CN115856886A - Cross-domain distributed MISO-ISAR radar forward-looking imaging detection method - Google Patents
Cross-domain distributed MISO-ISAR radar forward-looking imaging detection method Download PDFInfo
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Abstract
The invention relates to a cross-domain distributed MISO-ISAR radar forward-looking imaging detection method, which comprises the following steps: a plurality of movable transmitting nodes are arranged, and each transmitting node transmits orthogonal waveforms to form a transmitting array; the transmitting array moves according to the requirement, and a plurality of transmitting nodes transmit echoes; and setting a receiving station, receiving echoes by the receiving station, recovering a plurality of groups of two-dimensional imaging data sets, representing an observation sub-aperture by each group of echoes, and then carrying out multi-sub-aperture echo coherent fusion processing to obtain a fused optimal aperture image. The invention innovatively provides a multi-sub-aperture echo coherent fusion processing method, which is used for preprocessing on the basis of the MISO-ISAR technology, obtains the optimal features through fusion processing of image features, thereby obtaining the optimal aperture image, achieving the effect of high-definition two-dimensional imaging of the target, and realizing high-definition imaging resolution of the low-slow small target and the dense target in a detection airspace.
Description
Technical Field
The invention belongs to the technical field of radar detection imaging, and particularly relates to a cross-domain distributed MISO-ISAR radar forward-looking imaging detection method.
Background
The cross-domain distributed MISO-ISAR radar forward-looking imaging detection technology is a radar detection technology which collaboratively constructs an air-ground combined MISO-ISAR (multiple-transmitting single-receiving-inverse synthetic aperture) imaging configuration through an air distributed type irradiation platform and a ground silent receiving imaging platform in time and space, performs air multi-platform distributed type transmission and ground platform silent receiving, and performs ISAR two-dimensional high-resolution imaging on a target.
A time-varying distributed MISO-ISAR imaging system is adopted, a distributed array is assumed to be motionless, the radar is equivalent to a fixed station-distributed MISO-ISAR radar, transmitting nodes simultaneously transmit orthogonal radar waveforms, a B-ISAR system is formed between each transmitting node and each receiving node, a plurality of B-ISAR systems form a plurality of equivalent phase centers in space, each phase center simultaneously observes a target from a plurality of angles, virtual apertures formed by rotation between each equivalent phase center and the target can be spliced in a synthetic aperture time range, and then the distributed MISO-ISAR can obtain a synthetic aperture larger than the B-ISAR, so that the higher direction resolution capability of the target can be realized.
The existing research of the MISO-ISAR synthetic aperture, such as the research content in the literature, "moving target distributed radar imaging technology research", only relates to the synthesis between two apertures, and the research on the synthesis method between a plurality of apertures is still lacked.
Disclosure of Invention
The invention provides a forward-looking imaging detection method for a cross-domain distributed MISO-ISAR radar, which realizes multi-aperture feature fusion on the basis of distributed MISO-ISAR.
The technical scheme adopted by the invention is as follows: a cross-domain distributed MISO-ISAR radar forward-looking imaging detection method comprises the following steps:
a plurality of movable transmitting nodes are arranged, and each transmitting node transmits orthogonal waveforms to form a transmitting array;
the transmitting array moves according to requirements, and a plurality of transmitting nodes transmit echoes;
and setting a receiving station, receiving echoes by the receiving station, recovering a plurality of groups of two-dimensional imaging data sets, wherein each group of echoes represents an observation sub-aperture, and then carrying out multi-sub-aperture echo coherent fusion processing to obtain a fused optimal aperture image (namely the fused optimal two-dimensional imaging data set).
Further, the multi-sub-aperture echo coherent fusion processing method comprises:
respectively generating basic characteristic functions K according to the imaged characteristics m (x)，
K m (x)＝f(x 1 ,x 2 ...x n )，
Wherein x is 1 ,x 2 ...x n Respectively corresponding to different characteristics of the imaging process;
obtaining a comprehensive characteristic function according to the basic characteristic function:
with the constraint of
Wherein, K m (x) Is a basic feature function, M represents the mth basic feature function, M is the number of the basic feature functionsNumber, d m A weight representing the corresponding underlying feature function; according to d m Solving a comprehensive characteristic function; and performing feature fusion according to the comprehensive feature function, and solving each optimal fusion feature to realize optimal aperture image fusion.
