CN109633554A - Moving sound based on probabilistic data association reaches delay time estimation method - Google Patents
Moving sound based on probabilistic data association reaches delay time estimation method Download PDFInfo
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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
- G01S5/22—Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements
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- Radar, Positioning & Navigation (AREA)
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- Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
- Circuit For Audible Band Transducer (AREA)
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Abstract
The invention discloses a kind of, and the moving sound based on probabilistic data association reaches delay time estimation method.Reach the time delay of microphone using the source sound signal of Kalman prediction in previous moment in advance, broad sense cross correlation algorithm calculates the cross-spectral density distribution function of this time data, take out the time delay of the corresponding m arrival microphone of maximum m peak value on cross-spectral density distribution function, it combines the TDOA of prediction to calculate the probability that each time delay that this moment estimates may be TDOA using probabilistic correlation algorithm, this m time delay is obtained into the moment final TDOA using above-mentioned probability weight.In view of the time delay estimation of moving sound has continuity, PDA track algorithm is used in during TDOA continuously estimates by this method, multiple time delays that each moment is estimated regard the measurement of multiple targets in target following as, to convert tracking problem for more moment continuous T DOA estimation problems, the accuracy that moving sound reaches time delay estimation is considerably increased, to acoustic array process field value with higher.
Description
Technical field
The present invention relates to array signal fields, more particularly to microphone array signals processing and reach time delay estimation.
Background technique
Background of the invention is based on actual needs and generates.In recent years, it when unmanned plane rapidly becomes research hotspot, also brings
A series of problems, such as unmanned plane are black winged, seriously affect region security.Therefore unmanned plane defence is becoming national governments and army
The frontier just paid close attention to.The sound of unmanned plane have apparent feature, can effectively detect not Chu airflight object.It is logical
It crosses and sets up multiple microphone arrays, can effectively be collected into the signal of unmanned plane, but noise, signal existing for actual environment exist
Noise and some other reverberation, multi-path jamming in circuit arrangement transmission process etc., the voice signal being collected into very noise
Miscellaneous, when causing to position unmanned plane, the time delay (TDOA) that the signal being calculated reaches microphone will appear bigger
Error needs to improve TDOA algorithm for estimating to improve the accuracy of TDOA estimation.
In current research contents, common improved procedure is the information of the TDOA in conjunction with previous moment, by this moment
The value range of TDOA estimation function be limited in a section, to reduce the fluctuation of TDOA estimation, but this method pair
Initial value is more demanding, and requires to the flying speed of unmanned plane.Therefore, it is badly in need of a kind of next really effective estimation of new method
TDOA out, while can guarantee the faster speed of service and the lower cost of system.
Summary of the invention
In order to realize the tracking to unmanned plane voice signal time delay when mobile, the present invention uses microphone array sensor pair
Aerial unmanned plane voice signal is handled, and unmanned plane voice signal on the move can be effectively estimated and reach microphone time delay.
The technical solution adopted by the present invention to solve the technical problems is: a kind of moving sound based on probabilistic data association
Delay time estimation method is reached, is included the following steps:
(1) two groups of time domain acoustical signal x of microphone array t moment acquisition are calculated according to broad sense cross-correlation function1(t)、x2
(t) cross-spectral density distribution function
WhereinFor x1(t) and x2(t) product of Fourier transform results,For x1(t) and x2(t)
Frequency domain filter.
(2) it extractsThe corresponding m time delay of each moment maximum m peak value in function.
(3) the m time delay at each moment is regarded as to m measurement for reaching microphone time delay TDOA, and introduces TDOA's
Dynamical equation:
Xk+1=AXk+vk,
zk+1,i=CXk+wk,i,
Wherein XkIt is state of the TDOA at the k moment, vkBe mean value be 0, covariance matrix be Q white Gaussian noise.zk+1,i
It is the measurement in i-th of time delay at k+1 moment, wk,iBe mean value be 0, covariance matrix be R white Gaussian noise, and
C=[1 0],
(4) TDOA state is predicted using the predicted portions of Kalman filtering.
