CN110109058A - A kind of planar array deconvolution identification of sound source method - Google Patents
A kind of planar array deconvolution identification of sound source method Download PDFInfo
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- CN110109058A CN110109058A CN201910366448.8A CN201910366448A CN110109058A CN 110109058 A CN110109058 A CN 110109058A CN 201910366448 A CN201910366448 A CN 201910366448A CN 110109058 A CN110109058 A CN 110109058A
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- 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
- G01S5/20—Position of source determined by a plurality of spaced direction-finders
Abstract
The invention discloses a kind of planar array deconvolution identification of sound source methods, comprising steps of the traditional Wave beam forming of 1, calculating exports result；2, the equation group between Wave beam forming output result and sound source distribution is established；3, iterative solution sound source is distributed, and in the step, on the basis of broad sense degree of rarefication Adaptive matching tracks gSAMP, in conjunction with identification of sound source problem, constructs the source strength distributed problem solving method of suitable planar array.The solution have the advantages that: the present invention has high spatial resolution, can effectively remove secondary lobe, is accurately located each sound source, positioning accuracy is better than existing method OMP-DAMAS, and does not need the priori knowledge of sound-source signal degree of rarefication.
Description
Technical field
The invention belongs to sound field identification technology fields, and in particular to a kind of identification of sound source method of planar array.
Background technique
The identification of sound source method energy reliable recognition sound source of Wave beam forming based on microphone array has been obtained for extensive
Using wherein with deconvolution sound source imaging method (The deconvolution approach for the mapping of
Acoustic sources, DAMAS) it is the most classical.Document " Orthogonal matching pursuit applied to
the deconvolution approach for the mapping of acoustic sources inverse
Problem [J] ", Padois T, Berry A, Journal of the Acoustical Society of America,
2015,138 (6): 3678. (" orthogonal matching pursuit is applied to deconvolution sound source imaging inverse problem [J] ", Padois T, Berry
A, U.S.'s acoustic journal, 2015,138 (6): 3678.) proposing OMP-DAMAS method, by orthogonal matching pursuit algorithm
(Orthogonal matching pursuit, OMP) is applied to the solution of DAMAS inverse problem, improves the knowledge of Deconvolution Method
Other performance, in each iteration, OMP only select with residual error it is maximally related one column, i.e., characterization real sources to perception matrix
Column reconstruct original signal.But existing OMP-DAMAS method needs the priori knowledge of sound source degree of rarefication that (priori knowledge is
Refer to and need to determine the number of noise source in advance, determine the number of iterations according to the number, but this number is in practical applications simultaneously
It is not easy to determine), and when identifying more sound sources, target column is easy to be falsely dropped as column adjacent thereto, so as to cause accuracy of identification
Deteriorate, deviation occurs in positioning.
Summary of the invention
In view of the problems of the existing technology, the technical problem to be solved by the invention is to provide a kind of planar array is anti-
Convolution identification of sound source method, it does not have to the priori knowledge of sound-source signal degree of rarefication, and can improve the positioning accuracy of sound source.