Further, the method for solving each optimal fusion feature includes:
the synthetic feature function K (x) is expressed as follows:
the optimal fusion algorithm is equivalently found to be an optimization problem, to
Finding the minimum value, and d corresponding to the minimum value m Substituting the comprehensive characteristic function to obtain an optimal comprehensive characteristic function K y (x)；
Obtaining
The minimum constraints are as follows:
wherein ξ i Is a slack variable, C is a penalty factor, y i Are category labels. Xi i Is a relaxation variable, C is a penalty factor, y i The class target is given by performing multiple optimization assignments through a large number of samples and examples and combining engineering experience.
Further, the different features of the imaging process include: pixels, resolution, single synthetic aperture size, separation distance of apertures for the imaging process.
Further, the method also comprises the following step of fusing the sub-aperture:
and calculating a virtual aperture delta theta formed after multi-aperture fusion, and selecting a fusion method according to the virtual aperture delta theta to fuse the sub-apertures.
Further, the fusing the sub-apertures comprises: a total of P groups of echoes when
the fusion is performed using a conventional sub-aperture fusion method, where θ p The aperture representing the p-th set of echoes,
β 0 representing the double base angle of the radar at time Tp = 0.
Further, the fusing the sub-apertures comprises: a total of P groups of echoes when
The following fusion model was used:
s＝Aσ+e
wherein the content of the first and second substances,
s is a total echo signal formed by combining a plurality of echo signals S, A is an observation matrix corresponding to S, and the observation matrix A is determined by the rotating speed of the target relative to the P radars and the double base angle of the P-1 radars when Tp = 0; sigma is a single radar echo signal; e is the noise vector.
Compared with the prior art, the invention has the beneficial effects that:
the invention innovatively provides a multi-sub-aperture echo coherent fusion processing method, which is used for preprocessing on the basis of the MISO-ISAR technology, obtains the optimal features through fusion processing of image features, thereby obtaining the optimal aperture image, achieving the effect of high-definition two-dimensional imaging of the target, and realizing high-definition imaging resolution of the low-slow small target and the dense target in a detection airspace.
Drawings
Fig. 1 is a schematic diagram of multi-subaperture synthetic aperture fusion.
Detailed Description
The invention is further illustrated below with reference to the figures and examples.
The invention discloses a cross-domain distributed MISO-ISAR radar forward-looking imaging detection method, which comprises the following steps:
arranging a plurality of unmanned aerial vehicles in the air, wherein each unmanned aerial vehicle is a transmitting node, each transmitting node transmits orthogonal waveforms, and the unmanned aerial vehicles in the air form a transmitting array;
controlling the transmitting array to move according to requirements, and transmitting echoes by a plurality of transmitting nodes; and arranging a receiving station on the ground, receiving echoes by the receiving station, recovering a plurality of groups of two-dimensional imaging data sets, wherein each group of echoes represents an observation sub-aperture, and then carrying out multi-sub-aperture echo coherent fusion processing to obtain a fused optimal aperture image.
It is well known that the doppler resolution of moving object imaging is inversely proportional to the size of the synthetic aperture, with larger synthetic apertures giving higher resolution. In order to realize high-resolution imaging of a 'cluster low-slow small' target, an ultra-large synthetic aperture array must be synthesized, in ISAR imaging, the moving speed of the target, particularly the rotating angle of a relative radar in a synthetic aperture process, directly influences the size of a synthetic aperture, for the slow target, only single-base ISAR imaging is adopted, the slow movement of the target within the synthetic aperture time forms a very small rotating angle, and a large synthetic aperture cannot be formed for the small target.
The imaging performance and the realizability and the usability of the system are comprehensively considered, a time-varying distributed MISO-ISAR imaging system is provided, a plurality of unmanned aerial vehicles are arranged in the air, each unmanned aerial vehicle has the capability of transmitting and receiving echoes, each transmitting node transmits an orthogonal waveform, the aerial unmanned aerial vehicles form a transmitting array, and the transmitting array can be flexibly arranged and moved based on the system detection requirement; the ground vehicle is a receiving station, only receives and does not transmit, keeps the radio silent, and has high anti-detective and anti-interference capability.
