extended target, sigma is a scattering coefficient of the extended target,
Figure FDA0003303585440000011
as a sparse dictionary, AMIs defined as
Figure FDA0003303585440000012
fnIs the nth frequency point, tau is time delay, NxAnd NzThe grid numbers are respectively the direction and distance division;
length D of 2 according to pseudorandom m-sequence ac-1 ≧ MN and the primitive polynomial mapping table yields the polynomial g (x) ═ xc+xh+1, wherein M is the array element number of the equivalent virtual array corresponding to the MIMO through-wall radar array, and N is the frequency point number;
according to
Figure FDA0003303585440000013
Determining polynomial coefficients vc＝(v1,v2,v3,…vc) Generating a pseudo-random m-sequence a by a c-bit linear feedback shift register, wherein vcIs 0 or 1;
randomly selecting MN elements from the pseudorandom m sequence a to obtain a sequence a' as follows: a' ═ a1,a2,…aMN]；
By subjecting the sequence a' to Q1Q2Left shift of-1 cycle to Q1Q2Constructing a pseudo-random measurement matrix based on a pseudo-random m sequence by using the row vectors as follows:
Figure FDA0003303585440000021
the measurement vector y is thus obtained as follows:
y ═ Φ X, where Φ is a pseudo-random measurement matrix.
3. The method according to claim 2, wherein the constructing of the perception matrix from the pseudo-random measurement matrix and the sparse dictionary is specifically:
and constructing a perception matrix T by using the formula T ═ phi A.
4. The method of any one of claims 1 to 3, wherein the MIMO through-wall radar array is configured in a mode of two-end transmission, middle reception and time division multiplexing according to configuration indexes, and a uniform and non-redundant equivalent virtual array is obtained for data acquisition of through-wall imaging.
5. The method according to claim 4, wherein the configuration into a mode of two-end transmission intermediate reception and time division multiplexing according to the configuration index, and the data acquisition for obtaining a uniform and non-redundant equivalent virtual array for through-wall imaging specifically comprises:
calculating equivalent virtual aperture L of MIMO through-wall radar arrayEA：
Figure FDA0003303585440000022
Where η is the beam width factor, η is 0.886, λcIs the wavelength, ρ is the azimuthal resolution;
according to
Figure FDA0003303585440000023
And calculating the array element number M of the equivalent virtual array, wherein IGLL is the set grating lobe level.
6. The method of claim 3, wherein the obtaining of the initial value of the scattering coefficient of the extended target according to the back-projection imaging algorithm and the pre-estimating of the index set for the orthogonal matching pursuit compressed sensing algorithm according to the sensing matrix and the initial value of the scattering coefficient of the extended target are specifically:
setting an initial value sigma of the scattering coefficient0＝THw0Initial residual error w0Y, index set
Figure FDA0003303585440000031
The sparsity is K, and the iteration number is l;
solving the scattering coefficient of the extended target according to a back projection imaging algorithm as follows: sigmal＝THwlWhere T is the perceptual matrix;
finding out the index value corresponding to the largest K pixel values from the sigma to form an index set which is estimated and used for the orthogonal matching pursuit compressed sensing algorithm, wherein the index set comprises the following steps: omegal＝max_ind(σ,K)。
7. The method of claim 6, wherein the obtaining of the reconstructed new index set is performed by fully considering inter-pixel structure sparse prior information according to a markov random field first order neighborhood one-by-one transfer process, obtaining weights of interaction between each pixel and its neighboring pixels, and obtaining a joint probability distribution of each pixel value and a support vector, wherein the obtaining of the reconstructed new index set specifically comprises:
the support vector is denoted as λ, NiIs the set of the adjacent pixels of the ith pixel point,
Figure FDA0003303585440000032
is a complement of omega, an
Figure FDA0003303585440000033
The ith pixel of the one-dimensional signal sigma is transmitted one by one through a first-order neighborhood of a Markov random field to obtain structure sparse prior information among the pixels;
