CN109582919A - Method for parameter estimation when a kind of sky based on uniform linear array - Google Patents
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Abstract
The invention discloses method for parameter estimation when a kind of sky based on uniform linear array, this method carries out the DOA and frequency combined estimation method of spatial spectrum calculating using 2D-MUSIC algorithm by the way that the observation data of antenna array are carried out matrix recombination using Toeplitz (Teoplitz) restructing algorithm to the matrix after recombination.This method had both remained high-precision frequency and DOA estimation performance, the ability of the reconstruction algorithm processes coherent signal source Toeplitz separation is also had both simultaneously, the frequency of coherent signal ingredient and the high-precision of DOA are estimated so as to be realized using a kind of algorithm, reached the technical effect for significantly improving the estimated accuracy to the frequency and DOA of coherent signal ingredient.
Description
Technical field
This disclosure relates to array field of signal processing, two-dimensional array parameter Estimation aspect, more particularly to it is a kind of based on uniform
The method for parameter estimation when sky of linear array.
Background technique
Two-dimensional array parameter Estimation is a kind of main direction of studying of array signal process technique, be can be widely applied to military
And civil field, such as electronic countermeasure, communication, radar, sonar and medicine.Wherein, direction of arrival (direction of
Arrival, DOA) with frequency Combined estimator be one of its main research direction.DOA and frequency Combined estimator believe incoming wave
Number frequency and direction of arrival carry out Combined estimator, realize to the measurement of the frequency information of signal source and angle stationkeeping ability.Nearly 30
Nian Lai, for DOA and frequency Combined estimator project, estimation method when domestic and international researcher proposes many high-resolution skies,
In more typical algorithm for estimating have: two-dimentional multiple signal classification algorithm, the not political reform of Two Dimensional Rotating technology, Two-dimensional Maximum likelihood method
Deng.It is wherein the most typical with 2D-MUSIC algorithm, compared to other algorithms, with high stability and high angular resolution power spy
Point, to be widely studied.
2D-MUSIC method is algorithm the most typical in DOA and frequency Combined estimator, has that stability is good, angular resolution is high
The characteristics of.But the algorithm cannot correctly estimate the DOA of coherent signal ingredient present in incoming wave signal and frequency, and
Although the spatial smoothing method being derived on the basis of 2D-MUSIC algorithm has the processing capacity to coherent signal, but in frequency
There are contradictions between DOA estimated accuracy and coherent signal identification number, i.e. subarray is more, the coherent signal that can be distinguished
Number is more, but the useful space of array is smaller, and DOA estimated accuracy is lower；Conversely, subarray is fewer, the useful space of array
Bigger, estimated accuracy is higher, but it is fewer correctly to distinguish coherent signal number in incoming wave signal.
Therefore, need it is a kind of can frequency to multi signal and the DOA method that carries out high-precision estimation.
Summary of the invention
The technical problems to be solved by the invention: existing two-dimensional array method for parameter estimation is in frequency and DOA estimation essence
It is inverse relation between degree and coherent signal identification number.The present invention provides join when a kind of sky based on uniform linear array
Number estimation method, comprising:
From signal source into the aerial array arranged in one dimensional linear array each array element input signal, and acquire each array element
Data are observed, observation data vector matrix is obtained；
Parameter is estimated when observing data sky using the antenna that the observation data vector matrix building meets toeplitz matrix
The diagonal element of parameter Estimation matrix is any selected in the aerial array when counting matrix, and the antenna being made to observe data sky
The autocorrelation matrix of the observation data of array element；
Parameter Estimation matrix carries out singular value decomposition when observing data sky to the antenna, and according to singular value decomposition
Determine noise subspace；
To the noise subspace application 2D-MUSIC algorithm, noise subspace spectrum is obtained；
According to preset step-size in search and preset search lower limit, the signal source is searched for from noise subspace spectrum
The spectrum peak of number, and exported all spectrum peaks as estimates of parameters when sky.
Preferably, by the sight of remaining array element in the observation data vector set of the selected array element and the aerial array
The cross-correlation matrix of measured data vector set, as the antenna observe data sky when parameter Estimation matrix in except diagonal element with
Outer element.
Preferably, parameter Estimation matrix R when the antenna observation data skyTMeet:
Wherein, E { x1(n)x1(n)TBe the 1st array element as selected array element the auto-correlation for observing data vector set
Matrix, E { x1(n)xi(n)TBe the 1st array element observation data vector set and i-th of array element observation data vector set
Cross-correlation matrix, wherein i=1,2 ..., M, M are the quantity of the aerial array array element that includes, and T is the transposition of matrix, H
For the conjugation of matrix.
Preferably, the observation data for acquiring each array element obtain observation data vector matrix, comprising:
With preset sample frequency, within the preset sampling time, each array element in the aerial array is believed in input
Observation data are generated under the action of number to be acquired.
Preferably, the quantity of the signal source is at least two.
Preferably, parameter Estimation matrix carries out singular value decomposition when observing data sky to the antenna, and according to unusual
Value decomposition result determines noise subspace, comprising:
Parameter Estimation matrix carries out singular value decomposition when observing data sky to the antenna, obtains battle array in the aerial array
The characteristic value of first number；
All characteristic values are ranked up according to sequence from small to large is influenced by signal-to-noise ratio, by predetermined number preceding in sequence
The characteristic value of amount constitutes sub-eigenvalue set；
The noise subspace is constituted by the corresponding feature vector of characteristic value in the sub-eigenvalue set.
Preferably, number and institute of the quantity of characteristic value for array element in the aerial array in the sub-eigenvalue set
State the difference of the number of signal source.
Preferably, to the noise subspace application 2D-MUSIC algorithm, noise subspace spectrum is obtained, comprising:
The noise subspace is substituted into MUSIC algorithm power spectrum, noise subspace spectrum P is calculatedTMUSIC(f, θ):
Wherein, a (f, θ) is guiding vector, and G is the noise subspace；The spectrum of the noise subspace spectrum is by frequency f
It is indicated with direction of arrival θ.
Preferably, the corresponding frequency of each spectrum peak and direction of arrival searched out from noise subspace spectrum,
Using the estimated value of frequency and direction of arrival as signal source.
Preferably, estimates of parameters when being made of the sky the estimated value of the frequency of all signal sources and direction of arrival.
Compared with prior art, one or more embodiments in above scheme can have following advantage or beneficial to effect
Fruit:
In technical solution proposed by the invention, by the way that the observation data of antenna array are utilized Toeplitz (Top's benefit
Hereby) restructing algorithm carries out matrix recombination, then carries out the DOA of spatial spectrum calculating to the matrix after recombination using 2D-MUSIC algorithm
With frequency combined estimation method, high-precision frequency and DOA estimation performance are remained, while also having both Toeplitz restructing algorithm
The ability of coherent signal source separation is handled, so as to realize using a kind of algorithm to the frequency of coherent signal ingredient and DOA
High-precision is estimated, the technical effect for significantly improving the estimated accuracy to the frequency and DOA of coherent signal ingredient has been reached.
Detailed description of the invention
The detailed description for reading hereafter exemplary embodiment in conjunction with the accompanying drawings is better understood the scope of the present disclosure.Its
In included attached drawing be:
The flow diagram of method for parameter estimation when Fig. 1 shows the sky based on uniform linear array；
Fig. 2 shows one-dimensional aerial array schematic diagrames；
Fig. 3 shows the observation data simulation parsing result comprising coherent signal source；And
Fig. 4 is shown to the parsing result differentiated the case where multiple coherent signals in observation data.
Specific embodiment
From the detailed description provided hereinafter, it will be evident that the other application field of the disclosure.It is understood, however, that
Detailed description the being merely to illustrate property purpose of exemplary embodiment, accordingly, it is not intended that must limit the scope of the present disclosure.
2D-MUSIC algorithm in the prior art is algorithm the most typical in DOA and frequency Combined estimator, has and stablizes
The feature that property is good, angular resolution is high.But the algorithm cannot to the DOA of coherent signal ingredient present in incoming wave signal and frequency into
The correct estimation of row, and although the spatial smoothing method being derived on the basis of 2D-MUSIC algorithm has the place to coherent signal
Reason ability, but there are contradictions between frequency and DOA estimated accuracy and coherent signal identification number, i.e. and subarray is more, energy
The coherent signal number enough distinguished is more, but the useful space of array is smaller, and DOA estimated accuracy is lower；Conversely, subarray is got over
Few, the useful space of array is bigger, and estimated accuracy is higher, but it is fewer correctly to distinguish coherent signal number in incoming wave signal.
Do not have for the 2D-MUSIC algorithm of above-mentioned classics to the defect of the processing capacity of coherent signal and by its derivative
Estimated accuracy existing for spatial smoothing method out and coherent signal identification number between there are the problem of, the embodiment of the present invention
Method for parameter estimation when proposing a kind of sky based on uniform linear array.
The embodiment of the present invention will be described in detail below.Fig. 1 shows according to an embodiment of the present invention based on equal
The flow diagram of method for parameter estimation when the sky of even linear array.As shown in Figure 1, the present embodiment based on uniform linear array
Sky when method for parameter estimation mainly include step S101 to step S105.
Step S101, from signal source into the aerial array arranged in one dimensional linear array each array element input signal, and acquire
The observation data of each array element obtain observation data vector matrix.
Specifically, aerial array is equally distributed one dimensional linear array as shown in Figure 2.Wherein, array number M, adjacent battle array
First spacing distance is d, and sample frequency τ, data sampling rate is 1/ τ, and input signal number is p, and meets p < M.For frequency
Point number is M at the time of estimationt。
It is defeated at least two to array element each in aerial array within the preset sampling time with preset sample frequency
Generation observation data are acquired under the action of entering signal, and utilize the collected sight observed data and construct each array element respectively
Measured data vector set.It include collected all observation signals at various moments in the set.
Later, it constructs to obtain the observation of the antenna array using the observation data vector set of all array elements in antenna array
Data vector matrix x (n).
Step S102, ginseng when observing data sky using the antenna that observation data vector matrix building meets toeplitz matrix
Number estimated matrix, and the diagonal element of parameter Estimation matrix when antenna observation data sky is made to be any selected in aerial array
The autocorrelation matrix of the observation data of array element.
Specifically: firstly, calculating the autocorrelation matrix and the selected array element of the observation data vector set of selected array element
Observation data vector set and aerial array in remaining array element observation data vector set cross-correlation matrix.Next,
Using autocorrelation matrix and cross-correlation matrix as element, building obtains parameter Estimation matrix R when antenna observation data skyT.Wherein, square
Battle array RTDiagonal element be autocorrelation matrix, cross-correlation matrix as antenna observation data sky when parameter Estimation matrix in except diagonally
Element other than element is to construct the matrix.
The autocorrelation matrix R of the observation data vector set x (n) of some selected array elementxxExpression formula it is as follows:
Wherein, s (n) indicates the signal component in observation data x (n)；A (f, θ)=[a (f1,θ1),…,a(fp,θp)] be
The guiding matrix of s (n), σ2I is the autocorrelation matrix of additive noise vector e (n).
For example, using first array element as above-mentioned selection matrix.The observation data vector collection table of first array element
It is shown as x1(n), thus the autocorrelation matrix of the observation data vector set of first array element is expressed as E { x1(n)x1(n)T}.It should
The cross-correlation matrix of the observation data vector set of the observation data vector set and the second array element of first array element is expressed as E { x1
(n)x2(n)T}.The cross-correlation square of the observation data vector set of the observation data vector set and m-th array element of first array element
Matrix representation is E { x1(n)xM(n)T}.It can indicate that the observation data vector set of the first array element is any with remaining according to the same manner
The cross-correlation matrix of the observation data vector set of array element, details are not described herein.
