CN106680762A - Sound vector array orientation estimation method based on cross covariance sparse reconstruction - Google Patents
Sound vector array orientation estimation method based on cross covariance sparse reconstruction Download PDFInfo
- Publication number
- CN106680762A CN106680762A CN201611158285.7A CN201611158285A CN106680762A CN 106680762 A CN106680762 A CN 106680762A CN 201611158285 A CN201611158285 A CN 201611158285A CN 106680762 A CN106680762 A CN 106680762A
- Authority
- CN
- China
- Prior art keywords
- sound
- signal
- noise
- vibration velocity
- vector
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 239000013598 vector Substances 0.000 title claims abstract description 59
- 238000000034 method Methods 0.000 title claims abstract description 32
- 239000011159 matrix material Substances 0.000 claims abstract description 23
- 238000001228 spectrum Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 abstract description 9
- 230000008901 benefit Effects 0.000 abstract description 7
- 230000002401 inhibitory effect Effects 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 description 6
- 230000008859 change Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000004807 localization Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000001427 coherent effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000002708 enhancing effect Effects 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000003672 processing method Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 230000002337 anti-port Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- JEIPFZHSYJVQDO-UHFFFAOYSA-N ferric oxide Chemical compound O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000006798 recombination Effects 0.000 description 1
- 238000005215 recombination Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000017105 transposition Effects 0.000 description 1
- 230000004304 visual acuity Effects 0.000 description 1
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
- G01S3/80—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 ultrasonic, sonic or infrasonic waves
- G01S3/802—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)
- Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
- Circuit For Audible Band Transducer (AREA)
Abstract
The invention relates to a sound vector array orientation estimation method based on cross covariance sparse reconstruction. The method comprises steps of (a) acquiring sound vector array reception data, and generating vector array airspace sparse expression related to sound source signals in interested space THETA; (b) in each azimuth angle thetak, generating M*M-dimensional sound pressure-vibration velocity cross covariance matrix R(p+vc)(thetak); and (c) sufficiently using the irrelevance between the signals and the noise and independence between signals and between noise in the sound pressure-vibration velocity united processing, reducing the phi(vc)(thetak) in the cross covariance matrix into a K*K-dimensional diagonal matrix. According to the invention, a new sound source signal sparse expression form is constructed which is different from former forms where the vibration velocity in the vector array is only taken as scalar quantity which is the same as a sound pressure channel to be processed, and the advantages of the pressure-vibration velocity united processing are fully used, so the noise inhibiting ability of the array signal processing is greatly improved.
Description
Technical field
The present invention relates to a kind of acoustic vector sensor array direction estimation method based on the sparse reconstruct of cross covariance.
Background technology
The sharpest edges of acoustic vector array signal process technique are by the anti-noise ability and array manifold of vector sensor
Resolving power performance combines, therefore more single acoustic pressure battle array treatment estimates performance with orientation higher.But, it is existing
Acoustic vector sensor array signal processing technology be substantially based on Nehorai theoretical frames, its essence is by the vibration velocity of acoustic vector sensor array letter
Breath is processed as with acoustic pressure identical independence array element information, does not make full use of " acoustic pressure-vibration velocity " united information to process, because
This signal-noise ratio threshold is higher.In fact, in acoustic far field coherent source (signal source of limited dimension) signal acoustic pressure and vibration velocity
It is relevant, and the acoustic pressure of noise is incoherent with vibration velocity in isotropic noise, therefore based on acoustic pressure vibration velocity joint
The information processing technology will necessarily have stronger isotropic noise rejection ability.Based on this, Bai Xingyu etc. proposes acoustic pressure and shakes
Fast united signal processing method, the anti-noise ability of the high resolution of subspace class method and vector hydrophone is organically combined
Come, realize long-range high resolution DOA estimation (1 Bai Xingyu, based on the acoustic vector direction finding technology of united information treatment, the Doctor of engineering
Paper, Harbin Engineering University, 2006).Its core technology is that acoustic pressure vibration velocity Cross-covariance is carried out into Subspace Decomposition, from
And subspace class high-resolution space spectral method is expanded and is applied in vector array signals processing.
