CN107167776A - The adaptive beam-forming algorithm compensated based on subspace - Google Patents
The adaptive beam-forming algorithm compensated based on subspace Download PDFInfo
- Publication number
- CN107167776A CN107167776A CN201710529605.3A CN201710529605A CN107167776A CN 107167776 A CN107167776 A CN 107167776A CN 201710529605 A CN201710529605 A CN 201710529605A CN 107167776 A CN107167776 A CN 107167776A
- Authority
- CN
- China
- Prior art keywords
- mrow
- mover
- subspace
- msub
- signal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The present invention relates to Radar Signal Processing Technology field, specifically provide a kind of adaptive beam-forming algorithm compensated based on subspace, feature decomposition is carried out to radar sampling data covariance first, and utilize real desired signal steering vector and characteristic vector correlation, compensate the signal subspace of estimation, then desired signal steering vector is projected to the signal subspace of estimation, the steering vector for including desired signal in the signal subspace for illustrating estimation if projection value is larger, the signal subspace of estimation is exactly required signal subspace, it need not compensate, otherwise need to compensate the signal subspace of estimation, recycle the signal subspace of the correlation compensation estimation between desired signal steering vector and the characteristic vector of sample covariance matrix, obtain required signal subspace, finally optimal weight vector is asked for using the signal subspace of compensation, obtain adaptive antenna radiation pattern simultaneously.
Description
Technical field
The present invention relates to Radar Signal Processing Technology field, the Adaptive beamformer more particularly to compensated based on subspace
Algorithm.
Background technology
Wave beam forming is array signal and the important technology in antenna system, can be used for radar, electronics or communication
Interference investigation and moving communicating field.Adaptive beamformer, i.e. airspace filter, are a kind of real-time beam-forming technologies,
Estimate after arrival bearing, the parameters such as array compound excitation are adaptively adjusted according to the change of environment, complete optimum beam and formed, i.e.,
On the basis of DOA estimations, the change of sampling snap is relied on, formation allows useful signal to pass through, and suppresses to greatest extent
Interference and the directional diagram of noise.Ideally, the wave beam formed using adaptive spatial filtering can be by the larger master of gain
The arrival bearing of valve alignment target signal, while in the interference adaptively formed deeper null of incident direction.But, due to radar
There are a variety of non-ideal factors such as passage amplitude-frequency response inconsistency, angle estimation error in system, meanwhile, adaptive algorithm is actual
There is sampled data in and contain the problems such as echo signal, sample Limited Number, cause echo signal to be oriented to arrow
There is error in amount mismatch and covariance matrix so that adaptive beam Quality Down, have a strong impact on radar system anti-interference
Performance.
Subspace projection algorithm projects the steering vector of desired signal to signal space, to eliminate because of noise characteristic not
Disturb to improve the sane performance of algorithm caused by stable.Such algorithm can regard a kind of contraction or Beam Domain wave beam shape as
Into algorithm, the algorithm effectively reduces the dependence to fast umber of beats and reduces computation complexity, it require that signal source
Number as prior information, in low signal-to-noise ratio, because can not correctly estimate that information source number or desired signal are not comprised in
In the signal subspace of estimation, the algorithm will fail.Simultaneously as when information source number is more, the dimension of subspace is higher, right
Interference and noise suppression effect be not obvious, and the algorithm can also fail.
