CN113376630A - Radar imaging method, apparatus, electronic device, and computer-readable storage medium - Google Patents

Radar imaging method, apparatus, electronic device, and computer-readable storage medium Download PDF

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CN113376630A
CN113376630A CN202110757264.1A CN202110757264A CN113376630A CN 113376630 A CN113376630 A CN 113376630A CN 202110757264 A CN202110757264 A CN 202110757264A CN 113376630 A CN113376630 A CN 113376630A
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matrix
array element
scattering
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echo
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胡晓伟
郭艺夺
冯为可
何兴宇
王宇晨
冯存前
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Air Force Engineering University of PLA
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    • 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
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Abstract

The application relates to the technical field of radar, and discloses a radar imaging method, a radar imaging device, electronic equipment and a computer readable storage medium, wherein the method comprises the following steps: receiving, by a plurality of receiving array elements, received signals returned by a plurality of scattering points on the target object based on the transmission signals of the plurality of transmitting array elements; determining an initial sensing matrix according to each received signal; performing singular value decomposition on the initial sensing matrix to obtain a plurality of singular values; adjusting the plurality of singular values to obtain an adjusted singular value set; generating a target perception matrix according to the adjusted singular value set; and generating an image of the target object according to the target perception matrix. Therefore, the target perception matrix can be obtained by adjusting the singular value, so that the image of the target object can be generated according to the target perception matrix, and the radar imaging quality is improved.

Description

Radar imaging method, apparatus, electronic device, and computer-readable storage medium
Technical Field
The present application relates to the field of radar technology, and in particular, to a method, an apparatus, an electronic device, and a computer-readable storage medium for radar imaging.
Background
The Multiple-input Multiple-output (MIMO) radar technology has a wide application prospect in imaging a moving target object.
At present, most MIMO radar imaging methods require orthogonal emission waveforms, regular array structures and uniform sampling, but when sensing matrix ill-conditions are caused under the conditions that the emission waveforms are not ideal orthogonal waveforms, irregular array structures or non-uniform sampling and the like, clear images of target objects cannot be obtained by the existing MIMO radar imaging methods.
Therefore, when the transmitted waveform is not an ideal orthogonal waveform, an irregular array structure or non-uniform sampling, etc., how to improve the quality of MIMO radar imaging is a problem to be solved.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method, an apparatus, an electronic device, and a computer-readable storage medium for radar imaging, which are used to improve the quality of MIMO radar imaging when a transmitted waveform is not an ideal orthogonal waveform, an irregular array structure, or non-uniform sampling.
In a first aspect, an embodiment of the present application provides a radar imaging method, where the method includes:
and receiving the received signals returned by a plurality of scattering points on the target object based on the transmission signals of the plurality of transmission array elements through the plurality of receiving array elements.
An initial perceptual matrix is determined from each received signal received.
And carrying out singular value decomposition on the initial sensing matrix to obtain a plurality of singular values.
And adjusting the plurality of singular values to obtain an adjusted singular value set.
And generating a target perception matrix according to the adjusted singular value set.
And generating an image of the target object according to the target perception matrix.
In the implementation process, singular value decomposition is carried out on the sensing matrix to obtain a plurality of singular values, then the plurality of singular values are adjusted to obtain an adjusted singular value set, a target sensing matrix is further obtained according to the adjusted singular value set, finally an image of a target object is generated according to the target sensing matrix, and the singular values in the initial sensing matrix are adjusted, so that the imaging quality of the target object is improved when the initial sensing matrix is in a sick state.
With reference to the first aspect, in an embodiment, adjusting a plurality of singular values to obtain an adjusted set of singular values includes:
and screening out singular values higher than a first preset singular value threshold value from the plurality of singular values.
And obtaining an adjusted singular value set according to the screened singular values.
In the implementation process, singular values which are higher than a first preset singular value threshold value in the singular values are screened, and the singular values which are not higher than the first preset singular value threshold value in the singular values are removed, so that an adjusted singular value set is obtained, and the singular values are adjusted.
With reference to the first aspect, in an embodiment, adjusting a plurality of singular values to obtain an adjusted set of singular values includes:
and screening out singular values higher than a second preset singular value threshold value from the plurality of singular values.
And correcting the screened singular values to obtain at least one corrected singular value.
And obtaining an adjusted singular value set according to the corrected at least one singular value.
In the implementation process, singular values which are higher than a second preset singular value threshold value in the plurality of singular values are screened, the screened singular values are further corrected, so that a corrected singular value set is obtained, further, an image of a target object is generated according to the corrected singular value set, and accordingly the definition of the image is improved.
With reference to the first aspect, in an embodiment, determining an initial sensing matrix according to each received signal includes:
generating a scattering point for each scattering point at each receiving array element according to each received signal, and executing the following steps:
acquiring echo data of a scattering point at a receiving array element;
carrying out vector replacement on the echo data to generate vector data of the echo data;
and carrying out non-uniform compression sampling on the vector data by using the non-uniform compression sampling matrix to obtain a compressed echo matrix.
And generating a target echo matrix according to the compressed echo matrix of all scattering points at each receiving array element.
And decomposing the target echo matrix to obtain an initial scattering coefficient vector and an initial sensing matrix.
In the implementation process, echo data are generated according to each received signal, the echo data are further subjected to non-uniform sampling to obtain a compressed echo matrix, and then the compressed echo matrix is decomposed to obtain an initial sensing matrix, so that a data basis is provided for the decomposition of a subsequent sensing matrix.
With reference to the first aspect, in one embodiment, echo data of a scattering point at a receiving array element includes:
if there are M transmitting array elements, N receiving array elements, the reference point of the target object is O, the Q scattering point on the target object is Q, then the Q scattering point Q is obtained relative to the M transmitting array element TmAnd the Q scattering point Q is relative to the n receiving array element RnThe adjustment time delay.
Wherein the Q scattering point Q is relative to the m transmitting array element TmIs adjusted to a time delay of tauq,m＝(TmQ-TmO)/C, Q scattering point Q corresponding to n receiving array element RnIs adjusted to a time delay of tauq,n＝(RnQ-RnO)/C, C represents the speed of light, TmQ represents the mth transmitting array element TmThe distance to the qth scattering point Q,Tmo represents the m-th transmitting array element TmDistance to reference point O, RnQ denotes the nth receiving array element RnDistance to the qth scattering point Q, RnO denotes the nth receiving array element RnDistance to reference point O.
