CN113030939B - Sparse angle measurement method based on subarray space smoothing under main lobe interference - Google Patents
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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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- G01S13/66—Radar-tracking systems; Analogous systems
- G01S13/68—Radar-tracking systems; Analogous systems for angle tracking only
- G01S13/685—Radar-tracking systems; Analogous systems for angle tracking only using simultaneous lobing techniques
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- 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
- G01S7/38—Jamming means, e.g. producing false echoes
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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
- 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
- G01S7/415—Identification of targets based on measurements of movement associated with the target
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Abstract
The invention relates to a sparse angle measurement method based on subarray space smoothing under main lobe interference, which relates to the technical field of radar signal processing, and comprises the following steps of a, performing sliding window processing on an antenna array to form a subarray space smoothing network to obtain corresponding subarray space receiving data; b, solving a corresponding covariance matrix according to the data received by each subarray space; step c, solving the self-adaptive weight vector of each subarray space; step d, obtaining the self-adaptive output of each subarray space; e, constructing an angle atom library according to the central phase relation among the adaptive channels; step f, obtaining a sparse coefficient by adopting an orthogonal matching pursuit algorithm; and g, carrying out angle estimation according to the sparse coefficient. The method effectively improves the accuracy of the radar measuring target angle under the main lobe interference, and further improves the performance of the phased array radar.
Description
Technical Field
The invention relates to the technical field of radar signal processing, in particular to a sparse angle measurement method based on subarray spatial smoothing under main lobe interference.
Background
Electronic interference and radar anti-interference are accompanied, mutual game is realized, the mutual game is continuously developed in the mutual countermeasure process, the mutual countermeasure is a permanent theme in the radar development process, which party has advantages and masters the initiative of war, along with the continuous improvement of the status and the action of radar equipment in modern war, the war between the electronic interference and the radar anti-interference is also increasingly violent, when the electronic interference exists in the electromagnetic environment, the radar equipment must adopt corresponding anti-interference measures to provide basic guarantee for the radar to continuously exert the efficiency, and therefore, the radar anti-interference capability is an important war and technical index for checking the radar performance.
At present, main lobe interference is one of the problems in the radar field, which severely restricts the performance of radar equipment, and according to whether the interference is in the same direction with a target, the main lobe interference can be subdivided into two types, the first type is self-defense interference which has the same angle with the target space and is emitted by an interference pod carried by the target machine, the interference is resisted mainly by a multi-station passive positioning method at present, the second type is concomitant main lobe interference which enters a radar receiving main lobe and has a slightly different angle with the target and is emitted mainly by a team jammer, a missile-borne jammer, a towing jammer and the like which accompany around the target machine, and the current second type is the main problem at present, and the performance of phased array radar with higher angle measurement precision requirement is severely restricted, such as radar guidance and fire control radar. The main reasons are that the angle measurement method of the phased array radar is less under the main lobe interference, and the angle measurement precision of the phased array radar is poor under the main lobe interference, so that the requirements cannot be met.
Disclosure of Invention
Therefore, the invention provides a sparse angle measurement method based on subarray space smoothing under main lobe interference, which is used for solving the problem that a phased array radar cannot accurately measure a target angle through a sparse coefficient when main lobe interference exists in the prior art.
In order to achieve the above object, the present invention provides a sparse angle measurement method based on subarray spatial smoothing under main lobe interference, which comprises:
step a: performing sliding window processing on the antenna array to form a subarray space smooth network and obtain corresponding subarray space receiving data;
step b: solving a corresponding covariance matrix according to the data received by each subarray space;
step c: solving the self-adaptive weight vector of each subarray space;
step d: obtaining adaptive output of each subarray space;
step e: constructing an angle atom library according to the central phase relation among the respective adaptive channels;
step f: obtaining a sparse coefficient by adopting an orthogonal matching pursuit algorithm;
step g: carrying out angle estimation according to the sparse coefficient;
the array antenna is divided into a plurality of sub-arrays according to a smooth mode to form a sub-array space smooth network, then the sub-array space covariance matrix is obtained by utilizing data received by each sub-array, the sub-array space adaptive weight vector is further calculated, then the sub-array space adaptive output is obtained, an angle atom library is constructed according to the central phase relation of each sub-array space, and the target angle parameter is estimated by adopting an orthogonal matching pursuit algorithm.