The content of the invention
To overcome at least one defect that above-mentioned prior art is present, compensated the invention provides a kind of based on subspace
Adaptive beam-forming algorithm, comprises the following steps：
Step one, radar sampling data, i.e. radar return data are obtained, and the hits is estimated by equation below (1)
According to covariance matrix
Wherein []HRepresenting matrix conjugate transposition computing, X is K sampled data of radar system antenna linear array array element
Matrix, andM is the array element quantity of radar system antenna linear array；
Step 2, by equation below (2) to covariance matrixCarry out feature decomposition：
Wherein Λ s are target and interference signal characteristic value, and Λs={ υ1,υ2,…,υp+1, υ is covariance matrix's
Characteristic value, EsFor target and interference signals subspace, and Es={ e1,e2,…,ep+1, ΛnFor the characteristic value of noise, and Λn=
{υp+2,υp+3,…,υM, EnFor noise subspace, and En={ ep+2,ep+3,…,eM, p is the quantity of interference signal, and e is and υ
Corresponding characteristic vector；
Step 3, obtains goal orientation vectorIn the signal subspace E of estimationsIn projection u, such as formula (3)
It is shown：
Goal orientation vectorAnd am(θ0)=exp {-j2 π (m-1) d sin
θ0/ λ }, wherein m=1,2,3 ... M, θ0For the main beam direction of radar system antenna, d is between the array element of radar system antenna linear array
Away from λ is operation wavelength；
Step 4, the signal subspace E of estimation is judged by equation below (4)sWhether the steering vector of target is included
Wherein γ is the constant set according to radar system demand, if formula (4) is true, illustrates goal orientation vectorNot in the signal subspace E of estimationsIn, step 5 is performed, if formula (4) is false, illustrates the signal subspace of estimation
EsFor required signal subspace, step 6 is performed；
Step 5, is obtained by equation below (5) firstWith the coefficient correlation y (i) between characteristic vector e,
And be ranked up it according to order from big to small, it is contemplated that in order to obtain stablizing main lobe, composition signal subspace is empty
Between characteristic vector number be the bigger the better, but from suppress interference and noise from the point of view of, number is more few better, in order to obtain
Suppress interference and noise while main lobe must be stablized to greatest extent, balance is obtained between both using following formula, by such as
Lower formula (6) obtains the quantity l of required signal subspace,
(y (1)+...+y (l))/M ＞ ξ, 0 ＜ ξ ＜ 1 (6)；
Wherein ξ is the constant set according to radar system parameters, the signal subspace after being compensated obtained from being entered by formula (6)
SpaceAnd
Step 6, according to signal subspace and goal orientation vectorOptimal weight vector ω is asked for, such as formula (7) institute
Show：
WhereinFor the desired signal steering vector after being corrected by subspace, andSignal subspace
Space P value is as follows：
It is preferred that, constant γ span is 0.6 ＜ γ ＜ 1 in step 4.
It is preferred that, constant ξ value is 0.8 in step 5.
The adaptive beam-forming algorithm compensated based on subspace that the present invention is provided, is had the advantages that：
1st, the beamforming algorithm projected compared to conventional subspace, the present invention, which is utilized, expects signal guide vector and feature
The correlation of vector is compensated to the signal subspace of estimation, effectively improves subspace projection beamforming algorithm in low letter
The performance made an uproar than in the case of, improves the robustness of space domain self-adapted Anti-interference algorithm, improves radar system detection target outstanding
It is the ability of weak signal target；
2nd, the present invention is applied to the anti-interference application of onboard radar system, extends to planar array, advantageously reduces secondary lobe, obtains
The main lobe that must stablize, forms deeper null at interference, improves the target acquisition performance under radar chaff environment.
Brief description of the drawings
Fig. 1 is that Signal to Interference plus Noise Ratio of the present invention from prior art under different signal to noise ratio exports contrast curve；
Fig. 2 is the present invention and antenna radiation pattern of the prior art under -10dB signal to noise ratio；
Fig. 3 is the present invention and antenna radiation pattern of the prior art under 10dB signal to noise ratio.
Embodiment
To make the purpose, technical scheme and advantage of the invention implemented clearer, below in conjunction with the embodiment of the present invention
Accompanying drawing, the technical scheme in the embodiment of the present invention is further described in more detail.
It should be noted that：The embodiments described below with reference to the accompanying drawings are exemplary, it is intended to for explaining this hair
It is bright, and be not considered as limiting the invention.In the accompanying drawings, same or similar label represents same or like from beginning to end
Element or element with same or like function.Described embodiment is a part of embodiment of the invention, rather than entirely
The embodiment in portion, in the case where not conflicting, the feature in embodiment and embodiment in the application can be mutually combined.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
The present invention relates to Adaptive Anti-jamming technology in spatial domain in Radar Signal Processing, it is proposed that one kind is based on subspace compensation
Self-adapting airspace Anti-interference algorithm, it is adaptable to radar system Research on anti-interference technique with application, effectively improve subspace throwing
Performance of the shadow beamforming algorithm in the case of low signal-to-noise ratio, improves the robustness of space domain self-adapted Anti-interference algorithm, lifting
The ability of radar system detection target especially weak signal target.
