CN116908845B - MIMO radar super-resolution imaging method, MIMO radar and storage medium - Google Patents
MIMO radar super-resolution imaging method, MIMO radar and storage medium Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract
According to the MIMO radar super-resolution imaging method, the MIMO radar and the storage medium, a signal model based on phased emission beam scanning is constructed, echo signals are approximated by utilizing a space geometric model of a radar and a target, a convolution model of a target scattering coefficient and an antenna pattern is established, a target super-resolution imaging method based on compressed sensing is designed based on the convolution model, the resolution of adjacent targets is improved, meanwhile, contour information of the targets is reserved to a certain extent, the imaging resolution can be improved under the existing array channel number, and azimuth and/or pitching super-resolution estimation of millimeter wave radar imaging is realized, so that a millimeter wave radar imaging system based on the imaging method can provide a more reliable solution idea for automatic driving.
Description
Technical Field
The invention belongs to the field of radar scanning and imaging, and particularly relates to a super-resolution imaging method based on MIMO radar beam scanning, an MIMO radar and a storage medium.
Background
In the automatic driving system, an optical sensor typified by a camera and a radar sensor mainly including a laser radar and a millimeter wave radar constitute a road environment sensing system in automatic driving. The camera outputs image information to realize optical imaging; imaging the target three-dimensional structure by using laser radar output point cloud; the millimeter wave radar is mainly used for anti-collision early warning and target detection, and can be applied to adaptive cruise (ACC), auxiliary Lane Changing (LCA), blind Spot Detection (BSD) and the like, but is less in application in the imaging field. Under severe weather conditions such as fog, rain, snow and the like, the imaging performance of the camera and the laser radar is greatly reduced, and an effective imaging result of a scene cannot be obtained, so that the decision performance of an automatic driving system is greatly influenced; the millimeter wave radar is used as a sensor with stronger robustness under severe conditions, and if the sensor can image a foreground area, the adaptability of the whole automatic driving system to the environment can be improved, and the reliability of the automatic driving system can be improved.
Millimeter wave radar forward-looking imaging is mainly to perform two-dimensional imaging on a target scene in front in two dimensions of distance and azimuth. Imaging resolution is an important evaluation index in the field of forward-looking imaging, and can be divided into two dimensions, namely distance-wise resolution and azimuth-wise resolution. For distance resolutionIt is mainly related to the signal bandwidth of the radar, and the calculation formula is as follows:
(1)
wherein,indicating the speed of light +.>Representing the bandwidth of the radar signal. As the signal bandwidth of the millimeter wave radar can reach 2-3GHz generally, the distance resolution of the millimeter wave radar can be calculated to be about 5cm, and the requirements of most application scenes can be met. But for azimuthal resolution->The method mainly relates to the length of the physical aperture of the radar antenna, and the calculation formula is as follows:
(2)
wherein,representing the wavelength of the transmitted signal, ">Representation ofAperture length of radar antenna, < >>Indicating the azimuth angle of the target and the radar. For a uniform linear array, antenna aperture +.>，/>Representing the number of antenna elements of the linear array, +.>Representing the array element spacing.
Since millimeter wave radars have smaller volumes, if the resolution in azimuth is to be improved, the number of antennas of the radar needs to be increased, which means that the hardware cost is increased and the physical size of the radar is increased, which contradicts the advantage of small volume. Therefore, the azimuth resolution is an important factor for restricting the development of forward-looking imaging of the millimeter wave radar, and if the azimuth super-resolution estimation of the forward-looking imaging of the millimeter wave radar can be realized under the limit of the limited aperture size, the millimeter wave radar imaging system provides a more reliable solution for automatic driving.
Disclosure of Invention
The invention aims to provide a MIMO radar super-resolution imaging method, a MIMO radar and a storage medium, which can improve imaging resolution under the existing array channel number, realize super-resolution estimation of azimuth direction and/or elevation direction of millimeter wave radar imaging, and enable a millimeter wave radar super-resolution imaging system based on the imaging method to provide a more reliable solution for automatic driving.