Further, setting the wavelength of a linear frequency modulation signal transmitted by a phased array radar to be lambda, setting the array antenna to have N array elements, setting the array type to be an equidistant uniform linear array, setting the array element interval to be a half-wavelength, setting 1 target and M interferences to exist in an electromagnetic environment, and setting s 0 (n) represents a target echo signal, s m (n) represents an interference echo signal, wherein M =1, 2.. Wherein M, n represents a sampling point, the linear array is divided into L sub-arrays, the received data of the L sub-array is,
wherein, a ml Represents the steering vector of the mth source on the lth sub-matrix, and
a ml ＝[exp(-(l-1)μ m )，exp(-lμ m ),...,exp(-(N+l-L-1)μ m )] T ＝a m1 ·exp(-(l-1)μ m ) (2)
wherein, mu m ＝j2πd sinθ m /λ，θ m Representing a source s m (n) is further expressed by the formula (1),
wherein v is l (n) representing noise, interference plus noise sample data is obtained by remote sampling,
further, the interference plus noise covariance matrix received by the ith sub-array may be expressed as,
wherein the content of the first and second substances,
the energy of the disturbance is represented by,
representing the noise energy, equation (5) is re-expressed as,
further, the adaptive weight vector of the ith sub-array is expressed as,
wherein, a bl Beam steering vector, mu, representing the l-th sub-array b ＝j2πd sinθ b /λ，θ b Is beam pointing.
Further, the adaptive output signal of the ith sub-array is represented as,
wherein the interference suppressing part
Therefore, the formula (8) is rewritten as,
as can be seen, the first sub-array adaptively outputs y l And 1 st sub-array self-adaptive output y 1 The phase difference therebetween was exp [ (l-1) (. Mu.) ( b -μ 0 )]Due to beam pointing theta b Is known, so mu b Since the phase difference of each subarray is known, it is only the mu of the input signal 0 Relating, i.e. to the angle theta of the input signal 0 In relation to this, according to equation (9), the adaptive output of the L sub-arrays is expressed as,
y(n)＝[y 1 (n),y 2 (n),...,y L (n)] T ＝y 1 (n)[1,exp(-μ ε ),...,exp(-(L-1)μ ε )] T (10)
wherein, mu ε ＝μ 0 -μ b 。
Further, an angle atom library is constructed, sparse representation is carried out on the formula (10),
wherein, the first and the second end of the pipe are connected with each other,
for sparse coefficients, P represents the number of angle atoms, phi represents an angle atom library, phi is expressed as,
wherein the content of the first and second substances,
a(θ ηp )＝[1,exp(-j2πd/λ(sinθ ηp -sinθ b )),...,exp(-j2πd/λ(L-1)(sinθ ηp -sinθ b ))] T (13)
finding one of the angles theta in the angle atom library phi ηp Fitting the target angle theta 0 。
Further, a tag set is set
Setting an initialization residual vector r 0 Set k =1, and find the sum residual vector r in Φ k-1 The position of the most strongly correlated atom of (c),
wherein phi l For the l column of Φ, update Ω k ，
Ω k ＝Ω k-1 ∪{l k } (15)
The k-th iteration residue is updated to,
wherein, ω is k Representing sparse coefficients by minimizing the iterative residue r k The solution is carried out and the solution is carried out,
it is possible to obtain,
iteration is carried out through a repeated iteration formula until a convergence condition is met, and the iteration termination conditions are two, namely the known sparsity and the preset iteration step number are the first one, and the second one is that the iteration residue is smaller than a certain preset value zeta, namely | | | r k || 2 ≤ζ；
According to the sparse coefficient omega 1 The non-zero value position estimation of the target obtains the angle parameter information of the target.
Further, when windowing is performed on the adaptive weight vector in the step c, a rectangular weight or Chebyshev weight or Hamming weight or Hanning weight or Taylor weight is adopted.
Further, in the step f, the orthogonal matching pursuit algorithm is replaced by a sparse bayesian algorithm.
Compared with the prior art, the method has the advantages that an angle atom library is constructed by calculating the received data of the subarray space, the covariance matrix, the adaptive weight vector and the adaptive output, the sparse coefficient is obtained for estimating the angle, the accuracy of the radar measuring target angle under the main lobe interference is effectively improved, the method can ensure that the central phase relation of each subarray space is unchanged after the main lobe interference is inhibited, the method can simultaneously resist the main lobe interference, the side lobe interference or the multiple main lobe interference, and the method can be used for various phased array system radars of different platforms.
Furthermore, the method effectively improves the accuracy of interference plus noise sample data and further improves the accuracy of the radar measurement target angle under the main lobe interference by calculating the received data of the subarrays.
Furthermore, the method effectively improves the reliability of data and further improves the accuracy of the radar measurement target angle under the main lobe interference by calculating the interference and noise covariance matrix received by the subarray.
Furthermore, the method effectively improves the reliability of data and further improves the accuracy of the radar measurement target angle under the main lobe interference by calculating the self-adaptive weight vector of the subarray.
Furthermore, the method effectively improves the reliability of data by calculating the self-adaptive output of the subarrays, and further improves the accuracy of the angle of the target measured by the radar under the interference of the main lobe.
Furthermore, the method effectively improves the reliability of data by constructing an angle atom library, and further improves the accuracy of the target angle measured by the radar under the interference of the main lobe.
Furthermore, the method estimates the angle parameter information of the target by calculating the sparse coefficient, effectively improves the reliability of data, and further improves the accuracy of the radar measuring target angle under the main lobe interference.
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Fig. 1 is a schematic flow diagram of a sparse angle measurement method based on subarray spatial smoothing under main lobe interference according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described below with reference to examples; it should be understood that the specific embodiments described herein are merely illustrative of the invention and do not delimit the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principles of the present invention, and do not limit the scope of the present invention.
Fig. 1 is a schematic flow chart of a sparse angle measurement method based on subarray spatial smoothing under main lobe interference according to an embodiment of the present invention.
The invention provides a sparse angle measurement method based on subarray spatial smoothing under main lobe interference, which comprises the following steps:
step a: performing sliding window processing on the antenna array to form a subarray space smooth network to obtain corresponding subarray space receiving data;
step b: solving a corresponding covariance matrix according to the data received by each subarray space;
step c: solving the adaptive weight vector of each subarray space, wherein when windowing is carried out on the adaptive weight vector, a rectangular weight, a Chebyshev weight, a Hamming weight, a Hanning weight or a Taylor weight can be adopted;
step d: obtaining adaptive output of each subarray space;
step e: constructing an angle atom library according to the central phase relation among the respective adaptive channels;
step f: obtaining a sparse coefficient by adopting an orthogonal matching tracking algorithm, wherein the orthogonal matching tracking algorithm can be replaced by a sparse Bayesian algorithm;
step g: carrying out angle estimation according to the sparse coefficient;
the array antenna is divided into a plurality of sub-arrays according to a smooth mode to form a sub-array space smooth network, then the sub-array space covariance matrix is obtained by utilizing data received by each sub-array, the sub-array space adaptive weight vector is further calculated, then the sub-array space adaptive output is obtained, an angle atom library is constructed according to the central phase relation of each sub-array space, and the target angle parameter is estimated by adopting an orthogonal matching pursuit algorithm.
Specifically, the wavelength of a linear frequency modulation signal transmitted by a phased array radar is set to be lambda, an array antenna is set to have N array elements, the array type is set to be an equidistant uniform linear array, the array element interval is set to be a half-wavelength, 1 target and M interferences are set to exist in an electromagnetic environment, and s is set 0 (n) represents a target echo signal, s m (n) represents a disturbance echo signal, wherein M =1, 2.. And M, n represents a sampling point, the linear array is divided into L sub-arrays, the reception data of the L sub-array is,
wherein, a ml Represents the steering vector of the mth source on the lth sub-matrix, and
a ml ＝[exp(-(l-1)μ m )，exp(-lμ m ),...,exp(-(N+l-L-1)μ m )] T ＝a m1 ·exp(-(l-1)μ m ) (2)
wherein, mu m ＝j2πd sinθ m /λ，θ m Representing a source s m (n) is further expressed by the formula (1),
wherein v is l (n) representing noise, interference plus noise sample data obtained by remote sampling,
for example, the number of array elements N =20, the number of interference M =2, the number of smoothing sub-arrays L =15, each sub-array containing (M-L + 1) =6 array elements, the received interference plus noise sample data of the L (L =1,2, \8230; 15) th sub-array
Space domain steering vector a m1 ＝[1，exp(-μ m ),...,exp(-5μ m )] T 。
Specifically, the interference plus noise covariance matrix received by the ith sub-array is expressed as,
wherein, the first and the second end of the pipe are connected with each other,
the energy of the disturbance is represented by,
representing the noise energy, equation (5) is re-expressed as,
for example, R l Is a 6 x 6 matrix.
Specifically, the adaptive weight vector of the ith sub-array is expressed as,
wherein, a bl Beam steering vector, mu, representing the l-th sub-array b ＝j2πd sinθ b /λ，θ b Is beam pointing.
For example, the beam is pointed at θ b Equal to 0 degree, calculating to obtain mu b =0, adaptive weight vector w l ＝w 1 。
Specifically, the adaptive output signal of the l-th sub-array is represented as,
wherein the interference suppressing part
Therefore, the formula (8) is rewritten as,
as can be seen, the first sub-array adaptively outputs y l And 1 st sub-array self-adaptive output y 1 The phase difference between them is exp [ (l-1) (mu) b -μ 0 )]Due to beam pointing theta b Is known, therefore mu b The phase difference of the adaptive outputs of the respective sub-arrays is known only to mu of the input signal 0 Relating, i.e. to the angle theta of the input signal 0 In relation to this, according to equation (9), the adaptive output of the L sub-arrays is expressed as,
y(n)＝[y 1 (n),y 2 (n),...,y L (n)] T ＝y 1 (n)[1,exp(-μ ε ),...,exp(-(L-1)μ ε )] T (10)
wherein, mu ε ＝μ 0 -μ b 。
For example, the adaptive output y (n) = y 1 (n)[1,exp(-μ ε ),...,exp(-14μ ε )] T 。
Specifically, an angle atom library is constructed, expression (10) is sparsely expressed,
wherein, the first and the second end of the pipe are connected with each other,
for sparse coefficients, P represents the number of angle atoms, phi represents an angle atom library, phi is expressed as,
wherein the content of the first and second substances,
a(θ ηp )＝[1,exp(-j2πd/λ(sinθ ηp -sinθ b )),...,exp(-j2πd/λ(L-1)(sinθ ηp -sinθ b ))] T (13)
finding one of the angles theta in the angle atom library phi ηp Fitting the target angle theta 0 。
For example, the number of angular atoms P is 101, [ theta ] η1 ,...,θ η101 ]Take [ -2.5 °,. °,2.5 ° ]]Step by 0.05 degree, a (theta) ηp )＝[1,exp(-j2πd/λsinθ ηp ),...,exp(-j2πd/λ·14sinθ ηp )] T P =1, 2.., Φ is obtained by substituting 101 for formula (12).
Specifically, a tag set is set
Setting an initialization residual vector r 0 Set k =1, and find the sum residual vector r in Φ k-1 The position of the most strongly relevant atom of (c),
wherein phi l For the l column of Φ, update Ω k ，
Ω k ＝Ω k-1 ∪{l k } (15)
The k-th iteration residue is updated to,
wherein, ω is k Representing sparse coefficients by minimizing the iterative residue r k The solution is carried out and the solution is carried out,
it is possible to obtain,
iteration is carried out through a repeated iteration formula until a convergence condition is met, and the iteration termination conditions are two, namely the known sparsity and the preset iteration step number are the first one, and the second one is that the iteration residue is smaller than a certain preset value zeta, namely | | | r k || 2 ≤ζ；
For example, the target number is 1, the preset number of iteration steps k =1, ω 1 Is a 101-dimensional vector.