Each received signal is received at the nth receiving array element R according to the Q scattering point QnThe echo data at (a) is shown in expression (1):
Figure BDA0003148249280000041
wherein,
Figure BDA0003148249280000042
represents the m-th transmitting array element TmT denotes a fast time, N is 0, 1, …, N-1, M is 0, 1, …, M-1, f0Representing the carrier frequency, σqThe scattering coefficient of the scattering point Q is indicated.
Carrying out vector replacement on the echo data to generate vector data of the echo data, wherein the vector data comprises:
if it is
Figure BDA0003148249280000043
To represent
Figure BDA0003148249280000044
In a vector form, where L is
Figure BDA0003148249280000045
The length of (a) of (b),
Figure BDA0003148249280000046
bq＝[exp(-j2πf0τq，0)，…，exp(-j2πf0τq，m)，…，exp(-j2πf0τq,M-1)]∈CM×1then, the vector data of expression (1) is as shown in expression (2):
Figure BDA0003148249280000047
wherein, yq,nIndicating that the Q-th scattering point Q is at the n-th receiving array element RnVector data of the echo data at.
The method for carrying out non-uniform compression sampling on vector data by using a non-uniform compression sampling matrix to obtain a compressed echo matrix comprises the following steps:
using the nth receiving array element RnNon-uniform compression sampling matrix psi ofn∈RL′×LNon-uniform compression sampling is carried out on vector data, and an echo matrix after compression is obtained is shown as an expression (3):
Figure BDA0003148249280000048
wherein s isq，nIndicating that the Q-th scattering point Q is at the n-th receiving array element RnThe compressed echo matrix of (a) is,
Figure BDA0003148249280000049
l' represents the length of the compression echo.
Generating a compressed echo matrix of the qth scattering point Q at each receiving array element according to expression (3) is shown in expression (4):
Figure BDA00031482492800000410
wherein s isqRepresenting the compressed echo matrix at each receiving array element for the Q-th scattering point Q,
Figure BDA00031482492800000411
generating a target echo matrix according to the compressed echo matrix of all scattering points at each receiving array element, wherein the method comprises the following steps:
if the target object consists of J scattering points, the target echo matrix is shown in expression (5):
s＝Φθ (5)
wherein Φ is ═ ω0，…，ωq，…，ωJ-1]∈CL′N×JDenotes the initial perceptual matrix, θ ═ σ0，…，σq，…，σJ-1]T∈CJ×1Representing the initial scattering coefficient vector for all scattering points.
In the implementation process, the echo data of each scattering point on the target object at each receiving array element is obtained, and the target echo matrix is generated according to the echo data, so that the image of the target object is generated according to the echo matrix.
With reference to the first aspect, in an embodiment, performing singular value decomposition on the initial sensing matrix to obtain a plurality of singular values includes:
and if the length of the compressed echo matrix is smaller than that of the initial scattering coefficient vector, performing singular value decomposition on the initial sensing matrix to obtain a plurality of singular values.
In the implementation process, under the condition that the length of the compressed echo matrix is smaller than that of the initial scattering coefficient vector, singular value decomposition is carried out on the initial sensing matrix to obtain a plurality of singular values, so that the singular values can be conveniently adjusted subsequently.
With reference to the first aspect, in one implementation, generating an image of a target object according to a target perception matrix includes:
and determining a target scattering coefficient vector according to a sparse recovery algorithm and a target perception matrix.
And generating an image of the target object according to the target scattering coefficient vector.
In the implementation process, the target sensing matrix is obtained through the adjusted singular value, so that the adjustment of the ill-conditioned sensing matrix is realized, further, a target scattering coefficient vector is determined by using a sparse recovery algorithm and the target sensing matrix, so that the solution of the target scattering coefficient vector is realized, and further, an image of a target object can be generated according to the target scattering coefficient vector obtained by the solution.
In a second aspect, an embodiment of the present application provides an apparatus for radar imaging, where the apparatus includes:
and the receiving unit is used for receiving the received signals returned by the plurality of scattering points on the target object based on the transmitted signals of the plurality of transmitting array elements through the plurality of receiving array elements.
And the determining unit is used for determining the initial sensing matrix according to the received signals.
And the decomposition unit is used for carrying out singular value decomposition on the initial sensing matrix to obtain a plurality of singular values.
And the adjusting unit is used for adjusting the plurality of singular values to obtain the adjusted singular value set.
And the matrix generating unit is used for generating a target perception matrix according to the adjusted singular value set.
And the image generation unit is used for generating an image of the target object according to the target perception matrix.
With reference to the second aspect, in an embodiment, the adjusting unit is specifically configured to:
screening out singular values higher than a first preset singular value threshold value from the plurality of singular values;
and obtaining an adjusted singular value set according to the screened singular values.
With reference to the second aspect, in an embodiment, the adjusting unit is specifically configured to:
and screening out singular values higher than a second preset singular value threshold value from the plurality of singular values.
And correcting the screened singular values to obtain at least one corrected singular value.
And obtaining an adjusted singular value set according to the corrected at least one singular value.
With reference to the second aspect, in an embodiment, the determining unit is specifically configured to:
generating a scattering point for each scattering point at each receiving array element according to each received signal, and executing the following steps:
echo data of a scattering point at a receiving array element is acquired.
And carrying out vector replacement on the echo data to generate the vector data of the echo data.
And carrying out non-uniform compression sampling on the vector data by using the non-uniform compression sampling matrix to obtain a compressed echo matrix.
And generating a target echo matrix according to the compressed echo matrix of all scattering points at each receiving array element.
And decomposing the target echo matrix to obtain an initial scattering coefficient vector and an initial sensing matrix.
