CN107340499A - The sane low-sidelobe beam forming method rebuild based on covariance matrix - Google Patents
The sane low-sidelobe beam forming method rebuild based on covariance matrix Download PDFInfo
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- CN107340499A CN107340499A CN201710505821.4A CN201710505821A CN107340499A CN 107340499 A CN107340499 A CN 107340499A CN 201710505821 A CN201710505821 A CN 201710505821A CN 107340499 A CN107340499 A CN 107340499A
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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
- G01S7/023—Interference mitigation, e.g. reducing or avoiding non-intentional interference with other HF-transmitters, base station transmitters for mobile communication or other radar systems, e.g. using electro-magnetic interference [EMI] reduction techniques
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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
- G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
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- Computer Networks & Wireless Communication (AREA)
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Abstract
The invention discloses a kind of sane low-sidelobe beam forming method rebuild based on covariance matrix, comprise the following steps：1) reception signal of radar array is sampled, obtains signal phasor；2) according to sampled data, try to achieve and receive data covariance matrix and space Spectral structure, rebuild interference plus noise covariance matrix；3) according to the covariance matrix and steering vector of reconstruction, the MVDR models of addition secondary lobe constraint is solved with convex optimization method, obtain global optimum's weight vector；4) reception signal vector and the optimal weights vector obtained are subjected to multiplication operation, obtain sane Sidelobe adaptive beam.The Adaptive beamformer method of the present invention, not only robustness is good, and secondary lobe is low.
Description
Technical field
The invention belongs to the self-adaptive numerical integration algorithm technical field of Digital Array Radar, particularly a kind of Sidelobe
Robust adaptive beamforming method.
Background technology
Adaptive beamformer technology obtains in fields such as radio communication, radar, sonar, medical imaging, radio astronomys
Extensive use.The exact knowledge of conventional Adaptive beamformer hypothesis known desired steering vector, but actual medium wave
The performance that beam is formed is influenceed by error, causes Beam-former performance degradation, to correct deviation, sane adaptive beam shape
Arisen at the historic moment into technology.
For the excellent adaptive beam former of design performance, to consider robustness, minor level control and do
Suppression three aspect factor is disturbed, therefore this purpose can be reached using some technical measures.When containing echo signal in training data,
Beam-former hydraulic performance decline is particularly acute, and for such a situation, diagonal loading algorithm is by handling sampled data covariance square
Battle array, is closer in preferable interference plus noise matrix, namely in minimum variance distortionless response (MVDR) Beam-former
Object function in add a regular terms, can strengthen robustness, but this method lack strict theoretical foundation come it is accurate
Select optimal loading level.Feature decomposition beamforming algorithm feature based value is decomposed, using signal subspace characteristic, although this
Kind method can improve robustness, but subspace winding occurs in low signal-to-noise ratio so as to greatly reduce the property of Beam-former
Energy.Based on optimal beam forming algorithm under worst case, this algorithm is equivalent actually with diagonal loading algorithm, for it
Uncertain collection constant is difficult to determine under different backgrounds.
The above algorithm is constantly present the shortcomings that respective, it is impossible to meets wanting for Sidelobe while robustness is strengthened
Ask.
The content of the invention
It is an object of the invention to provide a kind of sane low-sidelobe beam forming method rebuild based on covariance matrix.
The technical solution for realizing the object of the invention is：A kind of sane low-sidelobe beam rebuild based on covariance matrix
Forming method, comprise the following steps：
Step 1, the reception signal to radar array sample, and obtain signal phasor；
Step 2, the data sampled according to step 1, try to achieve and receive data covariance matrix and space Spectral structure, rebuild interference
Plus noise covariance matrix；
Step 3, covariance matrix and steering vector according to reconstruction, addition secondary lobe constraint is solved with convex optimization method
MVDR models, obtain global optimum's weight vector；
Step 4, the signal phasor that step 1 is received and the optimal weights vector that step 3 is obtained carry out multiplication operation, obtain
Sane Sidelobe adaptive beam.
Compared with prior art, its remarkable advantage is the present invention：1) robustness of the invention is preferable：Present invention addition secondary lobe
The MVDR adaptive beam former models of constraint, this model are convex Optimized model, with MATLAB CVX tool boxes to optimization
Model is solved, and obtains optimal weights vector.Compared with existing classic algorithm, not only robustness is preferable, and disturbs suppression
System is deepened；2) secondary lobe is low：By the present invention in that the optimized variable designed by the use of array weight vector as adaptive beam, original
MVDR Adaptive beamformer models on add secondary lobe constraints, the performance requirement of Sidelobe is realized with this.