According to the sparse coefficient omega 1 The non-zero value position estimation of the target obtains the angle parameter information of the target.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention; various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A sparse angle measurement method based on subarray space smoothing under main lobe interference is characterized by comprising the following steps:
step a: performing sliding window processing on the antenna array to form a subarray space smooth network to obtain corresponding subarray space receiving data;
step b: solving a corresponding covariance matrix according to the data received by each subarray space;
step c: solving the self-adaptive weight vector of each subarray space;
step d: obtaining adaptive output of each subarray space;
step e: constructing an angle atom library according to the central phase relation among the respective adaptive channels;
step f: obtaining a sparse coefficient by adopting an orthogonal matching pursuit algorithm;
step g: carrying out angle estimation according to the sparse coefficient;
dividing the array antenna into a plurality of sub-arrays according to a smooth mode to form a sub-array space smooth network, solving a sub-array space covariance matrix by utilizing data received by each sub-array, further calculating a sub-array space adaptive weight vector, then obtaining the sub-array space adaptive output, constructing an angle atom library according to the central phase relation of each sub-array space, and estimating a target angle parameter by adopting an orthogonal matching pursuit algorithm; setting the wavelength of a linear frequency modulation signal transmitted by a phased array radar as lambda, setting an array antenna to have N array elements, setting the array type to be an equidistant uniform linear array, setting the array element interval to be a half-wavelength, setting 1 target and M interferences in an electromagnetic environment, and setting s 0 (n) represents a target echo signal, s m (n) represents an interference echo signal, wherein M =1, 2.. Wherein M, n represents a sampling point, the linear array is divided into L sub-arrays, the received data of the L sub-array is,
wherein, a ml Represents the steering vector of the mth source on the lth sub-matrix, and
a ml ＝[exp(-(l-1)μ m )，exp(-lμ m )，...，exp(-(N+l-L-1)μ m )] T
＝a m1 ·exp(-(l-1)μ m ) (2)
wherein, mu m ＝j2πd sinθ m /λ，θ m Representing a source s m (n) is further expressed by the formula (1),
wherein v is l (n) representing noise, interference plus noise sample data is obtained by remote sampling,
2. the sparse angle measurement method based on subarray spatial smoothing under main lobe interference according to claim 1, wherein the interference plus noise covariance matrix received by the ith subarray is represented as,
wherein the content of the first and second substances,
the energy of the disturbance is represented by,
representing the noise energy, equation (5) is re-expressed as,
3. the sparse angle measurement method based on subarray spatial smoothing under main lobe interference according to claim 2, wherein the adaptive weight vector of the ith subarray is represented as,
wherein, a bl Beam steering vector, mu, representing the l-th sub-array b ＝j2πd sinθ b /λ，θ b Is beam pointing.
4. The sparse angle measurement method based on subarray spatial smoothing under main lobe interference according to claim 3, wherein the adaptive output signal of the ith subarray is represented as,
wherein the interference suppressing part
Therefore, the formula (8) is rewritten as,
as can be seen, the first sub-array adaptively outputs y l And 1 st sub-array self-adaptive output y 1 The phase difference therebetween was exp [ (l-1) (. Mu.) ( b -μ 0 )]Due to beam pointing theta b Is known, therefore mu b The phase difference of the adaptive outputs of the respective sub-arrays is known only to the outputμ of incoming signal 0 Relating, i.e. to the angle theta of the input signal 0 In relation to this, according to equation (9), the adaptive output of the L sub-arrays is expressed as,
y(n)＝[y 1 (n)，y 2 (n)，...，y L (n)] T
＝y 1 (n)[1，exp(-μ ε )，...，exp(-(L-1)μ ε )] T (10)
wherein, mu ε ＝μ 0 -μ b 。
5. The sparse angle measurement method based on subarray spatial smoothing under main lobe interference according to claim 4, wherein an angle atom library is constructed to perform sparse representation on equation (10),
wherein the content of the first and second substances,
for sparse coefficients, P represents the number of angle atoms, phi represents an angle atom library, phi is expressed as,
wherein the content of the first and second substances,
a(θ ηp )＝[1，exp(-j2πd/λ(sinθ ηp -sinθ b ))，...，exp(-j2πd/λ(L-1)(sinθ ηp -sinθ b ))] T (13)
finding one of the angles theta in the angle atom library phi ηp To fit the target angle theta 0 。
6. The sparse angle measurement method based on subarray spatial smoothing under main lobe interference according to claim 5, wherein a label set is set
Setting an initialization residual vector r 0 Set k =1, and find the sum residual vector r in Φ k-1 The position of the most strongly relevant atom of (c),
wherein phi is l For the l column of Φ, update Ω k ，
Ω k ＝Ω k-1 ∪{l k } (15)
The k-th iteration residue is updated to,
wherein, ω is k Representing sparse coefficients by minimizing the iterative residue r k The solution is carried out and the solution is carried out,
it is possible to obtain,
iteration is carried out through a repeated iteration formula until a convergence condition is met, and the iteration termination conditions are two, namely the known sparsity and the preset iteration step number are the first one, and the second one is that the iteration residue is smaller than a certain preset value zeta, namely | | | r k || 2 ≤ζ；
According to the sparse coefficient omega 1 The non-zero value position estimation of the target obtains the angle parameter information of the target.
7. The sparse angle measurement method based on subarray spatial smoothing under main lobe interference according to claim 1, wherein when windowing is performed on the adaptive weight vector in step c, a rectangular weight or a chebyshev weight or a hamming weight or a hanning weight or a taylor weight is used.
8. The sparse angle measurement method based on subarray spatial smoothing under main lobe interference according to claim 1, wherein in step f, the orthogonal matching pursuit algorithm is replaced by a sparse bayesian algorithm.
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