With reference to the second aspect, in an embodiment, the determining unit is specifically configured to:
if there are M transmitting array elements, N receiving array elements, the reference point of the target object is O, the Q scattering point on the target object is Q, then the Q scattering point Q is obtained relative to the M transmitting array element TmAnd the Q scattering point Q is relative to the n receiving array element RnThe adjustment time delay of (2);
wherein the Q scattering point Q is relative to the m transmitting array element TmIs adjusted to a time delay of tauq,m＝(TmQ-TmO)/C, Q scattering point Q corresponding to n receiving array element RnIs adjusted to a time delay of tauq,n＝(RnQ-RnO)/C, C represents the speed of light, TmQ represents the mth transmitting array element TmDistance to the qth scattering point Q, TmO represents the m-th transmitting array element TmDistance to reference point O, RnQ denotes the nth receiving array element RnDistance to the qth scattering point Q, RnO denotes the nth receiving array element RnDistance to reference point O;
each received signal is received at the nth receiving array element R according to the Q scattering point QnThe echo data at (a) is shown in expression (1):
Figure BDA0003148249280000071
wherein,
Figure BDA0003148249280000072
represents the m-th transmitting array element TmT denotes a fast time, N is 0, 1, …, N-1, M is 0, 1, …, M-1, f0Representing the carrier frequency, σqRepresents the scattering coefficient of the scattering point Q;
the determination unit is specifically configured to:
if it is
Figure BDA0003148249280000073
To represent
Figure BDA0003148249280000074
In a vector form, where L is
Figure BDA0003148249280000075
The length of (a) of (b),
Figure BDA0003148249280000076
bq＝[exp(-j2πf0τq,0)，…，exp(-j2πf0τq，m)，…，exp(-j2πf0τq,M-1)]∈CM×1then, the vector data of expression (1) is as shown in expression (2):
Figure BDA0003148249280000077
wherein, yq,nIndicating that the Q-th scattering point Q is at the n-th receiving array element RnVector data of the echo data;
the determination unit is specifically configured to: using the nth receiving array element RnNon-uniform compression sampling matrix psi ofn∈RL′×LNon-uniform compression sampling is carried out on vector data, and an echo matrix after compression is obtained is shown as an expression (3):
Figure BDA0003148249280000078
wherein s isq,nIndicating that the Q-th scattering point Q is at the n-th receiving array element RnThe compressed echo matrix of (a) is,
Figure BDA0003148249280000081
l' represents the length of the compressed echo;
generating a compressed echo matrix of the qth scattering point Q at each receiving array element according to expression (3) is shown in expression (4):
Figure BDA0003148249280000082
wherein s isqRepresenting the compressed echo matrix at each receiving array element for the Q-th scattering point Q,
Figure BDA0003148249280000083
the determination unit is specifically configured to: if the target object consists of J scattering points, the target echo matrix is shown in expression (5):
s＝Φθ (5)
wherein Φ is ═ ω0，…，ωq，…，ωJ-1]∈CL′N×JDenotes the initial perceptual matrix, θ ═ σ0，…，σq，…，σJ-1]T∈CJ×1Representing the initial scattering coefficient vector for all scattering points.
With reference to the second aspect, in one embodiment, the decomposition unit specifically:
and if the length of the compressed echo matrix is smaller than that of the initial scattering coefficient vector, performing singular value decomposition on the initial sensing matrix to obtain a plurality of singular values.
With reference to the second aspect, in an embodiment, the image generating unit is specifically configured to:
and determining a target scattering coefficient vector according to a sparse recovery algorithm and a target perception matrix.
And generating an image of the target object according to the target scattering coefficient vector.
In a third aspect, an embodiment of the present application provides an electronic device, including:
the system comprises a processor, a memory and a bus, wherein the processor is connected with the memory through the bus, and the memory stores computer readable instructions which are used for realizing the method provided by any one of the implementation modes of the first aspect when being executed by the processor.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, performs the steps in the method as provided in any of the embodiments of the first aspect.
Additional features and advantages of the present application 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 present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a flowchart of a method of radar imaging according to an embodiment of the present disclosure;
FIG. 2 is a graph of imaging results under a uniform sampling and uniform array obtained using the related art;
FIG. 3 is a graph of imaging results for a non-uniform array using a related art technique;
FIG. 4 is a graph of non-uniform sampling and non-uniform array imaging results using a correlation technique;
FIG. 5 is a singular value distribution diagram provided in an embodiment of the present application;
FIG. 6 is another distribution diagram of singular values provided in an embodiment of the present application;
FIG. 7 is a graph of non-uniform sampling and non-uniform array imaging results provided by embodiments of the present application;
fig. 8 is a block diagram of an apparatus for radar imaging according to an embodiment of the present disclosure;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
First, some terms referred to in the embodiments of the present application will be described to facilitate understanding by those skilled in the art.
The terminal equipment: may be a mobile terminal, a fixed terminal, or a portable terminal such as a mobile handset, station, unit, device, multimedia computer, multimedia tablet, internet node, communicator, desktop computer, laptop computer, notebook computer, netbook computer, tablet computer, personal communication system device, personal navigation device, personal digital assistant, audio/video player, digital camera/camcorder, positioning device, television receiver, radio broadcast receiver, electronic book device, gaming device, or any combination thereof, including the accessories and peripherals of these devices, or any combination thereof. It is also contemplated that the terminal device can support any type of interface to the user (e.g., wearable device), and the like.
A server: the cloud server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and can also be a cloud server for providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, network service, cloud communication, middleware service, domain name service, security service, big data and artificial intelligence platform and the like.
The MIMO radar is a radar system with a new system, and has wide application prospect in the field of imaging of moving target objects. In the MIMO radar, a plurality of different waveforms are transmitted through a plurality of antennas, and after receiving processing, a large virtual aperture can be equivalently formed, so that coherent processing time is effectively reduced. To achieve this, ideal orthogonality of the transmitted waveforms is required, but it is difficult to achieve ideal orthogonality for multiple waveforms of the same frequency band.
Currently, there are two methods for imaging non-ideal orthogonal waveforms. Firstly, designing a new transmitting waveform with better orthogonality; another method is to improve the imaging quality of the non-ideal orthogonal waveform through some post-processing algorithms, and related technicians usually improve the imaging quality of the non-ideal orthogonal waveform through a sparse recovery algorithm, and when the imaging quality of the non-ideal orthogonal waveform is improved through the sparse recovery algorithm, most of the methods are based on a conventional and uniform MIMO array structure, and uniform sampling is performed in time, so that the freedom degrees of array design and echo sampling are limited.
However, the sparse recovery algorithm is invalid due to the fact that sensing matrix ill-condition is caused by some conditions such as non-ideal orthogonal waveforms, irregular array structures or non-uniform sampling, and therefore, when the transmitted waveforms are not the ideal orthogonal waveforms, the irregular array structures or the non-uniform sampling, how to improve the robustness of the MIMO radar imaging method is a problem to be solved.
Referring to fig. 1, fig. 1 is a flowchart of a method for radar imaging according to an embodiment of the present disclosure, where in the embodiment of the present disclosure, an execution subject of the method may be an electronic device, and optionally, the electronic device may be a server or a terminal device, but the present disclosure is not limited thereto.