This method carries out feature decomposition to radar sampling data covariance first, takes the corresponding characteristic vector of larger characteristic value
For signal subspace, and real desired signal steering vector and characteristic vector correlation are utilized, compensate the signal subspace sky of estimation
Between；
Then desired signal steering vector is projected to the signal subspace of estimation, is judged to estimate according to the size of absolute value
Signal subspace in whether there is desired signal steering vector, illustrate if projection value is larger estimation signal subspace
In include the steering vector of desired signal, the signal subspace of estimation is exactly required signal subspace, it is not necessary to compensated,
If projection value is smaller, illustrate not including in the signal subspace of estimation the steering vector of desired signal, it is necessary to estimation
Signal subspace is compensated；
The correlation compensation between desired signal steering vector and the characteristic vector of sample covariance matrix is recycled to estimate
Signal subspace, take the larger corresponding characteristic vector of wherein several coefficient correlations to reformulate signal subspace, obtain required
Signal subspace；
Optimal weight vector finally is asked for using the signal subspace of compensation, while adaptive antenna radiation pattern is obtained,
Specific embodiment：
Assuming that one dimensional linear array is made up of 10 isotropism array elements, wherein it is assumed that desired signal incoming wave angle is 0 °, width
For 6 °, two interference angles are respectively -20 ° and 50 °, and dry make an uproar than the fast umber of beats for 30dB, radar is 60.
Goal orientation vectorAnd
am(θ0)=exp {-j2 π (m-1) d sin θs0/ λ }, wherein m=1,2,3 ... M, θ0For the main ripple of radar system antenna
Shu Fangxiang, array element spacing the d=0.5 λ, λ of radar system antenna linear array are operation wavelength；
Utilize radar sampling data estimate covariance matrix
Wherein []HRepresenting matrix conjugate transposition computing；
Covariance estimated matrix feature decomposition：
Wherein Λs={ λ1,λ2,λ3Larger characteristic value is correspond to, represent
Target and interference signal characteristic value, Λn={ λ4,λ5,…,λ10Less characteristic value is correspond to, represent the characteristic value of noise, Es
={ e1,e2,e3Represent target and interference signal noise subspace, En={ e4,e5,…,e10Represent noise subspace；
Goal orientation vectorProjected to the signal subspace of estimation：
Echo signal steering vector can be expressed as to the projection matrix of the signal subspace of estimation
Judge whether the signal subspace of estimation includes the steering vector of desired signal：
Wherein 0 ＜ γ ＜ 1 are manually set according to radar system
γ=0.8 in constant, this patent, if above formula is true, illustrates that the steering vector of desired signal is not empty in the signal subspace of estimation
Between in, it is necessary to compensate it, if above formula is false, the signal subspace for illustrating estimation is exactly required signal subspace, nothing
Need compensation；
Using expecting signal guide vector and the correlation thermal compensation signal subspace of characteristic vector：
If not including the steering vector of desired signal in the signal subspace of estimation, it is necessary to compensate.Obtain first
Coefficient correlation between desired signal steering vector and characteristic vector
Then resequenced according to order from big to small.In order to suppress to disturb and make an uproar to greatest extent while stable main lobe is obtained
Sound, balance is obtained using following formula between both, (y (1)+...+y (l))/M ＞ ξ, 0 ＜ ξ ＜ 1, according to radar system parameters
Constant ξ=0.8 of setting, and then the signal subspace being compensated, P=[e1,…,el]；
Optimal weight vector is asked for according to signal subspace and desired signal steering vector：
WhereinFor the expectation after being corrected by subspace
Signal guide vector.
The output Signal to Interference plus Noise Ratio SINR of adaptive antenna is calculated by optimal weight vector：
Wherein RsFor the covariance matrix of desired signal, Ri+nAssisted for interference plus noise
Variance matrix；
Adaptive antenna directional diagram is calculated by optimal weight vector：
It is interference free performance evaluation index to export Signal to Interference plus Noise Ratio SINR and adaptive antenna directional diagram.
As shown in figure 1, SNR is signal to noise ratio, SNIR is Signal to Interference plus Noise Ratio, and LSMI represents diagonally to load Matrix Calculating
Algorithm for inversion, loading capacity is 6；ESB represents eigenspace projection beamforming algorithm.When there is error, true steering vector is represented
For：A=a (θ0)+[σ1,…,σM]H, σ herei, i=1 ..., the average that M represents to obey independent same distribution distribution is 0, standard deviation
For 0.1 Gaussian Profile.SINR can be exported with algorithm proposed by the present invention from Fig. 1 closest to the performance of optimal algorithm.Defeated
When entering SNR for 20dB, this patent improves 15.87dB than the output SINR of diagonal loading algorithm because diagonal loading with
SNR raising is inputted, because loading capacity is not enough, it is impossible to the loss that steering vector error band comes always, so hydraulic performance decline；Defeated
When entering SNR for -20dB, this patent algorithm improves 14.97dB than the output SINR of ESB algorithm, because in low signal-to-noise ratio,
The signal subspace of ESB estimations does not include the steering vector of expectation target signal, it is impossible to which in desired signal, arrival bearing forms master
Valve, forms signal cancellation phenomenon, reduces output SINR.
As shown in Fig. 2 when it is -10dB to input SNR, LSMI is used for minimum sidelobe level, but this patent algorithm exists
Most deep null is formd at interference, interference is effectively inhibited, compared to the reduction of ESB algorithms sidelobe level, secondary lobe is effectively inhibited
Noise.
As shown in figure 3, when it is 10dB to input SNR, because loading capacity is not enough, causing the sidelobe level liter of LSMI algorithms
Height, output SINR declines, and ESB algorithms estimate signal subspace due to that in high s/n ratio, can prepare, so possessing with this specially
The same superior performance of sharp algorithm.
The foregoing is only a specific embodiment of the invention, but protection scope of the present invention is not limited thereto, any
Those familiar with the art the invention discloses technical scope in, the change or replacement that can be readily occurred in, all should
It is included within the scope of the present invention.Therefore, protection scope of the present invention should using the scope of the claims as
It is accurate.
Claims (3)
1. a kind of adaptive beam-forming algorithm compensated based on subspace, it is characterised in that comprise the following steps：
Step one, radar sampling data are obtained, and estimate by equation below (1) covariance matrix of the sampled data
<mrow>
<mover>
<mi>R</mi>
<mo>^</mo>
</mover>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>K</mi>
</mfrac>
<msup>
<mi>XX</mi>
<mi>H</mi>
</msup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
Wherein []HRepresenting matrix conjugate transposition computing, X is the matrix of K sampled data of radar system antenna linear array array element,
AndM is the array element quantity of radar system antenna linear array；
Step 2, by equation below (2) to covariance matrixCarry out feature decomposition：
<mrow>
<mover>
<mi>R</mi>
<mo>^</mo>
</mover>
<mo>=</mo>
<msub>
<mi>E</mi>
<mi>s</mi>
</msub>
<msub>
<mi>&Lambda;</mi>
<mi>s</mi>
</msub>
<msubsup>
<mi>E</mi>
<mi>s</mi>
<mi>H</mi>
</msubsup>
<mo>+</mo>
<msub>
<mi>E</mi>
<mi>n</mi>
</msub>
<msub>
<mi>&Lambda;</mi>
<mi>n</mi>
</msub>
<msubsup>
<mi>E</mi>
<mi>n</mi>
<mi>H</mi>
</msubsup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
Wherein ΛsFor target and interference signal characteristic value, and Λs={ υ1,υ2,…,υp+1, υ is covariance matrixFeature
Value, EsFor target and interference signals subspace, and Es={ e1,e2,…,ep+1, ΛnFor the characteristic value of noise, and Λn={ υp+2,
υp+3,…,υM, EnFor noise subspace, and En={ ep+2,ep+3,…,eM, p is the quantity of interference signal, and e is corresponding with υ
Characteristic vector；
Step 3, obtains goal orientation vectorIn the signal subspace E of estimationsIn projection u, such as shown in formula (3)：
<mrow>
<mi>u</mi>
<mo>=</mo>
<msub>
<mi>E</mi>
<mi>s</mi>
</msub>
<msubsup>
<mi>E</mi>
<mi>s</mi>
<mi>H</mi>
</msubsup>
<mover>
<mi>a</mi>
<mo>^</mo>
</mover>
<mrow>
<mo>(</mo>
<msub>
<mi>&theta;</mi>
<mn>0</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
Goal orientation vectorAnd
am(θ0)=exp {-j2 π (m-1) d sin θs0/ λ }, wherein m=1,2,3 ... M, θ0For the main beam side of radar system antenna
To d is the array element spacing of radar system antenna linear array, and λ is operation wavelength；
Step 4, the signal subspace E of estimation is judged by equation below (4)sWhether the steering vector of target is included
<mrow>
<mi>n</mi>
<mi>o</mi>
<mi>r</mi>
<mi>m</mi>
<mrow>
<mo>(</mo>
<mi>u</mi>
<mo>)</mo>
</mrow>
<mo><</mo>
<mi>&gamma;</mi>
<mi>n</mi>
<mi>o</mi>
<mi>r</mi>
<mi>m</mi>
<mrow>
<mo>(</mo>
<mover>
<mi>a</mi>
<mo>^</mo>
</mover>
<mo>(</mo>
<msub>
<mi>&theta;</mi>
<mn>0</mn>
</msub>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mrow>
<mo>(</mo>
<mn>0</mn>
<mo><</mo>
<mi>&gamma;</mi>
<mo><</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
Wherein γ is the constant set according to radar system demand, if formula (4) is true, illustrates goal orientation vector
Not in the signal subspace E of estimationsIn, step 5 is performed, if formula (4) is false, illustrates the signal subspace E of estimationsFor institute
The signal subspace asked, performs step 6；
Step 5, is obtained by equation below (5) firstWith the coefficient correlation y (i) between characteristic vector e,
<mrow>
<mi>y</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mo>|</mo>
<msubsup>
<mi>e</mi>
<mi>i</mi>
<mi>H</mi>
</msubsup>
<mover>
<mi>a</mi>
<mo>^</mo>
</mover>
<mrow>
<mo>(</mo>
<msub>
<mi>&theta;</mi>
<mn>0</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mo>,</mo>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>M</mi>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
And be ranked up it according to order from big to small, the number of required signal subspace is obtained by equation below (6)
Measure l,
(y (1)+...+y (l))/M ＞ ξ, 0 ＜ ξ ＜ 1 (6)；
Wherein ξ is the constant set according to radar system parameters, the signal subspace after being compensated obtained from being entered by formula (6)And
Step 6, according to signal subspace and goal orientation vectorOptimal weight vector ω is asked for, shown in such as formula (7)：
<mrow>
<mi>&omega;</mi>
<mo>=</mo>
<mfrac>
<mrow>
<mi>i</mi>
<mi>n</mi>
<mi>v</mi>
<mrow>
<mo>(</mo>
<mover>
<mi>R</mi>
<mo>^</mo>
</mover>
<mo>)</mo>
</mrow>
<mo>*</mo>
<mover>
<mi>a</mi>
<mo>^</mo>
</mover>
</mrow>
<mrow>
<msup>
<mover>
<mi>a</mi>
<mo>^</mo>
</mover>
<mi>H</mi>
</msup>
<mo>*</mo>
<mi>i</mi>
<mi>n</mi>
<mi>v</mi>
<mrow>
<mo>(</mo>
<mover>
<mi>R</mi>
<mo>^</mo>
</mover>
<mo>)</mo>
</mrow>
<mo>*</mo>
<mover>
<mi>a</mi>
<mo>^</mo>
</mover>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
WhereinFor the desired signal steering vector after being corrected by subspace, andSignal subspace P's
Value is as follows：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>P</mi>
<mo>=</mo>
<msub>
<mi>E</mi>
<mi>s</mi>
</msub>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>n</mi>
<mi>o</mi>
<mi>r</mi>
<mi>m</mi>
<mrow>
<mo>(</mo>
<mi>u</mi>
<mo>)</mo>
</mrow>
<mo><</mo>
<mi>&gamma;</mi>
<mi>n</mi>
<mi>o</mi>
<mi>r</mi>
<mi>m</mi>
<mrow>
<mo>(</mo>
<mover>
<mi>a</mi>
<mo>^</mo>
</mover>
<mo>(</mo>
<msub>
<mi>&theta;</mi>
<mn>0</mn>
</msub>
<mo>)</mo>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>P</mi>
<mo>=</mo>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>n</mi>
<mi>o</mi>
<mi>r</mi>
<mi>m</mi>
<mrow>
<mo>(</mo>
<mi>u</mi>
<mo>)</mo>
</mrow>
<mo>&GreaterEqual;</mo>
<mi>&gamma;</mi>
<mi>n</mi>
<mi>o</mi>
<mi>r</mi>
<mi>m</mi>
<mrow>
<mo>(</mo>
<mover>
<mi>a</mi>
<mo>^</mo>
</mover>
<mo>(</mo>
<msub>
<mi>&theta;</mi>
<mn>0</mn>
</msub>
<mo>)</mo>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>.</mo>
</mrow>
2. the adaptive beam-forming algorithm according to claim 1 compensated based on subspace, it is characterised in that step 4
Middle constant γ span is 0.6 ＜ γ ＜ 1.
3. the adaptive beam-forming algorithm according to claim 1 compensated based on subspace, it is characterised in that step 5
Middle constant ξ value is 0.8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710529605.3A CN107167776B (en) | 2017-07-02 | 2017-07-02 | Adaptive beamforming algorithm based on subspace compensation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710529605.3A CN107167776B (en) | 2017-07-02 | 2017-07-02 | Adaptive beamforming algorithm based on subspace compensation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107167776A true CN107167776A (en) | 2017-09-15 |
| CN107167776B CN107167776B (en) | 2021-04-20 |
Family
ID=59827363
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710529605.3A Active CN107167776B (en) | 2017-07-02 | 2017-07-02 | Adaptive beamforming algorithm based on subspace compensation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107167776B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109600152A (en) * | 2018-12-17 | 2019-04-09 | 西北工业大学 | A kind of Adaptive beamformer method based on the transformation of subspace base |
| CN112130112A (en) * | 2020-09-20 | 2020-12-25 | 哈尔滨工程大学 | Information source number estimation method based on acoustic vector array joint information processing |
| CN112887001A (en) * | 2021-01-06 | 2021-06-01 | 西北工业大学 | Phase center compensation method based on signal incoming direction |
| CN113466899A (en) * | 2021-08-13 | 2021-10-01 | 电子科技大学 | Navigation receiver beam forming method based on small fast beat number under high signal-to-noise ratio environment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104270179A (en) * | 2014-09-12 | 2015-01-07 | 北京理工大学 | Self-adaptive beam forming method based on covariance reconstruction and guide vector compensation |
| CN105204006A (en) * | 2015-10-19 | 2015-12-30 | 电子科技大学 | Beam forming method based on subspace interference-plus-noise covariance matrix reconstruction |
| CN106443569A (en) * | 2016-09-14 | 2017-02-22 | 天津大学 | Robust adaptive beamforming method based on steering vector correction |
| KR101712425B1 (en) * | 2016-03-31 | 2017-03-06 | 한화시스템(주) | Method for Adaptive Beamforming of Digital Array Antenna and Program Stored in Storage for Executing the Same |
-
2017
- 2017-07-02 CN CN201710529605.3A patent/CN107167776B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104270179A (en) * | 2014-09-12 | 2015-01-07 | 北京理工大学 | Self-adaptive beam forming method based on covariance reconstruction and guide vector compensation |
| CN105204006A (en) * | 2015-10-19 | 2015-12-30 | 电子科技大学 | Beam forming method based on subspace interference-plus-noise covariance matrix reconstruction |
| KR101712425B1 (en) * | 2016-03-31 | 2017-03-06 | 한화시스템(주) | Method for Adaptive Beamforming of Digital Array Antenna and Program Stored in Storage for Executing the Same |
| CN106443569A (en) * | 2016-09-14 | 2017-02-22 | 天津大学 | Robust adaptive beamforming method based on steering vector correction |
Non-Patent Citations (2)
| Title |
|---|
| FENG SHEN ET AL.: "Robust Adaptive Beamforming Based on Steering Vector Estimation and Covariance Matrix Reconstruction", 《IEEE COMMUNICATIONS LETTERS》 * |
| 杨涛等: "基于波束域导向矢量估计的稳健自适应波束形成方法", 《电子与信息学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109600152A (en) * | 2018-12-17 | 2019-04-09 | 西北工业大学 | A kind of Adaptive beamformer method based on the transformation of subspace base |
| CN112130112A (en) * | 2020-09-20 | 2020-12-25 | 哈尔滨工程大学 | Information source number estimation method based on acoustic vector array joint information processing |
| CN112130112B (en) * | 2020-09-20 | 2022-09-27 | 哈尔滨工程大学 | Information source number estimation method based on acoustic vector array joint information processing |
| CN112887001A (en) * | 2021-01-06 | 2021-06-01 | 西北工业大学 | Phase center compensation method based on signal incoming direction |
| CN112887001B (en) * | 2021-01-06 | 2022-12-13 | 西北工业大学 | Phase center compensation method based on signal incoming direction |
| CN113466899A (en) * | 2021-08-13 | 2021-10-01 | 电子科技大学 | Navigation receiver beam forming method based on small fast beat number under high signal-to-noise ratio environment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107167776B (en) | 2021-04-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105137399B (en) | The radar self-adaption Beamforming Method filtered based on oblique projection | |
| CN107167776A (en) | The adaptive beam-forming algorithm compensated based on subspace | |
| CN109407055B (en) | Beam forming method based on multipath utilization | |
| CN107276658A (en) | The Beamforming Method reconstructed under coloured noise based on covariance matrix | |
| CN103293517B (en) | Diagonal-loading robust adaptive radar beam forming method based on ridge parameter estimation | |
| CN102944870A (en) | Robust covariance matrix diagonal loaded adaptive beam-forming method | |
| CN109959899A (en) | Projection Character pretreatment and the sparse reconstruct major lobe suppression restrainable algorithms of covariance matrix | |
| CN103942449A (en) | Feature interference cancellation beam forming method based on estimation of number of information sources | |
| CN107991659B (en) | Method for measuring height of low-elevation target of meter wave radar based on dictionary learning | |
| CN109814070B (en) | Distance fuzzy clutter suppression method based on auxiliary pulse | |
| CN107561502A (en) | A kind of portable high frequency groundwave radar Radio frequency interference suppressing method | |
| CN107340499A (en) | The sane low-sidelobe beam forming method rebuild based on covariance matrix | |
| CN105335336A (en) | Sensor array steady adaptive beamforming method | |
| CN103954941A (en) | Airborne phased array radar two-dimensional multi-pulse cognitive clutter suppression method | |
| CN110261826A (en) | A kind of coherent interference suppression method of null broadening | |
| CN101907702A (en) | Two-dimensional multi-pulse canceller for MIMO radar | |
| CN106788655A (en) | The relevant robust ada- ptive beamformer method of the interference of unknown mutual coupling information under array mutual-coupling condition | |
| CN108828586B (en) | Bistatic MIMO radar angle measurement optimization method based on beam domain | |
| CN113376584B (en) | Robust adaptive beam forming method based on improved diagonal loading | |
| CN106960083A (en) | A kind of robust adaptive beamforming method optimized based on main lobe beam pattern | |
| CN110727915A (en) | Robust self-adaptive beam forming method based on data correlation constraint | |
| CN109633563B (en) | Self-adaptive coherent beam forming method based on multipath information | |
| CN110261814B (en) | Beam forming method based on spatial spectrum reconstruction and direct estimation of steering vector | |
| CN114818793B (en) | Robust beam forming method based on auxiliary array elements | |
| CN107167803A (en) | The robust Beam Domain Adaptive beamformer method estimated based on steering vector mismatch |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |