CN104678350A - TLS-ESPRTT algorithm-based 2D DOA estimation in large scale MIMO system - Google Patents
TLS-ESPRTT algorithm-based 2D DOA estimation in large scale MIMO system Download PDFInfo
- Publication number
- CN104678350A CN104678350A CN201510104318.9A CN201510104318A CN104678350A CN 104678350 A CN104678350 A CN 104678350A CN 201510104318 A CN201510104318 A CN 201510104318A CN 104678350 A CN104678350 A CN 104678350A
- Authority
- CN
- China
- Prior art keywords
- submatrix
- matrix
- represent
- array
- tls
- 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
- G01S3/00—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received
- G01S3/02—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received using radio waves
- G01S3/14—Systems for determining direction or deviation from predetermined direction
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a method for estimating a 2D DOA (2-Dimensional Direction of Arrival) by utilizing a TLE ESPRIT (Total Least Square-Estimating Signal Parameter via Rotational in Variance Techniques) algorithm in a large scale MIMO system. The method comprises the following steps: dividing an area array into two partially-overlapped sub-arrays according to a received signal model of the large scale MIMO system, so that the two partially-overlapped sub-arrays meet the requirement for the array shift invariance of the ESPRIT algorithm; calculating a signal subspace of each sub-array according to a 1D ESPRIT (1-Dimensional ESPRIT) algorithm, and estimating elevation information according to a TLS rule; estimating an azimuth by utilizing a direction matrix of one of the sub-arrays, and automatically matching the azimuth with the elevation. According to the method, the problem about 2-dimensional DOA estimation in the large scale MIMO system is effectively solved by adopting the thought of the TLS-ESPRIT algorithm; the azimuth can be matched with the elevation automatically; the computing complexity is relatively low.
Description
Technical field
The present invention relates generally to future mobile field.
Background technology
Following mobile data services amount will exponentially increase, and traditional MIMO technology can not meet such demand.In the end of the year 2010, Bell Laboratory scientist Thomas L.Marzetta proposes extensive MIMO (Large Scale MIMO), is also the concept of Massive MIMO.Extensive MIMO as a kind of gordian technique of future broadband wireless communication systems, at the antenna that base station end configured number is huge, with degree of depth digging utilization Spatial Dimension unlimited resources, elevator system spectrum efficiency and power.Extensive mimo system adopts face battle array, cylindrical array or special-shaped battle array in base station end usually.In extensive mimo system, because the multiple antennas of base station configures, cause base station to obtain channel condition information (Channel State Information, CSI) very difficult, the precoding channel feedback that places one's entire reliance upon in traditional base station is no longer applicable.2D DOA is estimated as channel precoding and provides accurate spatial information (si), and the cost volume that realizes of optimum downlink precoding is significantly reduced.
In numerous extensive MIMO base station antenna configuration, URA is relatively simple, can make full use of the spatial information that horizontal dimensions and vertical dimensions provide simultaneously, therefore, be more suitable for applying in extensive mimo system.Under URA configuration, Received signal strength comprises vertical dimensions and horizontal dimensions information simultaneously, and 2D Mutual coupling is exactly to realize the position angle of incoming wave signal level dimension and the elevation estimate of vertical dimension.
Mutual coupling is as one of the important research content of Array Signal Processing, and at sonar, radar, the fields such as wireless communication system have important using value.At present, existing 1D DOA estimation theory has developed to obtain relative maturity, 1D DOA estimation method is as multiple signal classification (Multiple Signal Classification, MUSIC) algorithm, invariable rotary Subspace algorithm (Estimating Signal Parameter via Rotational In variance Techniques, ESPRIT) algorithm application is relatively more extensive, but is difficult to obtain practical application in two-dimensional estimation.Compare with 1D DOA, 2D DOA can state signal space position better, therefore studies 2D DOA and has more practical significance.When utilizing classical MUSIC algorithm to carry out two-dimensional estimation, need to carry out spectrum peak search, operand is large, and consumes a large amount of time.Two dimensional ESPRIT algorithm solves this problem preferably, but needing to match to final result just can obtain effective angle estimation.On the one hand, due to antenna number increase and 2D DOA estimate in the increase of parameter dimension, cause the increase of estimation procedure complexity.If still adopt traditional subspace class algorithm such as 2D MUSIC, 2D ESPRIT algorithm to estimate angle, the problems such as the large and angle pairing of algorithm complexity, calculated amount will be faced, make it can not be used for actual direction-finding system; On the other hand, due to the interference of noise, itself there is error in sampled signal.Sampled signal is directly carried out computing as signal itself by existing most of DOA algorithm for estimating, does not meet actual direction finding environment.
Summary of the invention
The present invention is directed to the deficiency that current 2D DOA estimation method exists, propose the 2D DOA estimation method based on TLS-ESPRIT algorithm in extensive mimo system, the thought of TLS-ESPRIT algorithm is adopted effectively to solve arrival direction estimation problem in extensive mimo system, not only automatically can realize position angle and elevation angle pairing, and computation complexity is relatively low.
The present invention adopts URA, to receive different directions incoming wave in base station end.According to the Received signal strength model of extensive mimo system, first face battle array is divided into two partly overlapping submatrixs, makes it meet the array motion immovability of ESPRIT algorithm requirement; Then the signal subspace of subarray is calculated according to 1D ESPRIT algorithm; Recycling TLS criterion, under noise disturbance condition, estimates accurate elevation information; Direction matrix finally by a wherein submatrix completes position angle and estimates, and automatically and the elevation angle match.
The present invention includes following steps:
Step one: base station adopts M × N to tie up URA, and consideration face battle array is positioned at XOZ plane, and supposing has K information source to incide this array, and array received signal model is as follows:
X=AS+N
Wherein X is that array received signal phasor is tieed up in MN × 1, and S is K × 1 dimension space signal phasor, and N is MN × 1 white Gaussian noise vector, and A is that MN × K ties up direction matrix, and concrete form is as follows:
Wherein
represent gram labor Roc inner product, a (u
i) be the direction vector of X-direction, a (v
i) be the direction vector of Z-direction.
Step 2: consider that base station end aerial array is positioned at XOZ plane, prolong Z-direction and M × N dimension URA is divided into two submatrixs with motion immovability, because the direction matrix of submatrix 1 and submatrix 2 differs a twiddle factor Φ, and this twiddle factor only comprises elevation information, specific as follows:
Φ=diag[exp(-2πd cosθ
1/λ) exp(-2πd cosθ
2/λ) ... exp(-2πd cosθ
K/λ)]
Wherein d is array element distance, and λ is velocity of wave, θ
i, i=1,2,3...K represent the elevation angle of i-th user.
Step 3, signal subspace E according to 1D ESPRIT algorithm computing array
s, definition selection matrix J
1, J
2, therefrom select the signal subspace that two submatrixs are corresponding
detailed process is as follows:
Definition
Then have:
Then the signal subspace of submatrix 1 and submatrix 2 correspondence is:
There is noise disturbance in step 4, consideration, according to TLS criterion, estimates elevation information, be specifically expressed as follows:
According to TLS criterion, definition:
Right
carry out Eigenvalues Decomposition:
And E is resolved into the submatrix of K × K, that is:
Calculate
and Eigenvalues Decomposition is carried out to Ψ:
Ψ=HΩH
-1=TΦT
-1
Eigenvalue λ in Ω
i, the diagonal entry in i=1,2...K homography Φ, H=T, calculates elevation estimate value:
θ
i=arccos{angle(λ
i)λ/2πd}
Wherein d is array element distance, and λ is velocity of wave, θ
i, i=1,2,3...K represent the elevation angle of i-th user, ()
hrepresent the conjugate transpose asking for matrix, ()
-1represent and ask for inverse of a matrix.
The direction matrix of step 5, submatrix 1 can by signal subspace
with the product representation of non-singular matrix H, utilize the column vector of its direction matrix to estimate position angle, concrete formula is as follows:
in the i-th, i=1,2...K column vector
meet:
Wherein
represent that i-th arranges front 1:M (N-2) individual element,
represent rear N-1:M (N-1) the individual element of the i-th row.
Order
position angle φ can be estimated according to above formula:
φ
i=arccos(angle(b
i)λ/(2πd sinθ
i))
Contemplated by the invention the noise that sampled signal exists, utilize TLS criterion, estimate the accurate elevation angle; Complete position angle by direction matrix column vector invariable rotary relation to estimate.The present invention only utilizes 1D DOA algorithm for estimating to realize 2D angle estimation, significantly reduces computation complexity, and can realize angle automatic matching.
