CN105842689A - High resolution radar fast imaging method based on generalized reflectivity model - Google Patents
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Abstract
The invention discloses a high resolution radar fast imaging method based on a generalized reflectivity model. According to the invention, the conventional radar imaging Bonn approximation model can be extended, and the anisotropic characteristic of the target and the characteristic of the target having different action effects on the signals of different frequency bands can be added to the model; the model is closer to the actual signal model, and the radar imaging effect can be enhanced, and the good foundation can be laid for realizing the high resolution radar imaging; by using three sparse characteristics of the target generalized reflectivity, the radar imaging system can be divided into the sub-apertures or sub-frequency bands for the approximation calculation; according to the system function characteristics, the imaging area can be divided into a series of sub-imaging areas, and then the imaging speed can be greatly accelerated; by adopting the dual transformation, the conventional radar imaging problem can be changed into the image processing problem based on the physical mechanism. The radar imaging precision can be guaranteed, and the radar imaging speed can be accelerated, and therefore the technical problem of the inability of realizing the large-scale high-resolution radar real-time imaging can be effectively solved.
Description
Technical Field
The invention relates to radar imaging technology, in particular to a high-resolution radar rapid imaging method based on a generalized reflectivity model.
Background
With the rapid development of economic society, radar imaging systems are now widely used in geoscience, medicine, and other various military and civil scenarios. Because the radar imaging system adopts high-frequency electromagnetic waves, the radar imaging system has stronger nonmetal penetration capability and can realize effective detection of hidden targets. For example: at night, remote sensing imaging is carried out aiming at the military base of the enemy, and the military operation of the enemy can be insights in time; in the anti-terrorism action and hostage rescue process, high-resolution imaging is carried out on a non-cooperative target behind a wall, so that powerful reference can be provided for formulating a rescue method; in public places such as airports, railway stations and the like, the passenger luggage is rapidly and harmlessly scanned, and the safety of lives and properties of people can be prevented from being damaged.
At present, an explosion element model and a Bonn approximation model are mostly adopted in a radar imaging model, amplitude attenuation of echo signals, anisotropic characteristics of imaging targets, different frequency band electromagnetic waves and the imaging targets are different in effect, and multiple scattering effect between the targets is neglected to a certain extent in the two models. From the above analysis, the current radar imaging model has a large deviation from the actual imaging system, so that an insurmountable obstacle is set for realizing high-resolution radar imaging. Improving the modeling accuracy is a fundamental approach to realize high-resolution radar imaging.
The radar imaging process is a typical electromagnetic inverse problem, and the morbidity and high computational complexity of the radar imaging process are always the focus of scientific research. The existing methods for solving the electromagnetic inverse problem mainly comprise two types: 1. the offset method comprises the following steps: the migration method is based on an explosion meta-model, and its assumed target is composed of a series of isolated points, and is generally implemented by using a back-projection algorithm or a time reversal algorithm. Typical algorithms are for example range-doppler. The offset method is limited to narrow band and small viewing angle far field imaging systems. 2. The chromatography method comprises the following steps: based on the born approximation model, it assumes that the target is a weakly scattering material. Although the imaging accuracy of the tomographic method is higher than that of the offset method, the computational complexity of the tomographic method is much higher than that of the offset method.
The analysis shows that: both the two solving methods have the defects of large calculation amount and suitability for a lower frequency band and a smaller target size, and the two methods can not be used for the problem of large-scale high-resolution imaging. At present, the radar imaging process is limited by the two oversimplified imaging models, so that a large number of ghosts exist in an imaging result, and serious burden is brought to target identification and classification in the post-processing process.
Therefore, how to provide a more accurate radar imaging model and improve the radar imaging efficiency under the condition of the existing radar hardware system, and realizing large-scale high-resolution radar real-time imaging is a key technical problem which is urgently needed to be solved by technical personnel in the field and has great challenge.
Disclosure of Invention
In order to solve the key technical problem, the invention provides a high-resolution radar rapid imaging method based on a generalized reflectivity model.