Angeliki Xenaki et al. propose compressed sensing Wave beam forming concept, using the space of array received data
It is openness to carry out sound source reconstruct (2 Angeliki Xenaki, Peter Gerstoft, Klaus
Mosegaard.Compressive beamforming.J.Acoust.Soc.Am.2014,136(1):260-271).Utilizing
In the sparse research reconstructed for target Bearing Estimation of array covariance matrix, Siyang Zhong et al. are proposed based on acoustic pressure
Compressed sensing Beamforming Method (3 Siyang Zhong, the Qingkai Wei, Xun of battle array data covariance matrix
Huang.Compressive sensingbeamforming based on covariance for acoustic imaging
With noisy measurements, J.Acoust.Soc.Am.2013,134 (5), 445-451), sound source waveform is estimated to ask
Topic is converted into sound power of a source estimation problem.Ning Chu et al. propose sound power of a source and location estimation side based on sparse reconstruct
Method (4 Ning Chu, Jos é Picheral, Ali Mohammad-djafari, Nicolas Gac.A robust super-
resolution approach with sparsity constraint in acoustic imaging).But above method
The separate prior information of sound source is with only, its shortcoming is identical with traditional acoustic pressure battle array treatment, it is impossible to reduce signal to noise ratio door
Limit.Shi Jie et al. proposes the vector array based on compressed sensing and focuses on localization method (clean when 5, Yang Desen, Shi Shengguo, Hu Bo, Zhu
In sharp be based on compressed sensing vector array focus on localization method Acta Physica Sinicas .2016,65 (2):024302,1-11) it is, fully sharp
With the spatial sparsity of sound source, the sparse signal model of vector array near field positioning is constructed, solved using l1 norms regularization method, it is real
The small accurate auditory localization taken soon is showed.Although the technology is processed using vector array, overcome coherent sound sources and differentiate difficult, tribute
Offer evaluation inaccurate, algorithm performance is degenerated seriously in practical application, and result of calculation relies on big snap and carries out data covariance estimation,
Algorithm iteration computational processing is huge to wait a series of complex problem, but it is still that acoustic pressure vibration velocity is considered as at autonomous channel
Reason, it is impossible to give full play to the advantage of acoustic pressure-vibration velocity Combined Treatment.
Inspired by above Physical Mechanism and processing method, this patent has paid close attention to acoustic pressure vibration velocity Cross-covariance
Spatial sparsity, invented a kind of compressed sensing Beamforming Method based on Cross-covariance, the method can be obtained simultaneously
Obtain sound power of a source and orientation estimated result.
The content of the invention
A kind of enhancing noise is provided present invention aim at the advantage of vector array acoustic pressure-vibration velocity Combined Treatment is made full use of
Rejection ability, realizes to sound source power in the reconstruct of evacuated space, can simultaneously obtain the base of sound power of a source and orientation estimated result
In the acoustic vector sensor array direction estimation method of the sparse reconstruct of cross covariance.
The object of the present invention is achieved like this：
A () obtains acoustic vector sensor array and receives data, the vector array on sound-source signal is generated in space Θ interested empty
Domain rarefaction is represented.
B () is in each azimuth angle thetakOn, generation M × M dimensions acoustic pressure-vibration velocity Cross-covariance R(p+vc)(θk)。
C () is made full use of in acoustic pressure-vibration velocity Combined Treatment, irrelevance and signal and letter between signal and noise
Between number, the independence between noise and noise, by the Φ in Cross-covariance(vc)(θk) turn to K × K dimension diagonal matrix.
D () is to M × M dimension Cross-covariances R(p+vc)(θk) deformed, generate new M2× 1 dimension cross covariance column vector
E () is obtained on signal powerRarefaction representation.
