CN111665501B - MIMO radar two-dimensional imaging method based on improved CBP - Google Patents

MIMO radar two-dimensional imaging method based on improved CBP Download PDF

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CN111665501B
CN111665501B CN202010603678.4A CN202010603678A CN111665501B CN 111665501 B CN111665501 B CN 111665501B CN 202010603678 A CN202010603678 A CN 202010603678A CN 111665501 B CN111665501 B CN 111665501B
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CN111665501A (en
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马月红
张伟涛
徐晴
惠蕙
王硕
朱唐永君
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Hebei Rongfa Information Technology Co ltd
Shijiazhuang Tiedao University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section

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Abstract

The invention discloses an MIMO radar two-dimensional imaging method based on improved CBPs, and relates to the technical field of millimeter wave MIMO radar imaging. The method comprises the following steps: establishing a near-field millimeter wave MIMO radar echo model; designing a group of matched filter functions to compensate phase errors introduced by an equivalent phase center principle; dividing an imaging area, detecting a target, and mapping the target to an imaging unit; extracting pixel units according to the probability to form an imaging pixel unit set; and sequentially imaging the sampling set pixel units by using a CBP algorithm, and reconstructing the rest pixel units according to a non-precise augmented Lagrange IALM algorithm to finish the two-dimensional imaging of the MIMO radar. The method greatly shortens the imaging time of the CBP algorithm, and can be widely applied to MIMO radar near-field imaging.

Description

MIMO radar two-dimensional imaging method based on improved CBP
Technical Field
The invention relates to the technical field of millimeter wave MIMO radar imaging, in particular to an MIMO radar two-dimensional imaging method based on improved CBP.
Background
The millimeter wave radar has the advantages of weather resistance and all-weather working capability, shows a series of advantages in the application of the millimeter wave in the fields of engineering and military, and plays a very important role in electromagnetic wave resources. The millimeter wave technology is mature day by day and is widely applied to the fields of communication radar, remote sensing, accurate guidance and automobile radar. The millimeter wave radar has stronger anti-interference capability due to the selectivity of the narrower bandwidth of the millimeter wave radar to atmospheric propagation and the larger Doppler frequency. With the continuous progress of unmanned technology, the performance requirements on radar are gradually improved, the military field comprises military reconnaissance, target detection/tracking and the like, the functions of distance measurement, speed measurement, angle measurement and the like of targets can be realized, the application value of high-resolution two-dimensional and three-dimensional imaging is particularly huge, large ground and sea surface scene information under a working environment can be provided, and the detection and identification precision of the military targets is greatly improved.
Disclosure of Invention
The invention aims to solve the technical problem of how to provide a two-dimensional imaging method of an MIMO radar, which can improve the imaging speed.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a MIMO radar two-dimensional imaging method based on improved CBP is characterized by comprising the following steps:
establishing a near-field millimeter wave MIMO radar echo model;
designing a group of matched filter functions to compensate phase errors introduced by an equivalent phase center principle;
dividing an imaging area, detecting a target, and mapping the target to an imaging unit;
extracting pixel units according to the probability to form an imaging pixel unit set;
and sequentially imaging the sampling set pixel units by using a CBP algorithm, and reconstructing the rest pixel units according to a non-precise augmented Lagrange IALM algorithm to finish the two-dimensional imaging of the MIMO radar.
The technical scheme is that the method for establishing the near-field millimeter wave MIMO radar echo model comprises the following steps:
let At(xt,yt) And Ar(xr,yr) Respectively, coordinates of a pair of transmitting and receiving antennas, for an object M (x) in the object arean,yn) At respective distances R from the transmitting antenna and the receiving antennat,n、Rr,nWherein:
Figure BDA0002560081790000021
Figure BDA0002560081790000022
the emission signals are:
s(t)＝exp[-j2πft] (3)
wherein f ═ f0+(n-1)Δf，f0Is the initial frequency;
the FMCW radar mixing signal is:
Sn(xt,yt；xr,yr；Kω)＝σ(xn,yn)exp[-jKω(Rt,n+Rr,n)] (4)
wherein KwSince 2 pi f/c is the spatial frequency, the signals received by the transmitting and receiving antenna in the target area are:
Figure BDA0002560081790000023
the further technical scheme is that a group of matched filter functions is designed, and a method for compensating phase errors introduced by an equivalent phase center principle comprises the following steps:
is equivalent to a self-transmitting and self-receiving antenna, and the position of the antenna is Ae(xe,ye) And then:
Figure BDA0002560081790000024
the compensation function is expressed as:
Figure BDA0002560081790000025
the compensated signals are:
Figure BDA0002560081790000026
the further technical solution is that the method for dividing the imaging area and detecting the target and mapping to the imaging unit is as follows:
establishing MIMO radar space coordinates, taking an MIMO radar array as an x axis, taking the array direction as a y axis vertically, and dividing an imaging area, wherein the MIMO radar is provided with Mt transmitting antennas and Mr receiving antennas, and the imaging area is divided into (x) receiving antennasmin,ymin)、(xmin,ymax)、(xmax,ymin)、(xmax,ymax) Four points are enclosed.
The further technical scheme is that the CBP algorithm is used for imaging the sampling set pixel units in sequence, and the method for reconstructing the rest pixel units according to the non-precise augmented Lagrangian IALM algorithm is as follows:
the distance up integration is done for equation (8) and summed along the azimuth direction:
if the designed MIMO array has Q sets of transceiving combinations in the azimuth direction, the imaging result of the nth pixel point is:
Figure BDA0002560081790000031
the matrix filling non-precise augmentation Lagrangian IALM algorithm solves the following problems:
Figure BDA0002560081790000032
the element in constraint E is 0, and the augmented lagrange function can be expressed as:
Figure BDA0002560081790000033
the iterative formula is as follows:
when the X is updated,
Figure BDA0002560081790000034
when the E is updated, the data of the E is updated,
Figure BDA0002560081790000035
the Y is updated, and the Y is updated,
Yk+1＝Yk+μk(M-Xk+1-Ek+1) (14)
if the elements of the whole area are imaged, the operation time is too long, pixel points in a certain proportion are randomly extracted from all pixel points corresponding to the imaging area, the extracted pixel points are imaged by using a convolution inverse projection algorithm to obtain imaging results of the pixel points, and then the values of all the pixel points are restored through a matrix filling algorithm, namely the imaging results of all the pixel points are finally obtained.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in: according to the method, the MIMO radar two-dimensional imaging CBP algorithm and the IALM algorithm are combined, two-dimensional imaging is carried out on the randomly extracted pixel points, and then the IALM algorithm is used for restoring the whole imaging area, so that the imaging time of the CBP algorithm is greatly shortened, and the method can be widely applied to near-field imaging of the MIMO radar.
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The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a flow chart of a method according to an embodiment of the invention;
FIG. 2 is a schematic diagram of the method of the present invention for introducing phase error using the principle of equivalent phase center;
FIG. 3 is a flow chart of the combination of CBP algorithm and IALM algorithm in the method according to the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the embodiment of the invention discloses a MIMO radar two-dimensional imaging method based on improved CBP, and the algorithm includes the following steps:
step 1): establishing a near-field millimeter wave MIMO radar echo model;
step 2): designing a group of matched filter functions for counteracting errors introduced by an equivalent phase center principle, and compensating the phase errors;
step 3): dividing an imaging area, detecting a target, and mapping the target to an imaging unit;
step 4): extracting pixel units according to the probability to form an imaging pixel unit set;
step 5): and sequentially imaging the sampling set pixel units by using a CBP algorithm, and reconstructing the rest pixel units according to an Inaccurate Augmented Lagrange (IALM) algorithm to finish the MIMO radar two-dimensional imaging, as shown in figure 3.
The above steps are described below with reference to specific methods:
the method for establishing the near-field millimeter wave MIMO radar echo model in the step 1) comprises the following steps:
let At(xt,yt) And Ar(xr,yr) Respectively, coordinates of a pair of transmitting and receiving antennas, for an object M (x) in the object arean,yn) At respective distances R from the transmitting antenna and the receiving antennat,n、Rr,nWherein:
Figure BDA0002560081790000051
Figure BDA0002560081790000052
the emission signals are:
s(t)＝exp[-j2πft] (3)
wherein f ═ f0+(n-1)Δf，f0Is the initial frequency.
The FMCW radar mixing signal is:
Sn(xt,yt；xr,yr；Kω)＝σ(xn,yn)exp[-jKω(Rt,n+Rr,n)] (4)
wherein Kw2 pi f/c is the spatial frequency. Therefore, the target area signal received by the transceiving antenna is:
Figure BDA0002560081790000053
as shown in fig. 2, a schematic diagram of introducing a phase error by using an equivalent phase center principle, and the method for introducing a matched filter function by using the step 2) of eliminating the equivalent phase center principle is as follows:
is equivalent to a self-transmitting and self-receiving antenna, and the position of the antenna is Ae(xe,ye) And then:
Figure BDA0002560081790000061
the compensation function can be expressed as:
Figure BDA0002560081790000062
the compensated signals are:
Figure BDA0002560081790000063
the step 3) of dividing the target area, detecting and mapping the target area to the imaging unit comprises the following steps:
and establishing a space coordinate of the MIMO radar, taking the MIMO radar array as an x axis, and taking the array direction as a y axis vertically. Dividing an imaging area, wherein the MIMO radar has Mt transmitting antennas and Mr receiving antennas, and the imaging area is formed by (x)min,ymin)、(xmin,ymax)、(xmax,ymin)、(xmax,ymax) Four points are enclosed.
And 4, extracting certain point target pixels according to the probability, forming a pixel set so as to improve the speed of an imaging algorithm, and selecting a specific point target in a target area for imaging.
As shown in fig. 3, in step 5, imaging is performed by using a CBP algorithm, and an imaging matrix is reconstructed by using an Imprecise Augmented Lagrangian (IALM) algorithm, and a method for obtaining a target image finally is as follows:
and 5.1) integrating the distance upwards on the formula (8) and summing along the azimuth direction. Assuming that the designed MIMO array has Q sets of transceiving combinations in the azimuth direction, the imaging result of the nth pixel point is:
Figure BDA0002560081790000064
step 5.2) matrix filling non-precise augmentation Lagrange (IALM) algorithm, which solves the following problems:
Figure BDA0002560081790000065
the element in constraint E is 0, and the augmented lagrange function can be expressed as:
Figure BDA0002560081790000066
the iterative formula is as follows:
when the X is updated,
Figure BDA0002560081790000071
when the E is updated, the data of the E is updated,
Figure BDA0002560081790000072
the Y is updated, and the Y is updated,
Yk+1＝Yk+μk(M-Xk+1-Ek+1) (14)
if the elements of the whole area are imaged, the operation time is too long, pixel points in a certain proportion are randomly extracted from all pixel points corresponding to the imaging area, the extracted pixel points are imaged by using a convolution inverse projection algorithm to obtain imaging results of the pixel points, and then the values of all the pixel points are restored through a matrix filling algorithm, namely the imaging results of all the pixel points are finally obtained.

Claims (1)

1. A MIMO radar two-dimensional imaging method based on improved CBP is characterized by comprising the following steps:
establishing a near-field millimeter wave MIMO radar echo model;
designing a group of matched filter functions to compensate phase errors introduced by an equivalent phase center principle;
dividing an imaging area, detecting a target, and mapping the target to an imaging unit;
extracting pixel units according to the probability to form an imaging pixel unit set;
sequentially imaging the sampling set pixel units by using a CBP algorithm, and reconstructing the rest pixel units according to a non-precise augmented Lagrange IALM algorithm to complete the two-dimensional imaging of the MIMO radar;
the method for establishing the near-field millimeter wave MIMO radar echo model comprises the following steps:
let At(xt,yt) And Ar(xr,yr) Respectively, coordinates of a pair of transmitting and receiving antennas, for an object M (x) in the object arean,yn) At respective distances R from the transmitting antenna and the receiving antennat,n、Rr,nWherein:
Figure FDA0003146202080000011
Figure FDA0003146202080000012
the emission signals are:
s(t)＝exp[-j2πft] (3)
wherein the transmission frequency f ═ f0+(n-1)Δf，f0Is the initial frequency;
the FMCW radar mixing signal is:
Sn(xt,yt；xr,yr；Kω)＝σ(xn,yn)exp[-jKω(Rt,n+Rr,n)] (4)
wherein KwSince 2 pi f/c is the spatial frequency, the signals received by the transmitting and receiving antenna in the target area are:
Figure FDA0003146202080000013
the method for designing a group of matched filter functions and compensating the phase error introduced by the equivalent phase center principle comprises the following steps:
is equivalent to a self-transmitting and self-receiving antenna, and the position of the antenna is Ae(xe,ye) And then:
Figure FDA0003146202080000014
the compensation function is expressed as:
Figure FDA0003146202080000021
the compensated signals are:
Figure FDA0003146202080000022
the method for dividing the imaging area and detecting the target and mapping to the imaging unit is as follows:
establishing MIMO radar space coordinates, taking an MIMO radar array as an x axis, taking the array direction as a y axis vertically, and dividing an imaging area, wherein the MIMO radar is provided with Mt transmitting antennas and Mr receiving antennas, and the imaging area is divided into (x) receiving antennasmin,ymin)、(xmin,ymax)、(xmax,ymin)、(xmax,ymax) Area surrounded by four points, xminIs the minimum value of the abscissa, xmaxIs the maximum value of the abscissa, yminIs the ordinate minimum, ymaxIs the maximum value of the ordinate;
the method for imaging the sampling set pixel units in sequence by using the CBP algorithm and reconstructing the rest pixel units according to the non-precise augmented Lagrange IALM algorithm comprises the following steps:
the distance up integration is done for equation (8) and summed along the azimuth direction:
if the designed MIMO array has Q sets of transceiving combinations in the azimuth direction, the imaging result of the nth pixel point is:
Figure FDA0003146202080000023
the matrix filling non-precise augmentation Lagrangian IALM algorithm solves the following problems:
Figure FDA0003146202080000024
wherein X is a band filling matrix, and E is a constraint matrix;
the element in constraint E is 0, and the augmented lagrange function can be expressed as:
Figure FDA0003146202080000025
wherein Y is a Lagrange multiplier, M is an original data matrix in coordinates, and mu is a penalty factor more than 0;
the iterative formula is as follows:
when the X is updated,
Figure FDA0003146202080000031
when the E is updated, the data of the E is updated,
Figure FDA0003146202080000032
the Y is updated, and the Y is updated,
Yk+1＝Yk+μk(M-Xk+1-Ek+1) (14)
if the elements of the whole area are imaged, the operation time is too long, pixel points in a certain proportion are randomly extracted from all pixel points corresponding to the imaging area, the extracted pixel points are imaged by using a convolution inverse projection algorithm to obtain imaging results of the pixel points, and then the values of all the pixel points are restored through a matrix filling algorithm, namely the imaging results of all the pixel points are finally obtained.
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