As an example, the specific implementation flow of the method shown in fig. 1 is as follows:
step 100: and receiving the received signals returned by a plurality of scattering points on the target object based on the transmission signals of the plurality of transmission array elements through the plurality of receiving array elements.
In one embodiment, the MIMO radar is an array of M transmitting array elements and N receiving array elements, and M signals with the same central frequency are transmitted to a target object to enable the target object to be subjected to MIMO radar
Figure BDA0003148249280000111
Represents the transmitted signal of the mth transmit array element, where t represents the fast time. Let point Q represent the qth scatter point on the target object. It is assumed that the position of the target object can be roughly obtained by parameter estimation, and the O point is a reference point of the target object. Each receiving array element receives the received signals returned by all scattering points on the target object.
Furthermore, according to the array of the M transmitting array elements and the N receiving array elements and the transmitting signals, envelope delay of different transmitting signals and recording time of different receiving signals are adjusted, so that the signal length of the target echo is reduced.
The Q scattering point Q is relative to the m transmission array element TmIs adjusted to a time delay of tauq,m＝(TmQ-TmO)/C, Q scattering point Q corresponding to n receiving array element RnIs adjusted to a time delay of tauq,n＝(RnQ-RnO)/C，CIndicating the speed of light, TmQ represents the mth transmitting array element TmDistance to the qth scattering point Q, TmO represents the m-th transmitting array element TmDistance to reference point O, RnQ denotes the nth receiving array element RnDistance to the qth scattering point Q, RnO denotes the nth receiving array element RnDistance to reference point O.
In the implementation process, the distance difference between the transmitting array element and the scattering point of the target object and the distance between the transmitting array element and the reference point of the target object are further calculated, and the time delay of each transmitting channel is adjusted according to the distance difference, so that different transmitting channel waveforms simultaneously reach the target object; the distance difference between the receiving array element and the scattering point of the target object and the distance between the receiving array element and the reference point of the target object are further calculated, and the time delay of each receiving channel is adjusted according to the distance difference, so that the echoes of different receiving channels have the same time delay.
Step 101: an initial perceptual matrix is determined from each received signal received.
Specifically, when step 101 is executed, the following steps may be adopted:
the first step is as follows: generating a scattering point for each scattering point at each receiving array element according to each received signal, and executing the following steps:
first, echo data of a scattering point at a receiving array element is acquired.
Specifically, each received signal is received at the nth receiving array element R according to the Q scattering point QnThe echo data at (a) is shown in expression (1):
Figure BDA0003148249280000121
wherein,
Figure BDA0003148249280000122
represents the m-th transmitting array element TmT denotes a fast time, N is 0, 1, …, N-1, M is 0, 1, …, M-1, f0Which is indicative of the carrier frequency,σqthe scattering coefficient of the scattering point Q is indicated.
Next, the echo data is subjected to vector replacement to generate vector data of the echo data.
In particular, if
Figure BDA0003148249280000123
To represent
Figure BDA0003148249280000124
In a vector form, where L is
Figure BDA0003148249280000125
The length of (a) of (b),
Figure BDA0003148249280000126
Figure BDA0003148249280000127
bq＝[exp(-j2πf0τq,0)，…，exp(-j2πf0τq,m)，…，exp(-j2πf0τq，M-1)]∈CM×1then, the vector data of expression (1) is as shown in expression (2):
Figure BDA0003148249280000131
wherein, yq,nIndicating that the Q-th scattering point Q is at the n-th receiving array element RnVector data of the echo data at.
And then, carrying out non-uniform compression sampling on the vector data by using the non-uniform compression sampling matrix to obtain a compressed echo matrix.
In particular, using the nth receiving array element RnNon-uniform compression sampling matrix psi ofn∈RL′×LNon-uniform compression sampling is carried out on vector data, and an echo matrix after compression is obtained is shown as an expression (3):
Figure BDA0003148249280000132
wherein s isq,nIndicating that the Q-th scattering point Q is at the n-th receiving array element RnThe compressed echo matrix of (a) is,
Figure BDA0003148249280000133
l' represents the length of the compression echo.
Generating a compressed echo matrix of the qth scattering point Q at each receiving array element according to expression (3) is shown in expression (4):
Figure BDA0003148249280000134
wherein s isqRepresenting the compressed echo matrix at each receiving array element for the Q-th scattering point Q,
Figure BDA0003148249280000135
the second step is that: generating a target echo matrix according to the compressed echo matrix of all scattering points at each receiving array element;
specifically, if the target object is composed of J scattering points, the target echo matrix obtained by all scattering points of the target object on all receiving array elements is shown in expression (5):
s＝Φθ (5)
where s denotes a target echo matrix, Φ ═ ω0，…，ωq，…，ωJ-1]∈CL′N×JDenotes the initial perceptual matrix, θ ═ σ0，…，σq，…，σJ-1]T∈CJ×1Representing the initial scattering coefficient vector for all scattering points.
In the implementation process, the echo data of each scattering point on the target object at each receiving array element is obtained, and the target echo matrix is generated according to the echo data, so that the image of the target object is generated according to the echo matrix.
The third step: and decomposing the target echo matrix to obtain an initial scattering coefficient vector and an initial sensing matrix.
Specifically, a target echo matrix s is decomposed to obtain an initial scattering coefficient vector θ and an initial sensing matrix Φ.
Step 102: and carrying out singular value decomposition on the initial sensing matrix to obtain a plurality of singular values.
Specifically, if the length of the compressed echo matrix is smaller than the length of the initial scattering coefficient vector, singular value decomposition is performed on the initial sensing matrix to obtain a plurality of singular values.
When the transmitted waveform has an ideal orthogonality and uniform MIMO array structure and is uniformly sampled in time, an initial scattering coefficient vector theta can be obtained by solving the expression (5), and an image of a target object can be obtained according to the initial scattering coefficient vector theta.
As an example, when the length of s is not less than theta, i.e. L' N ≧ J, the least square method (LS) can be used to solve for theta, i.e. theta ═ phi+s, wherein+Representing the pseudo-inverse matrix of phi.
In the case of high resolution, the length of s is usually less than θ, L' N<J, the theta may be solved using a sparse recovery algorithm, which may be smoothing l0(SL0) algorithm.