A time-varying distributed MISO-ISAR imaging system is adopted, a distributed array is assumed to be motionless, the radar is equivalent to a fixed station-distributed MISO-ISAR radar, transmitting nodes simultaneously transmit orthogonal radar waveforms, a B-ISAR system is formed between each transmitting node and each receiving node, a plurality of B-ISAR systems form a plurality of equivalent phase centers in space, each phase center simultaneously observes a target from a plurality of angles, virtual apertures formed by rotation between each equivalent phase center and the target can be spliced in a synthetic aperture time range, and then the distributed MISO-ISAR can obtain a synthetic aperture larger than the B-ISAR, so that the higher direction resolution capability of the target can be realized.
Due to the small and slow characteristics of the target, particularly for small and slow unmanned aerial vehicles with the flight speed below subsonic speed and the target size not exceeding one meter, the virtual synthetic aperture formed by the distributed MISO-ISAR is not enough to form a high-resolution image for subsequent classification and identification on the target, so that on the basis of distributed MISO-ISAR imaging, the distributed transmitting nodes are enabled to move as required, and the accumulation rotation angle of azimuth high-resolution imaging is provided.
In the synthetic aperture time range, a plurality of nodes transmit echoes, in a ground receiving station, after orthogonal demodulation, a plurality of groups of two-dimensional echo data sets can be recovered, each group of echoes represents an observation sub-aperture, and the echo signals are obtained from equivalent phase centers of different observation angles in space, so the sub-aperture echoes are positioned on different azimuth angles. If only these data are imaged separately, the imaging resolution is low because the synthetic aperture is short.
In order to achieve high-resolution imaging of clustered small targets, the coherent fusion method of multiple echo arrays obtained by MISO-ISAR must be studied, while existing studies only discuss synthesis between two apertures, and synthesis methods between multiple apertures are still lacking. Therefore, the invention provides a multi-sub-aperture echo coherent fusion processing method, which is used for fusing imaging images of multiple echoes to obtain an optimal image.
The multi-sub-aperture echo coherent fusion processing method comprises the following steps:
respectively generating basic characteristic functions K according to the imaged characteristics m (x)，
K m (x)＝f(x 1 ,x 2 ...x n )，
Wherein x is 1 ,x 2 ...x n Each corresponding to a different characteristic of the imaging process including, but not limited to, the pixel, resolution, single synthetic aperture size, separation distance of apertures, etc. of the imaging process.
Obtaining a comprehensive characteristic function according to the basic characteristic function:
with the constraint of
Wherein, K m (x) As a basis feature function, M denotes the mth basis feature function, M is the number of basis feature functions, d m A weight representing the corresponding underlying feature function; according to d m Solving a comprehensive characteristic function; performing feature fusion according to the comprehensive feature function, and solving each optimal fusion feature to realize optimal aperture image fusion;
the synthetic feature function K (x) may be expressed as follows:
the optimal fusion algorithm is equivalently found to be an optimization problem, pair
Finding the minimum value, and d corresponding to the minimum value m Substituting the comprehensive characteristic function to obtain an optimal comprehensive characteristic function K y (x)；
Obtaining
The minimum constraints are as follows:
wherein ξ i Is a relaxation variable, C is a penalty factor, y i Are category labels. Xi i 、C、y i And performing multiple optimization assignment giving through a large number of samples and examples and combining engineering experience.
The method has the advantages that the multiple images are fused through the algorithm, so that the fused optimal characteristics are obtained, the optimal aperture image is obtained, the effect of high-definition two-dimensional imaging of the target is achieved, and high-definition imaging resolution of the low-slow small target and the dense target in the detection space is realized.
Fusing the sub-aperture:
calculating the virtual aperture delta theta formed after multi-aperture fusion, and combining the sub-apertures delta theta as shown in FIG. 1 1 To delta theta P Obtaining delta theta after fusion, and selecting a fusion method according to the virtual aperture delta theta.
Fusing the sub-aperture specifically includes: assuming a common P set of echoes (i.e. P radars),
when the temperature is higher than the set temperature
When the temperature of the water is higher than the set temperature,
the fusion is performed using a conventional sub-aperture fusion method, where p represents the pth group of echoes, θ p The aperture representing the p-th set of echoes,
β 0 representing the double base angle of the radar at time Tp = 0. The conventional sub-aperture fusion method has been reported in the existing research, and reference can be specifically made to the third chapter of the article "research on distributed radar imaging technology for moving targets".
When in use
When the temperature of the water is higher than the set temperature,
the following fusion model was used: s = A σ + e
Wherein the content of the first and second substances,
s is the total echo signal obtained by combining a plurality of echo signals S, A is an observation matrix corresponding to S, and the observation matrix A is formed by an unknown number omega 1 ,ω 2 ···ω P And beta 02 ,β 03 ···β 0P It is determined that ω is the rotational speed of the target relative to the radar (P radars correspond to P ω), β 02 ,β 03 ···β 0P Is the bistatic angle of the target to P-1 radars at the time Tp = 0; sigma is a single radar echo signal; e is the noise vector.
Claims (7)
1. A cross-domain distributed MISO-ISAR radar forward-looking imaging detection method is characterized by comprising the following steps:
a plurality of movable transmitting nodes are arranged, and each transmitting node transmits orthogonal waveforms to form a transmitting array;
the transmitting array moves according to requirements, and a plurality of transmitting nodes transmit echoes;
and setting a receiving station, receiving echoes by the receiving station, recovering a plurality of groups of two-dimensional imaging data sets, representing an observation sub-aperture by each group of echoes, and then carrying out multi-sub-aperture echo coherent fusion processing to obtain a fused optimal aperture image.
2. The cross-domain distributed MISO-ISAR radar forward-looking imaging detection method of claim 1, wherein the multi-sub-aperture echo coherent fusion processing method is as follows:
respectively generating basic characteristic functions K according to the imaged characteristics m (x)，
K m (x)＝f(x 1 ,x 2 ...x n )，
Wherein x is 1 ,x 2 ...x n Respectively corresponding to different characteristics of the imaging process;
obtaining a comprehensive characteristic function according to the basic characteristic function:
with the constraint of
Wherein, K m (x) As a basis feature function, M denotes the mth basis feature function, M is the number of basis feature functions, d m A weight representing the corresponding underlying feature function; according to d m Solving a comprehensive characteristic function; and performing feature fusion according to the comprehensive feature function, and solving each optimal fusion feature to realize optimal aperture image fusion.
3. The cross-domain distributed MISO-ISAR radar forward-looking imaging detection method of claim 2, wherein said method for finding each optimal fusion feature is:
the synthetic feature function K (x) is expressed as follows:
the optimal fusion algorithm is equivalently found to be an optimization problem, pair
Finding the minimum value, and d corresponding to the minimum value m Substituting the comprehensive characteristic function to obtain an optimal comprehensive characteristic function K y (x)；
Obtaining
The minimum constraints are as follows: />
Wherein ξ i For the relaxation variable, C is a penalty factor and yi is a category label.
4. The cross-domain distributed MISO-ISAR radar forward-looking imaging detection method of claim 2, wherein the different features of the imaging process include: pixels, resolution, single synthetic aperture size, separation distance of apertures for the imaging process.
5. The cross-domain distributed MISO-ISAR radar forward-looking imaging detection method of claim 1, further comprising fusing sub-apertures:
and firstly, calculating a virtual aperture delta theta formed after the multi-aperture fusion, and selecting a fusion method delta theta according to the virtual aperture to fuse the sub-apertures.
6. The cross-domain distributed MISO-ISAR radar forward-looking imaging detection method of claim 5, wherein the fusing sub-apertures comprises: a total of P groups of echoes when
the fusion is performed using a conventional sub-aperture fusion method, where θ p An aperture representing the pth group of echoes;
β 0 representing the double base angle of the radar at Tp = 0.
7. The cross-domain distributed MISO-ISAR radar forward-looking imaging detection method of claim 5, wherein said fusing sub-apertures comprises: a total of P groups of echoes when
the following fusion model was used:
s＝Aσ+e
wherein the content of the first and second substances,
s is a total echo signal formed by combining a plurality of echo signals, A is an observation matrix corresponding to S, the observation matrix A is determined by the rotating speed of a target relative to P radars and the bistatic angle of P-1 radars when Tp =0, and sigma is a single radar echo signal; e is the noise vector.
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