(5) probability that each time delay is TDOA is calculated using Probabilistic Data Association Algorithm, then introduces Kalman filtering
Part is updated to be updated TDOA state.
Further, in step (1),Using phse conversion (PHAT) weighting function
Further, in step (4), the predicted portions for introducing Kalman filtering predict TDOA state:
Xk+1|k=AXk|k,
Pk+1|k=APk|kAT+Q,
Wherein Xk|kRepresent the state estimation of TDOA, Pk|kRepresent the covariance matrix of the state estimation error of TDOA, Xk+1|k
Represent one-step prediction TDOA state estimation, Pk+1|kRepresent the covariance matrix of one-step prediction TDOA state estimation error, ATRepresent A
Transposition.
Further, in step (5), the probability that each time delay is TDOA is calculated using following formula:
Wherein βi kRepresenting in i-th of time delay of k moment is the probability of TDOA, β0 kRepresenting in k moment all time delays is not
The probability of TDOA；
bk=λ (2 π)1/2|CPk|k-1CT+R-1|1/2,
Wherein λ is a constant,Indicate the residual error that i-th of time delay measures,
Further, in step (5), the update part for introducing Kalman filtering is updated TDOA state:
Kk=Pk|k-1CT(CPk|k-1CT+R-1),
Pk|k=β0 kPk|k-1+(1-β0 k)Pc k|k+Pd k,
Wherein
Pc k|k=[I-KkC]Pk|k-1,
TDOA estimated value is CX at this timek|k。
Moving sound proposed by the present invention based on probabilistic data association reaches delay time estimation method, can estimate in mobile
Unmanned plane voice signal reach microphone time delay, have it is low in cost, calculate the features such as rapid.The present invention has following excellent
Gesture:
(1) it under conditions of not increasing cost, is directly extracted using synchronization cross-spectral density distribution function more
A time delay is merged, and the precision of TDOA estimation is improved.
(2) it can work in the case where low signal-to-noise ratio is influenced serious environment by multipath effect, widen the work item of sound array
Part；
(3) the method for the present invention is simple and easy to do, and real-time is good, Yi Shixian.
Detailed description of the invention
Fig. 1 is to reach the diagram of microphone time delay；
Fig. 2 is that the peak value of the maximum cross-spectral density distribution function at more moment corresponds to the line of time delay；
Fig. 3 is that the peak value of the second largest cross-spectral density distribution function at more moment corresponds to the line of time delay；
Fig. 4 is by the continuous estimated result of the fused TDOA of multiple time delays；
Fig. 5 is that sound source or unmanned plane position used tetrahedral array model；
Fig. 6 is unmanned plane during flying positioning result.
Specific embodiment
The present invention is described in further detail below in conjunction with the drawings and specific embodiments.
A kind of moving sound based on probabilistic data association proposed by the present invention reaches delay time estimation method, including walks as follows
It is rapid:
(1) two groups of time domain acoustical signal x of microphone array t moment acquisition are calculated according to broad sense cross-correlation function1(t)、x2
(t) cross-spectral density distribution function
WhereinFor x1(t) and x2(t) product of Fourier transform results,For x1(t) and x2(t)
Frequency domain filter.
Phse conversion (PHAT) weighting function can be used
(2) it extractsThe corresponding m time delay of each moment maximum m peak value in function.
(3) the m time delay at each moment is regarded as to m measurement for reaching microphone time delay TDOA, and introduces TDOA's
Dynamical equation:
Xk+1=AXk+vk,
zk+1,i=CXk+wk,i,
Wherein XkIt is state of the TDOA at the k moment, vkBe mean value be 0, covariance matrix be Q white Gaussian noise.zk+1,i
It is the measurement in i-th of time delay at k+1 moment, wk,iBe mean value be 0, covariance matrix be R white Gaussian noise, and
C=[1 0],
(4) TDOA state is predicted using the predicted portions of Kalman filtering, specific as follows:
Xk+1|k=AXk|k,
Pk+1|k=APk|kAT+Q,
Wherein Xk|kRepresent the state estimation of TDOA, Pk|kRepresent the covariance matrix of the state estimation error of TDOA, Xk+1|k
Represent one-step prediction TDOA state estimation, Pk+1|kRepresent the covariance matrix of one-step prediction TDOA state estimation error, ATRepresent A
Transposition.
(5) probability that each time delay is TDOA is calculated using Probabilistic Data Association Algorithm, then introduces Kalman filtering
Part is updated to be updated TDOA state.
The formula for calculating the probability that each time delay is TDOA is as follows:
Wherein βi kRepresenting in i-th of time delay of k moment is the probability of TDOA, β0 kRepresenting in k moment all time delays is not
The probability of TDOA；
bk=λ (2 π)1/2|CPk|k-1CT+R-1|1/2,
Wherein λ is a constant,Indicate the residual error that i-th of time delay measures,
The formula that the update part of introducing Kalman filtering is updated TDOA state is as follows:
Kk=Pk|k-1CT(CPk|k-1CT+R-1),
Pk|k=β0 kPk|k-1+(1-β0 k)Pc k|k+Pd k,
Wherein
Pc k|k=[I-KkC]Pk|k-1,
TDOA estimated value is CX at this timek|k。
Attached drawing 1 is the diagram that a microphone reaches time delay, in the actual environment, microphone and unmanned plane or shifting
The distance of dynamic sound source is much bigger relative to the spacing between microphone array array element, so sound-source signal travels to microphone array
Parallel incidence can be regarded as by arranging different array elements.Sound-source signal reaches first microphone than reaching as can clearly see from the figure
The distance L ' that second microphone will be propagated more can be obtained by divided by the velocity of sound using this distance and reach time delay (TDOA).
After attached drawing 2 is continuous acquisition a few minutes data, data per second are found out into cross-power using broad sense cross correlation algorithm
Then spectrum density distribution function is directly believed the corresponding time delay of peak value maximum on cross-spectral density distribution function as sound
Number arrival microphone time delay (TDOA), then the TDOA continuously estimated is drawn as line graph.It can be seen from the figure that if directly taking
The corresponding time delay of maximum peak value obtains TDOA estimation curve and has bigger fluctuation point as TDOA, these fluctuation points are
It is generated by noise or multipath effect,
After attached drawing 3 is continuous acquisition a few minutes data, data per second are found out into cross-power using broad sense cross correlation algorithm
Spectrum density distribution function, then directly by the corresponding time delay of peak value second largest on cross-spectral density distribution function as sound
Signal reaches microphone time delay (TDOA), then the TDOA continuously estimated is drawn as line graph.From the figure, it can be seen that directly utilizing
It is different that the second largest peak value of cross-spectral density distribution function, which corresponds to the fluctuation point of the line that time delay is drawn compared to attached drawing 2,
, it is the place of fluctuation point in fig 2, but may be smoothed curve in attached drawing 3.This explanation is since noise etc. interferes, TDOA
The peak-peak of cross-spectral density distribution function is not necessarily corresponded to, may be second largest, the third-largest or even the fourth-largest peak value is corresponding
Time delay be only true TDOA.Therefore, we can introduce probabilistic data association (PDA) algorithm to calculate preceding several maximum peaks
Value corresponds to the probability that time delay point is true TDOA, recycles above-mentioned probability to weight time delay, TDOA can be improved in this way and estimate
The accuracy of meter.
Attached drawing 4 is illustrated carry out TDOA estimation with Time Delay Estimation Algorithms proposed by the present invention after obtain TDOA estimation connect
Line, it can be seen that compared to attached drawing 2 and attached drawing 3, the curve of attached drawing 4 does not have any fluctuation, and curve is very smooth, eliminates
The noise spot that front is generated by noise and multipath effect.
Attached drawing 5 is tetrahedral array model used by sound source or unmanned plane position, and L is array origin to Mike in figure
The distance of wind, r1It is the distance that No. 1 microphone reaches unmanned plane, S1,S2,S3,S4It is 1,2,3, No. 4 microphones respectively, enables (x0,
y0,z0) represent the space coordinate of unmanned plane, d1iSound-source signal is represented to reach No. 1 microphone and reach the distance of i microphone
Then difference introduces following equations:
x0 2+y0 2+(z0-L)2=r1 2,
x0 2+(y0-L)2+z0 2=(r1+b12)2,
Pass through above-mentioned equation, four equations, four unknown quantity (x0,y0,z0,r1), unmanned plane can be positioned.
Attached drawing 6 illustrate it is after we are positioned using above-mentioned array as a result, and with the GPS track of unmanned plane during flying into
Comparison is gone, it can be seen that can preferably track unmanned plane in most of region.
Above-described embodiment is used to illustrate the present invention, rather than limits the invention, in spirit of the invention and
In scope of protection of the claims, to any modifications and changes that the present invention makes, protection scope of the present invention is both fallen within.
Claims (5)
1. a kind of moving sound based on probabilistic data association reaches delay time estimation method, which comprises the steps of:
(1) two groups of time domain acoustical signal x of microphone array t moment acquisition are calculated according to broad sense cross-correlation function1(t)、x2(t) mutually
Power spectral density distribution function
WhereinFor x1(t) and x2(t) product of Fourier transform results,For x1(t) and x2(t) frequency domain
Filter.
(2) it extractsThe corresponding m time delay of each moment maximum m peak value in function.
(3) the m time delay at each moment is regarded as to m measurement for reaching microphone time delay TDOA, and introduces the dynamic of TDOA
Equation:
Xk+1=AXk+vk,
zk+1,i=CXk+wk,i,
Wherein XkIt is state of the TDOA at the k moment, vkBe mean value be 0, covariance matrix be Q white Gaussian noise.zk+1,iIt is in k+
The measurement of i-th of time delay at 1 moment, wk,iBe mean value be 0, covariance matrix be R white Gaussian noise, and
C=[1 0],
(4) TDOA state is predicted using the predicted portions of Kalman filtering.
(5) probability that each time delay is TDOA is calculated using Probabilistic Data Association Algorithm, then introduces the update of Kalman filtering
Part is updated TDOA state.
2. a kind of moving sound based on probabilistic data association according to claim 1 reaches delay time estimation method, special
Sign is, in the step (1),Using phse conversion (PHAT) weighting function
3. a kind of moving sound based on probabilistic data association according to claim 1 reaches delay time estimation method, special
Sign is that in the step (4), the predicted portions for introducing Kalman filtering predict TDOA state:
Xk+1|k=AXk|k,
Pk+1|k=APk|kAT+Q,
Wherein Xk|kRepresent the state estimation of TDOA, Pk|kRepresent the covariance matrix of the state estimation error of TDOA, Xk+1|kIt represents
One-step prediction TDOA state estimation, Pk+1|kRepresent the covariance matrix of one-step prediction TDOA state estimation error, ATRepresent turning for A
It sets.
4. a kind of moving sound based on probabilistic data association according to claim 1 reaches delay time estimation method, special
Sign is, in the step (5), calculates the probability that each time delay is TDOA using following formula:
Wherein βi kRepresenting in i-th of time delay of k moment is the probability of TDOA, β0 kRepresenting in k moment all time delays is not TDOA
Probability；
bk=λ (2 π)1/2|CPk|k-1CT+R-1|1/2,
Wherein λ is a constant,Indicate the residual error that i-th of time delay measures,
5. a kind of moving sound based on probabilistic data association according to claim 1 reaches delay time estimation method, special
Sign is that in the step (5), the update part for introducing Kalman filtering is updated TDOA state:
Kk=Pk|k-1CT(CPk|k-1CT+R-1),
Pk|k=β0 kPk|k-1+(1-β0 k)Pc k|k+Pd k,
Wherein
Pc k|k=[I-KkC]Pk|k-1,
TDOA estimated value is CX at this timek|k。
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111474521A (en) * | 2020-04-09 | 2020-07-31 | 南京理工大学 | Sound source positioning method based on microphone array in multipath environment |
| CN113640744A (en) * | 2021-08-20 | 2021-11-12 | 歌尔科技有限公司 | Sound source positioning method and audio equipment |
| CN113708859A (en) * | 2021-08-26 | 2021-11-26 | 大连工业大学 | Super-resolution multi-path quantity and time delay joint estimation method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103781095A (en) * | 2012-10-23 | 2014-05-07 | 华为技术有限公司 | Calibration method for TDOA (Time Difference Of Arrival) measurement errors, transmit points and system |
| US20140162686A1 (en) * | 2012-12-11 | 2014-06-12 | Transpacific Ip Management Group Ltd. | Femto-assisted location estimation in macro-femto heterogeneous networks |
| EP3203760A1 (en) * | 2016-02-08 | 2017-08-09 | Thomson Licensing | Method and apparatus for determining the position of a number of loudspeakers in a setup of a surround sound system |
| CN107102295A (en) * | 2017-04-13 | 2017-08-29 | 杭州电子科技大学 | The multisensor TDOA passive location methods filtered based on GLMB |
| CN107707324A (en) * | 2017-08-28 | 2018-02-16 | 西安电子科技大学 | A kind of acoustical signal delay time estimation method based on phase difference and maximal possibility estimation |
| WO2018119910A1 (en) * | 2016-12-29 | 2018-07-05 | 清华大学 | Method and apparatus for locating moving sound source |
| CN108398662A (en) * | 2018-01-23 | 2018-08-14 | 佛山市顺德区中山大学研究院 | A method of improving spatial positioning accuracy |
-
2019
- 2019-01-18 CN CN201910056039.8A patent/CN109633554A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103781095A (en) * | 2012-10-23 | 2014-05-07 | 华为技术有限公司 | Calibration method for TDOA (Time Difference Of Arrival) measurement errors, transmit points and system |
| US20140162686A1 (en) * | 2012-12-11 | 2014-06-12 | Transpacific Ip Management Group Ltd. | Femto-assisted location estimation in macro-femto heterogeneous networks |
| EP3203760A1 (en) * | 2016-02-08 | 2017-08-09 | Thomson Licensing | Method and apparatus for determining the position of a number of loudspeakers in a setup of a surround sound system |
| WO2018119910A1 (en) * | 2016-12-29 | 2018-07-05 | 清华大学 | Method and apparatus for locating moving sound source |
| CN107102295A (en) * | 2017-04-13 | 2017-08-29 | 杭州电子科技大学 | The multisensor TDOA passive location methods filtered based on GLMB |
| CN107707324A (en) * | 2017-08-28 | 2018-02-16 | 西安电子科技大学 | A kind of acoustical signal delay time estimation method based on phase difference and maximal possibility estimation |
| CN108398662A (en) * | 2018-01-23 | 2018-08-14 | 佛山市顺德区中山大学研究院 | A method of improving spatial positioning accuracy |
Non-Patent Citations (6)
| Title |
|---|
| D6PISTAGE DANS UNE AMBIANCE ENCOMBR等: ""Tracking in a Cluttered Environment With Probabilistic Data Association"", 《AUTOMATICA》 * |
| ILYAS POTAMITIS等: ""Tracking of Multiple Moving Speakers With Multiple Microphone Arrays"", 《IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING》 * |
| XIANYU CHANG等: ""A Surveillance System for Drone Localization and Tracking Using Acoustic Arrays"", 《2018 IEEE 10TH SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING WORKSHOP (SAM)》 * |
| 李元实: ""面向被动式传感器网络的多目标定位跟踪研究与系统设计"", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
| 杨露箐: "《多源信息融合理论与应用》", 28 February 2006, 北京邮电大学出版社 * |
| 潘泉: "《现代目标跟踪与信息融合》", 31 October 2009, 国防工业出版社 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111474521A (en) * | 2020-04-09 | 2020-07-31 | 南京理工大学 | Sound source positioning method based on microphone array in multipath environment |
| CN113640744A (en) * | 2021-08-20 | 2021-11-12 | 歌尔科技有限公司 | Sound source positioning method and audio equipment |
| CN113708859A (en) * | 2021-08-26 | 2021-11-26 | 大连工业大学 | Super-resolution multi-path quantity and time delay joint estimation method |
| CN113708859B (en) * | 2021-08-26 | 2024-03-22 | 大连工业大学 | Super-resolution multipath quantity and time delay joint estimation method |
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