In the SL0 algorithm, a key step is to project the unconstrained maximized solution θ 'onto the feasible set { θ | s ═ Φ θ }, resulting in the optimal solution θ ═ θ' - Φ θ }+(Φθ′-s)。
It should be noted that, in the embodiment of the present application, only the reconstruction algorithm based on the smoothed L0 norm (SL0 algorithm) is taken as an example for description, and in practical applications, the sparse recovery algorithm may also be an orthogonal matching pursuit algorithm (OMP algorithm) or a compressed sensing block sparse algorithm (BOMP algorithm), which is not limited herein.
However, when the emission waveforms are non-orthogonal and irregular array structures or the sampling is not uniform, the initial sensing matrix phi may have a pathological phenomenon, which causes the matrix inversion failure, that is, the pseudo-inverse matrix corresponding to the initial sensing matrix phi cannot be obtainedMatrix phi+。
The pseudo-inverse matrix phi corresponding to the initial sensing matrix phi cannot be obtained+The specific reasons are as follows:
performing singular value decomposition on the initial sensing matrix phi, wherein the decomposition expression is as follows:
Figure BDA0003148249280000151
wherein U is [ U ]0，…，ui，…，uL′N-1]∈CL′N×L′NAnd V ═ V0，…，vi，…，vL′N-1]∈CJ×L′NIs a unitary matrix with columns containing singular vectors, and Σ is a diagonal matrix containing singular values λiI is 0, 1, …, L' N-1. The pseudo-inverse matrix of Φ is represented as:
Figure BDA0003148249280000152
as an example, when λiWhen the value is 0, the solution result of (7), that is, the pseudo-inverse matrix Φ corresponding to the initial sensing matrix Φ does not exist+The solution fails.
As another example, when the echo is noisy, i.e. in the presence of noise
Figure BDA0003148249280000154
Where e represents the additive noise and e represents the echo matrix of the target object without noise. At this time, solving θ by LS can be expressed as:
Figure BDA0003148249280000153
from (8), when λ isiVery little, the noise may be amplified to the extent that the true echo is masked, and the solved θ may not accurately obtain the image of the target object.
Therefore, under the condition that the initial sensing matrix is ill-conditioned, the initial sensing matrix cannot be obtainedObtaining a pseudo inverse matrix phi corresponding to the initial sensing matrix phi+I.e. no image of the target object can be obtained.
Specifically, when L' N<J, performing singular value decomposition on the initial sensing matrix phi to obtain a plurality of singular values lambdai，i＝0，1，…，L′N-1。
In the implementation process, under the condition that the length of the compressed echo matrix is smaller than that of the initial scattering coefficient vector, singular value decomposition is carried out on the initial sensing matrix to obtain a plurality of singular values, so that the singular values can be conveniently adjusted subsequently.
Step 103: and adjusting the plurality of singular values to obtain an adjusted singular value set.
In one embodiment, when step 103 is performed, any one or combination of the following may be used:
the first method is as follows: and screening the plurality of singular values according to a first preset singular value threshold value to obtain an adjusted singular value set.
Specifically, from the plurality of singular values, a singular value higher than a first preset singular value threshold is screened out.
And obtaining an adjusted singular value set according to the screened singular values.
As an embodiment, the plurality of singular values are filtered by a method of truncating the singular values, and the adjusted singular value set is obtained.
Specifically, the first preset singular value threshold is λthrFrom a plurality of singular valuesiI is 0, 1, …, L' N-1, above a first predetermined singular value threshold λ is selectedthrObtaining adjusted sets of singular values
Figure BDA0003148249280000162
i＝0，1，…，X1-1, wherein X1Indicating the number of adjusted singular values. That is, the adjusted singular values are formulated as:
Figure BDA0003148249280000161
wherein the first preset singular value threshold lambdathr≥0。
The method for screening the plurality of singular values is also called a truncated singular value method, and the singular values in the initial sensing matrix are processed by the truncated singular value method, so that the adjustment of the ill-conditioned sensing matrix is realized.
In the implementation process, singular values which are higher than a first preset singular value threshold value in the singular values are screened, and singular values which are not higher than the first preset singular value threshold value in the singular values are removed, so that an adjusted singular value set is obtained.
The second method comprises the following steps: and screening and correcting the plurality of singular values according to a second preset singular value threshold value to obtain an adjusted singular value set.
Specifically, screening out singular values higher than a second preset singular value threshold from the plurality of singular values;
and correcting the screened singular values to obtain at least one corrected singular value.
And obtaining an adjusted singular value set according to the corrected at least one singular value.
As another embodiment, the second preset singular value threshold is λaFrom a plurality of singular valuesiI is 0, 1, …, L' N-1, and is higher than a second preset singular value threshold lambdaaObtaining the screened singular value
Figure BDA0003148249280000163
r＝0，1，…，X2-1, wherein X2Representing the number of singular values after screening.
Then, the screened singular values are:
Figure BDA0003148249280000171
it should be noted that the first preset singular value threshold λthrAnd a second predetermined singular value thresholdValue of lambdaaMay or may not be equal, X1And X2May or may not be equal. And a first preset singular value threshold lambdathrAnd a second predetermined singular value threshold lambdaaThe value of (b) may be 0.01, 0.001, or 0.002, but the present application is not limited thereto.
Further, the screened singular values are processed
Figure BDA00031482492800001716
And correcting to obtain at least one corrected singular value.
As an example, filtered singular values are calculated
Figure BDA0003148249280000172
r＝0，1，…，X2Average value of-1
Figure BDA0003148249280000173
And according to the mean value
Figure BDA0003148249280000174
For the screened singular value
Figure BDA0003148249280000175
And (6) correcting.
I.e. the mean value
Figure BDA0003148249280000176
Comprises the following steps:
Figure BDA0003148249280000177
if the filtered singular value is calculated
Figure BDA0003148249280000178
r＝0，1，…，X2In-1, there is a threshold λ of more than the third singular valuebWill be greater than a third singular value threshold lambdabCorrecting singular values of to mean values
Figure BDA0003148249280000179
Thereby obtaining a set of adjusted singular values, i.e. adjusted singular values
Figure BDA00031482492800001710
Is formulated as:
Figure BDA00031482492800001711
as another example, the filtered singular values are paired by a correction constant P
Figure BDA00031482492800001712
r＝0，1，…，X2-1, making a correction.
If the filtered singular value is calculated
Figure BDA00031482492800001713
r＝0，1，…，X2In-1, there is a threshold λ of more than the third singular valuebWill be greater than a third singular value threshold lambdabIs replaced by a correction constant P, thereby obtaining an adjusted set of singular values, i.e. adjusted singular values
Figure BDA00031482492800001714
Is formulated as:
Figure BDA00031482492800001715
in the implementation process, singular values which are higher than a second preset singular value threshold value in the plurality of singular values are screened, and the screened singular values are further corrected, so that an adjusted singular value set is obtained, and the accuracy of the adjusted singular values is improved.
Obtaining the adjusted singular value set through the first mode or the second mode, wherein the adjusted singular value set comprises the singular value setAdjusted singular values
Figure BDA0003148249280000181
Step 104: and generating a target perception matrix according to the adjusted singular value set.
Further, generating a target sensing matrix phi according to the adjusted singular value set*The object perception matrix phi*Comprises the following steps:
Figure BDA0003148249280000182
further, a target perception matrix Φ is calculated*Corresponding pseudo-inverse matrix phipinvThen pseudo inverse matrix phipinvComprises the following steps:
Figure BDA0003148249280000183
wherein X represents the number of adjusted singular values obtained when the first mode is adopted
Figure BDA0003148249280000184
When X is equal to X1When the second mode obtains the adjusted singular value
Figure BDA0003148249280000185
When X is equal to X2。
Step 105: and generating an image of the target object according to the target perception matrix.
Specifically, when step 105 is executed, the following steps may be adopted:
step a: and determining a target scattering coefficient vector according to a sparse recovery algorithm and a target perception matrix.
As an example, the SL0 algorithm is utilized to sense the matrix phi according to the target*Calculating a target scattering coefficient vector theta*。
Namely, in the SL0 algorithm, the initialization steps are:
θ0＝Φpinvs (16)
the projection steps are as follows:
θ*＝θ′-Φpinv(Φ*θ′-s) (17)
step b: and generating an image of the target object according to the target scattering coefficient vector.
Specifically, the target scattering coefficient vector θ is calculated by the above-described method*And according to the target scattering coefficient vector theta*Generating an image of the target object, i.e. a target scattering coefficient vector theta*The form of the target object is transformed to obtain the image of the target object.
In the implementation process, the target sensing matrix is obtained through the adjusted singular value, so that the adjustment of the ill-conditioned sensing matrix is realized, further, a target scattering coefficient vector is determined by using a sparse recovery algorithm and the target sensing matrix, so that the solution of the target scattering coefficient vector is realized, and further, an image of a target object can be generated according to the target scattering coefficient vector obtained by the solution.
Consider a linear MIMO array with 4 transmit elements and 40 receive elements. The receiving array element spacing and the transmitting array element spacing are 3.735m and 149.4m respectively. 4 polyphase coded signals with a code length of 300 are transmitted. The bandwidth is 600MHz and the center frequency is 10 GHz. These transmitted signals are not ideal orthogonal signals, they have low autocorrelation side lobes, but high cross-correlation side lobes. Consider a point scattering target consisting of 330 scattering points, 5km from radar. The theoretical resolutions in the range and azimuth directions are 0.25m and 0.251m, respectively.
Under the condition of uniform sampling and uniform MIMO array, the echo of the target object is uniformly sampled at the frequency of 600MHz by adopting a joint block sparse recovery algorithm, and a two-dimensional image of the target object is obtained, as shown in FIG. 2, FIG. 2 is an imaging result diagram under the uniform sampling and uniform array obtained by using the related technology.
Consider the case of a non-uniform array. From the above uniform array, 20 receive array elements were randomly selected. Under the condition that other parameters are not changed, a joint block sparse recovery algorithm is adopted to recover High Resolution Range Profiles (HRRPs) of the target, the imaging result of the non-uniform array is shown in FIG. 3, and FIG. 3 is a diagram of the imaging result of the non-uniform array obtained by using the related technology. The result shows that the nonuniform array is adopted, the number of receiving array elements is less, and the imaging effect is poor.
Further, non-uniform sampling is considered with non-uniform arrays. Randomly selected 30% of the data from the uniformly sampled echoes is used as the non-uniformly sampled echo. The imaging results using non-uniform sampling and non-uniform array are shown in fig. 4, and fig. 4 is a graph of the imaging results using non-uniform sampling and non-uniform array obtained using the related art. Clearly, the joint block sparse recovery algorithm is ineffective because the echoes are randomly down-sampled in both time and space.
As an example, with the radar imaging method proposed in the present application, an image of a target object is generated under non-uniform sampling and non-uniform array conditions. The imaging area is the same as fig. 4, and the imaging area is divided into grids, and the size of the grids is set to the theoretical resolution. Other parameters are the same as in fig. 4. A perceptual matrix is constructed, the singular values of which are shown in fig. 5 and 6. Fig. 6 is another singular value distribution diagram provided in the embodiment of the present application, and as can be seen from fig. 5 and 6, zero singular values and very small singular values exist. This means that the perceptual matrix is ill-conditioned due to non-orthogonal waveforms, non-uniform arrays and non-uniform sampling. In this case, the computational imaging method cannot be directly applied, and adjustment of singular values is required.
Further, the adjusted singular value is obtained in the first mode of the present application, wherein the first preset singular value threshold is 0.01, the pseudo-inverse of the sensing matrix is calculated according to equation (15), and the target object is imaged by using the modified SL0 algorithm based on equations (16) and (17). The imaging result obtained by the method of the present application is shown in fig. 7, and fig. 7 is a non-uniform sampling and non-uniform array imaging result graph provided by the embodiment of the present application.
Therefore, the two-dimensional image of the target object is obtained by the joint block sparse recovery algorithm only under the conditions of uniform sampling and uniform MIMO array, imaging is carried out under the conditions of non-uniform sampling and non-uniform array, and the final imaging effect is poor.
Referring to fig. 8, fig. 8 is a block diagram of a radar imaging apparatus according to an embodiment of the present disclosure, and an apparatus 800 shown in fig. 8 corresponds to the method in fig. 1, and includes various functional modules capable of implementing the method in fig. 1.
In one embodiment, the apparatus 800 shown in FIG. 8 includes:
a receiving unit 801, configured to receive, through the multiple receiving array elements, received signals returned by the multiple scattering points on the target object based on the transmission signals of the multiple transmitting array elements.
A determining unit 802, configured to determine an initial sensing matrix according to each received signal;
a decomposition unit 803, configured to perform singular value decomposition on the initial sensing matrix to obtain a plurality of singular values.
An adjusting unit 804, configured to adjust the plurality of singular values to obtain an adjusted singular value set.