according to the one-by-one transfer process of the first-order neighborhood of the Markov random field, the weight of the interaction between the ith pixel point and the pixel in the neighborhood is expressed as:
Figure FDA0003303585440000034
wherein
Figure FDA0003303585440000035
Is a normalized conditional probability density function;
λiwhen 1, the ith pixel value σiIs non-zero, λiWhen-1, it corresponds to the i-th pixel value σiIs zero, σiAnd λi、
Figure FDA0003303585440000036
The joint probability distribution of (c) is:
Figure FDA0003303585440000037
further taken from the natural logarithm, two functions are defined, respectively:
Figure FDA0003303585440000038
note the book
Figure FDA0003303585440000041
λiThe estimation of (d) is:
Figure FDA0003303585440000042
study is lung2-Ρ1And ε is the adaptive relaxation parameter for separating pixel values, when |. σi|≥ε，I(σi) 1, whereas, I (σ)i) γ is 0:
Figure FDA0003303585440000043
wherein rho is a weight parameter satisfying
Figure FDA0003303585440000044
Thereby obtaining
Figure FDA0003303585440000045
The solution of (a) is:
Figure FDA0003303585440000046
by
Figure FDA0003303585440000047
New index set with corresponding index position reconstruction
Figure FDA0003303585440000048
8. The method according to claim 7, wherein the recalculating the extended target scattering coefficient according to the new index set by using the orthogonal matching pursuit compressed sensing algorithm until the convergence condition is satisfied, and using the extended target scattering coefficient at this time for two-dimensional imaging specifically comprises:
obtaining the scattering coefficient of the extended target by the least square method according to the new index set
Figure FDA0003303585440000049
The update of (1) is:
Figure FDA00033035854400000410
where y is the measurement vector and the corresponding residual is updated as:
Figure FDA00033035854400000411
if it is
Figure FDA00033035854400000412
Or l>When K is reached, the loop iteration is stopped, and the scattering coefficient of the extended target at the moment is measured
Figure FDA00033035854400000413
For two-dimensional imaging, where ξ is the convergence threshold.
9. A structural sparse imaging apparatus of a MIMO through-wall radar, the apparatus comprising:
the receiving module is used for receiving echo signals of the extended target collected by the MIMO through-wall radar array;
the construction module is used for carrying out sparse transformation on the echo signals of the extended target by adopting a sparse dictionary, constructing a pseudo-random measurement matrix based on a pseudo-random m sequence for compression sampling, and further constructing a perception matrix according to the pseudo-random measurement matrix and the sparse dictionary, wherein the pseudo-random m sequence is generated by a c-bit linear feedback shift register;
the estimation module is used for obtaining an initial value of a scattering coefficient of an extended target according to a back projection imaging algorithm and estimating an index set for an orthogonal matching pursuit compressed sensing algorithm according to a sensing matrix and the initial value of the scattering coefficient of the extended target;
the new index set generation module is used for fully considering the structure sparse prior information among the pixels according to the one-by-one transmission process of the first-order neighborhoods of the Markov random field, obtaining the weight of the interaction between each pixel point and the pixel among the neighborhoods of the pixel point, obtaining the joint probability distribution of each pixel value and the occupied weight, and further obtaining a new reconstructed index set;
and the extended target scattering coefficient calculating module is used for recalculating the extended target scattering coefficient by adopting an orthogonal matching pursuit compressed sensing algorithm according to the new index set until a convergence condition is met, and using the extended target scattering coefficient at the moment for two-dimensional imaging.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for structural sparse imaging of MIMO through-the-wall radar according to any one of claims 1 to 8.
CN201810079149.1A 2018-01-26 2018-01-26 Structure sparse imaging method and device of MIMO through-wall radar Active CN108562897B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810079149.1A CN108562897B (en) 2018-01-26 2018-01-26 Structure sparse imaging method and device of MIMO through-wall radar

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810079149.1A CN108562897B (en) 2018-01-26 2018-01-26 Structure sparse imaging method and device of MIMO through-wall radar

Publications (2)

Publication Number Publication Date
CN108562897A CN108562897A (en) 2018-09-21
CN108562897B true CN108562897B (en) 2022-01-11

Family

ID=63529974

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810079149.1A Active CN108562897B (en) 2018-01-26 2018-01-26 Structure sparse imaging method and device of MIMO through-wall radar

Country Status (1)

Country Link
CN (1) CN108562897B (en)

Families Citing this family (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109298422B (en) * 2018-11-14 2022-06-28 湖南华诺星空电子技术有限公司 Synthetic aperture imaging optimization processing method and device for through-wall radar
CN109459752B (en) * 2018-11-19 2021-12-31 桂林电子科技大学 Resource self-adaptive scheduling method for inverse synthetic aperture radar two-dimensional sparse imaging
CN109696672B (en) * 2018-12-29 2020-09-15 南京汇君半导体科技有限公司 High-resolution through-wall radar imaging method based on spatial structure correlation
CN109799499B (en) * 2019-01-28 2023-04-28 沈阳航空航天大学 Wall parameter estimation method of through-wall radar
CN110031838B (en) * 2019-05-10 2021-03-12 沈阳航空航天大学 Through-wall radar wall parameter estimation method without reference to transmitted waveform
CN111239730B (en) * 2020-01-19 2021-11-23 浙江大学 Electromagnetic non-line-of-sight imaging method based on time reversal and compressed sensing
CN111766575B (en) * 2020-06-08 2023-04-21 桂林电子科技大学 Self-focusing sparse imaging method of through-wall radar and computer equipment
CN112150614B (en) * 2020-09-24 2023-05-02 济南大学 Cement three-dimensional simulation model establishment method and system based on microstructure image
CN113376630A (en) * 2021-07-05 2021-09-10 中国人民解放军空军工程大学 Radar imaging method, apparatus, electronic device, and computer-readable storage medium
CN113777574A (en) * 2021-08-30 2021-12-10 深圳市道通智能汽车有限公司 Sparse array grating lobe arrangement method, device and related equipment
CN113917461B (en) * 2021-10-21 2022-10-28 苏州威陌电子信息科技有限公司 MIMO radar imaging method and system

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101498787A (en) * 2008-01-29 2009-08-05 电子科技大学 Linear array three-dimensional imaging synthetic aperture radar fast imaging method
CN103064071A (en) * 2012-10-25 2013-04-24 西安电子科技大学 Radar target attribute scattering center feature extraction method based on sparse decomposition
CN105911544A (en) * 2016-05-09 2016-08-31 西安理工大学 Spread spectrum through-the-wall radar imaging method based on compression perception technology
CN105917249A (en) * 2014-01-13 2016-08-31 三菱电机株式会社 Method and system for reconstructing scene behind wall
CN106226765A (en) * 2016-09-12 2016-12-14 桂林电子科技大学 A kind of building layout formation method and system
CN107132518A (en) * 2017-06-07 2017-09-05 陕西黄河集团有限公司 A kind of range extension target detection method based on rarefaction representation and time-frequency characteristics
CN107316306A (en) * 2017-06-23 2017-11-03 电子科技大学 A kind of diameter radar image fast partition method based on Markov model

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102183762B (en) * 2011-03-15 2012-09-05 北京航空航天大学 Method for acquiring and imaging data of compressive sensing synthetic aperture radar
US9971019B2 (en) * 2013-07-22 2018-05-15 Mitsubishi Electric Research Laboratories, Inc. System and method for through-the-wall-radar-imaging using total-variation denoising
US9915730B2 (en) * 2015-07-23 2018-03-13 Mitsubishi Electric Research Laboratories, Inc. Method and system for through-the-wall radar imaging
CN106249233B (en) * 2016-09-21 2018-11-09 清华大学 Forward direction mixing match tracing method and system towards multipolarization through-wall radar imaging

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101498787A (en) * 2008-01-29 2009-08-05 电子科技大学 Linear array three-dimensional imaging synthetic aperture radar fast imaging method
CN103064071A (en) * 2012-10-25 2013-04-24 西安电子科技大学 Radar target attribute scattering center feature extraction method based on sparse decomposition
CN105917249A (en) * 2014-01-13 2016-08-31 三菱电机株式会社 Method and system for reconstructing scene behind wall
CN105911544A (en) * 2016-05-09 2016-08-31 西安理工大学 Spread spectrum through-the-wall radar imaging method based on compression perception technology
CN106226765A (en) * 2016-09-12 2016-12-14 桂林电子科技大学 A kind of building layout formation method and system
CN107132518A (en) * 2017-06-07 2017-09-05 陕西黄河集团有限公司 A kind of range extension target detection method based on rarefaction representation and time-frequency characteristics
CN107316306A (en) * 2017-06-23 2017-11-03 电子科技大学 A kind of diameter radar image fast partition method based on Markov model

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
Sparse and low-rank matrix decomposition based micro-motion target indication in through-the-wall radar;Lei Qiu等;《Radar,Sonar and Navigation》;20170201;全文 *
THROUGH-THE-WALL RADAR IMAGING BASED ON MODIFIED BAYESIAN COMPRESSIVE SENSING;Qisong Wu;《2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP) 》;20140904;全文 *
Two-Level Block Matching Pursuit for Polarimetric Through-Wall Radar Imaging;Xueqian Wang等;《IEEE Transactions on Geoscience and Remote Sensing》;20171109;全文 *
穿墙雷达扩展目标自聚焦稀疏成像方法;晋良念等;《雷达科学与技术》;20171231(第6期);全文 *
组合字典下超宽带穿墙雷达自适应稀疏成像方法;晋良念等;《电子与信息学报》;20160531;第38卷(第5期);全文 *
超宽带穿墙雷达稀疏成像技术研究;戴耀辉;《万方智搜》;20181219;全文 *

Also Published As

Publication number Publication date
CN108562897A (en) 2018-09-21

Similar Documents

Publication Publication Date Title
CN108562897B (en) Structure sparse imaging method and device of MIMO through-wall radar
CN105917249B (en) Method and system for reconstructing the scene after wall
Ender A brief review of compressive sensing applied to radar
Tang et al. Multipolarization through-wall radar imaging using low-rank and jointly-sparse representations
Pu et al. OSRanP: A novel way for radar imaging utilizing joint sparsity and low-rankness
CN110109050B (en) Unknown mutual coupling DOA estimation method based on sparse Bayes under nested array
KR101524793B1 (en) Apparatus and method for estimating direction of arrival using array antenna
CN108896990B (en) Building wall imaging method and device by using coupled mode dictionary learning
CN105699969A (en) A maximum posterior estimated angle super-resolution imaging method based on generalized Gaussian constraints
Yang et al. High-resolution and wide-swath SAR imaging via Poisson disk sampling and iterative shrinkage thresholding
CN112147608A (en) Rapid Gaussian gridding non-uniform FFT through-wall imaging radar BP method
CN115356678A (en) Sparse array DOA estimation method based on DPNALM algorithm
CN115236584A (en) Meter-wave radar low elevation angle estimation method based on deep learning
CN117129994B (en) Improved backward projection imaging method based on phase compensation nuclear GNSS-SAR
CN115825944B (en) Single-snapshot multi-target incoming wave direction estimation method based on external radiation source radar
CN117420550A (en) Through-wall radar imaging method based on maximum echo energy selection back projection
Gurbuz et al. Sparse ground-penetrating radar imaging method for off-the-grid target problem
CN111896929A (en) DOD/DOA estimation algorithm of non-uniform MIMO radar
CN110133656B (en) Three-dimensional SAR sparse imaging method based on decomposition and fusion of co-prime array
Wu et al. Compressed sensing techniques for altitude estimation in multipath conditions
KR102531755B1 (en) Radar image generation mehtod and apparatus for performing the same
CN115128607A (en) Cross MIMO array radar system and three-dimensional imaging method thereof
Duman et al. Analysis of compressive sensing based through the wall imaging
CN112068083A (en) Signal reconstruction method and device and radar
Song et al. An Effective Image Reconstruction Enhancement Method with Convolutional Reweighting for Near-field SAR

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20180921

Assignee: Guilin Dazhi Technology Co.,Ltd.

Assignor: GUILIN University OF ELECTRONIC TECHNOLOGY

Contract record no.: X2022450000183

Denomination of invention: A Structure Sparse Imaging Method and Device for MIMO Through wall Radar

Granted publication date: 20220111

License type: Common License

Record date: 20221125

Application publication date: 20180921

Assignee: Guilin Feimin Electronic Technology Co.,Ltd.

Assignor: GUILIN University OF ELECTRONIC TECHNOLOGY

Contract record no.: X2022450000182

Denomination of invention: A Structure Sparse Imaging Method and Device for MIMO Through wall Radar

Granted publication date: 20220111

License type: Common License

Record date: 20221125

EE01 Entry into force of recordation of patent licensing contract