The present invention is described in further detail with reference to the accompanying drawings and detailed description.
Brief description of the drawings
Fig. 1 is the main flow chart for the sane low-sidelobe beam forming method that the present invention is rebuild based on covariance matrix.
Fig. 2 is the flow chart of interference plus noise covariance matrix reconstruction procedures in Fig. 1.
Fig. 3 is secondary lobe when being constrained to -30dB, the beam pattern obtained using the inventive method.
When Fig. 4 is that secondary lobe is constrained to -30dB, using output SINR during the inventive method with the variation diagram of fast umber of beats.
Embodiment
The present invention increases minor level constraints, constructs Sidelobe MVDR on the basis of MVDR Beam-formers
Optimized model；According to the incident angle range of desired signal, using Capon spatial spectrum location modes to interference-plus-noise covariance
Matrix is rebuild, and obtains accurate interference plus noise covariance matrix；Increase minor level constraint bar is taken to afterwards
In the MVDR Beam-formers of part, the globally optimal solution for meeting minor level constraint is solved using convex optimization method, optimization
Power output maximum is used as performance basis for estimation, meets under constraints, obtains optimal adaptive weight coefficient.
The present general inventive concept is：On the basis of MVDR Beam-formers, according to desired signal incident area and sky
Between Spectral structure, rebuild interference plus noise covariance matrix, using array weight vector be used as optimized variable, construction addition secondary lobe constrain
The convex Optimized model of MVDR Beam-formers, and this model is solved.
A kind of sane low-sidelobe beam forming method rebuild based on covariance matrix of the present invention, is comprised the following steps：
Step 1, the reception signal to radar array sample, and obtain signal phasor；
Step 2, the data sampled according to step 1, try to achieve and receive data covariance matrix and space Spectral structure, rebuild interference
Plus noise covariance matrix；The interference plus noise covariance matrix of reconstruction is：
In formula, P (θ) is space Spectral structure；RXIt is positive semidefinite matrix to receive data covariance matrix；D (θ) is space
The steering vector of angle, θ；Θ is desired signal region, and interference signal is not in this region；Represent that desired signal arrives
Region outside up to scope；H represents conjugate transposition；I is interference, and n is noise.
Step 3, covariance matrix and steering vector according to reconstruction, addition secondary lobe constraint is solved with convex optimization method
MVDR models, obtain global optimum's weight vector；
Described global optimum's weight vector is：
minimize wHRXw
subject to wHa(θsThe θ of)=1 is expectation signal angle
|wHa(θj)|2≤εθj∈ΘSL(j=1 ..., J)
ΘSL=[- 90 °, θs1]∪[θs2,90°]
In formula, wHRXW is power output, i.e. object function；RXIt is positive positive semidefinite square to receive data covariance matrix
Battle array；a(θs) it is it is expected signal angle θsSteering vector；a(θj) be secondary lobe constraint steering vector；[-90°,θs1]∪
[θs2, 90 °] and it is secondary lobe constraint；θjFor the J centrifugal pump taken in secondary lobe constraint；ε is sidelobe reduction level, with dB tables
Show.
Step 4, the signal phasor that step 1 is received and the optimal weights vector that step 3 is obtained carry out multiplication operation, obtain
Sane Sidelobe adaptive beam.Determine sane Sidelobe adaptive beam be：
Y=wHx
In formula, x is the signal phasor received in step 1；W is the optimal weights vector obtained in step 3.
The robustness of the present invention is preferable, the MVDR adaptive beam former models of present invention addition secondary lobe constraint, this mould
Type is convex Optimized model, and Optimized model is solved with MATLAB CVX tool boxes, obtains optimal weights vector.With
Some classic algorithms are compared, and not only robustness is preferable, and AF panel is deepened.
It is described in more detail below.
Fig. 1 gives the process chart that array weight vector is obtained using the present invention.Wherein array number is 16, and secondary lobe is about
Beam region is [- 90 °, -10 °] and [10 °, 90 °], and desired signal angle is 0 °.Concrete operation step is as follows：
Step 1, the reception signal to radar array sample, and obtain signal phasor；
Step 2, the data sampled according to step 1, try to achieve and receive data covariance matrix and space Spectral structure, rebuild interference
Plus noise covariance matrix；
Interference plus noise covariance matrix step is specially in the step 2：
Capon spatial spectrums are
P (θ) is space Spectral structure；RXIt is positive positive semidefinite matrix to receive data covariance matrix；D (θ) is Space Angle
Spend θ steering vector.Composed using Capon, interference plus noise covariance matrix is redeveloped into
Θ is desired signal region, and interference signal is not in this region；Represent desired signal coverage area it
Outer region；I is interference, and n is noise.
Fig. 2 gives the process of interference plus noise covariance matrix reconstruction, and concrete operation step is as follows：
(1) obtained using sampled data and receive data covariance matrix RX；
(2) the reception data covariance matrix obtained by (1) asks for spatial spectrum；
(3) spatial spectrum obtained by (2) rebuilds interference plus noise covariance matrix
Step 3, covariance matrix and steering vector according to reconstruction, addition secondary lobe constraint is solved with convex optimization method
MVDR models, obtain global optimum's weight vector；
It is prior art that steering vector is tried to achieve in the step 3, is not illustrated herein；Weight vector asks for step tool
Body is：
Consider that the signal in array antenna far field space receives, between desired signal and interference, disturb between interference mutually
It is uncorrelated.Noise is zero mean Gaussian white noise, and noise and signal and interference are orthogonal.
The desired homogeneous linear array that array is made up of N number of array element, each array element are isotropic antenna, and array element spacing is
d；Carrier wavelength is λ, receive arrival bearing be θ narrow band signal x (t), on reception signal steering vector for a (θ)=[1,
e-j2πdsinθ/λ,…,e-j2π(N-1)dsinθ/λ]T, array weight vector is w=[w1,w2,...wN]T, then adaptive beam former
Export and be：Y=wHx；The pattern function of array is F (θ)=wHa(θ)。
The MVDR models of following addition secondary lobe constraint are solved with convex optimization method, obtain weight vector w,
Wherein, wHRXW is power output, i.e. object function；RXIt is positive positive semidefinite square to receive data covariance matrix
Battle array；a(θs) it is it is expected signal angle θsSteering vector；a(θj) be secondary lobe constraint steering vector；[-90°,θs1]∪
[θs2, 90 °] and it is secondary lobe constraint；θjFor the J centrifugal pump taken in secondary lobe constraint；ε is sidelobe reduction level, with dB tables
Show.
In embodiment, θs1=-10 °, θs2=10 °, J=162, ε=- 30dB.
Step 4, the signal phasor that step 1 is received and the optimal weights vector that step 3 is obtained carry out multiplication operation, obtain
Sane Sidelobe adaptive beam.
In the step 4, the sane Sidelobe adaptive beam finally determined is：
Y=wHx (4)
X is the signal phasor received in step 1；W is the optimal weights vector obtained in step 3.
Fig. 3 is secondary lobe when being constrained to -30dB, and the beam pattern designed, desired signal angle is 0 °, disturbs angle
For -30 ° and 20 °, as seen from the figure, the sane low-sidelobe beam forming method rebuild based on covariance matrix that the present invention designs,
Directional diagram points to 0 ° of desired signal angle, and can control secondary lobe well, is upwardly formed in the disturber of -30 ° and 20 ° very deep
Null suppresses to disturb.
Fig. 4 is exports SINR with the variation diagram of fast umber of beats, and as seen from the figure, when fast umber of beats changes, output SINR is basic
It is constant, therefore there is good robustness.
Verified through embodiment, the present invention can controlled based on the sane low-sidelobe beam forming method that covariance matrix is rebuild
Good robustness is maintained while compared with Sidelobe.
Claims (4)
1. a kind of sane low-sidelobe beam forming method rebuild based on covariance matrix, it is characterised in that comprise the following steps：
Step 1, the reception signal to radar array sample, and obtain signal phasor；
Step 2, the data sampled according to step 1, try to achieve and receive data covariance matrix and space Spectral structure, rebuild interference plus make an uproar
Sound covariance matrix；
Step 3, covariance matrix and steering vector according to reconstruction, the MVDR moulds of addition secondary lobe constraint are solved with convex optimization method
Type, obtain global optimum's weight vector；
Step 4, the signal phasor that step 1 is received and the optimal weights vector that step 3 is obtained carry out multiplication operation, and it is sane to obtain
Sidelobe adaptive beam.
2. the sane low-sidelobe beam forming method according to claim 1 rebuild based on covariance matrix, its feature are existed
In the interference plus noise covariance matrix rebuild in step 2 is：
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In formula, P (θ) is space Spectral structure；RXIt is positive semidefinite matrix to receive data covariance matrix；D (θ) is space angle θ
Steering vector；Θ is desired signal region, and interference signal is not in this region；Represent desired signal coverage area
Outside region；H represents conjugate transposition；I is interference, and n is noise.
3. the sane low-sidelobe beam forming method according to claim 1 rebuild based on covariance matrix, its feature are existed
In global optimum's weight vector described in step 3 is：
minimize wHRXw
subject to wHa(θsThe θ of)=1 is expectation signal angle
|wHa(θj)|2≤εθj∈ΘSL(j=1 ..., J)
ΘSL=[- 90 °, θs1]∪[θs2,90°]
In formula, wHRXW is power output, i.e. object function；RXIt is positive positive semidefinite matrix to receive data covariance matrix；a
(θs) it is it is expected signal angle θsSteering vector；a(θj) be secondary lobe constraint steering vector；[-90°,θs1]∪[θs2,
90 °] it is secondary lobe constraint；θjFor the J centrifugal pump taken in secondary lobe constraint；ε is sidelobe reduction level, is represented with dB.
4. the sane low-sidelobe beam forming method according to claim 1 rebuild based on covariance matrix, its feature are existed
In the sane Sidelobe adaptive beam that step 4 determines is：
Y=wHx
In formula, x is the signal phasor received in step 1；W is the optimal weights vector obtained in step 3.
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Cited By (8)
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| CN108445486A (en) * | 2018-03-13 | 2018-08-24 | 南京理工大学 | It is rebuild and the modified Beamforming Method of steering vector based on covariance matrix |
| CN108761402A (en) * | 2018-03-19 | 2018-11-06 | 南京理工大学 | Steady low-sidelobe beam forming method based on Minimum square error |
| CN109799486A (en) * | 2019-01-09 | 2019-05-24 | 西安科技大学 | A kind of adaptive and difference beam forming method |
| CN110045334A (en) * | 2019-02-28 | 2019-07-23 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Sidelobe null Beamforming Method |
| CN110673119A (en) * | 2019-10-30 | 2020-01-10 | 海鹰企业集团有限责任公司 | Compressed sensing-based non-regularization orientation estimation method and system |
| CN110687528A (en) * | 2019-10-30 | 2020-01-14 | 海鹰企业集团有限责任公司 | Method and system for generating adaptive beam former |
| CN112230226A (en) * | 2020-09-23 | 2021-01-15 | 浙江大学 | Adaptive beam former design method based on Bayes compressed sensing algorithm |
| CN115825875A (en) * | 2023-02-14 | 2023-03-21 | 南京理工大学 | Robust low sidelobe beam forming method for improving objective function and constraint |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108445486A (en) * | 2018-03-13 | 2018-08-24 | 南京理工大学 | It is rebuild and the modified Beamforming Method of steering vector based on covariance matrix |
| CN108761402A (en) * | 2018-03-19 | 2018-11-06 | 南京理工大学 | Steady low-sidelobe beam forming method based on Minimum square error |
| CN109799486A (en) * | 2019-01-09 | 2019-05-24 | 西安科技大学 | A kind of adaptive and difference beam forming method |
| CN110045334A (en) * | 2019-02-28 | 2019-07-23 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Sidelobe null Beamforming Method |
| CN110673119A (en) * | 2019-10-30 | 2020-01-10 | 海鹰企业集团有限责任公司 | Compressed sensing-based non-regularization orientation estimation method and system |
| CN110687528A (en) * | 2019-10-30 | 2020-01-14 | 海鹰企业集团有限责任公司 | Method and system for generating adaptive beam former |
| CN112230226A (en) * | 2020-09-23 | 2021-01-15 | 浙江大学 | Adaptive beam former design method based on Bayes compressed sensing algorithm |
| CN112230226B (en) * | 2020-09-23 | 2022-12-27 | 浙江大学 | Adaptive beam former design method based on Bayes compressed sensing algorithm |
| CN115825875A (en) * | 2023-02-14 | 2023-03-21 | 南京理工大学 | Robust low sidelobe beam forming method for improving objective function and constraint |
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