As a result, when selected array element is the first array element, parameter Estimation matrix R when the antenna of building observes data skyTIt can be with
It is expressed as form:
Wherein, E { x1(n)x1(n)TBe the 1st array element as selected array element the auto-correlation for observing data vector set
Matrix, E { x1(n)xi(n)TBe the 1st array element observation data vector set and i-th of array element observation data vector set
Cross-correlation matrix, wherein i=1,2 ..., M, M are the quantity of the aerial array array element that includes, and T is the transposition of matrix, and H is square
The conjugation of battle array.
Step S103, parameter Estimation matrix R when observing data sky to antennaTSingular value decomposition is carried out, and according to singular value
Decomposition result determines noise subspace.
Specifically: due to for different fiAnd θiValue, guiding vector a (fi,θi) mutual statistical independence, and in information source only
There are information source 1 and information source 2 relevant, and there is different direction of arrival, that is, meets f1=f2≠fi, i=3,4 ..., p and θ1≠θ2.By
In RTFor symmetrical matrix, then there is unitary matrice U, makes its satisfaction
UHRTU=Σ (3)
Wherein, U=[u1,u2,…,uM], and meet UUH=I；I indicates (Mt-1)×(Mt- 1) unit matrix；
To UHRTU carries out singular value decomposition, obtains M characteristic root of element number of array
Due to knowing that guiding matrix A expires column rank, rank (APA is releasedT)=rank (P)=p, then have:
Then RTCharacteristic value be
Work as signal-to-noise ratioIt is sufficiently high, so thatThan additive noise variances sigma2When obvious big, it is easy matrix RTPreceding p
A big characteristic value (signal characteristic value) distinguishes with subsequent small characteristic value (noise characteristic value).Therefore, by all characteristic values
Σ=diag (λ1,λ2…,λM) be ranked up according to being influenced sequence from big to small by signal-to-noise ratio, i.e. λ1≥λ2≥λ3…≥
λM, later by p preceding in sequence eigenvalue λs1,λ2,…,λpConstitute dominant eigenvalue set, remaining (M-p) a characteristic value
λp+1,λp+2,…,λMConstitute sub-eigenvalue set.It, can be by unitary matrice U points according to dominant eigenvalue set and sub-eigenvalue set
At two parts, i.e.,
U=[S | G]=[u1,u2,…,up|up+1,…,uM… (6)
Wherein, the space for the corresponding feature vector of dominant eigenvalue set interior element for including is signal subspace S=[u1,
u2,…,up].The space for the corresponding feature vector of sub-eigenvalue set interior element for including is noise subspace G=[up+1,
up+2,…,uM]。
Step S104 obtains noise subspace spectrum to noise subspace application 2D-MUSIC algorithm.Specifically, according to 2D-
The definition of MUSIC algorithm spatial spectrum provides following noise subspace spectrum PTMUSICThe expression formula of (f, θ):
Wherein, G is noise subspace；The spectrum of noise subspace spectrum is indicated by frequency f and direction of arrival θ；A (f, θ) is
Guiding vector meets:
Wherein, fsFor sample frequency；C indicates the light velocity；D indicates the interval in aerial array between adjacent array element, MtFor for
Point number, M at the time of Frequency EstimationaFor the guiding vector number for Mutual coupling.
Step S105 searches for signal from noise subspace spectrum according to preset step-size in search and preset search lower limit
The spectrum peak of source number, and exported all spectrum peaks as estimates of parameters when sky.
Specifically: calculating the corresponding spatial spectrum P of 2D-TMUSIC algorithm using formula (7)TMUSIC(fi,θi), wherein fi=
fmin+ (i-1) △ f, θi=(i-1) △ θ；△ θ and △ f indicate search stepping, fminIndicate frequency search lower bound.
It is searched for using traversal and determines PTMUSIC(fi,θi) p spectrum peak, and by the corresponding frequency of each spectrum peak and
Direction of arrival is as the frequency of signal source and the estimated value of direction of arrival.
It later, will be by spectrum peak (f1,θ1),…,(fp,θp) indicate the frequency of all signal sources and estimating for direction of arrival
Estimates of parameters exports when evaluation is as sky.
Fig. 3 is shown in the case where there is coherent signal, utilizes the sky proposed by the invention based on antenna observation data
When method for parameter estimation to observation data emulation parsing result.
If information source is 3 narrow band signals, centre frequency is [1.1,1.1,1.15] GHz, and DOA is [10 °, 20 °, 30 °],
Its amplitude is 1；Input signal-to-noise ratio is 10dB, sample frequency 1GHz, MtIt is 200, element number of array M=10.From the emulation of Fig. 3
As a result as can be seen that it is proposed by the invention based on antenna observation data sky when method for parameter estimation can correctly identify
The coherent signal ingredient of different direction of arrival in signal source, as frequency is two coherent signals of 1.10GHz in figure；We simultaneously
Method remains 2D-MUSIC algorithm to the separating capacity of incoherent signal, as frequency is the signal of 1.15GHz in figure.
Fig. 4 is shown using method for parameter estimation when the sky proposed by the invention based on antenna observation data to observation number
The parsing result differentiated according to the case where interior multiple coherent signals.
If information source is 9 narrow band signals, DOA is respectively [- 60, -40 °, -20 °, 10 °, 0 °, 10 °, 20 °, 40 °, 60], in
Frequency of heart is 0.99GHz, and amplitude is 1；Input signal-to-noise ratio is 10dB, sample frequency 1GHz, MtIt is 200, array element
Number M=10, can be seen that from the simulation result of Fig. 4 as element number of array M=10, proposed by the invention to be observed based on antenna
Method for parameter estimation can correctly identify 9 coherent source in signal source when the sky of data, and therefore, be concerned with identifing source number
Ability is determined by element number of array completely.
In technical solution proposed by the invention, by the way that the observation data of antenna array are utilized Toeplitz (Top's benefit
Hereby) restructing algorithm carries out matrix recombination, carries out the DOA and frequency of spatial spectrum calculating to the matrix after recombination using 2D-MUSIC algorithm
Rate combined estimation method, this method had both remained high-precision frequency it can be seen from the parsing result of Fig. 3 and Fig. 4 and DOA estimates
Performance is counted, while also having both the ability of the reconstruction algorithm processes coherent signal source Toeplitz separation, so as to utilize a kind of calculation
Method, which is realized, estimates the frequency of coherent signal ingredient and the high-precision of DOA, has reached the frequency significantly improved to coherent signal ingredient
The technical effect of rate and the estimated accuracy of DOA.
While it is disclosed that embodiment content as above but described only to facilitate understanding the present invention and adopting
Embodiment is not intended to limit the invention.Any those skilled in the art to which this invention pertains are not departing from this
Under the premise of the disclosed spirit and scope of invention, any modification and change can be made in the implementing form and in details,
But protection scope of the present invention still should be subject to the scope of the claims as defined in the appended claims.
Claims (10)
1. method for parameter estimation when a kind of sky based on uniform linear array characterized by comprising
From signal source into the aerial array arranged in one dimensional linear array each array element input signal, and acquire the observation of each array element
Data obtain observation data vector matrix；
Parameter Estimation square when observing data sky using the antenna that the observation data vector matrix building meets toeplitz matrix
Battle array, and the diagonal element of parameter Estimation matrix when antenna observation data sky is made to be any selected array element in the aerial array
Observation data autocorrelation matrix；
Parameter Estimation matrix carries out singular value decomposition when observing data sky to the antenna, and is determined according to singular value decomposition
Noise subspace；
To the noise subspace application 2D-MUSIC algorithm, noise subspace spectrum is obtained；
According to preset step-size in search and preset search lower limit, the signal source number is searched for from noise subspace spectrum
Spectrum peak, and using all spectrum peaks as sky when estimates of parameters export.
2. the method according to claim 1, wherein by the observation data vector set of the selected array element and institute
The cross-correlation matrix for stating the observation data vector set of remaining array element in aerial array, joins when observing data sky as the antenna
Element in number estimated matrix in addition to diagonal element.
3. according to the method described in claim 2, it is characterized in that, parameter Estimation matrix R when the antenna observes data skyTIt is full
Foot:
Wherein, E { x1(n)x1(n)TBe the 1st array element as selected array element the auto-correlation square for observing data vector set
Battle array, E { x1(n)xi(n)TIt is the observation data vector set of the 1st array element and the observation data vector set of i-th of array element
Cross-correlation matrix, wherein i=1,2 ..., M, M are the quantity for the array element that the aerial array includes, and T is the transposition of matrix, and H is
The conjugation of matrix.
4. according to the method in any one of claims 1 to 3, which is characterized in that the observation data for acquiring each array element obtain
To observation data vector matrix, comprising:
With preset sample frequency, within the preset sampling time, to each array element in the aerial array in input signal
The lower observation data that generate of effect are acquired.
5. according to the method described in claim 4, it is characterized in that, the quantity of the signal source is at least two.
6. method according to any one of claims 1 to 3, which is characterized in that parameter when observing data sky to the antenna
Estimated matrix carries out singular value decomposition, and determines noise subspace according to singular value decomposition, comprising:
Parameter Estimation matrix carries out singular value decomposition when observing data sky to the antenna, obtains array element in the aerial array
Several characteristic values；
All characteristic values are ranked up according to sequence from small to large is influenced by signal-to-noise ratio, by predetermined quantity preceding in sequence
Characteristic value constitutes sub-eigenvalue set；
The noise subspace is constituted by the corresponding feature vector of characteristic value in the sub-eigenvalue set.
7. method according to claim 6, which is characterized in that the quantity of characteristic value is the day in the sub-eigenvalue set
The difference of the number of the number of array element and the signal source in linear array.
8. method according to claim 1, which is characterized in that the noise subspace application 2D-MUSIC algorithm, obtain
Noise subspace spectrum, comprising:
The noise subspace is substituted into MUSIC algorithm power spectrum, noise subspace spectrum P is calculatedTMUSIC(f, θ):
Wherein, a (f, θ) is guiding vector, and G is the noise subspace；The spectrum of the noise subspace spectrum is by frequency f and wave
It is indicated up to direction θ.
9. method according to claim 8 characterized by comprising
The corresponding frequency of each spectrum peak and direction of arrival searched out from noise subspace spectrum, as described in one
The frequency of signal source and the estimated value of direction of arrival.
10. method according to claim 9, which is characterized in that by the frequency of all signal sources and the estimated value of direction of arrival
Estimates of parameters when constituting the sky.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110018438A (en) * | 2019-04-23 | 2019-07-16 | 北京邮电大学 | A kind of Wave arrival direction estimating method and device |
| CN110212966A (en) * | 2019-06-11 | 2019-09-06 | 哈尔滨工业大学 | Mutual coupling of antenna bearing calibration based on importance resampling under the conditions of a kind of coherent source |
| CN110531311A (en) * | 2019-08-27 | 2019-12-03 | 武汉大学深圳研究院 | A kind of LTE external illuminators-based radar DOA estimation method based on matrix recombination |
| CN110542880A (en) * | 2019-08-13 | 2019-12-06 | 唐晓杰 | DOA estimation strategy under partial overlapping condition of frequency points |
| CN110888106A (en) * | 2019-11-21 | 2020-03-17 | 南京航空航天大学 | Angle and frequency joint estimation augmented DOA matrix method |
| WO2021248792A1 (en) * | 2020-06-08 | 2021-12-16 | 浙江大学 | Single-bit quantized signal virtual domain statistic reconstruction-based co-prime array direction of arrival estimation method |
| CN114157538A (en) * | 2021-11-22 | 2022-03-08 | 清华大学 | Wireless signal arrival angle estimation method and system based on dual-channel receiver |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104515969A (en) * | 2013-09-29 | 2015-04-15 | 电子科技大学 | Hexagonal array-based coherent signal two-dimensional DOA (Direction of Arrival) estimation method |
| CN105589056A (en) * | 2015-12-15 | 2016-05-18 | 吉林大学 | Multi-objective near-and-far field mixed source positioning method |
| CN105891770A (en) * | 2016-06-07 | 2016-08-24 | 天津大学 | DOA (Direction Of Arrival) estimation method for coherence circulation balance signal |
| CN106054123A (en) * | 2016-06-06 | 2016-10-26 | 电子科技大学 | Sparse L-shaped array and two-dimensional DOA estimation method thereof |
| CN108398659A (en) * | 2018-02-11 | 2018-08-14 | 华南理工大学 | A kind of Wave arrival direction estimating method that pencil of matrix is combined with rooting MUSIC |
-
2018
- 2018-11-28 CN CN201811434175.8A patent/CN109582919A/en not_active Withdrawn
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104515969A (en) * | 2013-09-29 | 2015-04-15 | 电子科技大学 | Hexagonal array-based coherent signal two-dimensional DOA (Direction of Arrival) estimation method |
| CN105589056A (en) * | 2015-12-15 | 2016-05-18 | 吉林大学 | Multi-objective near-and-far field mixed source positioning method |
| CN106054123A (en) * | 2016-06-06 | 2016-10-26 | 电子科技大学 | Sparse L-shaped array and two-dimensional DOA estimation method thereof |
| CN105891770A (en) * | 2016-06-07 | 2016-08-24 | 天津大学 | DOA (Direction Of Arrival) estimation method for coherence circulation balance signal |
| CN108398659A (en) * | 2018-02-11 | 2018-08-14 | 华南理工大学 | A kind of Wave arrival direction estimating method that pencil of matrix is combined with rooting MUSIC |
Non-Patent Citations (1)
| Title |
|---|
| 蒲磊: "基于块Toeplitz矩阵的频率和波达方向联合估计方法", 《电子世界》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110018438A (en) * | 2019-04-23 | 2019-07-16 | 北京邮电大学 | A kind of Wave arrival direction estimating method and device |
| CN110018438B (en) * | 2019-04-23 | 2020-09-25 | 北京邮电大学 | Direction-of-arrival estimation method and device |
| CN110212966A (en) * | 2019-06-11 | 2019-09-06 | 哈尔滨工业大学 | Mutual coupling of antenna bearing calibration based on importance resampling under the conditions of a kind of coherent source |
| CN110212966B (en) * | 2019-06-11 | 2022-07-08 | 哈尔滨工业大学 | Antenna mutual coupling correction method based on importance resampling under coherent source condition |
| CN110542880A (en) * | 2019-08-13 | 2019-12-06 | 唐晓杰 | DOA estimation strategy under partial overlapping condition of frequency points |
| CN110531311A (en) * | 2019-08-27 | 2019-12-03 | 武汉大学深圳研究院 | A kind of LTE external illuminators-based radar DOA estimation method based on matrix recombination |
| CN110888106A (en) * | 2019-11-21 | 2020-03-17 | 南京航空航天大学 | Angle and frequency joint estimation augmented DOA matrix method |
| WO2021248792A1 (en) * | 2020-06-08 | 2021-12-16 | 浙江大学 | Single-bit quantized signal virtual domain statistic reconstruction-based co-prime array direction of arrival estimation method |
| US11567161B2 (en) | 2020-06-08 | 2023-01-31 | Zhejiang University | Method for estimating the direction-of-arrival of a coprime array based on virtual domain statistics reconstruction of single-bit quantized signal |
| CN114157538A (en) * | 2021-11-22 | 2022-03-08 | 清华大学 | Wireless signal arrival angle estimation method and system based on dual-channel receiver |
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