Accompanying drawing explanation
Fig. 1 is the FB(flow block) of 2D DOA method in the extensive mimo system that proposes of the present invention;
Fig. 2 is extensive MIMO 3D model schematic;
Fig. 3 is that the URA that base station end adopts divides schematic diagram;
Fig. 4 is the arrival direction estimation method flow diagram based on TLS-ESPRIT algorithm in the extensive mimo system that proposes of the present invention.
Embodiment
In order to make the object, technical solutions and advantages of the present invention clearly, below in conjunction with accompanying drawing, the present invention is described in further detail:
Figure 1 shows that the FB(flow block) of 2D DOA method in the extensive mimo system that the present invention proposes, base station end configuration M × N ties up URA, wherein M represents URA horizontal dimension antenna number, N represents URA vertical dimension antenna number, ensure that each transmit antennas can both simultaneously processing horizontal dimension and the information in vertical dimensions, it is that K is individual that user side sends signal.According to the Received signal strength model of extensive mimo system, first face battle array is divided into two partly overlapping submatrixs, is calculated the signal subspace of subarray by 1D ESPRIT algorithm; Then utilize TLS criterion, estimate elevation information; Finally utilize the direction matrix of a wherein submatrix to estimate to complete position angle, and realize automatic matching.
Fig. 2 is extensive MIMO 3D model schematic, and base station height is h, and user is highly h
u, base station end configuration is positioned at the M capable N row URA of X-Z plane, and array element distance is d, and sending signal number is K.For each send signal have a position angle φ and elevation angle theta corresponding with it, system model is as follows:
X=AS+N
Wherein X is that array received signal phasor is tieed up in MN × 1, and S is K × 1 dimension space signal phasor, and N is MN × 1 white Gaussian noise vector, and A is that MN × K ties up direction matrix, under concrete form:
a(u
i)=[1 exp(-j2πdsinθ
icosφ
i/λ) ... exp(-j(M-1)2πdsinθ
icosφ
i/λ)]
T
a(v
i)=[1 exp(-j2πdcosθ
i/λ) ... exp(-j(N-1)2πdcosθ
i/λ)]
T
Wherein
represent gram labor Roc inner product, a (u
i) be the direction vector of X-direction, a (v
i) be the direction vector of Z-direction, θ
iand φ
irepresent respectively
ithe elevation angle of individual user and position angle, i=1,2,3...K, d are array element distance, and λ is velocity of wave, ()
trepresent and ask for transpose of a matrix.
Fig. 3 is that the URA that base station end adopts divides schematic diagram, consider that base station end aerial array is positioned at XOZ plane, prolong Z-direction and planar array is divided into two submatrixs with motion immovability, two submatrix array numbers are identical, the direction matrix of submatrix 1 and submatrix 2 differs a twiddle factor Φ, this twiddle factor only comprises elevation information θ, and concrete form is as follows:
A
1Φ=A
2
Φ=diag[exp(-2πd cosθ
1/λ) exp(-2πd cosθ
2/λ) ... exp(-2πd cosθ
K/λ)]
Wherein A
1, Α
2be respectively the direction matrix of submatrix 1 and submatrix 2, d is array element distance, and λ is velocity of wave, θ
i, i=1,2,3...K represent the elevation angle of i-th user.
Fig. 4 is the arrival direction estimation method flow diagram based on TLS-ESPRIT algorithm in extensive mimo system, and specific implementation step is as follows:
Step 41, as shown in Figure 3, the URA being positioned at X-Z face is divided into two partly overlapping submatrixs along Z axis, the direction matrix of submatrix 1 and submatrix 2 differs a twiddle factor Φ, and this twiddle factor only comprises elevation information θ.
Step 42, according to 1D ESPRIT algorithm, the covariance matrix of first computing array output:
Wherein N is the fast umber of beats of signal sampling, then to R
xXcarry out Eigenvalues Decomposition, obtain signal subspace E
s:
Wherein σ
2for noise variance, ()
hrepresent the conjugate transpose asking for matrix.
Step 43, definition selection matrix J
1, J
2signal subspace E
sin select signal subspace corresponding to two submatrixs
specifically be expressed as follows:
Definition:
Then have:
Wherein
represent gram labor Roc inner product.The signal subspace that so submatrix is corresponding is:
Step 44, estimate the elevation angle according to TLS criterion, detailed process is as follows:
Under A, ideal conditions, the signal subspace E of submatrix 1 and submatrix 2
1, E
2with corresponding direction matrix A
1, Α
2open into similar subspace, so the non-singular matrix T of existence anduniquess, following formula set up:
A
1=E
1T A
2=E
2T
According to conclusion A in Fig. 3
1Φ=A
2, can E be obtained
1t Φ T
-1=E
2; Definition Ψ=T Φ T
-1, then E is had
1Ψ=E
2.Wherein, ()
-1represent and ask for inverse of a matrix.
There is error in B, actual samples signal, defences the signal subspace that difference solves jointly
all there is noise disturbance:
Utilize TLS criterion to solve Ψ more more accurate than traditional E SPRTI algorithm, be specifically expressed as follows:
Definition:
Right
carry out Eigenvalues Decomposition:
And E is resolved into the submatrix of K × K, namely
Calculate
and Eigenvalues Decomposition is carried out to Ψ:
Ψ=HΩH
-1=TΦT
-1
Eigenvalue λ in Ω
i, the diagonal entry in i=1,2...K homography Φ, H=T, calculates elevation estimate value:
θ
i=arccos{angle(λ
i)λ/2πd}
Wherein d is array element distance, and λ is velocity of wave, ()
hrepresent the conjugate transpose asking for matrix, ()
-1represent and ask for inverse of a matrix.
Step 45, utilizes the direction matrix of submatrix 1 to estimate position angle, and the direction matrix of submatrix 1 correspondence by the product representation of signal subspace and above-mentioned non-singular matrix H, specifically can be expressed as follows:
The direction matrix A of submatrix 1
1:
A
1=[α
1α
2… α
i… α
K]
M(N-1)×K
Wherein α
iconcrete form be:
By α
iconcrete form can find out, A
1in i-th arrange front 1:M (N-2) individual element and rear N-1:M (N-1) individual element meets invariable rotary relation:
Utilize the signal subspace of submatrix 1
direction matrix corresponding to submatrix is gone out with the product estimation of non-singular matrix H:
Step 46, utilizes
the invariable rotary relation of middle column vector is by twiddle factor
solve, estimate position angle φ, concrete formula is as follows:
φ
i=arccos(angle(b
i)λ/(2πd sinθ
i))
Wherein d is array element distance, and λ is velocity of wave, φ
i, i=1,2,3...K represent the position angle of i-th user, ()
+represent the pseudoinverse asking for matrix.
Be more than that the preferred embodiments of the present invention are described in detail, should be appreciated that preferred embodiment only in order to the present invention is described, instead of in order to limit the scope of the invention.
Claims (5)
1. in extensive mimo system based on the 2D DOA estimation method of TLS-ESPRIT algorithm, it is characterized in that, comprise step: base station end adopts M × N to tie up uniform rectangular face battle array (URA), according to the Received signal strength model of extensive mimo system, first face battle array is divided into two partly overlapping submatrixs, makes it meet the array motion immovability of ESPRIT algorithm requirement; And then the signal subspace of subarray is calculated according to 1D ESPRIT algorithm; Then utilize TLS criterion, realize elevation estimate; The direction matrix of a wherein submatrix is finally utilized to estimate corresponding position angle.
2. 2D DOA estimation method according to claim 1, is characterized in that, described two partly overlapping submatrixs are submatrix 1 and submatrix 2, submatrix 1 direction matrix A
1with submatrix 2 direction matrix A
2meet relation: A
1Φ=A
2, wherein include elevation information in matrix Φ.
3. 2D DOA estimation method according to claim 1, it is characterized in that, the described signal subspace according to 1D ESPRIT algorithm calculating subarray is: according to 1D ESPRIT algorithm, the covariance matrix of pair array sampled data carries out Eigenvalues Decomposition, obtains signal subspace E
s, from E
sin select the signal subspace corresponding with submatrix 1 and submatrix 2
with
that is:
wherein J
1, J
2represent corresponding selection matrix respectively.