The matrix generating unit 805 is configured to generate a target sensing matrix according to the adjusted singular value set.
And an image generating unit 806, configured to generate an image of the target object according to the target perception matrix.
In an embodiment, the adjusting unit 804 is specifically configured to:
and screening out singular values higher than a first preset singular value threshold value from the plurality of singular values.
And obtaining an adjusted singular value set according to the screened singular values.
In an embodiment, the adjusting unit 804 is specifically configured to:
and screening out singular values higher than a second preset singular value threshold value from the plurality of singular values.
And correcting the screened singular values to obtain at least one corrected singular value.
And obtaining an adjusted singular value set according to the corrected at least one singular value.
In an embodiment, the determining unit 802 is specifically configured to:
generating a scattering point for each scattering point at each receiving array element according to each received signal, and executing the following steps:
echo data of a scattering point at a receiving array element is acquired.
And carrying out vector replacement on the echo data to generate the vector data of the echo data.
And carrying out non-uniform compression sampling on the vector data by using the non-uniform compression sampling matrix to obtain a compressed echo matrix.
And generating a target echo matrix according to the compressed echo matrix of all scattering points at each receiving array element.
And decomposing the target echo matrix to obtain an initial scattering coefficient vector and an initial sensing matrix.
With reference to the second aspect, in an embodiment, the determining unit 802 is specifically configured to:
if there are M transmitting array elements, N receiving array elements, the reference point of the target object is O, the Q scattering point on the target object is Q, then the Q scattering point Q is obtained relative to the M transmitting array element TmAnd the Q scattering point Q is relative to the n receiving array element RnThe adjustment time delay.
Wherein the Q scattering point Q is relative to the m transmitting array element TmIs adjusted to a time delay of tauq,m＝(TmQ-TmO)/C, Q scattering point Q corresponding to n receiving array element RnIs adjusted to a time delay of tauq,n＝(RnQ-RnO)/C, C represents the speed of light, TmQ represents the mth transmitting array element TmDistance to the qth scattering point Q, TmO represents the m-th transmitting array element TmDistance to reference point O, RnQ denotes the nth receiving array element RnDistance to the qth scattering point Q, RnO denotes the nth receiving array element RnDistance to reference point O.
Each received signal is received at the nth receiving array element R according to the Q scattering point QnThe echo data at (a) is shown in expression (1):
Figure BDA0003148249280000221
wherein,
Figure BDA0003148249280000222
represents the m-th transmitting array element TmT denotes a fast time, N is 0, 1, …, N-1, M is 0, 1, …, M-1, f0Representing the carrier frequency, σqThe scattering coefficient of the scattering point Q is indicated.
The determining unit 802 is specifically configured to:
if it is
Figure BDA0003148249280000223
To represent
Figure BDA0003148249280000224
In a vector form, where L is
Figure BDA0003148249280000225
The length of (a) of (b),
Figure BDA0003148249280000226
aq，n＝exp(-j2πf0τq，n)，
bq＝[exp(-j2πf0τq,0)，…，exp(-j2πf0τq，m)，…，exp(-j2πf0τq,M-1)]∈CM×1then, the vector data of expression (1) is as shown in expression (2):
Figure BDA0003148249280000227
wherein, yq,nIndicating that the Q-th scattering point Q is at the n-th receiving array element RnVector data of the echo data at.
The determining unit 802 is specifically configured to: using the nth receiving array element RnNon-uniform compression sampling matrix psi ofn∈RL ′×LNon-uniform compression sampling is carried out on vector data, and an echo matrix after compression is obtained is shown as an expression (3):
Figure BDA0003148249280000231
wherein s isq,nIndicating that the Q-th scattering point Q is at the n-th receiving array element RnThe compressed echo matrix of (a) is,
Figure BDA0003148249280000232
l' represents the length of the compression echo.
Generating a compressed echo matrix of the qth scattering point Q at each receiving array element according to expression (3) is shown in expression (4):
Figure BDA0003148249280000233
wherein s isqRepresenting the compressed echo matrix at each receiving array element for the Q-th scattering point Q,
Figure BDA0003148249280000234
the determining unit 802 is specifically configured to: if the target object consists of J scattering points, the target echo matrix is shown in expression (5):
s＝Φθ (5)
wherein Φ is ═ ω0，…，ωq，…，ωJ-1]∈CL′N×JDenotes the initial perceptual matrix, θ ═ σ0，…，σq，…，σJ-1]T∈CJ×1Representing the initial scattering coefficient vector for all scattering points.
In one embodiment, decomposition unit 803 specifically:
and if the length of the compressed echo matrix is smaller than that of the initial scattering coefficient vector, performing singular value decomposition on the initial sensing matrix to obtain a plurality of singular values.
In one embodiment, the image generation unit 806 is specifically configured to:
and determining a target scattering coefficient vector according to a sparse recovery algorithm and a target perception matrix.
And generating an image of the target object according to the target scattering coefficient vector.
It should be noted that the apparatus 800 shown in fig. 8 can implement the processes of the method for radar imaging in the embodiment of the method in fig. 1. The operations and/or functions of the various modules in the apparatus 800 are each intended to implement a corresponding flow in the method embodiment in fig. 1. Reference may be made specifically to the description of the above method embodiments, and a detailed description is appropriately omitted herein to avoid redundancy.
Referring to fig. 9, fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure, where the electronic device 900 shown in fig. 9 may include: at least one processor 910, such as a CPU, at least one communication interface 920, at least one memory 930, and at least one communication bus 940. Wherein the communication bus 940 is used for realizing direct connection communication of the components. In this embodiment, the communication interface 920 of the device in this application is used for performing signaling or data communication with other node devices. The memory 930 may be a high-speed RAM memory or a non-volatile memory (e.g., at least one disk memory). The memory 930 may optionally be at least one memory device located remotely from the processor. The memory 930 stores computer readable instructions, which when executed by the processor 910, cause the electronic device to perform the method processes described above with reference to fig. 1.
An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a server, the computer program implements the method process shown in fig. 1.
In the several embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. The above-described system embodiments are merely illustrative, and for example, the division of the system apparatus into only one logical functional division may be implemented in other ways, and for example, a plurality of apparatuses or components may be combined or integrated into another system, or some features may be omitted, or not implemented.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (10)

1. A method of radar imaging, the method comprising:
receiving, by a plurality of receiving array elements, received signals returned by a plurality of scattering points on the target object based on the transmission signals of the plurality of transmitting array elements;
determining an initial sensing matrix according to each received signal;
performing singular value decomposition on the initial sensing matrix to obtain a plurality of singular values;
adjusting the plurality of singular values to obtain an adjusted singular value set;
generating a target perception matrix according to the adjusted singular value set;
and generating an image of the target object according to the target perception matrix.
2. The method of claim 1, wherein the adjusting the plurality of singular values to obtain an adjusted set of singular values comprises:
screening out singular values higher than a first preset singular value threshold value from the plurality of singular values;
and obtaining the adjusted singular value set according to the screened singular value.
3. The method of claim 1, wherein the adjusting the plurality of singular values to obtain an adjusted set of singular values comprises:
screening out singular values higher than a second preset singular value threshold value from the plurality of singular values;
correcting the screened singular values to obtain at least one corrected singular value;
and obtaining the adjusted singular value set according to the corrected at least one singular value.
4. The method according to any of claims 1-3, wherein said determining an initial perceptual matrix from the received signals comprises:
generating a scattering point for each scattering point at each receiving array element according to each received signal, and executing the following steps:
acquiring echo data of a scattering point at a receiving array element;
carrying out vector replacement on the echo data to generate vector data of the echo data;
carrying out non-uniform compression sampling on the vector data by using a non-uniform compression sampling matrix to obtain a compressed echo matrix;
generating a target echo matrix according to the compressed echo matrix of all scattering points at each receiving array element;
and decomposing the target echo matrix to obtain an initial scattering coefficient vector and the initial perception matrix.
5. The method of claim 4, wherein the echo data of one scattering point at one receiving array element comprises:
if there are M transmitting array elements, N receiving array elements, the reference point of the target object is O, the Q scattering point on the target object is Q, then the Q scattering point Q is obtained relative to the M transmitting array element TmAnd said Q-th scattering point Q is relative to the n-th receiving array element RnThe adjustment time delay of (2);
wherein the Q scattering point Q is relative to the m transmitting array element TmIs adjusted to a time delay of tauq，m＝(TmQ-TmO)/C, the Q scattering point Q is relative to the n receiving array element RnIs adjusted to a time delay of tauq，n＝(RnQ-RnO)/C, C represents the speed of light, TmQ represents the m-th transmitting array element TmDistance to said qth scattering point Q, TmO represents the m-th transmitting array element TmDistance to said reference point O, RnQ represents the nth receiving array element RnDistance, R, to said qth scattering point QnO represents the n-th receiving array element RnA distance to the reference point O;
the received signals are respectively directed to the Q scattering point Q at the n receiving array element RnThe echo data at (a) is shown in expression (1):
Figure FDA0003148249270000021
wherein,
Figure FDA0003148249270000022
represents the m-th transmitting array element TmT denotes a fast time, N is 0, 1, …, N-1, M is 0, 1, …, M-1, f0Representing the carrier frequency, σqRepresents the scattering coefficient of the scattering point Q;
the performing vector replacement on the echo data to generate vector data of the echo data includes:
if it is
Figure FDA0003148249270000023
To represent
Figure FDA0003148249270000024
In a vector form, where L is
Figure FDA0003148249270000025
The length of (a) of (b),
Figure FDA0003148249270000031
aq，n＝exp(-j2πf0τq，n)，
bq＝[exp(-j2πf0τq,0)，…，exp(-j2πf0τq,m)，…，exp(-j2πf0τq,M-1)]∈CM×1then the vector data of expression (1) is as shown in expression (2):
Figure FDA0003148249270000036
wherein, yq,nIndicating that said Q scattering point Q is at the n receiving array element RnVector data of the echo data;
the non-uniform compression sampling is performed on the vector data by using the non-uniform compression sampling matrix to obtain a compressed echo matrix, and the method comprises the following steps:
using said nth receiving array element RnNon-uniform compression sampling matrix psi ofn∈RL′×LAnd carrying out non-uniform compression sampling on the vector data to obtain a compressed echo matrix as shown in an expression (3):
Figure FDA0003148249270000032
wherein s isq，nIndicating that said Q scattering point Q is at the n receiving array element RnThe compressed echo matrix of (a) is,
Figure FDA0003148249270000033
l' represents the length of the compressed echo;
generating a compressed echo matrix of the qth scattering point Q at each receiving array element according to the expression (3) is shown in the expression (4):
Figure FDA0003148249270000034
wherein s isqRepresenting a compressed echo matrix of said Q-th scattering point Q at each receiving array element,
Figure FDA0003148249270000035
generating a target echo matrix according to the compressed echo matrix of all scattering points at each receiving array element, including:
if the target object consists of J scattering points, the target echo matrix is shown in expression (5):
s＝Φθ (5)
wherein Φ is ═ ω0，…，ωq，…，ωJ-1]∈CL′N×JRepresenting said initial perceptual matrix, θ ═ σ0，…，σq，…，σJ-1]T∈CJ×1The initial scattering coefficient vectors representing all scattering points.
6. The method of claim 4, wherein the performing singular value decomposition on the initial sensing matrix to obtain a plurality of singular values comprises:
and if the length of the compressed echo matrix is smaller than that of the initial scattering coefficient vector, performing singular value decomposition on the initial sensing matrix to obtain a plurality of singular values.
7. The method according to any one of claims 1-3, wherein said generating an image of said target object from said target perception matrix comprises:
determining a target scattering coefficient vector according to a sparse recovery algorithm and the target perception matrix;
and generating an image of the target object according to the target scattering coefficient vector.
8. An apparatus for radar imaging, the apparatus comprising:
a receiving unit, configured to receive, through a plurality of receiving array elements, received signals returned by a plurality of scattering points on a target object based on transmission signals of a plurality of transmitting array elements;
a determining unit, configured to determine an initial sensing matrix according to each received signal;
the decomposition unit is used for carrying out singular value decomposition on the initial sensing matrix to obtain a plurality of singular values;
an adjusting unit, configured to adjust the plurality of singular values to obtain an adjusted singular value set;
the matrix generating unit is used for generating a target perception matrix according to the adjusted singular value set;
and the image generating unit is used for generating an image of the target object according to the target perception matrix.
9. An electronic device, comprising:
a processor, a memory, and a bus, the processor being connected to the memory through the bus, the memory storing computer readable instructions for implementing the method of any one of claims 1-7 when the computer readable instructions are executed by the processor.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed by a server, implements the method of any one of claims 1-7.
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