It is realized the technical problem to be solved by the present invention is to technical solution in this way, it includes
Step 1 calculates traditional Wave beam forming output result
The calculating formula of Wave beam forming output quantity are as follows:
In formula, C=∑r′C (r ')=∑r′q(r′)v*(r′)vT(r ') is the full cross-spectrum matrix under frequency domain；
R ' is sound source position vector, and q (r ') is the place r ' source strength, and subscript T and * respectively indicate transposition and conjugation；V=[vm
(r)] indicate that focus point is the guiding column vector of r, vm(r) steering vector of m-th of microphone is indicated；L is that all elements are 1
Matrix, w=[| vm|]2；
Step 2, establish Wave beam forming output result and sound source distribution between equation group
B=Aq
In formula, b=[b (r)] is that N-dimensional Wave beam forming exports column vector, and N is mesh point number；A=[psf (r | r ')] it is N
The point propagation function matrix of × N-dimensional；Q is that the sound source of N-dimensional is distributed column vector；
Step 3, iterative solution sound source distribution
1) t=1, residual error r, are initializedt-1=b, supported collectionMatrix Λt-1=0；
2) related coefficient h=A, is calculatedHrt-1, subscript H indicates transposition conjugation, select in h before absolute value S element it is right
The index answered is denoted as { λi}I=1,2 ... S；
3) Γ, is enabledt=Γt-1∪{λi}I=1,2 ... S；Calculate least square solution:
θtIt is N-dimensional source strength distribution estimated vector to be solved,It indicates according to ΓtThe index for including selects propagation function
Constructed intermediary matrix is arranged in matrix A accordingly；
4) index, is foundFor the index of sound source distributing vector；
5) J, is enabledt=Jt-1∪ μ, Λt=Λt-1∪aμ, aμFor the μ column vector of A；Source strength distribution estimation is updated again:
6) residual error vector, is updated
If 7), ‖ rt‖2> ε then enables t=t+1 return to step 2), otherwise exports result；
8) sound source distributing vector estimated value q, is obtained in JtThere are nonzero value, J in placetFor final index set, value is
Preferably, the 2 of above-mentioned steps 3) in, parameter S value is S < D and S < M/D, D are the number of sound source, and M is microphone
Number.
Preferably, the 7 of above-mentioned steps 3) in, ε chooses 10-3To 10-6Value in range.
The solution have the advantages that:
The present invention is to construct the deconvolution of suitable planar array on the basis of the tracking of broad sense degree of rarefication Adaptive matching
Identification of sound source method.According to analogue simulation and verification experimental verification: the present invention has high spatial resolution, can effectively remove secondary lobe,
It is accurately located each sound source, positioning accuracy is better than existing method OMP-DAMAS, and the priori for not needing sound-source signal degree of rarefication is known
Know, overall performance is better than OMP-DAMAS.
Detailed description of the invention
Detailed description of the invention of the invention is as follows:
Fig. 1 is planar array sampling model of the invention；
Fig. 2 is the flow chart of iterative solution algorithm of the invention；
Fig. 3 is the identification of sound source effect picture of the emulation present invention and OMP-DAMAS；
Fig. 4 is the identification image of the present invention and OMP-DAMAS.
Specific embodiment
Present invention will be further explained below with reference to the attached drawings and examples:
The present invention the following steps are included:
Step 1 calculates traditional Wave beam forming output result
Planar of microphones sampling model is as shown in Figure 1, it is assumed that sound source to be measured is all fallen in known sound source plane, wheat
Gram wind planar array is located at the front of sound source plane, and the sound pressure signal that microphone receives is by being changed to frequency after inverse Fourier transform
Full cross-spectrum Matrix C under domain, is indicated with following formula:
C=∑r′C (r ')=∑r′q(r′)v*(r′)vT(r′) (1)
In formula (1), r ' is sound source position vector, and q (r ') is the place r ' source strength, and subscript T and * respectively indicate transposition and conjugation；v
=[vm(r)] indicate that focus point is the guiding column vector of r, vm(r) steering vector of m-th of microphone is indicated, expression formula is such as
Under:
In formula (2),Indicate complex unit, k=2 π f/c indicates wave number, and f is frequency of sound wave, and c is velocity of wave.R table
Show position vector of the array center to focus point, rmPosition vector of the expression array center to m-th of microphone.
According to document " four kinds of typical Wave beam forming identification of sound source clarification methods [J] ", Yang Yang, Chu Zhigang, data acquisition
With processing, 2014,29 (2): 316-326 is recorded, the calculating formula of Wave beam forming output quantity are as follows:
In formula (3), l be all elements be 1 matrix, w=[| vm|]2。
Step 2, establish Wave beam forming output result and sound source distribution between equation group
Formula (1) substitution formula (3) is obtained:
In formula (4), and psf (r | r ') it is known as array point propagation function, indicate that there are unit source strengths at the only position r '
When point sound source, the point sound source is to the Wave beam forming pressure contribution amount generated at the position r, as r=r ', psf=1.As it can be seen that r
The Wave beam forming output quantity at the place of setting can be expressed as each point source of sound the point Wave beam forming pressure contribution multiplied by corresponding source strength after
It sums again, Deconvolution Algorithm Based on Frequency constructs the line between Wave beam forming output result, array point propagation function harmony source distribution accordingly
Property equation group:
B=Aq (5)
In formula (5), b=[b (r)] is that N-dimensional Wave beam forming exports column vector, and N is mesh point number；A=[psf (r | r ')]
For N × N-dimensional point propagation function matrix；Q is that the sound source of N-dimensional is distributed column vector；Wherein A, b are it is known that q is unknown.