F () is using the vector array cross covariance column vector for having obtainedWith super complete G Φ(vc)(θk) weigh
Structure sparse signal matrix
The whole azimuth angle thetas of (g) traversalk(k=1,2 ..., K), repeat step (b) to (f) obtains each angle, θkOn
Sound-source signal power estimated result.
H () draws orientation spectrogram according to all azimuthal sound-source signal power estimation values.
I () determines sound source incoming wave orientation and power relative size simultaneously by the spectrum peak position and intensity of spatial spectrum.
The beneficial effects of the invention are as follows：
1) new sound-source signal rarefaction representation form is constructed, this form vibration velocity by vector array different from the past is led to
Road is only regarded solely as harmony pressure passageway identical scalar and is processed, but takes full advantage of the excellent of acoustic pressure-vibration velocity Combined Treatment
Gesture, greatly improves the noise inhibiting ability of Array Signal Processing.
2) the spatial domain compression property of the sound source information received using vector array, makes Array Signal Processing no longer be directly to obtain
Orientation estimated result (can only obtain azimuthal estimate) is obtained, and can be the joint for obtaining sound bearing and power simultaneously
Estimated result.
3) vector array is taken full advantage of to port and starboard ambiguity resolution capability, is suppressed completely in the spatial spectrum after sparse reconstruct
Fuzzy message.
4) high space high-resolution performance is shown naturally in spatial spectrum, only sound source is cashed out in orientation where sound source
Performance number, and completely without power leakage on adjacent orientation.
5) preferable orientation estimation effect can be still obtained under fewer snapshots.
Brief description of the drawings
Fig. 1 is vector array sparse signal schematic diagram；
Fig. 2 is orientation spectrogram comparing result；
Fig. 3 is change curve of the power estimation error with signal to noise ratio；
Fig. 4 is change curve of the orientation evaluated error with signal to noise ratio.
Specific embodiment
The present invention is described further below in conjunction with the accompanying drawings.
The method makes full use of vector array acoustic pressure vibration velocity Combined Treatment advantage, sparse on dimensional orientation using sound source
Property, by constructing the rarefaction representation of novel acoustic vector sensor array cross-covariance vector, performance is estimated in effectively enhancing acoustic vector sensor array orientation.
A () obtains acoustic vector sensor array and receives data, the vector array on sound-source signal is generated in space Θ interested empty
Domain rarefaction is represented.
Consider that single far field single-frequency point sound-source signal is incided in the uniform two dimension acoustic vector linear array of M units, array element spacing d mono-
As be chosen for sound wave half-wavelength (Fig. 1 show vector array sparse signal schematic diagram).The sparse letter of vector array how soon generation is taken
Number matrix form it is as follows：
Y(p)=AS+N(p) (1a)
Y(vx)=A Φ(vx)S+N(vx) (1b)
Y(vy)=A Φ(vy)S+N(vy) (1c)
Above formula is deployable to be expressed as：
Wherein, S is that K × L ties up source signal matrix, and K is the whole azimuth numbers in the Θ of space, and L is fast umber of beats, skIt is right
Should be in 1 × L dimensional signal vectors of kth arrival bearing；A is conventional linear array steering vector matrix, wherein element(m=1,2 ..., M, k=1,2 ..., K) is to reach the corresponding to the signal on kth arrival bearing
The steering vector element of m array elements, d is array element spacing, and f is frequency of sound wave, and c is the velocity of sound in water；Subscript ()(p)、(.)(vx)With
(.)(vy)Represent respectively corresponding to acoustic pressure, x to vibration velocity and y to the variable of vibration velocity, vector or matrix.Φ(vx)And Φ(vy)Respectively
Corresponding to x K × K dimension diagonal matrix are to vibration velocity and y to the unit vector matrix of vibration velocity channel.Consider ambient noise interference,
N(p)、N(vx)And N(vy)Respectively M × L dimensions acoustic pressure, x are to vibration velocity and y to vibration velocity channel noise matrix.WithPoint
Not Wei the acoustic pressure of m hydrophones, x tie up noise data vector to 1 × L of vibration velocity channel to vibration velocity and y.Y(p)、Y(vx)And Y(vy)Point
Not Wei M × L dimension acoustic pressure, x to vibration velocity and y to vibration velocity data matrix,WithRespectively the acoustic pressure of m array elements,
X is to vibration velocity and y to vibration velocity 1 × L dimension data vectors.