The invention discloses a high-resolution radar rapid imaging method based on a generalized reflectivity model, which comprises the following steps of:
1) establishing a radar imaging system, and acquiring radar scattering data:
the radar imaging system comprises T transmitters and R transmittersThe receivers, the frequency number of the transmitted signals is F, the transmitters sequentially transmit signals to the target imaging area, all the receivers receive echo signals, and after the t-th transmitter transmits signals, the echo signals received by all the receivers are yF,t＝[yF,t,1；уF,t,2；…уF,t,R]Wherein, T and R are natural numbers which are more than or equal to 2 respectively, T is 1,2, …, T and F are natural numbers which are more than or equal to 2;
2) establishing a generalized reflectivity model:
the generalized reflectivity model is established on the basis of a traditional Bonn approximation model and comprises anisotropic characteristics and frequency characteristics of an imaging target, namely the reflectivity of the imaging target under different angle transmitters is different and the reflectivity of the imaging target under different working frequencies is different;
3) data transformation and data integration:
a) a receiver receives a frequency domain echo signal;
b) according to the structural characteristics of radar system functions and a generalized reflectivity model, a radar imaging system is divided into K sub-apertures or sub-frequency bands, a target imaging area is divided into B sub-imaging areas, and frequency domain echo signals are combined and sorted according to the division results to obtain echo data functions y related to the B sub-imaging area and the K sub-aperture or sub-frequency band(k,b)Wherein K is 1,2, …, K, B is 1,2, …, B, radar imaging system function A is constructed according to free space dyadic Green function(k,b)Then the generalized reflectivity x(k,b)Satisfies equation (1):
y(k,b)＝A(k,b)x(k,b)+n(k,b)(1)
wherein n is(k,b)Expressing the model error, the radar imaging problem is converted into the solution of the unknown number x in the equation (1)(k,b)The inverse problem of (2);
c) multiplying the system function A on both sides of equation (1)(k,b)Conjugate transpose matrix ofNamely, the dual transformation is carried out on the equation (1) to obtain an equation (2):
wherein,is a system function that represents the processing of the image,is a representation of the back projection imaging result, n(k,b)The dual transformation converts the traditional radar imaging problem into a radar image processing problem based on a physical mechanism;
4) parallel imaging of the sub-imaging regions:
based on the generalized reflectivity model, parallel imaging is carried out on each sub-imaging area according to a gradient iterative algorithm aiming at equation (2), and the generalized reflectivity is obtained through the mth iteration of the kth sub-aperture or sub-band under the kth sub-imaging areaSatisfies equation (3):
wherein,for the step size factor of the mth iteration of the kth sub-aperture or sub-band under the mth sub-imaging region,a gradient function for the mth iteration of the kth sub-aperture or sub-band under the mth sub-imaging region;
imaging results x of all K sub-apertures under the b-th sub-imaging area by utilizing a generalized reflectivity model(k,b)Carrying out image fusion to obtain an imaging result x of the b-th sub-imaging area(b)：
Wherein N represents x(k,b)N represents the nth element, N is 1,2, …, N, p and q are norm indexes;
5) image fusion:
imaging result x of B sub-imaging areas(b)And performing image fusion to obtain a high-resolution radar imaging image X of a complete target imaging area.
In the step 1), the radar imaging system is suitable for various commonly used radar imaging systems; each transmitter of the radar imaging system sequentially transmits electromagnetic waves, and all receivers receive echo signals simultaneously.
In the step 2), the generalized reflectivity model expands the original Bonn approximation model, and assumes that induced currents generated by emission signals with different angles and different frequencies at the imaging target are different. As known from the electromagnetic integration method, the scattered field received by the receiver can be expressed as formula (5):
E(rs；f,rt)＝iωμ0∫Vdr＇G(rs,r＇；f)·J(r＇,f；rt) (5)
wherein, G (r)sR'; f) is a three-dimensional dyadic Green function in free space, rtTo be the location of the transmitter(s),rsis the position of the receiver, r 'is the position of the imaged object, f is the operating frequency, ω is the angular operating frequency, J (r', f; r)t) Means that the induction current, mu, generated at the target r' by the electromagnetic wave emitted by the t-th transmitter and varying with the operating frequency f0Is the permeability in vacuum. From the generalized reflectivity model, equation (5) can be abbreviated as equation (6):
y(f,t)＝A(f,t)x(f,t)+n(f,t)f＝1,2,…,F,t＝1,2,…,T (6)
wherein, F is the number of frequencies, and T is the number of transmitters.
Solving equation (6) directly results in a sharp increase in the number of unknowns that need to be solved, making the imaging problem more complex. The invention utilizes three sparse characteristics of an imaging target and provides a method for dividing the sub-aperture and the sub-frequency band to reduce the number of unknowns, thereby reducing the imaging difficulty. Three sparse characteristics of the imaged object include:
1. when the generalized reflectivity x is fixed(f,t)Medium transmitter t and frequency f, generalized reflectivity x(f,t)Must be sparse within some transform domain;
2. for a set of generalized reflectivity column vectors x(f,t)F1, 2, …, F, T1, 2, …, T all describe induced currents generated by the emitted signal at the target and thus have a common physical basis, the generalized reflectivity { x }(f,t)Has the structural property of joint sparsity, which can be measured with a (p, q) mixed norm (equation 4);
3. due to a set of generalized reflectivities x(f,t)Described are the induced currents on the imaging target, let x(f,t)Is a column vector and thus the matrix X resulting from its transverse combination is a low rank matrix.
Based on the above three sparsity characteristics, equation (6) can be solved by dividing the sub-apertures and sub-bands. Equation (6) can be simplified to equation (7):
y(k)＝A(k)x(k)+n(k)(7)
where K is 1,2, …, K denotes the number of sub-apertures or sub-bands.
In step 3), if the echo signal received by the receiver is a time domain echo signal, the time domain echo signal is transformed into a frequency domain echo signal by using a Fast Fourier Transform (FFT).
In step 3), in order to implement distributed computation, the invention uses two structural characteristics of the image processing system function to respectively provide two different sub-imaging region division methods:
i. neglecting far field effects: dividing a target imaging area into B sub-imaging areas with overlapped edges by utilizing the characteristic of relatively weak correlation or action effect between two elements with relatively long distance, and neglecting the influence between the elements with relatively long distance;
neighbor constant method: the interaction between two adjacent elements is approximately a constant, the reflectivity of the adjacent elements is set to be the same value in the same equation, namely the original target imaging area is divided into B mutually staggered sub-imaging areas, and the solved large-scale multi-element linear equation set is approximately a series of small-scale multi-element linear equations.
Constructing an echo data function y from the structure of the sub-apertures and the sub-imaging regions(k,b)And radar imaging system function A(k,b)。
In the formula (1), the same product is multiplied by (A)(k,b))*Thus, the traditional radar imaging problem is converted into an image processing problem based on a physical mechanism.
In step 4), based on the generalized reflectivity model, performing distributed solution on the equation (2) according to a gradient iterative algorithm, and performing parallel imaging on each sub-imaging area in the following specific imaging process:
a) the imaging results at each sub-aperture or sub-band are iteratively calculated:
i. iterating the m times, calculating the gradient function of the k sub-aperture or sub-band under the b sub-imaging area
iterating the mth time, updating the step size factor of the kth sub-aperture or sub-band under the kth sub-imaging region
iterating the mth time to update the generalized reflectivity of the kth sub-aperture or sub-band under the kth sub-imaging region
Judging whether an iteration condition is met, if so, entering the step b), and if not, returning to the step i);
b) imaging result x of different sub-apertures of the b-th sub-imaging region(k,b)Merge into an image x(b)。
In step 5), determining an image fusion method according to the method for dividing the neutron imaging region in step b) of step 3; if the sub-imaging region division method adopts a far-field effect neglecting method, the image fusion adopts a weighted average method; if the sub-imaging region division method adopts the neighbor constant method, the image fusion adopts an interpolation method.
The invention has the advantages that:
the method expands the Bonn approximation model of the existing radar imaging, adds the characteristics that the anisotropy characteristic of the target is different from the action effect of the target on signals of different frequency bands into the model, and provides a generalized reflectivity model for the first time; the model is closer to an actual signal model, the radar imaging effect is enhanced, and a model foundation is laid for realizing high-resolution radar imaging; the method has the advantages that the radar imaging system is divided into sub-apertures or sub-frequency bands for approximate calculation by utilizing three sparse characteristics of the target generalized reflectivity; the imaging region is divided into a series of sub-imaging regions according to the system function characteristics, namely, a large-scale electromagnetic inverse problem is converted into a series of small-scale electromagnetic inverse problems, so that the imaging speed is greatly accelerated; further, the method converts the traditional radar imaging problem into an image processing problem based on a physical mechanism by utilizing the pair-even transformation; the invention not only ensures the radar imaging precision, but also accelerates the radar imaging speed, and effectively solves the technical problem that the large-scale high-resolution radar real-time imaging cannot be carried out.
Drawings
Fig. 1 is a schematic structural diagram of a three-dimensional simulation system of a scene corresponding to a first embodiment and a second embodiment of a generalized reflectivity model-based high-resolution radar fast imaging method according to the present invention;
fig. 2 is a diagram of imaging results obtained by the embodiment of the fast imaging method for high-resolution radar based on the generalized reflectivity model according to the present invention, wherein the diagrams (a) to (f) are respectively the imaging results of the divided sub-imaging areas B with the number of 1, 3, 9, 27, 63 and 127;
fig. 3 is an imaging result diagram obtained by the second embodiment of the high-resolution radar fast imaging method based on the generalized reflectivity model according to the present invention, wherein the diagrams (a) to (f) are respectively the imaging result diagrams with the number of the divided sub-imaging areas B being 1, 4, 8, 12, 24 and 32.
Detailed Description
The invention will be further elucidated by means of specific embodiments in the following with reference to the drawing.
Example one
In this embodiment, the structure of the three-dimensional simulation system is as shown in fig. 1, and the radar imaging system adopts a transmit-receive split Multiple-Input Multiple-Output antenna technology MIMO (Multiple-Input Multiple-Output) radar imaging system.
The high-resolution radar rapid imaging method based on the generalized reflectivity model comprises the following steps:
1) establishing a radar imaging system, and acquiring radar scattering data:
the radar imaging system comprises 1-4 transmitters and 240 receivers, the bandwidth of a transmitted signal is 1-3 Ghz, the transmitter transmits a signal to a target imaging area, the receivers receive an echo signal, and after the t-th transmitter transmits the signal, the echo signal at each receiver can be represented as yF,t＝[yF,t,1；уF,t,2；…уF,t,R]Four triangles in fig. 1 represent 4 transmitters 1-4, dots on the same three-dimensional plane are radar receivers and are all located on a plane y equal to 0, signals transmitted by the radar are Gaussian modulated pulse waves, a three-dimensional cartoon character target is located right in front of a radar imaging system, the dielectric constant of the three-dimensional cartoon character target is 50, the head of the three-dimensional cartoon character target is composed of a sphere with the diameter of 0.44m, arms and legs of the three-dimensional cartoon character target are composed of cylinders with the diameter of 0.1m, the total height of the cartoon character is about 1.8m, other system parameters are drawn on fig. 1, and a target imaging area is divided into square grids with the size of 0.1m × 0.1.1 m × 0.1.1 m.
2) Establishing a generalized reflectivity model:
the generalized reflectivity model is based on a traditional Bonn approximation model, and comprises the anisotropic characteristic of an imaging target and the characteristic that different frequency bands have different effects on the imaging target, namely that induced currents generated by emission signals with different angles and different frequencies at the imaging target are different. Here four transmitters represent four sub-apertures.
3) Data transformation and data integration:
a) because the echo signal received by the receiver is a time domain echo signal, the time domain echo signal is transformed into a frequency domain echo signal by using Fast Fourier Transform (FFT);
b) according to the structural characteristics of radar system functions, according to a generalized reflectivity model, a radar imaging system is divided into 4 sub-apertures, namely each transmitter is a sub-aperture, a target imaging area is divided into B sub-imaging areas according to a method of ignoring far-field effect, and the target imaging area is divided into B sub-imaging areas, wherein B is 1, 3, 9, 27, 63 and 127 respectively; according to the division result, the frequency domain echo signals are combined and sorted to obtain an echo data function y related to the b-th sub-imaging area and the k-th sub-aperture(k,b)Constructing a system function A of radar imaging according to a dyadic Green function in free space(k,b)And satisfies the following conditions:
y(k,b)＝A(k,b)x(k,b)+n(k,b)
the radar imaging problem is converted to solve for the unknown x in equation (1)(k,b)Wherein x is(k,b)Generalized reflectivity;
c) dual transformation: multiplication by matrix (A) at both ends of the upper equation(k,b))*Obtaining a system function B of image processing(k,b)And back projection imaging result z(k,b)：
The traditional radar imaging problem is converted into a radar image processing problem based on a physical mechanism.
4) Parallel imaging of the sub-imaging regions: based on the generalized reflectivity model, parallel imaging is carried out on each sub-imaging area according to a gradient iterative algorithm aiming at equation (2), and the generalized reflectivity is obtained through the mth iteration of the kth sub-aperture under the kth sub-imaging areaEquation (3) is satisfied:
wherein,is the step size factor of the mth iteration of the kth sub-aperture under the mth sub-imaging region,a gradient function for the mth iteration of the kth sub-aperture under the mth sub-imaging region;
imaging results x of all K sub-apertures under the b-th sub-imaging area by using a generalized reflectivity model(k,b)(K is more than or equal to 1 and less than or equal to K, B is more than or equal to 1 and less than or equal to B) image fusion is carried out to obtain an imaging result x of the B-th sub-imaging area(b)(1≤b≤B). This example uses a (1,2) mixed norm, which is processed as follows:
wherein N represents x(k,b)N represents the nth element.
5) Image fusion:
the imaging results of the B sub-imaging regions are subjected to image fusion to obtain a high-resolution radar imaging result of a complete target imaging region, and different imaging effects are obtained respectively by different numbers of the divided sub-imaging regions, as shown in fig. 2. The relationship between the divided sub-imaging regions of different numbers and the average imaging time is shown in table 1 below:
TABLE 1
As can be seen from table 1, as the number of sub-imaging regions increases, the average imaging time decreases exponentially, and it can be seen that the division of the sub-imaging regions greatly accelerates the imaging speed. However, as can be seen from fig. 2, as the number of sub-imaging regions increases, the imaging effect is further deteriorated, and the target becomes less visible. It can be seen that the imaging speed and imaging effect need to be effectively compromised. However, it is also found from fig. 2 and table 1 that the imaging time is short and the imaging effect is good, such as c and d in the figure.
Example two
In this embodiment, the target imaging region is divided into B sub-imaging regions according to the second sub-imaging region dividing method in B) of step 3), that is, the neighbor constant method, and this embodiment divides the target imaging region into B ═ 1, 4, 8, 12, 24, and 32 sub-imaging regions, respectively. The other steps are the same as those of the first embodiment. The number of the divided sub-imaging regions is different, and different imaging effects are respectively obtained, as shown in fig. 3.
In this embodiment, the relationship between the sub-imaging regions with different numbers and the average imaging time is shown in table 2 below:
TABLE 2
As can be seen from table 2, example two verifies the correctness of the conclusion of the example: with the increase of the number of the sub-imaging regions, the average imaging time is exponentially reduced, and it can be seen that the division of the sub-imaging regions greatly accelerates the imaging speed. However, as can be seen from fig. 3, the imaging effect is slightly deteriorated as the number of sub-imaging regions increases. Therefore, the imaging speed and the imaging effect need to be effectively compromised. However, it is also found from fig. 3 and table 2 that the imaging time is short and the imaging effect is good, such as c and d in the figure.
Finally, it is noted that the disclosed embodiments are intended to aid in further understanding of the invention, but those skilled in the art will appreciate that: various substitutions and modifications are possible without departing from the spirit and scope of the invention and the appended claims. Therefore, the invention should not be limited to the embodiments disclosed, but the scope of the invention is defined by the appended claims.
