CN104991236B - A kind of single base MIMO radar not rounded signal coherence source Wave arrival direction estimating method - Google Patents
A kind of single base MIMO radar not rounded signal coherence source Wave arrival direction estimating method Download PDFInfo
- Publication number
- CN104991236B CN104991236B CN201510340888.8A CN201510340888A CN104991236B CN 104991236 B CN104991236 B CN 104991236B CN 201510340888 A CN201510340888 A CN 201510340888A CN 104991236 B CN104991236 B CN 104991236B
- Authority
- CN
- China
- Prior art keywords
- matrix
- signal
- data
- dimension
- vector
- 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.)
- Active
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
- 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
Abstract
The invention belongs to Radar Technology field, and in particular to a kind of single base MIMO radar not rounded signal coherence source Wave arrival direction estimating method.Obtain signal sample data；The docking collection of letters number carries out a dimension-reduction treatment；The space smoothing processing being modified to the data matrix after dimensionality reduction；Feature decomposition is carried out to the correlation matrix after smooth；(5) rooting multinomial is constructed, the angle estimation of target is obtained.The method docking collection of letters number of the present invention carries out a dimension-reduction treatment, greatly improves computational efficiency, and hardly estimation performance is impacted；The characteristic of not rounded signal is made full use of, so as to improve the utilization rate to receiving data, the performance of DOA estimations is improved；It is modified by the decorrelation LMS method to space smoothing, the situation for making it be applied to not rounded signal direction-finding can effectively estimate the direction of arrival of coherent under not rounded signal condition.
Description
Technical field
The invention belongs to Radar Technology field, and in particular to a kind of single base MIMO radar not rounded signal coherence source ripple reaches side
To method of estimation.
Background technology
In recent years, the MIMO technology thought of the communications field is incorporated into field of radar, the MIMO (Multiple- of proposition
Input Multiple-Output, multiple-input and multiple-output) radar system causes extensive concern.MIMO radar makes full use of
Signal diversifying, spatial domain diversity gain, obtain the free degree higher compared with conventional radar, its target detection capabilities and parameter estimation capabilities
Widely approve Deng obtaining.Due to the potential plurality of advantages of MIMO radar, the research and development on MIMO radar is quick, wherein
Direction of arrival (Direction Of Arrival, abbreviation DOA) estimation problem of MIMO radar is emphasis research topic therein.
DOA estimation method based on traditional phased-array radar often receives data matrix using circle signal construction, does not account for non-
The presence of circle signal, but in practical application, the use of the not rounded signal such as BPSK, AM is more and more extensive, therefore, makes full use of non-
It is a theoretical important topic of Estimation of Spatial Spectrum that the characteristic of circle signal, which carries out DOA estimations,.In addition, encountered in actual environment
It is frequently not independent between signal source, but with certain correlation, is even concerned with, this will cause wide variety of
Subspace Decomposition class method, such as MUSIC (multiple signal classification) methods and ESPRIT (being based on rotational invariance technology) method,
Performance severe exacerbation is even entirely ineffective, so, the DOA estimations in coherent signal source are also a weight in Mutual coupling field
Want problem.
For the DOA estimations based on MIMO radar, general approach is to be transplanted conventional method, such as by subspace class
MUSIC methods be applied to two-dimentional MUSIC methods, dimensionality reduction MUSIC methods etc. of MIMO radar, but such method often amount of calculation
It is larger, and it is not suitable for the method using not rounded signal direction-finding, when there is coherent, performance drastically deteriorates, it is difficult to realize
Effective DOA estimations.
For the DOA estimations of not rounded signal, generally by matrix-expand, DOA is carried out then in conjunction with subspace class method
Estimation.In the direction-finding method of numerous utilization not rounded signals, the NC (not rounded) that Pascal Charg é were proposed equal to 2001-
MUSIC methods have certain representativeness.This method to receiving data matrix by being extended, with reference to Root-MUSIC methods
Obtain DOA estimations.But when there is coherent, the performance of NC-MUSIC methods drastically deteriorates, and estimated accuracy and success rate are obvious
Less than the situation for only existing independent source.