For formula (5), existing DAMAS method solves strength of sound source distribution, OMP- using Gauss iteration method
DAMAS then reconstructs q by orthogonal matching pursuit algorithm using the sparse characteristic of sound source distribution vector.
Step 3, iterative solution sound source distribution
At document " the broad sense degree of rarefication Adaptive matching tracing algorithm [J/OL] for CS ", Ma Yushuan, Liu Cui are rung, Guo Zhi
Great waves, Wang Baozhu, computer engineering and application, (the starting paper of network, publication address arehttp://kns.cnki.net/kcms/ detail/11.2127.TP.20181115.1637.006.html, network starting date is 2018-11-19), the broad sense of proposition
On the basis of degree of rarefication Adaptive matching tracks gSAMP, in conjunction with identification of sound source problem, iterative solution process such as Fig. 2 of the invention
Shown, its step are as follows:
1) t=1, residual error r, are initializedt-1=b, supported collectionMatrix Λt-1=0；Λt-1It is member
Element is all 0 matrix, and it is the intermediate quantity in iterative process that Γ and J, which are null set/matrixes,.
2) related coefficient h=A, is calculatedHrt-1, subscript H indicates transposition conjugation, select in h before absolute value S element it is right
The index answered is denoted as { λi}I=1,2 ... S；
3) Γ, is enabledt=Γt-1∪{λi}I=1,2 ... S；Calculate least square solution:
θtIt is N-dimensional source strength distribution estimated vector to be solved,It indicates according to ΓtThe index for including selects propagation function
Constructed intermediary matrix is arranged in matrix A accordingly；
The formula indicates to solve a vector thetat, so thatReach it is minimum (b andIt is known), calculated result
ForθtIt is in fact exactly the unknown number in equation.It is obtained by calculation to θtEstimated value.
4) index, is foundFor the index of sound source distributing vector；
5) J, is enabledt=Jt-1∪ μ, Λt=Λt-1∪aμ, aμFor the μ column vector of A；Source strength distribution estimation is updated again:
6) residual error vector, is updated
If 7), ‖ rt‖2> ε then enables t=t+1 return to step 2), otherwise exports result；
8) sound source distributing vector estimated value q, is obtained in JtThere are nonzero value, J in placetFor final index set, value is
Iteration all can be in J each timetNew index is added in this set the inside, and index can be regarded as sound source coordinate,
It is exactly JtIt is the set of final all coordinates for recognizing sound source.
Above-mentioned iterative formula is related to the selection of two parameters S and ε；Wherein S is the atomic quantity selected in each iteration,
Relative to the single atom of OMP-DAMAS (atom here is the column referred in point propagation function matrix, i.e. S perseverance is selection 1),
Increasing the atom number being selected into every time can effectively improve the probability for successfully restoring original signal.To parameter S in present method invention
Selection, provide recommended formula (S < D and S < M/D), D be sound source number, M be microphone number.S in practical applications
It only needs to take a relatively small integer (such as 2) greater than 1 that can achieve the effect that improve acoustic source location accuracy.
ε is the threshold value for stopping iteration, as residual error rt2 norms when decaying to certain numerical value, original signal can be by
Atom approximation rarefaction representation is selected.When ε chooses the larger value, it can quickly stop iteration after finding main sound source；ε chooses smaller value
When, it is able to achieve after finding secondary sound source and just stops iteration, generally choose 10-3To 10-6Value in range.
Analogue simulation test
Accuracy of the invention is established for verifying, is compared with OMP-DAMAS, the raising of inventive energy, carry out sound are probed into
Identifing source analogue simulation.
In specific position, the point sound source of radiation intensity and frequency sound waves is set, identifies that front 2m × 2m's is flat using array
Face region, sound source plan range array 1m, by sound source plane it is discrete be 21 × 21 mesh points.Emulation assumes that sound source is 6 non-
Relevant monopole point sound source, coordinate are (- 0.6,0,1), (0.6,0,1), (- 0.2,0.5,1), (0.2,0.5,1), (-
0.2, -0.5,1), (0.2, -0.5,1) m, calculating separately frequency of source is the imaging in the case of 2000Hz, 4000Hz and 6000Hz
As a result, effective acoustic pressure is 0.02Pa, corresponding sound pressure level is 60dB, calculates each microphone reception sound pressure signal according to formula (1) is positive
Cross-spectrum matrix, and add signal-to-noise ratio be 10dB white Gaussian noise；Setting focus sound source face, be based respectively on OMP-DAMAS and
Present method invention reconstruct strength of sound source is distributed and is imaged.Wherein OMP-DAMAS is 6 times according to priori knowledge setting the number of iterations.
And present method invention setting maximum number of iterations is 20 times, takes S=4, ε=10-6.Its imaging results reference data acoustic pressure is divided
Shellfish conversion, setting display dynamic range are 10dB.
For the effect of the analogue simulation as shown in figure 3, real sources position is marked with white " ten " word in Fig. 3, amplitude is black
Point is that " main lobe " indicates sound source position.
As seen from Figure 3, when frequency of source is 2000Hz, there are two the positioning of sound source deviation occurs by OMP-DAMAS, and
When frequency of source is 6000Hz, OMP-DAMAS is lost a sound source, and is reconstructed a false sound valve in non-sound source position.
And sound source position has then all been accurately positioned in present method invention at three frequencies, obtains clear shrewd sound source image.
Verification test
For the correctness for examining simulation result, the loudspeaker of steady-state signal excitation is subjected to verification experimental verification as sound source.It adopts
WithCompany, 0.65m array diameter, the 36 channel C ombo arrays sampling acoustic pressure letter for integrating 4958 type microphones
Number.Each received sound pressure signal of microphone acquires simultaneously through PULSE 3560D type data collection system and is transferred to PULSE
Spectrum analysis is carried out in LABSHOP, obtains the cross-spectrum matrix of sound pressure signal, and setting sample frequency is 16384Hz, signal Jia Hanning
Window, using 64 sections of average, 66.7% Duplication, every section of duration 0.25s, corresponding frequency resolution are 4Hz.
The frequency-region signal that frequency is 2000Hz is extracted after array samples sound pressure signal to imported into after MATLAB writes
It is imaged in reason program, Fig. 4 is the recognition imaging figure for testing speaker sound.
As can be seen from Figure 4, the position of a sound source deviates from a mesh point, this method in the image of OMP-DAMAS
Invention has then accurately identified the position of four loudspeakers, and the present invention improves the positioning accuracy of sound source.
Claims (3)
1. a kind of planar array deconvolution identification of sound source method, includes the following steps,
Step 1 calculates traditional Wave beam forming output result
The calculating formula of Wave beam forming output quantity are as follows:
In formula, C=∑r′C (r ')=∑r′q(r′)v*(r′)vT(r ') is the full cross-spectrum matrix under frequency domain；
R ' is sound source position vector, and q (r ') is the place r ' source strength, and subscript T and * respectively indicate transposition and conjugation；V=[vm(r)] it indicates
Focus point is the guiding column vector of r, vm(r) steering vector of m-th of microphone is indicated；L is the matrix that all elements are 1, w
=[| vm|]2；
Step 2, establish Wave beam forming output result and sound source distribution between equation group
B=Aq
In formula, b=[b (r)] is that N-dimensional Wave beam forming exports column vector, and N is mesh point number；A=[psf (r | r ')] it is N × N
The point propagation function matrix of dimension；Q is that the sound source of N-dimensional is distributed column vector；
It is characterized in that:
Step 3, iterative solution sound source distribution
1) t=1, residual error r, are initializedt-1=b, supported collectionMatrix At-1=0；
2) related coefficient h=A, is calculatedHrt-1, subscript H indicates transposition conjugation, selects in h rope corresponding to S element before absolute value
Draw and is denoted as { λi}I=1,2...S；
3) Γ, is enabledt=Γt-1∪{λi}I=1,2...S；Calculate least square solution:
θtIt is N-dimensional source strength distribution estimated vector to be solved,It indicates according to ΓtThe index for including selects propagation function matrix A
In arrange constructed intermediary matrix accordingly；
4) index, is foundFor the index of sound source distributing vector；
5) J, is enabledt=Jt-1∪ μ, At=At-1∪aμ, aμFor the μ column vector of A；Source strength distribution estimation is updated again:
6) residual error vector, is updated
If 7), | | rt||2> ε then enables t=t+1 return to step 2), otherwise exports result；
8) sound source distributing vector estimated value q, is obtained in JtThere are nonzero value, J in placetFor final index set, value θt。
2. planar array deconvolution identification of sound source method according to claim 1, it is characterized in that: the 2 of step 3) in, ginseng
Number S value is S < D and S < M/D, D are the number of sound source, and M is microphone number.
3. planar array deconvolution identification of sound source method according to claim 1 or 2, it is characterized in that: in above-mentioned steps 3
7) in, ε chooses 10-3To 10-6Value in range.
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| CN111551943A (en) * | 2020-05-19 | 2020-08-18 | 中国科学院声学研究所 | DAMAS 2-based sparse array high-resolution three-dimensional acoustic imaging method and system |
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| CN113658606A (en) * | 2021-08-19 | 2021-11-16 | 中国人民解放军海军工程大学 | Beam forming method based on self-adaptive compressed sensing under low signal-to-noise ratio condition |
| CN113962250A (en) * | 2021-09-14 | 2022-01-21 | 西南交通大学 | Self-adaptive minimum correlation generalized deconvolution method |
| CN114741652A (en) * | 2022-06-10 | 2022-07-12 | 杭州兆华电子股份有限公司 | Deconvolution high-resolution imaging method and system based on acoustic image instrument |
| CN115113139A (en) * | 2022-05-12 | 2022-09-27 | 苏州清听声学科技有限公司 | Sound source identification method and device based on microphone array and electronic equipment |
| CN116008911A (en) * | 2022-12-02 | 2023-04-25 | 南昌工程学院 | Orthogonal matching pursuit sound source identification method based on novel atomic matching criteria |
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| CN111551943B (en) * | 2020-05-19 | 2022-07-12 | 中国科学院声学研究所 | DAMAS 2-based sparse array high-resolution three-dimensional acoustic imaging method and system |
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| CN112198476B (en) * | 2020-10-16 | 2023-10-27 | 昆明理工大学 | Three-dimensional positioning method of mobile sound source based on stereoscopic vision and beam forming |
| CN113658606B (en) * | 2021-08-19 | 2023-08-08 | 中国人民解放军海军工程大学 | Beam forming method based on self-adaptive compressed sensing under low signal-to-noise ratio condition |
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| CN115113139A (en) * | 2022-05-12 | 2022-09-27 | 苏州清听声学科技有限公司 | Sound source identification method and device based on microphone array and electronic equipment |
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| CN115113139B (en) * | 2022-05-12 | 2024-02-02 | 苏州清听声学科技有限公司 | Sound source identification method and device based on microphone array and electronic equipment |
| CN114741652A (en) * | 2022-06-10 | 2022-07-12 | 杭州兆华电子股份有限公司 | Deconvolution high-resolution imaging method and system based on acoustic image instrument |
| CN116008911A (en) * | 2022-12-02 | 2023-04-25 | 南昌工程学院 | Orthogonal matching pursuit sound source identification method based on novel atomic matching criteria |
| CN116008911B (en) * | 2022-12-02 | 2023-08-22 | 南昌工程学院 | Orthogonal matching pursuit sound source identification method based on novel atomic matching criteria |
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