Claims (6)
1. A high-resolution radar rapid imaging method based on a generalized reflectivity model is characterized by comprising the following steps:
1) establishing a radar imaging system, and acquiring radar scattering data:
the radar imaging system comprises T transmitters and R receivers, the frequency number of the transmitted signals is F, the transmitters sequentially transmit signals to a target imaging area, all the receivers receive echo signals, and after the T-th transmitter transmits the signals, the echo signals received by all the receivers are y in sequenceF,t＝[yF,t,1；yF,t,2；…yF,t,R]Wherein, T and R are natural numbers which are more than or equal to 2 respectively, T is 1,2, …, T and F are natural numbers which are more than or equal to 2;
2) establishing a generalized reflectivity model:
the generalized reflectivity model is established on the basis of a Bonn approximation model and comprises anisotropic characteristics and frequency characteristics of an imaging target, namely the reflectivity of the imaging target under different angle transmitters is different and the reflectivity of the imaging target under different working frequencies is different;
3) data transformation and data integration:
a) a receiver receives a frequency domain echo signal;
b) according to the structural characteristics of radar system functions and a generalized reflectivity model, a radar imaging system is divided into K sub-apertures or sub-frequency bands, a target imaging area is divided into B sub-imaging areas, and frequency domain echo signals are combined and sorted according to the division results to obtain echo data functions y related to the B sub-imaging area and the K sub-aperture or sub-frequency band(k,b)Wherein k is 1,2, …, k, B is 1,2, …, B, radar imaging system function A is constructed according to free space dyadic Green function(k,b)Then the generalized reflectivity x(k,b)Satisfies equation (1):
y(k,b)＝A(k,b)x(k,b)+n(k,b)(1)
wherein n is(k,b)Expressing the model error, the radar imaging problem is converted into the solution of the unknown number x in the equation (1)(k,b)The inverse problem of (2);
c) multiplying the system function A on both sides of equation (1)(k,b)Conjugate transpose matrix ofNamely, the dual transformation is carried out on the equation (1) to obtain an equation (2):
wherein,is a system function that represents the processing of the image,is a representation of the back projection imaging result, n(k,b)The dual transformation converts the radar imaging problem into a radar image processing problem based on a physical mechanism;
4) parallel imaging of the sub-imaging regions:
based on the generalized reflectivity model, each sub-imaging area is subjected to parallel imaging according to a gradient iterative algorithm aiming at equation (2),
obtaining generalized reflectivity through m iteration of kth sub-aperture or sub-band under the b-th sub-imaging areaSatisfies equation (3):
wherein,for the step size factor of the mth iteration of the kth sub-aperture or sub-band under the mth sub-imaging region,a gradient function for the mth iteration of the kth sub-aperture or sub-band under the mth sub-imaging region;
imaging results x of all K sub-apertures under the b-th sub-imaging area by utilizing a generalized reflectivity model(k,b)Carrying out image fusion to obtain an imaging result x of the b-th sub-imaging area(b)：
Wherein N represents x(k,b)N represents the nth element, N is 1,2, …, N, p and q are norm indexes;
5) image fusion:
imaging result x of B sub-imaging areas(b)And performing image fusion to obtain a high-resolution radar imaging image X of a complete target imaging area.
2. The imaging method according to claim 1, wherein in the step 3) a), if the echo signal received by the receiver is a time domain echo signal, the time domain echo signal is transformed into a frequency domain echo signal using a fast fourier transform FFT.
3. The imaging method as set forth in claim 1, wherein in b) of step 3), the sub-imaging region division includes two different methods:
i. neglecting far field effects: dividing a target imaging area into B sub-imaging areas with overlapped edges by utilizing the characteristic of relatively weak correlation or action effect between two elements with relatively long distance, and neglecting the influence between the elements with relatively long distance;
neighbor constant method: the interaction between two adjacent elements is approximately a constant, the reflectivity of the adjacent elements is set to be the same value in the same equation, namely the original target imaging area is divided into B mutually staggered sub-imaging areas, and the solved large-scale multi-element linear equation set is approximately a series of small-scale multi-element linear equation sets.
4. The imaging method according to claim 1, wherein in step 4), based on the generalized reflectivity model, the equation (2) is solved in a distributed manner according to a gradient iterative algorithm, and the specific imaging process for parallel imaging of each sub-imaging region is as follows:
a) the imaging results at each sub-aperture or sub-band are iteratively calculated:
i. iterating the m times, calculating the gradient function of the k sub-aperture or sub-band under the b sub-imaging area
iterating the mth time, updating the step size factor of the kth sub-aperture or sub-band under the kth sub-imaging region
iterating the mth time to update the generalized reflectivity of the kth sub-aperture or sub-band under the kth sub-imaging region
Judging whether an iteration condition is met, if so, entering the step b), and if not, returning to the step i);
b) imaging result x of different sub-apertures of the b-th sub-imaging region(k,b)Merge into an image x(b)。
5. The imaging method according to claim 1, wherein in step 5), the method of image fusion is determined according to the method of sub-imaging region division.
6. The imaging method according to claim 5, wherein if the sub-imaging region division method employs a method of ignoring far-field effects, the image fusion employs a method of weighted averaging; if the sub-imaging region division method adopts the neighbor constant method, the image fusion adopts an interpolation method.
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