The physical significance of the sparse signal model is as shown in Figure 1.Director space domain Θ where sound source is divided between K etc.
Every uniform azimuth constitute { θ1,θ2,...,θk,...,θK, then each of which dimensional orientation θkWith a pair of sound bearing 1
Should, simultaneously as only existing N number of real sources (N ＜ ＜ K) in space.On Θ, there is signal at only N number of azimuth, that is, construct
There was only the Wave data .y of wherein corresponding N row nonzero elements in the K × L dimensional signal matrixes S for going out(p)、y(vx)And y(vy)Essence
On be S rarefaction representation, the reconstruct of signal S is realized following with the united new method of acoustic pressure-vibration velocity.
B () is in each azimuth angle thetakOn, generation M × M dimensions acoustic pressure-vibration velocity Cross-covariance R(p+vc)(θk)。
In θkOn, construct vibration velocity composite signal Y(vc)(θk)=cos θkY(vx)+sinθkY(vy).And then generate M × M dimension sound
Pressure-vibration velocity Cross-covariance：
R(p+vc)(θk)=E { Y(p)(Y(vc)(θk))*}=E { Y (cos θkY(vx)+sinθkY(vy))*} (3)
Wherein, E { } is mathematic expectaion,*It is conjugate transposition operator.
C () is made full use of in acoustic pressure-vibration velocity Combined Treatment, irrelevance and signal and letter between signal and noise
Between number, the independence between noise and noise, by the Φ in Cross-covariance(vc)(θk) turn to K × K dimension diagonal matrix.
Using the irrelevance between signal and noise, Operation Nature meets(i
=1,2 ..., K, j=1,2 ..., K), and between signal and signal, the independence between noise and noise
With(now requiring i ≠ j), then can be by the Φ in Cross-covariance(vc)(θk) K × K dimensions are turned to angular moment
Battle array.
Have：
D () is to M × M dimension Cross-covariances R(p+vc)(θk) deformed, generate new M2× 1 dimension cross covariance column vector
The Cross-covariance R that further M × M can be tieed up(p+vc)Shown recombination method, generates M according to the following formula2×1
The cross covariance column vector of dimension
E () is obtained on signal powerRarefaction representation.
Wherein, G is M2× K ties up restructuring transformation matrix.For reconstruction signal matrix is tieed up in K × 1, its physical significance represents sparse
The power of signal.It is M2× 1 dimension restructuring noise matrix.
F () is using the vector array cross covariance column vector for having obtainedWith super complete G Φ(vc)(θk) weigh
Structure sparse signal matrix
Sparse signal processing procedure is expressed as following optimization：
Wherein ε is noise constraints parameter, | | | |1Represent l1Norm, | | | |2Represent l1Norm, min is represented and is taken minimum value.
S.t. implication is to make the l of left side formula on the right side of meeting under formula constraints1Norm minimum.Above formula is a underdetermined equation, can
Recover source signal power with from vector array cross covariance column vector, so as to obtain indirectly in θkSignal power in orientation is estimated
As a result.This sparse linear regression problem can carry out regularization to second order error by using low-order mode, and using CVX tool boxes
Effectively solve the optimization problem.
The whole azimuth angle thetas of (g) traversalk(k=1,2 ..., K), repeat step (b) to (f) obtains each angle, θkOn
Sound-source signal power estimated result.
H () draws orientation spectrogram according to all azimuthal sound-source signal power estimation values.
I () determines sound source incoming wave orientation and power relative size simultaneously by the spectrum peak position and intensity of spatial spectrum.
The specific embodiment to content of the invention each several part is illustrated above.New method makes full use of vector array sound
The advantage of pressure-vibration velocity Combined Treatment, strengthen noise inhibiting ability, realize to sound source power evacuated space reconstruct, can be simultaneously
Obtain sound power of a source and orientation estimated result.Simulation example is analyzed below.
Example one：Orientation spectrogram comparative analysis
Instance parameter sets as follows：Single single-frequency frequency of source is 1kHz, 45 ° of its incident orientation angle.For ease of analysis, will
Sound power of a source value is 1.Vector array element number of array 7, array element spacing is chosen for the half-wavelength 0.75m of incident acoustic wave.System is adopted
Sample rate is 10kHz.The velocity of sound is taken as 1480m/s in water.Azimuth sweep scope is 0 ° to 360 °, 1 ° of scanning step.Will in emulation
The sparse reconstruct of auto-covariance matrix is used in new method and document [3] that the sparse reconstruct of Cross-covariance is utilized in this patent
Method is analyzed.The orientation spectrogram comparing result of two methods is as shown in Fig. 2 (a) represents document [3] method in figure
Result of calculation, (b) represents result of calculation of the invention, and the abscissa in figure represents azimuth, and ordinate represents estimation power.
Example two：Amplitude Estimation and orientation evaluated error analysis under different signal to noise ratios
Simulation parameter is constant, amplitude Estimation and orientation the evaluated error analysis under different signal to noise ratios, signal to noise ratio excursion
It is -5dB to 20dB.Fig. 3 show change curve of the power estimation error with signal to noise ratio.Fig. 4 show orientation evaluated error with
The change curve of signal to noise ratio.
Simulation result in Comprehensive example can be seen that the excellent of the abundant acoustic pressure vibration velocity Combined Treatment of new method in the present invention
Gesture, with anti-port and starboard ambiguity ability, the orientation especially when signal to noise ratio is relatively low estimates that performance has significantly raising.The method
Due to being reconstructed to sound power of a source, can be significantly improved background fluctuations rejection ability.
Claims (1)
1. a kind of acoustic vector sensor array direction estimation method based on the sparse reconstruct of cross covariance, it is characterised in that the method is included such as
Lower step：
A () obtains acoustic vector sensor array and receives data, the vector array spatial domain on sound-source signal is generated in space Θ interested dilute
Thinization is represented；
B () is in each azimuth angle thetakOn, generation M × M dimensions acoustic pressure-vibration velocity Cross-covariance R(p+vc)(θk)；
C () is made full use of in acoustic pressure-vibration velocity Combined Treatment, the irrelevance and signal and signal between signal and noise it
Between, the independence between noise and noise, by the Φ in Cross-covariance(vc)(θk) turn to K × K dimension diagonal matrix；
D () is to M × M dimension Cross-covariances R(p+vc)(θk) deformed, generate new M2× 1 dimension cross covariance column vector
E () is obtained on signal powerRarefaction representation；
F () is using the vector array cross covariance column vector for having obtainedWith super complete G Φ(vc)(θk) dilute to reconstruct
Dredge signal matrix
The whole azimuth angle thetas of (g) traversalk(k=1,2 ..., K), repeat step (b) to (f) obtains each angle, θkOn sound
Source signal power estimated result；
H () draws orientation spectrogram according to all azimuthal sound-source signal power estimation values；
I () determines sound source incoming wave orientation and power relative size simultaneously by the spectrum peak position and intensity of spatial spectrum.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611158285.7A CN106680762B (en) | 2016-12-15 | 2016-12-15 | A kind of acoustic vector sensor array direction estimation method based on the sparse reconstruct of cross covariance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611158285.7A CN106680762B (en) | 2016-12-15 | 2016-12-15 | A kind of acoustic vector sensor array direction estimation method based on the sparse reconstruct of cross covariance |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106680762A true CN106680762A (en) | 2017-05-17 |
| CN106680762B CN106680762B (en) | 2019-11-26 |
Family
ID=58869351
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611158285.7A Active CN106680762B (en) | 2016-12-15 | 2016-12-15 | A kind of acoustic vector sensor array direction estimation method based on the sparse reconstruct of cross covariance |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106680762B (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107202975A (en) * | 2017-05-25 | 2017-09-26 | 哈尔滨工程大学 | A kind of a burst of first attitude error rectification method of two-dimensional vector |
| CN107728109A (en) * | 2017-09-18 | 2018-02-23 | 哈尔滨工程大学 | A kind of noncooperative target radiated noise measurement and positioning technology |
| CN107966677A (en) * | 2017-11-16 | 2018-04-27 | 黑龙江工程学院 | A kind of circle battle array mode domain direction estimation method based on space sparse constraint |
| CN108181611A (en) * | 2017-12-11 | 2018-06-19 | 东南大学 | Compressed sensing underwater multiple targets method based on subspace |
| CN109696657A (en) * | 2018-06-06 | 2019-04-30 | 南京信息工程大学 | A kind of coherent sound sources localization method based on vector hydrophone |
| CN109870671A (en) * | 2017-12-05 | 2019-06-11 | 常熟海量声学设备科技有限公司 | A kind of high-resolution efficient DOA algorithm for estimating of robustness |
| CN109884592A (en) * | 2019-03-04 | 2019-06-14 | 浙江大学 | A kind of auditory localization emulation mode towards low frequency Gaussian noise source |
| CN110082741A (en) * | 2019-03-14 | 2019-08-02 | 哈尔滨工程大学 | A kind of super-resolution DOA estimate algorithm based on pseudo- data reconstruction |
| CN110082712A (en) * | 2019-03-14 | 2019-08-02 | 哈尔滨工程大学 | A kind of acoustic vector circle battle array Coherent Targets direction estimation method |
| CN111175691A (en) * | 2019-11-29 | 2020-05-19 | 北京理工大学 | Bilateral sparse nested array design method for direction of arrival estimation |
| CN111679248A (en) * | 2020-05-15 | 2020-09-18 | 黑龙江工程学院 | Target azimuth and distance combined sparse reconstruction positioning method based on seabed horizontal L-shaped array |
| CN112285647A (en) * | 2020-09-30 | 2021-01-29 | 中国船舶重工集团公司七五0试验场 | Signal orientation high-resolution estimation method based on sparse representation and reconstruction |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110035346A1 (en) * | 2009-05-13 | 2011-02-10 | Arman Melkumyan | Method and system for data analysis and synthesis |
| CN103399312A (en) * | 2013-07-15 | 2013-11-20 | 哈尔滨工程大学 | Temporal-spatial joint filtering high-resolution DOA (Direction of Arrival) estimation method based on compressed sensing technology |
| CN105676168A (en) * | 2015-12-02 | 2016-06-15 | 江苏科技大学 | Acoustic vector array DOA estimation method |
-
2016
- 2016-12-15 CN CN201611158285.7A patent/CN106680762B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110035346A1 (en) * | 2009-05-13 | 2011-02-10 | Arman Melkumyan | Method and system for data analysis and synthesis |
| CN103399312A (en) * | 2013-07-15 | 2013-11-20 | 哈尔滨工程大学 | Temporal-spatial joint filtering high-resolution DOA (Direction of Arrival) estimation method based on compressed sensing technology |
| CN105676168A (en) * | 2015-12-02 | 2016-06-15 | 江苏科技大学 | Acoustic vector array DOA estimation method |
Non-Patent Citations (2)
| Title |
|---|
| 孙国仓: "浅海矢量声场及其信号处理", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 朱伟: "米波数字阵列雷达低仰角测高方法", 《万方数据》 * |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107202975A (en) * | 2017-05-25 | 2017-09-26 | 哈尔滨工程大学 | A kind of a burst of first attitude error rectification method of two-dimensional vector |
| CN107202975B (en) * | 2017-05-25 | 2020-04-03 | 哈尔滨工程大学 | Two-dimensional vector array element attitude error correction method |
| CN107728109A (en) * | 2017-09-18 | 2018-02-23 | 哈尔滨工程大学 | A kind of noncooperative target radiated noise measurement and positioning technology |
| CN107966677A (en) * | 2017-11-16 | 2018-04-27 | 黑龙江工程学院 | A kind of circle battle array mode domain direction estimation method based on space sparse constraint |
| CN107966677B (en) * | 2017-11-16 | 2021-04-13 | 黑龙江工程学院 | Circular array modal domain orientation estimation method based on space sparse constraint |
| CN109870671A (en) * | 2017-12-05 | 2019-06-11 | 常熟海量声学设备科技有限公司 | A kind of high-resolution efficient DOA algorithm for estimating of robustness |
| CN108181611B (en) * | 2017-12-11 | 2020-06-30 | 东南大学 | Subspace-based compressed sensing high-resolution array processing method |
| CN108181611A (en) * | 2017-12-11 | 2018-06-19 | 东南大学 | Compressed sensing underwater multiple targets method based on subspace |
| CN109696657A (en) * | 2018-06-06 | 2019-04-30 | 南京信息工程大学 | A kind of coherent sound sources localization method based on vector hydrophone |
| CN109884592A (en) * | 2019-03-04 | 2019-06-14 | 浙江大学 | A kind of auditory localization emulation mode towards low frequency Gaussian noise source |
| CN110082712A (en) * | 2019-03-14 | 2019-08-02 | 哈尔滨工程大学 | A kind of acoustic vector circle battle array Coherent Targets direction estimation method |
| CN110082741A (en) * | 2019-03-14 | 2019-08-02 | 哈尔滨工程大学 | A kind of super-resolution DOA estimate algorithm based on pseudo- data reconstruction |
| CN110082741B (en) * | 2019-03-14 | 2022-07-15 | 哈尔滨工程大学 | Super-resolution angle of arrival estimation calculation method based on pseudo data reconstruction |
| CN111175691A (en) * | 2019-11-29 | 2020-05-19 | 北京理工大学 | Bilateral sparse nested array design method for direction of arrival estimation |
| CN111679248A (en) * | 2020-05-15 | 2020-09-18 | 黑龙江工程学院 | Target azimuth and distance combined sparse reconstruction positioning method based on seabed horizontal L-shaped array |
| CN111679248B (en) * | 2020-05-15 | 2023-04-21 | 黑龙江工程学院 | Target azimuth and distance combined sparse reconstruction positioning method based on seabed horizontal L-shaped array |
| CN112285647A (en) * | 2020-09-30 | 2021-01-29 | 中国船舶重工集团公司七五0试验场 | Signal orientation high-resolution estimation method based on sparse representation and reconstruction |
| CN112285647B (en) * | 2020-09-30 | 2023-10-27 | 中国船舶重工集团公司七五0试验场 | Signal azimuth high-resolution estimation method based on sparse representation and reconstruction |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106680762B (en) | 2019-11-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106680762B (en) | A kind of acoustic vector sensor array direction estimation method based on the sparse reconstruct of cross covariance | |
| CN105676168B (en) | A kind of acoustic vector sensor array direction estimation method | |
| CN104730491B (en) | A kind of virtual array DOA estimation method based on L-type battle array | |
| CN106802403B (en) | Acoustic vector sensors two-dimensional array MUSIC decorrelation LMS method for parameter estimation | |
| CN106199505B (en) | A kind of acoustic vector circle steady direction estimation method in battle array mode domain | |
| CN104166120B (en) | A kind of acoustic vector justifies battle array robust wideband MVDR direction estimation methods | |
| CN107544051A (en) | Wave arrival direction estimating method of the nested array based on K R subspaces | |
| Lei et al. | Localization of low-frequency coherent sound sources with compressive beamforming-based passive synthetic aperture | |
| CN114527427A (en) | Low-frequency beam forming sound source positioning method based on spherical microphone array | |
| CN110082712B (en) | Acoustic vector circular array coherent target azimuth estimation method | |
| Liang et al. | Two-dimensional DOA estimation method of acoustic vector sensor array based on sparse recovery | |
| CN111812581B (en) | Spherical array sound source direction-of-arrival estimation method based on atomic norms | |
| CN112285647A (en) | Signal orientation high-resolution estimation method based on sparse representation and reconstruction | |
| CN110501669B (en) | Central symmetry acoustic vector circular array fast space spectrum compression super-resolution direction estimation method | |
| Park et al. | Incipient tip vortex cavitation localization using block-sparse compressive sensing | |
| Wang et al. | Unambiguous broadband direction of arrival estimation based on improved extended frequency-difference method | |
| Yang et al. | Sparse spatial spectral estimation in directional noise environment | |
| CN106908754A (en) | L-type acoustic vector-sensor array row ESPRIT decorrelation LMS method for parameter estimation | |
| CN106483193A (en) | A kind of method for quick estimating is reached based on the ripple of High-order Cumulant | |
| CN105842656B (en) | Space-time frequency direction estimation method based on Jacobi rotary joint diagonalization | |
| Zhao et al. | Passive estimation of target velocity based on cross-spectrum histogram | |
| CN106154220B (en) | L-type simplifies acoustic vector-sensor array column multi-parameter Combined estimator quaternary counting method | |
| CN113687297A (en) | Acoustic vector sensor DOA estimation method based on matrix decomposition under data deficiency | |
| Shang et al. | Combined noise reduction and DOA estimation algorithm for MEMS vector hydrophone based on variational mode decomposition | |
| CN108181611B (en) | Subspace-based compressed sensing high-resolution array processing method |
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 | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20231205 Address after: 15F, Shipbuilding Building, No. 258, Nantong Street, Nangang District, Harbin, Heilongjiang, 150000 Patentee after: Harbin Engineering University Asset Management Co.,Ltd. Patentee after: Harbin Haixin Technology R&D Partnership Enterprise (L.P.) Address before: 150001 Intellectual Property Office, Harbin Engineering University science and technology office, 145 Nantong Avenue, Nangang District, Harbin, Heilongjiang Patentee before: HARBIN ENGINEERING University |
|
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20240201 Address after: Room 402-13, 4th Floor, West Zone, Harbin Shipbuilding Electronics World, No. 258 Nantong Street, Nangang District, Harbin City, Heilongjiang Province, 150000 Patentee after: Harbin Hachuan Haixin Technology Co.,Ltd. Country or region after: China Address before: 15F, Shipbuilding Building, No. 258, Nantong Street, Nangang District, Harbin, Heilongjiang, 150000 Patentee before: Harbin Engineering University Asset Management Co.,Ltd. Country or region before: China Patentee before: Harbin Haixin Technology R&D Partnership Enterprise (L.P.) |
|
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20240802 Address after: No. 1806, Unit 1, No. 202-1 Changjiang Road, Economic and Technological Development Zone, Yantai City, Shandong Province, China 264006 Patentee after: Yantai Haixin Tuofei Marine Technology Co.,Ltd. Country or region after: China Address before: Room 402-13, 4th Floor, West Zone, Harbin Shipbuilding Electronics World, No. 258 Nantong Street, Nangang District, Harbin City, Heilongjiang Province, 150000 Patentee before: Harbin Hachuan Haixin Technology Co.,Ltd. Country or region before: China |
|
| TR01 | Transfer of patent right |