The MIMO radar super-resolution imaging method is suitable for a multiple-input multiple-output array radar system, and comprises the following steps:
step 1, in the process of MIMO radar beam scanning, calculating the phase deviation value of each transmitting channel according to the scanned angle, and superposing the transmitting signals of each transmitting channel after phase deviation to synthesize a transmitting beam forming signal；
Step 2, transmitting the beam formed signalAfter the target reflection, the echo signals are respectively received by the receiving channels>；
Step 3, the radar receiver pairs echo signalsDeclivity results in a difference signal +.>Difference frequency signal after declivating each receiving channel +.>After distance dimension FFT, synthesizing to obtain beam forming signal +.>The beam formed signalObtaining a two-dimensional complex echo signal comprising distance and angle through approximation processing>；
Step 4, combining the two-dimensional complex echo signalsIs divided into two parts, one part is an antenna pattern complex matrix composed of antenna patterns with different scanning angles +.>A part of the complex scattering coefficient distribution matrix is the complex scattering coefficient distribution matrix of each point in the target scene>；
Step 5, combining the two-dimensional complex echo signalsThe convolution process of carrying out weighted summation on scattering coefficient distribution matrixes of all azimuth dimensions in the same distance unit by using antenna pattern complex matrixes formed by antenna patterns with different scanning angles is expressed as follows:
(3)
wherein, the total sampling point number of the radar imaging view distance dimension is assumed to bePThe number of scan direction angles isKThe total sampling point number of the antenna pattern in the azimuth dimension isThen->Complex matrix of echo signals representing multiple scans, the size of which is +.>；/>Antenna pattern complex matrix composed of antenna patterns with different scanning angles, the size is +.>；/>A complex scattering coefficient distribution matrix for each point in the target scene, the size of the complex scattering coefficient distribution matrix is +.>The method comprises the steps of carrying out a first treatment on the surface of the p is a distance dimension sampling point, q is an azimuth dimension sampling point; k is the kth scan direction angle;
step 6, representing a convolution model in a matrix operation mode:
the convolution model is:
wherein,is a noise distribution complex matrix with the size of +.>；
Respectively and individually draw out、/>And->Corresponding columns in (a) to obtain an echo convolution form of a target azimuth scattering coefficient distribution matrix and an antenna pattern on the same distance unit: />，/>For the azimuth scattering coefficient distribution matrix to be reconstructed, n is the complex matrix extracted from the noise distribution matrix +.>Corresponding columns of (3);
step 7, azimuth scattering coefficient distribution matrix to be reconstructedRegarded as a sparse target, established as +.>Target equation for norm minimization:
(4)
wherein,representing the wavelength of the transmitted signal;
step 8, atAdding a differential constraint term on the basis of norm minimization, and increasing the continuity of scattering points in a reconstruction result by utilizing the differential constraint, wherein the continuity is defined as an objective function of a Fused-LASSO minimization problem, and the specific form is as follows:
（5）
wherein,is a differential matrix>Representing the fusion parameters, non-zero and positive;
and 9, solving the objective function by adopting a convex optimization method, and obtaining a final iteration expression after calculating the corresponding augmented Lagrangian function through variable substitution, and performing an iteration solving process to realize super-resolution imaging of the sparse target in the radar beam scanning process.
According to the convex optimization method, an objective function is solved by adopting an alternating direction multiplier method ADMM, and after a variable is replaced to obtain a corresponding augmented Lagrangian function, a final iterative expression is obtained, wherein the method is specifically as follows:
(6)
(7)
(8)
(9)
(10)
wherein,representing constraint item variables, respectively->Representing the variable values of the kth iteration respectively,representing the updated values after the k+1st iteration, respectively,>lagrangian multipliers, respectively representing the kth iteration,>respectively represent the updated Lagrangian multiplier after the (k+1) th iteration,/->Represents the conjugate transpose of the matrix,penalty term parameters, respectively>Respectively represent soft threshold functions, ">Representing the identity matrix.
A MIMO radar comprising:
the signal transmitting module is used for calculating according to the scanning angle in the process of MIMO radar beam scanningThe phase shift value of each transmitting channel, and the transmitting signals of each transmitting channel after phase shift are overlapped to form transmitting beam forming signals；
A signal receiving module for transmitting the beam forming signalAfter the target reflection, the echo signals are respectively received by the receiving channels>；
A signal synthesis module for receiving echo signalsDeclivity results in a difference signal +.>After that, the difference frequency signal after declivating each receiving channel is +.>After distance dimension FFT, synthesizing to obtain beam forming signal +.>The beam forming signal +.>Obtaining a two-dimensional complex echo signal comprising distance and angle through approximation processing>；
A signal processing module for processing the two-dimensional complex echo signalsIs divided into two parts, one part is an antenna pattern complex matrix composed of antenna patterns with different scanning angles +.>A part of the complex scattering coefficient distribution matrix is the complex scattering coefficient distribution matrix of each point in the target scene>；
By combining two-dimensional complex echo signalsThe convolution process of carrying out weighted summation on scattering coefficient distribution matrixes of all azimuth dimensions in the same distance unit by using antenna pattern complex matrixes formed by antenna patterns with different scanning angles is expressed as follows:
(3)
wherein, the total sampling point number of the radar imaging view distance dimension is assumed to bePThe number of scan direction angles isKThe total sampling point number of the antenna pattern in the azimuth dimension isThen->Complex matrix of echo signals representing multiple scans, the size of which is +.>；/>Antenna pattern complex matrix composed of antenna patterns with different scanning angles, the size is +.>；/>A complex scattering coefficient distribution matrix for each point in the target scene, the size of the complex scattering coefficient distribution matrix is +.>The method comprises the steps of carrying out a first treatment on the surface of the p is a distance dimension sampling point, q is an azimuth dimension sampling point; k is the kth scan direction angle;
the convolution model is represented by a form of matrix operation, and is:
wherein,is a noise distribution complex matrix with the size of +.>；
Respectively and individually draw out、/>And->Corresponding columns in (a) to obtain an echo convolution form of a target azimuth scattering coefficient distribution matrix and an antenna pattern on the same distance unit: />，/>For the azimuth scattering coefficient distribution matrix to be reconstructed, n is the complex matrix extracted from the noise distribution matrix +.>Corresponding columns of (3);
directional scattering coefficient distribution matrix to be reconstructedRegarded as a sparse target, established as +.>Norm minimumThe objective equation:
（4）
wherein,representing the wavelength of the transmitted signal;
at the position ofAdding a differential constraint term on the basis of norm minimization, and increasing the continuity of scattering points in a reconstruction result by utilizing the differential constraint, wherein the continuity is defined as an objective function of a Fused-LASSO minimization problem, and the specific form is as follows:
（5）
wherein,is a differential matrix>Representing the fusion parameters, non-zero and positive;
and solving the objective function by adopting a convex optimization method, calculating a corresponding augmented Lagrangian function through variable substitution, obtaining a final iterative expression, and carrying out an iterative solving process to realize super-resolution imaging of a sparse target in the radar beam scanning process.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the processing steps described for any of the MIMO radar super-resolution imaging methods described above.
A radar super-resolution imaging chip comprising an internally packaged integrated circuit substrate for performing the processing steps described in any of the MIMO radar super-resolution imaging methods described above.
The application of the MIMO radar in the field of motor vehicles uses the MIMO radar which is installed on the motor vehicle to operate as a vehicle-mounted radar.
The invention provides an effective millimeter wave radar super-resolution imaging algorithm based on an imaging mode of phase control transmitting beam scanning, constructs a signal model based on phase control transmitting beam scanning, approximates echo signals by utilizing a space geometric model of a radar and a target, establishes a convolution model of a target scattering coefficient and an antenna pattern, and provides a target super-resolution imaging method based on compressed sensing based on the convolution model, so that the resolution of adjacent targets is improved, the contour information of the targets is reserved to a certain extent, the imaging resolution is improved under the condition of the number of the existing array channels, and the azimuth and/or elevation super-resolution estimation of millimeter wave radar imaging is realized.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a graph of signal-to-noise ratio set to 5dB and two target angles set to { respectively、/>An imaging result map of a conventional beam scan (DBF);
FIG. 3 is a graph of signal-to-noise ratio set to 5dB and two target angles set to { respectively、/>An imaging result map of the present invention;
FIG. 4 is a graph of signal-to-noise ratio set to 5dB and two target angles set to { respectively、/>Imaging junction of conventional beam scanning (DBF) at time }Fruit map;
FIG. 5 is a graph of signal-to-noise ratio set to 5dB and two target angles set to { respectively、/>Imaging results of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, it being apparent that the described embodiments are only some, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention adopts the imaging thought of MIMO (multiple input multiple output) radar beam scanning. The emission beam forming can be understood as that emission signals of a plurality of channels are weighted and radiated outwards, so that narrower beam directions are formed in different directions in space, and if the angle of the beam directions is continuously changed, the beam directions are scanned and directed within a certain angle range, and then the beam scanning of a target scene can be realized, and a scanning echo signal is obtained.
Example 1
As shown in fig. 1, a super-resolution imaging method of a MIMO radar is provided in the first embodiment, which is suitable for a MIMO array radar system, and includes the following steps:
step 1, in the process of MIMO radar beam scanning, calculating the phase deviation value of each transmitting channel according to the scanned angle, and superposing the transmitting signals of each transmitting channel after phase deviation to synthesize a transmitting beam forming signal：
Assume that during beam scanning, different scanning moments correspond to one beam direction, and the beam during kth direction angle scanningThe heart direction angle isThe transmitting array and the receiving array are linear arrays which are arranged at equal intervals, and the array element interval of the transmitting array is +.>The array element number of the transmitting array is M, and the transmitting signals of each transmitting channel after phase shift are overlapped to form a transmitting beam forming signal +.>；
Step 2, transmitting the beam formed signalAfter the target reflection, the echo signals are respectively received by the receiving channels>：
Assume that the number of array elements of a receiving array of the radar isNThe radar imaging view contains Q azimuth sampling points, and the distance between the Q-th azimuth sampling point and the antenna phase center isThe q-th azimuth sampling point has an azimuth included angle of +.>Each receiving channel can receive echo signal +_ during the kth direction angle scanning>；
Step 3, the radar receiver pairs echo signalsDeclivity results in a difference signal +.>For each of the receiversDifference frequency signal after channel declivity +.>After distance dimension FFT, synthesizing to obtain beam forming signal +.>The beam formed signalObtaining a two-dimensional complex echo signal comprising distance and angle through approximation processing>；
Step 4, combining the two-dimensional complex echo signalsIs divided into two parts, one part is an antenna pattern complex matrix composed of antenna patterns with different scanning angles +.>A part of the complex scattering coefficient distribution matrix is the complex scattering coefficient distribution matrix of each point in the target scene>；
Step 5, combining the two-dimensional complex echo signalsThe convolution process of carrying out weighted summation on scattering coefficient distribution matrixes of all azimuth dimensions in the same distance unit by using antenna pattern complex matrixes formed by antenna patterns with different scanning angles is expressed as follows:
(3)
wherein, the total sampling point number of the radar imaging view distance dimension is assumed to bePThe number of scan direction angles isKTotal number of sampling points of antenna pattern in azimuth dimensionIs thatThen->Complex matrix of echo signals representing multiple scans, the size of which is +.>；/>Antenna pattern complex matrix composed of antenna patterns with different scanning angles, the size is +.>；/>A complex scattering coefficient distribution matrix for each point in the target scene, the size of the complex scattering coefficient distribution matrix is +.>The method comprises the steps of carrying out a first treatment on the surface of the p is a distance dimension sampling point, q is an azimuth dimension sampling point; k is the kth scan direction angle;
step 6, representing a convolution model in a matrix operation mode:
in discrete echoes, signals are represented by a matrix, and a convolution model represented by a form of matrix operation is:
wherein,is a noise distribution complex matrix with the size of +.>；
Respectively and individually draw out、/>And->Corresponding columns in (a) to obtain an echo convolution form of a target azimuth scattering coefficient distribution matrix and an antenna pattern on the same distance unit: />，/>For the azimuth scattering coefficient distribution matrix to be reconstructed, n is the complex matrix extracted from the noise distribution matrix +.>Corresponding columns of (3);
step 7, azimuth scattering coefficient distribution matrix to be reconstructedRegarded as a sparse target, established as +.>Target equation for norm minimization:
(4)
wherein,representing the wavelength of the transmitted signal;
step 8, atAdding a differential constraint term on the basis of norm minimization, and increasing continuity of scattering points in a reconstruction result by utilizing the differential constraint:
since cars, trees, pedestrians, etc. in a radar front view scene have continuous reflection surfaces, these objects cannot be regarded as only individual scattering points, and also comprise aAnd (3) definite expansibility. In this application scenario, not only is the radar required to be able to resolve adjacent targets, but also its profile information needs to be maintained, and scattering points located within the same target profile have spatially distributed continuity. Thus, to meet the above needs, the present invention is toAdding a differential constraint term on the basis of norm minimization>The continuity of scattering points in the reconstruction result is increased by using differential constraint, which is defined as an objective function of the Fused-LASSO (Fused-Least Absolute Shrinkage and Selection Operator) minimization problem, and the specific form is as follows:
（5）
wherein,is a differential matrix>Representing the fusion parameters, non-zero and positive;
step 9, solving an objective function by adopting a convex optimization method, obtaining a final iteration expression after calculating a corresponding augmented Lagrangian function through variable substitution, and performing an iteration solving process to realize super-resolution imaging of a sparse target in a radar beam scanning process;
in this embodiment, an Alternate Direction Multiplier Method (ADMM) is selected to solve the objective function, and after the variable substitution obtains the corresponding augmented lagrangian function, a final iteration expression is obtained, where the iteration formula of the ADMM is as follows:
(6)
(7)
(8)
(9)
(10)
wherein,representing constraint item variables, respectively->Representing the variable values of the kth iteration respectively,representing the updated values after the k+1st iteration, respectively,>lagrangian multipliers, respectively representing the kth iteration,>respectively represent the updated Lagrangian multiplier after the (k+1) th iteration,/->Represents the conjugate transpose of the matrix,penalty term parameters, respectively>Respectively represent soft threshold functions, ">Representing the identity matrix.
To verify the inventionThe validity of the system is verified by adopting a simulation experiment method, and all steps and conclusions are verified to be correct on matlab. The simulation experiment sets the signal-to-noise ratio to 5dB, and when the two target angles are set as { respectively、And {>、/>When in use, compared with the traditional beam scanning (DBF), the Fused-LASSO algorithm can obviously distinguish two peaks, can obtain a good resolution effect, and obviously improves the azimuth resolution of imaging. The effects of the present invention can be further illustrated by comparison of the simulated imaging diagrams of fig. 2-5.
Example two
An embodiment of the present invention provides a MIMO radar, including:
the signal transmitting module is used for calculating the phase deviation value of each transmitting channel according to the scanning angle in the process of MIMO radar beam scanning, and superposing the transmitting signals of each transmitting channel after phase deviation to synthesize the transmitting beam forming signals；
A signal receiving module for transmitting the beam forming signalAfter the target reflection, the echo signals are respectively received by the receiving channels>；
A signal synthesis module for receiving echo signalsDeclivity results in a difference signal +.>After that, the difference frequency signal after declivating each receiving channel is +.>After distance dimension FFT, synthesizing to obtain beam forming signal +.>The beam forming signal +.>Obtaining a two-dimensional complex echo signal comprising distance and angle through approximation processing>；
A signal processing module for processing the two-dimensional complex echo signalsIs divided into two parts, one part is an antenna pattern complex matrix composed of antenna patterns with different scanning angles +.>A part of the complex scattering coefficient distribution matrix is the complex scattering coefficient distribution matrix of each point in the target scene>；
By combining two-dimensional complex echo signalsThe convolution process of carrying out weighted summation on scattering coefficient distribution matrixes of all azimuth dimensions in the same distance unit by using antenna pattern complex matrixes formed by antenna patterns with different scanning angles is expressed as follows:
(3)
wherein, the total sampling point number of the radar imaging view distance dimension is assumed to bePThe number of scan direction angles isKThe total sampling point number of the antenna pattern in the azimuth dimension isThen->Complex matrix of echo signals representing multiple scans, the size of which is +.>；/>Antenna pattern complex matrix composed of antenna patterns with different scanning angles, the size is +.>；/>A complex scattering coefficient distribution matrix for each point in the target scene, the size of the complex scattering coefficient distribution matrix is +.>The method comprises the steps of carrying out a first treatment on the surface of the p is a distance dimension sampling point, q is an azimuth dimension sampling point; k is the kth scan direction angle;
the convolution model is represented by a form of matrix operation, and is:
wherein,is a noise distribution complex matrix with the size of +.>；
Respectively and individually draw out、/>And->Corresponding columns in (a) to obtain an echo convolution form of a target azimuth scattering coefficient distribution matrix and an antenna pattern on the same distance unit: />，/>For the azimuth scattering coefficient distribution matrix to be reconstructed, n is the complex matrix extracted from the noise distribution matrix +.>Corresponding columns of (3);
directional scattering coefficient distribution matrix to be reconstructedRegarded as a sparse target, established as +.>Target equation for norm minimization:
（4）
wherein,representing the wavelength of the transmitted signal;
at the position ofAdding a differential constraint term on the basis of norm minimization, and increasing the continuity of scattering points in a reconstruction result by utilizing the differential constraint, wherein the continuity is defined as an objective function of a Fused-LASSO minimization problem, and the specific form is as follows:
（5）
wherein,is a differential matrix>Representing the fusion parameters, non-zero and positive;
and solving the objective function by adopting a convex optimization method, calculating a corresponding augmented Lagrangian function through variable substitution, obtaining a final iterative expression, and carrying out an iterative solving process to realize super-resolution imaging of a sparse target in the radar beam scanning process.
Example III
The third embodiment of the present invention also provides a computer-readable storage medium having instructions stored therein, which when executed on a computer, cause the computer to perform the processing steps described in the method of the first embodiment.
Example IV
The fourth embodiment of the present invention also provides a radar super-resolution imaging chip, which includes an integrated circuit substrate encapsulated therein, where the integrated circuit substrate is configured to perform the processing steps described in the method of the first embodiment.
Example five
The fifth embodiment of the invention provides an application of the MIMO radar in the field of motor vehicles, and the MIMO radar using the second embodiment is installed on the motor vehicles to operate as a vehicle-mounted radar.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (7)
1. The MIMO radar super-resolution imaging method is suitable for a multiple-input multiple-output array radar system, and is characterized by comprising the following steps of:
step 1, in the process of MIMO radar beam scanning, calculating the phase deviation value of each transmitting channel according to the scanned angle, and superposing the transmitting signals of each transmitting channel after phase deviation to synthesize a transmitting beam forming signal；
Step 2, transmitting the beam formed signalAfter being reflected by the target, the echo signals are respectively received by each receiving channel；
Step 3, the radar receiver pairs echo signalsDeclivity results in a difference signal +.>Difference frequency signal after declivating each receiving channel +.>After distance dimension FFT, synthesizing to obtain beam forming signal +.>The beam forming signal +.>Obtaining a two-dimensional complex echo signal comprising distance and angle through approximation processing>；
Step 4, combining the two-dimensional complex echo signalsIs divided into two parts, one part is an antenna pattern complex matrix composed of antenna patterns with different scanning angles +.>A part of the complex scattering coefficient distribution matrix is the complex scattering coefficient distribution matrix of each point in the target scene>；
Step 5, combining the two-dimensional complex echo signalsThe convolution process of carrying out weighted summation on scattering coefficient distribution matrixes of all azimuth dimensions in the same distance unit by using antenna pattern complex matrixes formed by antenna patterns with different scanning angles is expressed as follows:
(3)
wherein, the total sampling point number of the radar imaging view distance dimension is assumed to bePThe number of scan direction angles isKTotal of antenna patterns in azimuth dimensionThe sampling point number isThen->A complex matrix of echo signals representing multiple scans, the complex matrix being of the size；/>Antenna pattern complex matrix composed of antenna patterns with different scanning angles, the size is +.>；/>A complex scattering coefficient distribution matrix for each point in the target scene, the size of the complex scattering coefficient distribution matrix is +.>The method comprises the steps of carrying out a first treatment on the surface of the p is a distance dimension sampling point, q is an azimuth dimension sampling point; k is the kth scan direction angle;
step 6, representing a convolution model in a matrix operation mode:
the convolution model is:
wherein,is a noise distribution complex matrix with the size of +.>；
Respectively and individually draw out、/>And->Corresponding columns in (a) to obtain an echo convolution form of a target azimuth scattering coefficient distribution matrix and an antenna pattern on the same distance unit: />，/>For the azimuth scattering coefficient distribution matrix to be reconstructed, n is the complex matrix extracted from the noise distribution matrix +.>Corresponding columns of (3);
step 7, azimuth scattering coefficient distribution matrix to be reconstructedRegarded as a sparse target, established as +.>Target equation for norm minimization:
（4）
wherein,representing the wavelength of the transmitted signal;
step 8, atAdding a differential constraint term on the basis of norm minimization, and adding the dispersion in the reconstruction result by utilizing the differential constraintThe continuity of the shot, defined as the objective function of the Fused-LASSO minimization problem, is specified as follows:
（5）
wherein,is a differential matrix>Representing the fusion parameters, non-zero and positive;
and 9, solving the objective function by adopting a convex optimization method, and obtaining a final iteration expression after calculating the corresponding augmented Lagrangian function through variable substitution, and performing an iteration solving process to realize super-resolution imaging of the sparse target in the radar beam scanning process.
2. The super-resolution imaging method of claim 1, wherein the convex optimization method is characterized in that an objective function is solved by selecting an alternate direction multiplier method ADMM, and after variable substitution to obtain a corresponding augmented lagrangian function, a final iterative expression is obtained, and the method is specifically as follows:
(6)
(7)
(8)
(9)
(10)
wherein,representing constraint item variables, respectively->Representing the variable values of the kth iteration respectively,representing the updated values after the k+1st iteration, respectively,>lagrangian multipliers, respectively representing the kth iteration,>respectively represent the updated Lagrangian multiplier after the (k+1) th iteration,/->Represents the conjugate transpose of the matrix,penalty term parameters, respectively>Respectively represent soft threshold functions, ">Representing the identity matrix.
3. A MIMO radar, comprising:
the signal transmitting module is used for calculating the phase offset value of each transmitting channel according to the scanning angle in the process of MIMO radar beam scanning, and transmitting each transmitting channelThe phase-shifted transmission signals are superimposed to synthesize a transmission beam forming signal；
A signal receiving module for transmitting the beam forming signalAfter the target reflection, the echo signals are respectively received by the receiving channels>；
A signal synthesis module for receiving echo signalsDeclivity results in a difference signal +.>After that, the difference frequency signal after declivating each receiving channel is +.>After distance dimension FFT, synthesizing to obtain beam forming signal +.>The beam forming signal +.>Obtaining a two-dimensional complex echo signal comprising distance and angle through approximation processing>；
A signal processing module for processing the two-dimensional complex echo signalsIs divided into two parts, one part is an antenna pattern formed by different scanning anglesAntenna pattern complex matrix>A part of the complex scattering coefficient distribution matrix is the complex scattering coefficient distribution matrix of each point in the target scene>；
By combining two-dimensional complex echo signalsThe convolution process of carrying out weighted summation on scattering coefficient distribution matrixes of all azimuth dimensions in the same distance unit by using antenna pattern complex matrixes formed by antenna patterns with different scanning angles is expressed as follows:
(3)
wherein, the total sampling point number of the radar imaging view distance dimension is assumed to bePThe number of scan direction angles isKThe total sampling point number of the antenna pattern in the azimuth dimension isThen->A complex matrix of echo signals representing multiple scans, the complex matrix being of the size；/>Antenna pattern complex matrix composed of antenna patterns with different scanning angles, the size is +.>；/>A complex scattering coefficient distribution matrix for each point in the target scene, the size of the complex scattering coefficient distribution matrix is +.>The method comprises the steps of carrying out a first treatment on the surface of the p is a distance dimension sampling point, q is an azimuth dimension sampling point; k is the kth scan direction angle;
the convolution model is represented by a form of matrix operation, and is:
wherein,is a noise distribution complex matrix with the size of +.>；
Respectively and individually draw out、/>And->Corresponding columns in (a) to obtain an echo convolution form of a target azimuth scattering coefficient distribution matrix and an antenna pattern on the same distance unit: />，/>For the azimuth scattering coefficient distribution matrix to be reconstructed, n is the complex matrix extracted from the noise distribution matrix +.>Corresponding columns of (3);
directional scattering coefficient distribution matrix to be reconstructedRegarded as a sparse target, established as +.>Target equation for norm minimization:
（4）
wherein,representing the wavelength of the transmitted signal;
at the position ofAdding a differential constraint term on the basis of norm minimization, and increasing the continuity of scattering points in a reconstruction result by utilizing the differential constraint, wherein the continuity is defined as an objective function of a Fused-LASSO minimization problem, and the specific form is as follows:
（5）
wherein,is a differential matrix>Representing the fusion parameters, non-zero and positive;
and solving the objective function by adopting a convex optimization method, calculating a corresponding augmented Lagrangian function through variable substitution, obtaining a final iterative expression, and carrying out an iterative solving process to realize super-resolution imaging of a sparse target in the radar beam scanning process.
4. A MIMO radar according to claim 3, wherein the convex optimization method is implemented by solving an objective function by using an alternate direction multiplier method ADMM, and obtaining a final iterative expression after obtaining a corresponding augmented lagrangian function by variable substitution, as follows:
according to the convex optimization method, an objective function is solved by adopting an alternating direction multiplier method ADMM, and after a variable is replaced to obtain a corresponding augmented Lagrangian function, a final iterative expression is obtained, wherein the method is specifically as follows:
(6)
(7)
(8)
(9)
(10)
wherein,representing constraint item variables, respectively->Representing the variable values of the kth iteration respectively,representing the updated values after the k+1st iteration, respectively,>lagrangian multipliers, respectively representing the kth iteration,>respectively represent the updated Lagrangian multiplier after the (k+1) th iteration,/->Represents the conjugate transpose of the matrix,penalty term parameters, respectively>Respectively represent soft threshold functions, ">Representing the identity matrix.
5. A MIMO radar according to claim 3 or 4, wherein: the MIMO radar is mounted on a motor vehicle to operate as an on-board radar.
6. A computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the processing steps described for a MIMO radar super-resolution imaging method according to any one of claims 1-2.
7. A radar super-resolution imaging chip comprising an internally packaged integrated circuit substrate, characterized in that: the integrated circuit substrate is used for executing the processing steps described in the MIMO radar super-resolution imaging method of any one of claims 1-2.
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