4. 2D DOA estimation method according to claim 3, is characterized in that, describedly utilizes TLS criterion, and realizing elevation estimate is: according to TLS criterion, estimates accurate elevation information, definition
Right
carry out Eigenvalues Decomposition and obtain eigenmatrix E, and E is resolved into the submatrix of K × K, that is:
Calculate
and Eigenvalues Decomposition is carried out to Ψ:
Ψ=HΩH
-1=TΦT
-1
Eigenvalue λ in Ω
i, the diagonal entry in i=1,2...K homography Φ, H=T, calculates elevation estimate value:
θ
i=arccos{angle(λ
i)λ/2πd}
Wherein d is array element distance, and λ is velocity of wave, θ
irepresent the elevation angle of i-th signal, ()
hrepresent the conjugate transpose asking for matrix, ()
-1represent and ask for inverse of a matrix.
5. 2DDOA method of estimation according to claim 1, is characterized in that, the direction matrix of a described utilization wherein submatrix estimates that corresponding position angle concrete grammar is: the direction matrix of submatrix 1 can by its signal subspace
with the product representation of non-singular matrix H:
the invariable rotary relation of utilization orientation rectangular array vector can estimate corresponding position angle φ:
φ
i=arccos(angle(b
i)λ/(2πdsinθ
i))
Wherein
Represent
i-th arranges front 1:M (N-2) individual element,
represent rear N-1:M (N-1) the individual element of the i-th row, d is array element distance, and λ is velocity of wave, θ
irepresent the elevation angle of i-th signal, φ
irepresent the
ithe position angle of individual signal, ()
+represent the pseudoinverse asking for matrix.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510104318.9A CN104678350B (en) | 2015-03-10 | 2015-03-10 | Arrival direction estimation based on TLS ESPRIT algorithms in extensive mimo system |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510104318.9A CN104678350B (en) | 2015-03-10 | 2015-03-10 | Arrival direction estimation based on TLS ESPRIT algorithms in extensive mimo system |
Publications (2)
Publication Number | Publication Date |
---|---|
CN104678350A true CN104678350A (en) | 2015-06-03 |
CN104678350B CN104678350B (en) | 2017-07-28 |
Family
ID=53313679
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201510104318.9A Active CN104678350B (en) | 2015-03-10 | 2015-03-10 | Arrival direction estimation based on TLS ESPRIT algorithms in extensive mimo system |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN104678350B (en) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106291453A (en) * | 2016-09-18 | 2017-01-04 | 电子科技大学 | A kind of two-dimentional ripple based on beam space conversion reaches angle assay method |
CN109188386A (en) * | 2018-08-30 | 2019-01-11 | 河海大学 | Based on the MIMO radar high-resolution method for parameter estimation for improving two dimensional ESPRIT algorithm |
CN109633521A (en) * | 2019-01-25 | 2019-04-16 | 西安电子科技大学 | Face battle array estimating two-dimensional direction-of-arrival method based on subspace reconstruct |
CN113504504A (en) * | 2021-06-04 | 2021-10-15 | 华南理工大学 | Underwater high-precision one-dimensional DOA estimation method |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20040253987A1 (en) * | 2003-05-26 | 2004-12-16 | Lg Electronics Inc. | Method and device for determining distributed signal sources of a base station |
CN102608565A (en) * | 2012-03-23 | 2012-07-25 | 哈尔滨工程大学 | Direction-of-arrival estimation method on basis of uniform circular array |
-
2015
- 2015-03-10 CN CN201510104318.9A patent/CN104678350B/en active Active
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20040253987A1 (en) * | 2003-05-26 | 2004-12-16 | Lg Electronics Inc. | Method and device for determining distributed signal sources of a base station |
CN102608565A (en) * | 2012-03-23 | 2012-07-25 | 哈尔滨工程大学 | Direction-of-arrival estimation method on basis of uniform circular array |
Non-Patent Citations (5)
Title |
---|
TADEU N. FERREIRA ETC.: "Low Complexity Covariance-based DOA Estimation Algorithm", 《15TH EUROPEAN SIGNAL PROCESSING CONFERENCE(EUSIPCO 2007)》 * |
吴湘淋等: "未知噪声背景下的波达方向及载波频率三维联合估计方法", 《西北工业大学学报》 * |
姚林宏等: "冲击噪声下的改进TLS-ESPRIT算法研究", 《长春大学学报》 * |
张裕峰: "多径传播条件下的波达方向估计算法研究", 《中国博士学位论文全文数据库 信息科技辑》 * |
郑植等: "低复杂度相干分布源二维DOA解耦估计方法", 《电波科学学报》 * |
Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106291453A (en) * | 2016-09-18 | 2017-01-04 | 电子科技大学 | A kind of two-dimentional ripple based on beam space conversion reaches angle assay method |
CN106291453B (en) * | 2016-09-18 | 2018-12-18 | 电子科技大学 | A kind of two-dimentional direction of arrival measuring method based on beam space transformation |
CN109188386A (en) * | 2018-08-30 | 2019-01-11 | 河海大学 | Based on the MIMO radar high-resolution method for parameter estimation for improving two dimensional ESPRIT algorithm |
CN109633521A (en) * | 2019-01-25 | 2019-04-16 | 西安电子科技大学 | Face battle array estimating two-dimensional direction-of-arrival method based on subspace reconstruct |
CN109633521B (en) * | 2019-01-25 | 2022-09-06 | 西安电子科技大学 | Area array two-dimensional direction of arrival estimation method based on subspace reconstruction |
CN113504504A (en) * | 2021-06-04 | 2021-10-15 | 华南理工大学 | Underwater high-precision one-dimensional DOA estimation method |
CN113504504B (en) * | 2021-06-04 | 2023-06-20 | 华南理工大学 | Underwater high-precision one-dimensional DOA estimation method |
Also Published As
Publication number | Publication date |
---|---|
CN104678350B (en) | 2017-07-28 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Kikuchi et al. | Pair-matching method for estimating 2-D angle of arrival with a cross-correlation matrix | |
Wen et al. | 3-D positioning method for anonymous UAV based on bistatic polarized MIMO radar | |
CN103091671B (en) | Bidimensional wave arrival direction estimation method based on non-concentric electromagnetic vector array radar | |
Liu et al. | Joint estimation of DOA and TDOA of multiple reflections in mobile communications | |
CN105611627A (en) | Method for estimating AOA of WLAN access point based on double antennas | |
CN103941221A (en) | Method for estimating parameters of space stretching electromagnetic vector sensor array | |
CN104515969B (en) | Hexagonal array-based coherent signal two-dimensional DOA (Direction of Arrival) estimation method | |
CN102253363A (en) | Device for estimating two-dimensional direction of arrival (DOA) of coherent signals based on L array and method thereof | |
CN108020812B (en) | Two-dimensional DOA estimation method based on special three-parallel line array structure | |
CN105259550A (en) | Compressed sensing-based multi-input and multi-output radar two-dimensional angle estimation method | |
CN109254272B (en) | Two-dimensional angle estimation method of concurrent polarization MIMO radar | |
CN104375115A (en) | Polarization sensitive array based non-circular signal DOA and polarization parameter joint estimation method | |
CN106021637A (en) | DOA estimation method in co-prime array based on iteration sparse reconstruction | |
CN104678350A (en) | TLS-ESPRTT algorithm-based 2D DOA estimation in large scale MIMO system | |
CN104991236A (en) | Monostatic MIMO radar non-circular signal coherent source DOA (Direction Of Arrival) estimation method | |
Yang et al. | 2D DOA estimation of coherently distributed noncircular sources | |
CN104035069A (en) | Partly-corrected symmetrical uniform linear array based narrow-band near-field signal source positioning method | |
Li et al. | Reduced-dimensional ESPRIT for direction finding in monostatic MIMO radar with double parallel uniform linear arrays | |
Wu et al. | Direction finding in bistatic MIMO radar with direction-dependent mutual coupling | |
CN104237843A (en) | Estimation method of two-dimensional central direction of arrival for distributed sources | |
CN108872930A (en) | Extend aperture two dimension Joint diagonalization DOA estimation method | |
CN103364762A (en) | Estimation method for arriving direction of monostatic MIMO radar based on random array manifolds | |
Li et al. | Root‐music based angle estimation for MIMO radar with unknown mutual coupling | |
Roemer et al. | Efficient 1-D and 2-D DOA estimation for non-circular sourceswith hexagonal shaped ESPAR arrays | |
Alibi et al. | 2D DOA estimation method based on channel state information for uniform circular array |
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 |