It is general to use space smoothing class method and matrix reconstruction class method for the DOA estimations of coherent, wherein, with
Front-rear space smooth is representative.But when carrying out DOA estimations using not rounded signal, directly apply front-rear space smooth broken
Bad original subspace structure, so that reliable DOA estimated results can not be obtained.Therefore, not rounded signal how is made full use of
Characteristic, it is technical problem urgently to be resolved hurrily to carry out the coherent DOA estimations based on single base MIMO radar.
The content of the invention
Present invention aims at propose a kind of single base of use not rounded signal progress coherent Mutual coupling
MIMO radar not rounded signal coherence source Wave arrival direction estimating method.
The object of the present invention is achieved like this：
A kind of single base MIMO radar not rounded signal coherence source Wave arrival direction estimating method includes：
(1) signal sample data is obtained：
X (l)=AS (l)+N (l), wherein, X (l)=[x1(l),x2(l),...,xMN(l)]TMN × 1 when for fast umber of beats being l
Dimension receives data vector, and M and N are respectively emission array and receiving array array number, and A is array manifold matrix, and S (l) is that K × 1 is tieed up
Narrow band signal vector, signal type is not rounded signal, and N (l) is that additive noise vector is tieed up in MN × 1, and noise type is multiple Gauss white noise
Sound, l=1 ..., L,
To receive steering vector,For transmitting steering vector, symbolRepresent Kronecker product, θkTable
Show the incident angle of k-th of information source, zk=exp (- j π sin (θk)), k=1,2 ..., K,
For the multiple initial phase of k-th of signal, S0(t) it is real vectorial for K × 1 dimension；
(2) the docking collection of letters number carries out a dimension-reduction treatment：
XRD(l)=W-1GHX (l)=BS (l)+W-1GHN (l), wherein, XRD(l) it is (M+N-1) after dimensionality reduction is changed
× 1 dimension data vector, W and G are dimensionality reduction transition matrix, and B is (M+N-1) × K dimension array manifold squares after dimensionality reduction is changed
Battle array, B=[b (θ1),b(θ2),...,b(θK)],Steering vector after being changed for dimensionality reduction,
Wherein, Gn=[O1 IM O2]∈RM×(M+N-1), n=1,2 ..., N, O1And O2Point
Not Wei M × (n-1) and M × (N-n) dimension null matrix, IMUnit matrix is tieed up for M,
(3) the space smoothing processing being modified to the data matrix after dimensionality reduction：
RfbFor to data correlation matrix RsubThe 2 of the space smoothing processing acquisition being modified ×
(M+N-P) square formation is tieed up, wherein P is the number of times of space smoothing,
XRDpBy XRDPth row to pth+M+N-1-P rows element constitute, J be 2 × (M+N-P) tie up switching matrix, its counter-diagonal
On element be 1, the element in other positions is 0；
(4) feature decomposition is carried out to the correlation matrix after smooth：
To RfbFeature decomposition is carried out, corresponding 2 × (M+N-P) -2 × K of 2 × (M+N-P) -2 × K smaller characteristic value is obtained
The noise subspace U of individual characteristic vector composition；
(5) rooting multinomial is constructed, the angle estimation of target is obtained：
It is divided to two structure identical submatrixs for above and below by noise subspace U,Utilize U1And U2Construct rooting
MultinomialWherein
B (z)=[1, z, z2,...,zM+N-2]T, z=exp (- j π sin (θ)),Angle () represents to take
The phase of plural number, obtains root of polynomial, and wherein K is that the DOA of correspondence target estimates to the root that closest unit is justified.
The beneficial effects of the present invention are：
The method docking collection of letters number of the present invention carries out a dimension-reduction treatment, greatly improves computational efficiency, and hardly to estimating
Meter performance is impacted；The characteristic of not rounded signal is made full use of, so as to improve the utilization rate to receiving data, DOA is improved
The performance of estimation；It is modified by the decorrelation LMS method to space smoothing, the situation for making it be applied to not rounded signal direction-finding can
Effectively to estimate the direction of arrival of coherent under not rounded signal condition.
Brief description of the drawings
Fig. 1 is the method schematic diagram of the present invention.
Fig. 2 this method (NC-Root-MUSIC) is with Root-MUSIC method DOA estimated result mean square errors with signal to noise ratio
Change contrast simulation figure.
Fig. 3 is that this method (NC-Root-MUSIC) estimates change of the success rate with signal to noise ratio with Root-MUSIC methods DOA
Contrast simulation figure.
Embodiment
The present invention is described further below in conjunction with the accompanying drawings.
The method comprises the following steps：
Step (10), the signal data received to the receiving array carry out dimension-reduction treatment, obtain after dimension-reduction treatment
Signal data XRD(l)；
Step (20), to data vector XRDThe space smoothing processing being modified, the correlation matrix R after obtaining smoothlyfb；
Step (30), to RfbFeature decomposition is carried out, noise subspace is obtained, and constructs rooting multinomial, information source is obtained
DOA estimates.
Wherein, step (10) includes：
Step (110), the data for obtaining all matched filter outputs of receiving array, construct dimensionality reduction transition matrix G, and G
W is constructed, wherein,Gn=[O1 IM O2]∈RM×(M+N-1), n=1,2 ..., N, O1And O2Respectively
The null matrix tieed up for M × (n-1) and M × (N-n), IMUnit matrix is tieed up for M,
Step (120), using above-mentioned two data conversion matrix to receive data handle, obtain the data after dimensionality reduction
XRD(l)。
Wherein, step (20) includes：
Step (210), utilize XRDPth row to pth+M+N-1-P rows element constitute XRDp；Then X is utilizedRDpConstitute number
According to extended matrixCalculate the correlation matrix of extended matrix
Step (220), sum-average arithmetic is carried out to all data correlation matrixConstruct 2 × (M+N-P)
Element on the switching matrix J of dimension, its counter-diagonal is that the element in 1, other positions is 0, then utilizes RsubCalculated with J
The space smoothing matrix of amendment
Wherein, step (30) includes：
Step (310), to RfbFeature decomposition is carried out, 2 × (M corresponding by 2 × (M+N-P) -2 × K small characteristic value is obtained
+ N-P) -2 × K characteristic vector composition noise subspace U；
Step (320), the preceding M+N-P using U and rear M+N-P rows constitute two sub- matrix Us1And U2, utilize U1And U2Construction
Operator D1、D2、D3,Utilize D1、
D2And D3Construct rooting multinomialWherein b (z)=[1, z, z2,...,zM+N-2]T, z=exp (- j π sin
(θ)),Angle () expressions take the phase of plural number, obtain root of polynomial, wherein K is to most connecing
The root of nearly unit circle is the DOA estimations of correspondence target.
When there is correlation between each information source, the order of signal covariance matrix has different degrees of loss, so as to drop
The performance of low DOA estimations.Space smoothing processing is a kind of effective ways that decorrelation LMS is carried out to signal.Its basic thought is to pass through
Even linear array is divided into several overlapped submatrixs, the array manifold structure of each submatrix is identical, to the covariance of each submatrix
Matrix carries out sum-average arithmetic computing, and the order of signal covariance matrix is restored, so as to realize decorrelation LMS.Front-rear space smooth
In addition to forward direction space smoothing, each submatrix to be carried out to be conjugated and reversely reconstitute a subarray, then respectively to process before
The covariance matrix of the submatrix of backward space smoothing processing carries out sum-average arithmetic processing, constitutes total covariance matrix, realizes solution
It is relevant.
Traditional space smoothing algorithm is carried out smoothly to whole signal covariance matrix, but is worked as and utilized not rounded signal to carry out
During direction finding, the structure of covariance matrix is changed, therefore, and this method is applied directly to the covariance of not rounded signal data
Correct DOA estimations can not be obtained in matrix disposal.Array is divided into multiple submatrixs by the present invention, utilizes the data of each submatrix
It is extended respectively, calculates data covariance matrix, carry out sum-average arithmetic, then using the space smoothing processing of amendment, in profit
While with not rounded characteristics of signals, it is ensured that new covariance matrix and former covariance matrix identical structure, so as to realize
Decorrelation LMS.
The present invention will be described in detail below in conjunction with the accompanying drawings.
As shown in figure 1, the method for the present invention is carried out to the reception data vector of single base MIMO radar receiving array first
Dimension-reduction treatment, is then handled using the space smoothing that the characteristic of not rounded signal is modified to the data matrix after dimensionality reduction, obtained
Data correlation matrix, feature decomposition is carried out to it, so that noise subspace is obtained, it is finally many using noise subspace construction rooting
Item formula, obtains root of polynomial, obtains the DOA estimations of coherent.
The present invention is based on single base MIMO radar, and its array structure uses even linear array, and emission array array number is M, is connect
It is N to receive array elements number, and array element spacing is half-wavelength.Assuming that there is K arrowband far field objects in same investigative range, it enters
Firing angle is respectively θ1,θ2,...,θK, then array manifold matrix can be constructed：
Wherein,To receive steering vector,For hair
Penetrate steering vector, symbolRepresent Kronecker (Kronecker) product, zk=exp (- j π sin (θk)), k=1,2 ..., K, θk
Represent the incident angle of k-th of information source.
Tie up arrowband not rounded signal phasor in K × 1
S (l)=[s1(l),s2(l),...,sK(l)]T=Φ S0(l)
Wherein,For the multiple initial phase of k-th of signal, S0(l) for the dimension of K × 1 it is real to
Amount.
When then fast umber of beats is l, the output data vector for receiving all matched filters of array is
X (l)=AS (l)+N (l)
Wherein, X (l)=[x1(l),x2(l),...,xMN(l)]TFor the dimension data vector of MN × 1, N (l)=[n1(l),n2
(l),...,nMN(l)]TAdditive noise vector is tieed up for MN × 1, noise type is white complex gaussian noise, l=1 ..., L.
Fig. 2 is concerned with completely in signal, and emission array array number is 8, and receiving array array number is 6, and array element spacing is
Half-wavelength, fast umber of beats is 1280, and information source number is 2.
Fig. 3 is concerned with completely in signal, and emission array array number is 8, and receiving array array number is 6, and array element spacing is
Half-wavelength, fast umber of beats is 1280, and information source number is 2.
There is more redundancy in above-mentioned data vector, and do not make full use of the feature of not rounded signal, because
This, if directly carrying out direction finding using it not only results in unnecessary computation burden, and have lost a part of useful information.This
Invention carries out dimension-reduction treatment to above-mentioned data vector first, obtains the data vector after dimensionality reduction, then utilizes the spy of not rounded signal
Property data are extended and space smoothing, ensure computational efficiency while, take full advantage of the characteristic of not rounded signal.
Construct dimensionality reduction transition matrix：
Wherein, Gn=[O1 IM O2]∈RM×(M+N-1), n=1,2 ..., N, O1And O2For dimension be M × (n-1) and M ×
(N-n) null matrix, IMUnit matrix is tieed up for M.
Then the data vector after dimensionality reduction is
XRD(l)=W-1GHX (l)=BS (l)+W-1GHN(l)
Wherein, B=[b (θ1),b(θ2),...,b(θK)]=W-1GHA,Turn for dimensionality reduction
Steering vector after changing.
By dimension-reduction treatment, former data vector is tieed up by MN × 1 is converted to the dimension of (M+N-1) × 1, eliminates substantial amounts of redundancy letter
Breath.The space smoothing processing being modified using not rounded characteristics of signals to the data vector after dimensionality reduction：
Wherein, XRDpFor by XRDThe virtual submatrix data that are constituted to pth+M+N-1-P row elements of pth row, calculate YpPhase
Close matrixAnd P all correlation matrixes is carried out before sum-average arithmetic is obtained to smooth covariance matrixThen obtain the covariance matrix after smoothing processing：
Wherein, the element on the switching matrix that J ties up for 2 × (M+N-P), its counter-diagonal is the element in 1, other positions
It is 0.
To the signal covariance matrix R after smoothed processingfbEigenvalues Decomposition is carried out, then be can obtain by 2 × (M+N-P) -2
The noise subspace U that corresponding 2 × (M+N-P) -2 × K characteristic vector of the smaller characteristic values of × K is constituted, utilizes U preceding M+
N-P and rear M+N-P rows constitute two sub- matrix Us1And U2：
Utilize U1And U2Construct operator D1、D2、D3：
Wherein, b (z)=[1, z, z2,...,zM+N-2]T, z=exp (- j π sin (θ)).
Utilize D1、D2And D3Rooting multinomial：
Root of polynomial is obtained, the root z that wherein K justifies to closest unit is chosen, z is calculated,
Angle () expressions take the phase of plural number, that is, obtain the DOA estimations of correspondence target.
Compared with existing DOA estimation method, side is reached using single base MIMO radar not rounded signal coherence source of the invention ripple
To method of estimation, the amount of calculation of single base MIMO radar direction finding can be reduced.Using the present invention by front-rear space smooth method
It is modified according to the structure of the covariance matrix of not rounded signal data, combines front-rear space smooth and surveyed with not rounded signal
To effectively reducing the correlation received between signal, realize effective estimation to coherent angle of arrival.In summary,
The present invention can effectively estimate the direction of arrival of coherent based on single base MIMO radar using not rounded signal.
Claims (1)
1. a kind of single base MIMO radar not rounded signal coherence source Wave arrival direction estimating method, it is characterised in that：
(1) signal sample data is obtained：
X (l)=AS (l)+N (l), wherein, X (l)=[x1(l),x2(l),...,xMN(l)]TThe dimension of MN when for fast umber of beats being l × 1
Data vector is received, M and N are respectively emission array and receiving array array number, and A is array manifold matrix, and S (l) is that K × 1 is tieed up
Narrow band signal vector, signal type is not rounded signal, and N (l) is that additive noise vector is tieed up in MN × 1, and noise type is multiple Gauss white noise
Sound, l=1 ..., L,
To receive steering vector,For transmitting steering vector, symbolRepresent Kronecker product, θkTable
Show the incident angle of k-th of information source, zk=exp (- j π sin (θk)), k=1,2 ..., K；
(2) the docking collection of letters number carries out a dimension-reduction treatment：
XRD(l)=W-1GHX (l)=BS (l)+W-1GHN (l), wherein, XRD(l) tieed up for (M+N-1) × 1 after dimensionality reduction is changed
Data vector, W and G are dimensionality reduction transition matrix, and B is (M+N-1) × K dimension array manifold matrixes after dimensionality reduction is changed, B=
[b(θ1),b(θ2),...,b(θK)],Steering vector after being changed for dimensionality reduction,Wherein, Gn=[O1 IM O2]∈RM×(M+N-1), n=1,2 ..., N, O1And O2Respectively M ×
(n-1) and M × (N-n) dimension null matrix, IMUnit matrix is tieed up for M,
(3) the space smoothing processing being modified to the data matrix after dimensionality reduction：
RfbFor to data correlation matrix Rsub2 × (the M+N- that the space smoothing processing being modified is obtained
P' square formation) is tieed up, wherein P' is the number of times of space smoothing, XRDpBy
XRD(l) element of pth row to pth+M+N-1-P' rows is constituted, and J is the switching matrix that 2 × (M+N-P') is tieed up, its counter-diagonal
On element be 1, the element in other positions is 0；
(4) feature decomposition is carried out to the correlation matrix after smooth：
To RfbFeature decomposition is carried out, 2 × (M+N-P') -2 × K smaller characteristic value is obtained corresponding 2 × (M+N-P') -2 × K
The noise subspace U of characteristic vector composition；
(5) rooting multinomial is constructed, the angle estimation of target is obtained：
It is divided to two structure identical submatrixs for above and below by noise subspace U,Utilize U1And U2Construct rooting multinomialWherein
B (z)=[1, z, z2,...,zM+N-2]T, z=exp (- j π sin (θ)),Angle () represents to take
The phase of plural number, obtains root of polynomial, and wherein K is that the DOA of correspondence target estimates to the root that closest unit is justified.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510340888.8A CN104991236B (en) | 2015-06-18 | 2015-06-18 | A kind of single base MIMO radar not rounded signal coherence source Wave arrival direction estimating method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510340888.8A CN104991236B (en) | 2015-06-18 | 2015-06-18 | A kind of single base MIMO radar not rounded signal coherence source Wave arrival direction estimating method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104991236A CN104991236A (en) | 2015-10-21 |
| CN104991236B true CN104991236B (en) | 2017-10-31 |
Family
ID=54303072
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510340888.8A Active CN104991236B (en) | 2015-06-18 | 2015-06-18 | A kind of single base MIMO radar not rounded signal coherence source Wave arrival direction estimating method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104991236B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107229041A (en) * | 2016-03-23 | 2017-10-03 | 西安中兴新软件有限责任公司 | A kind of MIMO radar Wave arrival direction estimating method and device |
| CN107037393B (en) * | 2017-05-19 | 2019-10-11 | 西安电子科技大学 | Non-circular signal wave based on nested array reaches bearing estimate method |
| CN107576940B (en) * | 2017-07-10 | 2021-04-13 | 西安邮电大学 | Low-complexity single-base MIMO radar non-circular signal angle estimation method |
| CN107290732B (en) * | 2017-07-11 | 2020-05-22 | 哈尔滨工程大学 | Single-base MIMO radar direction finding method for large-quantum explosion |
| CN108363049A (en) * | 2018-03-09 | 2018-08-03 | 西安电子科技大学 | Coherent MIMO radar angle estimating method under nonstationary noise |
| CN109828252B (en) * | 2019-04-02 | 2023-02-28 | 河海大学 | MIMO radar parameter estimation method |
| CN110873866A (en) * | 2019-12-03 | 2020-03-10 | 海南大学 | Monostatic MIMO radar target angle estimation method under cross-coupling condition |
| CN111273218A (en) * | 2020-03-09 | 2020-06-12 | 上海无线电设备研究所 | Coherent source direction-of-arrival estimation method based on multilayer co-prime array |
| CN111781593A (en) * | 2020-07-09 | 2020-10-16 | 深圳大学 | Low data volume coherent signal DOA estimation method, device, equipment and medium |
| CN112639511B (en) * | 2020-12-01 | 2022-07-01 | 华为技术有限公司 | Method and device for estimating number of information sources and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101150345A (en) * | 2006-09-18 | 2008-03-26 | 中国人民解放军空军装备研究院雷达与电子对抗研究所 | Direction measurement method applicable to phase interference signal source under non stabilized noise background |
| CN103364762A (en) * | 2013-07-13 | 2013-10-23 | 西安电子科技大学 | Estimation method for arriving direction of monostatic MIMO radar based on random array manifolds |
| CN103760527A (en) * | 2014-01-23 | 2014-04-30 | 西安电子科技大学 | Method for direction of arrival estimation of coherent source of single-base MIMO radar |
| CN103777197A (en) * | 2013-12-24 | 2014-05-07 | 南京航空航天大学 | Orientation estimation method of dimension descending propagation operator in monostatic MIMO radar |
| CN103901417A (en) * | 2014-04-02 | 2014-07-02 | 哈尔滨工程大学 | Low-complexity space target two-dimensional angle estimation method of L-shaped array MIMO radar |
| CN103954950A (en) * | 2014-04-25 | 2014-07-30 | 西安电子科技大学 | Direction-of-arrival estimation method based on sample covariance matrix sparsity |
| CN103983952A (en) * | 2014-05-16 | 2014-08-13 | 哈尔滨工程大学 | Low-complexity receiving and transmitting angle joint estimation method for non-circular signal double-base MIMO radar |
| CN104251989A (en) * | 2014-10-09 | 2014-12-31 | 哈尔滨工程大学 | Compression spatial spectrum-based single base MIMO (Multiple Input Multiple Output) radar target DOA (Direction of Arrival) estimation method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101351123B1 (en) * | 2007-03-23 | 2014-01-14 | 삼성전자주식회사 | Method and apparatus for coherent source doa estimation |
| KR101414574B1 (en) * | 2012-11-05 | 2014-07-03 | 한남대학교 산학협력단 | A Spatially Smoothed Auto-convolution based DOA Estimating Method for Correlated Signals in the Presence of Correlated Antenna Noises |
-
2015
- 2015-06-18 CN CN201510340888.8A patent/CN104991236B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101150345A (en) * | 2006-09-18 | 2008-03-26 | 中国人民解放军空军装备研究院雷达与电子对抗研究所 | Direction measurement method applicable to phase interference signal source under non stabilized noise background |
| CN103364762A (en) * | 2013-07-13 | 2013-10-23 | 西安电子科技大学 | Estimation method for arriving direction of monostatic MIMO radar based on random array manifolds |
| CN103777197A (en) * | 2013-12-24 | 2014-05-07 | 南京航空航天大学 | Orientation estimation method of dimension descending propagation operator in monostatic MIMO radar |
| CN103760527A (en) * | 2014-01-23 | 2014-04-30 | 西安电子科技大学 | Method for direction of arrival estimation of coherent source of single-base MIMO radar |
| CN103901417A (en) * | 2014-04-02 | 2014-07-02 | 哈尔滨工程大学 | Low-complexity space target two-dimensional angle estimation method of L-shaped array MIMO radar |
| CN103954950A (en) * | 2014-04-25 | 2014-07-30 | 西安电子科技大学 | Direction-of-arrival estimation method based on sample covariance matrix sparsity |
| CN103983952A (en) * | 2014-05-16 | 2014-08-13 | 哈尔滨工程大学 | Low-complexity receiving and transmitting angle joint estimation method for non-circular signal double-base MIMO radar |
| CN104251989A (en) * | 2014-10-09 | 2014-12-31 | 哈尔滨工程大学 | Compression spatial spectrum-based single base MIMO (Multiple Input Multiple Output) radar target DOA (Direction of Arrival) estimation method |
Non-Patent Citations (3)
| Title |
|---|
| 单基地MIMO雷达降维酉ESPRIT算法;文才等;《系统工程与电子技术》;20140630;第36卷(第6期);1062-1067 * |
| 基于特殊阵列的相干信源二维测向新方法;刁鸣等;《系统工程与电子技术》;20070331;第29卷(第3期);338-340 * |
| 非圆信号扩展传播算子DOA估计求根算法;刘振霞等;《空军工程大学学报（自然科学版）》;20110831;第12卷(第4期);58-63 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104991236A (en) | 2015-10-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104991236B (en) | A kind of single base MIMO radar not rounded signal coherence source Wave arrival direction estimating method | |
| CN106980106B (en) | Sparse DOA estimation method under array element mutual coupling | |
| CN107290730B (en) | Bistatic MIMO radar angle estimation method under cross-coupling condition | |
| CN103901395B (en) | Coherent signal direction of arrival Dynamic Tracking under a kind of impulsive noise environment | |
| CN102213761B (en) | Multi-target location method of bistatic common-address multi-input-multi-output radar | |
| Zhang et al. | Joint transmission and reception diversity smoothing for direction finding of coherent targets in MIMO radar | |
| CN108303683B (en) | Single-base MIMO radar real-value ESPRIT non-circular signal angle estimation method | |
| CN108732549A (en) | A kind of array element defect MIMO radar DOA estimation method based on covariance matrix reconstruct | |
| CN106526530A (en) | Propagation operator-based 2-L type array two-dimensional DOA estimation algorithm | |
| CN105403874B (en) | Nonuniform noise owes standing wave arrival direction estimating method | |
| CN104698433A (en) | Single-snapshot data-based coherent signal DOA (direction of arrival) estimating method | |
| CN108020812B (en) | Two-dimensional DOA estimation method based on special three-parallel line array structure | |
| CN108462521A (en) | The anti-interference realization method of adaptive array antenna | |
| CN104515969B (en) | Hexagonal array-based coherent signal two-dimensional DOA (Direction of Arrival) estimation method | |
| CN109143197B (en) | 2D-DOA and polarization parameter estimation method of polarization MIMO radar based on auxiliary array element | |
| CN103760527B (en) | Single base MIMO radar coherent source Wave arrival direction estimating method | |
| CN109254272B (en) | Two-dimensional angle estimation method of concurrent polarization MIMO radar | |
| CN107315162A (en) | Far field DOA Estimation in Coherent Signal method with Wave beam forming is converted based on interpolation | |
| CN107290709A (en) | The relatively prime array Wave arrival direction estimating method decomposed based on vandermonde | |
| CN107589399A (en) | Based on the relatively prime array Wave arrival direction estimating method for sampling virtual signal singular values decomposition more | |
| CN105403871B (en) | A kind of bistatic MIMO radar array target angle estimation and mutual coupling error calibration method | |
| CN106526531A (en) | Improved propagation operator two-dimensional DOA estimation algorithm based on three-dimensional antenna array | |
| CN103983952A (en) | Low-complexity receiving and transmitting angle joint estimation method for non-circular signal double-base MIMO radar | |
| CN107124216A (en) | A kind of Capon robust adaptive beamforming method and system for array error | |
| CN107037393A (en) | Not rounded signal wave based on nested array reaches bearing estimate method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |