CN109696657A - A kind of coherent sound sources localization method based on vector hydrophone - Google Patents
A kind of coherent sound sources localization method based on vector hydrophone Download PDFInfo
- Publication number
- CN109696657A CN109696657A CN201910145175.4A CN201910145175A CN109696657A CN 109696657 A CN109696657 A CN 109696657A CN 201910145175 A CN201910145175 A CN 201910145175A CN 109696657 A CN109696657 A CN 109696657A
- Authority
- CN
- China
- Prior art keywords
- matrix
- vector
- signal
- formula
- array
- 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
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/18—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using ultrasonic, sonic, or infrasonic waves
Abstract
The invention discloses a kind of coherent sound sources localization method based on vector hydrophone acquires sound-source signal using vector hydrophone arrays, and obtains the covariance matrix of acquisition signal；Signal decorrelation LMS is carried out using the method that subarray extracts data is not overlapped；The signal subspace that covariance matrix after decorrelation LMS is estimated using least square method, is solved according to vector hydrophone arrays flow pattern invariable rotary characteristic, finally obtains the arrival direction estimation of multiple coherent sound sources.The method of the present invention estimates that coherent sound sources azimuth accuracy is high, it is easy to accomplish, additionally it is possible to effectively overcome the problems, such as azimuth ambiguity and coherent sound sources problem interfering with each other.
Description
Technical field
The present invention relates to vector hydrophone arrays field of sound source location in the case of coherent sound sources, specially a kind of to be based on vector
The coherent sound sources localization method of hydrophone.
Background technique
Vector hydrophone is two to three velocity hydrophones by omnidirectional distribution in sound field under water plus an acoustic pressure water
Device is listened to form.Due to its recognition capability more superior than traditional pressure hydrophone, vector hydrophone arrays are under water in signal processing
Played important function, and under water identification, bay acoustics inverting and subsurface communication (see document: Song A, Abdi A,
Badiey M,et al.Experimental Demonstration of Underwater Acoustic
Communication by Vector Sensors[J].IEEE Journal of Oceanic Engineering,2011,
36 (3): 454-461.) etc. be widely used in fields.In the past ten years, many sons based on vector hydrophone
Space technology is suggested, such as MUSIC and ESPRIT, and there are also 2D underwater signal orientation is estimated using vector hydrophone arrays
(see document: He J, Liu Z.Efficient underwater two-dimensional coherent source
localization with linear vector-hydrophone array[J].Signal Processing,2009,89
(9):1715-1722.)。
Approach described above is all made of incoherent signal, i.e. signal covariance matrix has full order.However, this assume
Multipath propagation deliberates interference and exists and be concerned with or be often not suitable in the case where highly relevant.Coherent signal can reduce
The order of incoming signal covariance matrix can not correctly estimate the position of sound source to seriously reduce the performance of technology.Therefore, it learns
Persons for sound source coherence problems carried out it is a large amount of research and propose as maximum likelihood method, Search Space Smoothing and
The solutions such as Toeplitz method.
In order to handle coherent signal, document (Tao J, Chang W, Shi Y.Direction- with vector hydrophone arrays
finding of coherent sources via'particle-velocity-field smoothing'[J].Iet
Radar Sonar&Navigation, 2008,2 (2): 127-134.) propose the smooth skill of vector by data correlation matrix
Art restores the order of signal subspace.However, this vector smoothing technique needs geometrical plane array or 2D iterative search to estimate
Count the two-dimensional directional of incoming signal.Document (Liu S, Yang L, Xie Y, et al.2D DOA Estimation for
Coherent Signals with Acoustic Vector-Sensor Array[J].Wireless Personal
Communications, 2017,95 (2): 1285-1297) a kind of use ESPRIT estimation incoming signal elevation angle is proposed, pass through
Modified array manifold matches to obtain azimuthal method of coherent signal.This method is with respect to document (Gu J F, Wei P, Tai
H M.2-D direction-of-arrival estimation of coherent signals using cross-
Correlation matrix [J] .Signal Processing, 2008,88 (1): 75-85.) in calculating process operate letter
Just, it is more accurate to position.
The above method is all decoupled method, i.e., first obtains pitch angle further according to pitch angle and acquire azimuth, and then obtain phase
The orientation of dry sound source.It follows that the evaluated error of pitch angle will affect azimuthal accurate estimation, positioning accuracy will be received
Extreme influence.
Summary of the invention
The present invention is directed to the deficiency that existing vector hydrophone positions coherent sound sources, proposes a kind of based on vector hydrophone
Coherent sound sources localization method, with solve spectrum peak search bring calculation amount and substep estimation bring error the problems such as.This hair
The bright vector hydrophone arrays suitable for arbitrary structures, can be realized angle estimation automatic matching.
In order to solve the above technical problems, the present invention provides a kind of coherent sound sources localization method based on vector hydrophone,
Include the following steps:
Step 1: acquiring K sound-source signal using M member vector hydrophone arrays, establish vector hydrophone arrays and receive data
Model；
Step 2: acquiring covariance matrix according to signal is received, carried out using the method that subarray extracts data is not overlapped
Signal decorrelation LMS；
Step 3: using transformation matrix, the acoustic pressure vector sum vibration velocity vector after extraction decorrelation LMS in array manifold；
Step 4: the signal subspace of covariance matrix after decorrelation LMS is estimated using least square method；
Step 5: being solved to obtain the two-dimentional DOA of coherent sound sources according to vector hydrophone arrays flow pattern invariable rotary characteristic
Estimation.
Vector hydrophone arrays receive data module and establish specific steps in step 1 are as follows:
Assuming that the narrowband plane wave signal s that K wavelength is λi(t), i=1,2 ..., K is incident on M member vector water from far field
Listen on device array, and in sound field between each noise, it is irrelevant between noise and signal, have L in K signalmaxA relevant sound
Source, if the pitch angle of sound source isAzimuth is φi, i=1,2 ..., K, then the array stream of vector hydrophone
Type vector are as follows:
In formula,
Each vector hydrophone arrays receive data model are as follows:
In formula,Indicate the array manifold vector of m-th of array element, nm(t) noise that m-th of array element receives, m are indicated
=1,2 ..., M, t=1,2 ..., N, ψi=(2 π d/ λ) γi, i=1,2 ... K, wherein M indicates that array number, N indicate signal
Number of snapshots, K indicate sound source number.
Step 2 is specific as follows:
Array received signal can be obtained by formula (2):
X (t)=As (t)+n (t), t=1,2 ..., N (3)
In formula,
The correlation matrix of array received signal are as follows:
In formula,Indicate the correlation matrix of signal；
Correlation matrix is first divided into LmaxA submatrix, LmaxIndicate that the number of coherent sound sources, the dimension of each submatrix are 4 (M-
Lmax+ 1) × 4M, first of submatrix are labeled as Rl, l=1,2 ... lmax, by RxThe the 4th (l-1)+1 row to 4 (M-Lmax+ 1) row group
At constructing a new matrix R by these submatrixs:
In formula, the dimension of matrix R is 4 (M-Lmax+1)×4MLmax；
According to the correlation matrix of array received signal:
Bring matrix R into, available::
In formula,
Due to
So data covariance matrix R when group battle array array element number >=K after decorrelation LMS is full rank.
Step 3 is specific as follows:
The acoustic pressure vector sum vibration velocity vector in matrix R array manifold is extracted using transition matrix J, obtains one newly
MatrixIt can be expressed as
In formula,J=[J1 J2 J3 J4],eiBe i-th of component be 1 other be all zero 4 (M=Lmax+1)×1
Unit vector；
Segmentation indicates the array manifold matrix after conversionIt can be segmented and be write as:
In formula,
Thus A is obtainedjAnd A4Relationship:
Aj=A4Γj, j=1,2,3 (10)
In formula, Γ1=diag { α1,α2,…αK, Γ2=diag { β1,β2,…βK, Γ3=diag { γ1,γ2,…
γK, three matrixes are all K × K diagonal matrix；
Pass through estimated matrix ГjThe value of middle element obtains the orientation of i-th of sound source
Step 4 is specific as follows:
MatrixThe corresponding characteristic vector of K maximal eigenvector and the array manifold vector of K sound source be linearly to close
System, it is hereby achieved that:
In formula
Uj=AjT, j=1,2,3,4 (14)
From U4=A4T can derive A4=U4T-1.By U1=A1T=A4Γ1The available U of T1=U4T-1Γ1T, similarly
It is available, U2=U4T-1Γ2T, U3=U4T-1Γ3T；
We define
Λj=T-1ΓjT, j=1,2,3 (15)
Then ΛjCharacteristic value be matrix ΓiDiagonal element.So structural matrix ΛiEstimated value, calculate its feature
Value, so that it may obtain the orientation of signalFormula (14) can be write as
Uj=U4Λj (16)
It can be released from above formulaMatrixThe corresponding feature vector of K maximum eigenvalue to constitute signal subspace special
Levy vector；
Assuming that UjAnd ΛjEstimated value be respectivelyWithSignal subspace is estimated using least square method:
It obtainsEstimated value are as follows:
Step 5 specifically:
First acquireCharacteristic value in it is available
It enables:
Obtain azimuth and the pitch angle of each sound source:
The utility model has the advantages that compared with prior art, the present invention having the advantage that
(1) present invention uses linear vector hydrophone array, compares common L-type array, and array structure is more easy.
(2) present invention is subtracted while retaining larger array aperture using the submatrix extracting method decorrelation LMS not overlapped
Few influence of the coherent sound sources to location estimation.
(3) present invention makes full use of the array manifold structure and its invariable rotary characteristic of vector hydrophone, it is ensured that positioning is estimated
It is more stable to count effect.
(4) case study on implementation of the invention shows that the present invention is more preferable than conventional method locating effect.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Fig. 2 is array junctions composition of the invention.
Fig. 3 is coherent sound sources positioning result analogous diagram of the present invention in three different directions.
Fig. 4 is the relativity of the present invention with PM, ESPRIT-AMM method difference SNR and RMSE.
Fig. 5 is the relativity of the present invention with PM, ESPRIT-AMM method difference number of snapshots and RMSE.
Fig. 6 is the relationship of present invention difference SNR and RMSE in different array numbers.
Specific embodiment
The present invention is further explained with reference to the accompanying drawings and examples.
As shown in Figure 1, the present invention the following steps are included:
Step 1: acquiring K sound-source signal using M member vector hydrophone arrays, establish vector hydrophone arrays and receive data
Model；
Assuming that the narrowband plane wave signal s that K wavelength is λi(t), i=1,2 ..., K is incident on M member vector water from far field
Listen on device array, and in sound field between each noise, it is irrelevant between noise and signal, as shown in Figure 2.There is L in K signalmax
A coherent signal, if the pitch angle of signal isAzimuth is φi, i=1,2 ..., K, then vector hydrophone
Array manifold vector be
In formula,
The reception data model of each vector hydrophone are as follows:
In formula,Indicate the array manifold vector of m-th of array element, nm(t) noise that m-th of array element receives, m are indicated
=1,2 ..., M, t=1,2 ..., N, ψi=(2 π d/ λ) γi, i=1,2 ... K, wherein M indicates that array number, N indicate signal
Number of snapshots, K indicate sound source number.
Step 2: acquiring covariance matrix according to signal is received, carried out using the method that subarray extracts data is not overlapped
Signal decorrelation LMS；
Array received signal can be obtained by formula (2):
X (t)=As (t)+n (t) t=1,2 ..., N (3)
In formula,
The correlation matrix of array received signal are as follows:
In formula,Indicate the correlation matrix of signal；
In order to which by coherent signal decorrelation LMS, correlation matrix is first divided into LmaxA submatrix, the dimension of each submatrix are 4
(M-Lmax+1)×4M.First of submatrix is labeled as Rl, l=1,2 ... lmax, by RxThe the 4th (l-1)+1 row to 4 (M-Lmax+ 1) row
Composition.By these submatrixs, we construct a new matrix R:
In formula, the dimension of matrix R is 4 (M-Lmax+1)×4MLmax。
It can be write as segmented version using formula (4) and formula (5) matrix R, as follows:
In formula,
Due to
So data covariance matrix R when group battle array array element number >=K after decorrelation LMS is full rank.
Step 3: using transition matrix, the acoustic pressure vector sum vibration velocity vector after extraction decorrelation LMS in array manifold；
The acoustic pressure vector sum vibration velocity vector in matrix R array manifold is extracted using transition matrix J, obtains one newly
MatrixIt can be expressed as
In formula,J=[J1 J2 J3 J4],eiBe i-th of component be 1 other be all zero 4 (M-Lmax+1)×1
Unit vector.Since each column of matrix J are orthogonal with other column, so the order of matrix J is equal to 4 (M-Lmax+ 1), according to formula (7)
Matrix can be immediately arrived atOrder be equal to K.By to matrixFeature decomposition, available K orthogonal vectors constitute letter
Work song space, i.e.,The linear space of column vector.
Array manifold matrixIt can be segmented and be write as
In formula,
It is hereby achieved that AjAnd A4Relationship:
Aj=A4Γj, j=1,2,3 (10)
In formula, Γ1=diag { α1,α2,…αK, Γ2=diag { β1,β2,…βK, Γ3=diag { γ1,γ2,…
γK, three matrixes are all K × K diagonal matrix.Formula (10) shows each matrix to (Aj,A4) relationship, therefore, Ke Yitong
Cross estimated matrix ΓiThe value of middle element obtains the orientation of i-th of sound source
Step 4: the signal subspace of covariance matrix after decorrelation LMS is estimated using least square method；
Two orthogonal subspaces can be divided into using feature decomposition, one is K dimensional signal subspace, maximum comprising K
The corresponding feature vector of characteristic value；The other is [4 (M-Lmax+ 1)-K] dimension noise subspace.By to matrixFeature point
Solution, obtains noise subspace and signal subspace.If signal subspace is Us, then have
So signal subspace UsIt can be expressed as
T is the nonsingular matrix of K × K.From formula (12) as can be seen that matrixThe corresponding spy of K maximal eigenvector
The array manifold vector for levying vector and K sound source is linear relationship.It is available by formula (9) and (12)
In formula
Uj=AjT, j=1,2,3,4 (14)
From U4=A4T can derive A4=U4T-1.By U1=A1T=A4Γ1The available U of T1=U4T-1Γ1T, similarly
It is available, U2=U4T-1Γ2T, U3=U4T-1Γ3T。
We define
Λj=T-1ΓjT, j=1,2,3 (15)
Then ΛjCharacteristic value be matrix ΓiDiagonal element.So structural matrix ΛiEstimated value, calculate its feature
Value, so that it may obtain the orientation of signalFormula (14) can be write as
Uj=U4Λj (16)
It can be released from above formulaMatrixThe corresponding feature vector of K maximum eigenvalue to constitute signal subspace special
Levy vector.Assuming that UjAnd ΛjEstimated value be respectivelyWithThe U estimated by signal subspace feature vector4Not
Accurately, so being estimated using LS-ESPRIT formula (16)
It obtainsEstimated value be
Step 5: being solved to obtain the two-dimentional DOA of coherent sound sources according to vector hydrophone arrays flow pattern invariable rotary characteristic
Estimation.
FromCharacteristic value in it is availableTherefore, αi,βi,γiValue can be with table
It is shown as
Finally, obtaining azimuth and the pitch angle of each sound source
Result of implementation, as seen in figures 3-6:
Parameter used in following embodiment is as follows:
Fig. 3 is the coherent sound sources positioning result analogous diagram of three different directions.Vector hydrophone arrays number is 10, array element
Between be divided into λ/2 d=, wherein λ indicate sound wave wavelength, 3 coherents respectively from 100 emulation in SNR=0dB and SNR=20dB respectively
As a result.
Fig. 4 is the relativity of the present invention with PM, ESPRIT-AMM method difference SNR and RMSE.Vector hydrophone arrays
Number is 10, and λ/2 d=are divided between array element, and wherein λ indicates the wavelength of sound wave.The root-mean-square error of angle estimation is
J indicates that Monte Carlo experiment number, K indicate the number of sound source,WithIndicate k-th target in testing for j time
Angle-of- arrival estimation value.Taking number of snapshots is 500, and SNR ranges are 0dB~20dB, when Monte Carlo experiment number is 200 times, respectively
The mean square error of a method positioning result, the location estimation performance of verification method.
Fig. 5 is the relativity of the present invention with PM, ESPRIT-AMM method difference number of snapshots and RMSE.Vector- sensor linear array
Column number is 10, is divided into λ/2 d=between array element, and wherein λ indicates the wavelength of sound wave.Take signal-to-noise ratio be 20dB, number of snapshots be 100~
1000, when Monte Carlo experiment number is 200 times, the mean square error of each method positioning result verifies the positioning of each method
Estimate performance.
Fig. 6 is the relationship of present invention difference SNR and RMSE in different array numbers.Vector hydrophone arrays number is 10,
λ/2 d=are divided between array element, wherein λ indicates the wavelength of sound wave.It is 500 in number of snapshots, signal-to-noise ratio 20dB, Monte Carlo experiment
Number is 200 times, when array number is respectively 5,8,10, carries out simulation comparison to two coherent sound sources positioning results.
Claims (6)
1. a kind of coherent sound sources localization method based on vector hydrophone, characterized by the following steps:
Step 1: acquiring K sound-source signal using M member vector hydrophone arrays, establish vector hydrophone arrays and receive data mould
Type；
Step 2: acquiring covariance matrix according to signal is received, carry out signal using the method that subarray extracts data is not overlapped
Decorrelation LMS；
Step 3: using transformation matrix, the acoustic pressure vector sum vibration velocity vector after extraction decorrelation LMS in array manifold；
Step 4: the signal subspace of covariance matrix after decorrelation LMS is estimated using least square method；
Step 5: being estimated according to the two-dimentional DOA that vector hydrophone arrays flow pattern invariable rotary characteristic is solved to obtain coherent sound sources
Meter.
2. the according to claim a kind of coherent sound sources localization method based on vector hydrophone, which is characterized in that step 1
Middle vector hydrophone arrays receive data module and establish specific steps are as follows:
Assuming that the narrowband plane wave signal s that K wavelength is λi(t), i=1,2 ..., K is incident on M member vector hydrophone from far field
On array, and in sound field between each noise, it is irrelevant between noise and signal, have L in K signalmaxA coherent sound sources, if
The pitch angle of sound source isAzimuth is φi, i=1,2 ..., K, then the array manifold of vector hydrophone is sweared
Amount are as follows:
In formula,
Each vector hydrophone arrays receive data model are as follows:
In formula,Indicate the array manifold vector of m-th of array element, nm(t) noise that m-th of array element of expression receives, m=1,
2 ..., M, t=1,2 ..., N, ψi=(2 π d/ λ) γi, i=1,2 ... K, wherein M indicates that array number, N indicate the fast of signal
Umber of beats, K indicate sound source number.
3. a kind of coherent sound sources localization method based on vector hydrophone according to claim 2, which is characterized in that described
Step 2 is specific as follows:
Array received signal can be obtained by formula (2):
X (t)=As (t)+n (t), t=1,2 ..., N (3)
In formula,
The correlation matrix of array received signal are as follows:
In formula,Indicate the correlation matrix of signal；
Correlation matrix is first divided into LmaxA submatrix, LmaxIndicate that the number of coherent sound sources, the dimension of each submatrix are 4 (M-Lmax+
1) × 4M, first of submatrix are labeled as Rl, l=1,2 ... lmax, by RxThe the 4th (l-1)+1 row to 4 (M-Lmax+ 1) row composition leads to
These submatrixs are crossed, a new matrix R is constructed:
In formula, the dimension of matrix R is 4 (M-Lmax+1)×4MLmax；
According to the correlation matrix of array received signal:
Bring matrix R into, available:
In formula,
Due to
So data covariance matrix R when group battle array array element number >=K after decorrelation LMS is full rank.
4. a kind of coherent sound sources localization method based on vector hydrophone according to claim 3, which is characterized in that described
Step 3 is specific as follows:
The acoustic pressure vector sum vibration velocity vector in matrix R array manifold is extracted using transition matrix J, obtains a new matrixIt can be expressed as
In formula,ei
Be i-th of component be 1 other be all zero 4 (M-Lmax+ 1) × 1 unit vector；
Segmentation indicates the array manifold matrix after conversionIt can be segmented and be write as:
In formula,
It is hereby achieved that AjAnd A4Relationship:
Aj=A4Γj, j=1,2,3 (10)
In formula, Γ1=diag { α1,α2,…αK, Γ2=diag { β1,β2,…βK, Γ3=diag { γ1,γ2,…γK, three
A matrix is all K × K diagonal matrix；
Pass through estimated matrix ΓjThe value of middle element obtains the orientation of i-th of sound source
5. a kind of coherent sound sources localization method based on vector hydrophone according to claim 4, which is characterized in that described
Step 4 is specific as follows:
MatrixThe corresponding characteristic vector of K maximal eigenvector and the array manifold vector of K sound source be linear relationship, by
This is available:
In formula
Uj=AjT, j=1,2,3,4 (14)
From U4=A4T can derive A4=U4T-1.By U1=A1T=A4Γ1The available U of T1=U4T-1Γ1T, similarly can be with
It obtains, U2=U4T-1Γ2T, U3=U4T-1Γ3T；
We define
Λj=T-1ΓjT, j=1,2,3 (15)
Then ΛjCharacteristic value be matrix ΓiDiagonal element.So structural matrix ΛiEstimated value, calculate its characteristic value, just
The orientation of available signalFormula (14) can be write as
Uj=U4Λj (16)
It can be released from above formulaMatrixThe corresponding feature vector of K maximum eigenvalue constitute signal subspace feature to
Amount；
Assuming that UjAnd ΛjEstimated value be respectively UjAnd Λj, signal subspace is estimated using least square method:
It obtainsEstimated value are as follows:
6. a kind of coherent sound sources localization method based on vector hydrophone according to claim 5, it is characterised in that: described
Step 5 specifically:
First acquireCharacteristic value in it is available
It enables:
Obtain azimuth and the pitch angle of each sound source:
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2018105736344 | 2018-06-06 | ||
| CN201810573634 | 2018-06-06 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109696657A true CN109696657A (en) | 2019-04-30 |
| CN109696657B CN109696657B (en) | 2022-10-14 |
Family
ID=66234969
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910145175.4A Active CN109696657B (en) | 2018-06-06 | 2019-02-27 | Coherent sound source positioning method based on vector hydrophone |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109696657B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112269163A (en) * | 2020-09-30 | 2021-01-26 | 黑龙江工程学院 | Underwater sound source azimuth depth cooperative tracking method based on single three-dimensional vector hydrophone at bottom of seat |
| CN113138366A (en) * | 2020-01-17 | 2021-07-20 | 中国科学院声学研究所 | Single-vector hydrophone orientation estimation method based on deep migration learning |
| CN113325365A (en) * | 2021-05-18 | 2021-08-31 | 哈尔滨工程大学 | Quaternion-based coherent signal two-dimensional DOA estimation method |
| CN113534041A (en) * | 2021-05-31 | 2021-10-22 | 中国船舶重工集团公司第七一五研究所 | High-resolution DOA estimation method of single-vector hydrophone based on ECKART filter |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103605108A (en) * | 2013-07-29 | 2014-02-26 | 哈尔滨工程大学 | High-precision remote direction estimation method of acoustic vector array |
| CN105005038A (en) * | 2015-06-18 | 2015-10-28 | 许昌学院 | Improved acoustic vector array coherent source DOA estimation algorithm |
| CN105301563A (en) * | 2015-11-10 | 2016-02-03 | 南京信息工程大学 | Double sound source localization method based on consistent focusing transform least square method |
| CN106680762A (en) * | 2016-12-15 | 2017-05-17 | 哈尔滨工程大学 | Sound vector array orientation estimation method based on cross covariance sparse reconstruction |
| CN106872934A (en) * | 2017-02-22 | 2017-06-20 | 西安电子科技大学 | L-type Electromagnetic Vector Sensor Array decorrelation LMS ESPRIT method for parameter estimation |
| CN106997037A (en) * | 2017-02-22 | 2017-08-01 | 西安电子科技大学 | Acoustic vector-sensor array column space rotates decorrelation LMS angle-of- arrival estimation method |
-
2019
- 2019-02-27 CN CN201910145175.4A patent/CN109696657B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103605108A (en) * | 2013-07-29 | 2014-02-26 | 哈尔滨工程大学 | High-precision remote direction estimation method of acoustic vector array |
| CN105005038A (en) * | 2015-06-18 | 2015-10-28 | 许昌学院 | Improved acoustic vector array coherent source DOA estimation algorithm |
| CN105301563A (en) * | 2015-11-10 | 2016-02-03 | 南京信息工程大学 | Double sound source localization method based on consistent focusing transform least square method |
| CN106680762A (en) * | 2016-12-15 | 2017-05-17 | 哈尔滨工程大学 | Sound vector array orientation estimation method based on cross covariance sparse reconstruction |
| CN106872934A (en) * | 2017-02-22 | 2017-06-20 | 西安电子科技大学 | L-type Electromagnetic Vector Sensor Array decorrelation LMS ESPRIT method for parameter estimation |
| CN106997037A (en) * | 2017-02-22 | 2017-08-01 | 西安电子科技大学 | Acoustic vector-sensor array column space rotates decorrelation LMS angle-of- arrival estimation method |
Non-Patent Citations (3)
| Title |
|---|
| ELIO: "Space Time MUSIC:Consistent Signal Subspace Estimation for Wideband Sensor Arrays", 《IEEE TRANSACTIONS ON SIGNAL PROCESSING》 * |
| MATS VIBERG: "sensor array processing based on subspace fitting", <IEEE TRANSACTIONS ON SIGNAL PROCESSING> * |
| 郭业才等: "自适应多通道双L 型麦克风阵列声源定位算法", 《系统仿真学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113138366A (en) * | 2020-01-17 | 2021-07-20 | 中国科学院声学研究所 | Single-vector hydrophone orientation estimation method based on deep migration learning |
| CN113138366B (en) * | 2020-01-17 | 2022-12-06 | 中国科学院声学研究所 | Single-vector hydrophone orientation estimation method based on deep migration learning |
| CN112269163A (en) * | 2020-09-30 | 2021-01-26 | 黑龙江工程学院 | Underwater sound source azimuth depth cooperative tracking method based on single three-dimensional vector hydrophone at bottom of seat |
| CN113325365A (en) * | 2021-05-18 | 2021-08-31 | 哈尔滨工程大学 | Quaternion-based coherent signal two-dimensional DOA estimation method |
| CN113534041A (en) * | 2021-05-31 | 2021-10-22 | 中国船舶重工集团公司第七一五研究所 | High-resolution DOA estimation method of single-vector hydrophone based on ECKART filter |
| CN113534041B (en) * | 2021-05-31 | 2024-04-09 | 中国船舶重工集团公司第七一五研究所 | Single-vector hydrophone high-resolution DOA estimation method based on ECKART filter |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109696657B (en) | 2022-10-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111123192B (en) | Two-dimensional DOA positioning method based on circular array and virtual extension | |
| CN109696657A (en) | A kind of coherent sound sources localization method based on vector hydrophone | |
| CN108375763B (en) | Frequency division positioning method applied to multi-sound-source environment | |
| Famoriji et al. | An intelligent deep learning-based direction-of-arrival estimation scheme using spherical antenna array with unknown mutual coupling | |
| CN106483493B (en) | A kind of sparse double parallel linear array and estimating two-dimensional direction-of-arrival method | |
| CN108663653B (en) | Direction-of-arrival estimation method based on L-shaped electromagnetic vector sensor array | |
| CN112130111B (en) | Single-snapshot two-dimensional DOA estimation method in large-scale uniform cross array | |
| Zhao et al. | Open-lake experimental investigation of azimuth angle estimation using a single acoustic vector sensor | |
| Sun et al. | Improving the performance of a vector sensor line array by deconvolution | |
| CN106249225B (en) | Sparse circle acoustic vector-sensor array column quaternary number ESPRIT method for parameter estimation | |
| CN103364772A (en) | Target low elevation estimation method based on real number field generalized multiple-signal sorting algorithm | |
| CN112285647B (en) | Signal azimuth high-resolution estimation method based on sparse representation and reconstruction | |
| CN104502904A (en) | Torpedo homing beam sharpening method | |
| CN106249196A (en) | Three-component acoustic vector sensors thinned array quaternary number ambiguity solution method | |
| CN108469599B (en) | Acoustic vector sensor amplitude weighting MUSIC direction-finding method | |
| CN108318855A (en) | Near-field and far-field mixed signal source positioning method based on uniform circular array | |
| CN106970348B (en) | Electromagnetic Vector Sensor Array decorrelation LMS two dimension MUSIC method for parameter estimation | |
| CN109541573A (en) | A kind of element position calibration method being bent hydrophone array | |
| He et al. | Particle-velocity-field difference smoothing for coherent source localization in spatially nonuniform noise | |
| CN112305497B (en) | DOA estimation direction finding fuzzy elimination method for near field microphone array | |
| Lu et al. | Robust novel EM-based direction-of-arrival estimation technique for wideband source signals | |
| Liu et al. | Research on high resolution algorithm of sound source localization based on microphone array | |
| Lai et al. | DOA estimation using vector sensor arrays | |
| CN109683128A (en) | Single snap direction-finding method under impulsive noise environment | |
| CN115792792B (en) | Direction finding method and system based on covariance matrix iterative estimation under extremely low sample |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |