CN109633553B - Mobile sound source arrival time delay estimation method based on dynamic programming algorithm - Google Patents

Mobile sound source arrival time delay estimation method based on dynamic programming algorithm Download PDF

Info

Publication number
CN109633553B
CN109633553B CN201910055840.0A CN201910055840A CN109633553B CN 109633553 B CN109633553 B CN 109633553B CN 201910055840 A CN201910055840 A CN 201910055840A CN 109633553 B CN109633553 B CN 109633553B
Authority
CN
China
Prior art keywords
time delay
time
sound source
tdoa
dynamic programming
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.)
Active
Application number
CN201910055840.0A
Other languages
Chinese (zh)
Other versions
CN109633553A (en
Inventor
陈积明
吴泽先
史治国
常先宇
杨超群
吴均峰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang University ZJU
Original Assignee
Zhejiang University ZJU
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Zhejiang University ZJU filed Critical Zhejiang University ZJU
Priority to CN201910055840.0A priority Critical patent/CN109633553B/en
Publication of CN109633553A publication Critical patent/CN109633553A/en
Application granted granted Critical
Publication of CN109633553B publication Critical patent/CN109633553B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

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
    • G01S5/22—Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Circuit For Audible Band Transducer (AREA)

Abstract

The invention discloses a mobile sound source arrival time delay estimation method based on a dynamic programming algorithm. The method comprises the steps of firstly collecting data of continuous n moments by utilizing the characteristic that time delay estimation of a mobile sound source has coherence, taking out m time delays corresponding to maximum m peak values on a cross-power spectral density distribution function at each moment, then taking out any one time delay from the m time delays, connecting the n time delays after continuously taking the n moments, calculating a connecting line with minimum time delay fluctuation by utilizing a dynamic programming algorithm, and simultaneously, introducing the peak value of the cross-power spectral density distribution function to correct the selection of the time delay connecting line. After the minimum fluctuation time delay connection line is selected, the time delay of the last moment of the connection line is used as the time delay TDOA of the signal arrival, and the process can be repeated by utilizing a sliding time window to obtain the subsequent TDOA. The method fully utilizes the time delay continuity of the multi-frame mobile sound source signals, simultaneously considers the influence of the multipath effect on the sound source time delay, extracts the most correct time delay and has higher value in the field of sound array processing.

Description

Mobile sound source arrival time delay estimation method based on dynamic programming algorithm
Technical Field
The present invention relates to the field of array signals, and in particular to microphone array signal processing and arrival delay estimation.
Background
The background of the invention arises on the basis of practical requirements. In recent years, when the unmanned aerial vehicle rapidly becomes a research hotspot, a series of problems are brought, such as black flight of the unmanned aerial vehicle, and the safety of the area is seriously affected. Unmanned aerial vehicle defense is becoming a new area of major concern for governments and military parties of various countries. Unmanned aerial vehicle's sound has obvious characteristic, can effectively detect the object of flying in the air. By erecting a plurality of microphone arrays, signals of the unmanned aerial vehicle can be effectively collected, but noise existing in the actual environment, noise of the signals in the transmission process of circuit equipment, some other reverberation, multi-path interference and the like are very noisy, so that when the unmanned aerial vehicle is positioned, a relatively large error occurs in the time delay of the estimated signals reaching a microphone (TDOA), and in order to improve the accuracy of TDOA estimation, improvement can be performed in the process of estimating the TDOA.
In the current research, the TDOA estimate at the current time is generally corrected by using the TDOA at the previous time, but the method only depends on the TDOA at the previous time, and has poor redundancy and is easy to generate errors. Therefore, a new method for estimating the TDOA at the current time by combining the TDOAs at a plurality of previous times is urgently needed, and meanwhile, the higher operation speed and the lower cost of the system can be ensured. The TDOA can be effectively calculated by the method, and the accuracy of subsequent signal analysis is improved.
Disclosure of Invention
In order to realize the tracking of the sound signal time delay when the unmanned aerial vehicle moves, the invention adopts the microphone array sensor to process the sound signal of the unmanned aerial vehicle in the air, and can effectively calculate the sound signal of the unmanned aerial vehicle in the movement to reach the microphone TDOA.
The technical scheme adopted by the invention for solving the technical problems is as follows: a mobile sound source arrival time delay estimation method based on a dynamic programming algorithm comprises the following steps:
(1) two groups of time domain acoustic signals x collected at t moment of microphone array are calculated according to generalized cross-correlation function1(t)、x2(t) cross-power spectral density distribution function
Figure BDA0001952415660000011
Figure BDA0001952415660000012
Wherein
Figure BDA0001952415660000013
Is x1(t) and x2(t) the product of the Fourier transform results,
Figure BDA0001952415660000014
is x1(t) and x2(t) a frequency domain filter;
(2) extracting to obtain
Figure BDA0001952415660000015
And the function continuously processes m time delays corresponding to m maximum peak values of each time in n time moments.
(3) According to the consistency of the time delay estimation of the mobile sound source, finding a curve with the minimum fluctuation in time delay connected curves at n moments by using the following formula, and calculating the time delay TDOA of the microphone:
Figure BDA0001952415660000021
wherein T ishRepresenting the time delay at the h-th instant, ThTaken from the set [ t ]h,1,th,2,...,th,m]，th,iRepresenting the time delay corresponding to the ith maximum peak value at the h moment; phRepresents ThThe peak value of the cross-power spectral density distribution function corresponding to the obtained time delay; a is used for coming PhAnd ThIs adjusted to the same order of magnitude.
(4) And (3) solving the formula (2) through a dynamic programming algorithm to obtain the final TDOA at the nth moment.
(5) And obtaining the TDOA at each moment after the time by using the sliding time window.
Further, in the step (1),
Figure BDA0001952415660000022
using phase-shift (PHAT) weighting functions
Figure BDA0001952415660000023
Further, in the step (4), the following dynamic programming algorithm is used to solve the formula (2):
Figure BDA0001952415660000024
wherein i, v ═ 1, …, m; h 2., n;
Figure BDA0001952415660000025
the ith intermediate variable representing the h-th time instant, here the initial value
Figure BDA0001952415660000026
Is set as the magnitude of the v-th peak of the cross-power spectral density distribution function at the first moment, and the final result is obtained by the iterative formula (3)
Figure BDA0001952415660000027
The final TDOA is calculated using the following formula:
Figure BDA0001952415660000028
the method for estimating the arrival time delay of the mobile sound source based on the dynamic programming algorithm can calculate the arrival time delay of the sound signal of the unmanned aerial vehicle in motion to the microphone, and has the characteristics of low cost, quick calculation and the like. The invention has the following advantages:
(1) the information of the multi-time sound source signals is fully fused, and the accuracy of TDOA estimation is improved.
(2) And the peak value of the cross-power spectral density distribution function is introduced during the time delay of the fusion moment signal, so that the influence of the multipath effect on the microphone time delay is eliminated.
(3) The whole calculation process is optimized by fully utilizing a dynamic programming algorithm, so that the calculation steps are simplified, the calculation complexity is reduced, and the calculation efficiency is accelerated.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a multi-time multi-delay scatter plot;
FIG. 3 is a plot of minimum fluctuation delay;
FIG. 4 is a subsequent TDOA trace result after using the incorrect TDOA initial value;
FIG. 5 shows the subsequent TDOA tracking results after using the correct initial TDOA value.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples.
The invention provides a mobile sound source arrival time delay estimation method based on a dynamic programming algorithm, which comprises the following steps:
(1) two groups of time domain acoustic signals x collected at t moment of microphone array are calculated according to generalized cross-correlation function1(t)、x2(t) cross-power spectral density distribution function
Figure BDA0001952415660000031
Figure BDA0001952415660000032
Wherein
Figure BDA0001952415660000033
Is x1(t) and x2(t) the product of the Fourier transform results,
Figure BDA0001952415660000034
is x1(t) and x2(t) a frequency domain filter;
Figure BDA0001952415660000035
phase shift (PHAT) weighting functions may be employed
Figure BDA0001952415660000036
(2) Extracting to obtain
Figure BDA0001952415660000037
And the function continuously processes m time delays corresponding to m maximum peak values of each time in n time moments.
(3) According to the consistency of the time delay estimation of the mobile sound source, finding a curve with the minimum fluctuation in time delay connected curves at n moments by using the following formula, and calculating the time delay TDOA of the microphone:
Figure BDA0001952415660000038
wherein T ishRepresenting the time delay at the h-th instant, ThTaken from the set [ t ]h,1,th,2,...,th,m]，th,iRepresenting the time delay corresponding to the ith maximum peak value at the h moment; phRepresents ThThe peak value of the cross-power spectral density distribution function corresponding to the obtained time delay; a is used for coming PhAnd ThIs adjusted to the same order of magnitude.
(4) And (3) solving the formula (2) through a dynamic programming algorithm to obtain the final TDOA at the nth moment.
Solving equation (2) using the following dynamic programming algorithm:
Figure BDA0001952415660000039
wherein i, v ═ 1, …, m; h 2., n;
Figure BDA00019524156600000310
the ith intermediate variable representing the h-th time instant, here the initial value
Figure BDA00019524156600000311
Is set as the magnitude of the v-th peak of the cross-power spectral density distribution function at the first moment, and the final result is obtained by the iterative formula (3)
Figure BDA00019524156600000312
The final TDOA is calculated using the following formula:
Figure BDA0001952415660000041
(5) and obtaining the TDOA at each moment after the time by using the sliding time window.
Fig. 1 is an implementation flowchart of an embodiment, first, a microphone array collects sound signals, first, continuously collects data for about 10 seconds, processes the data for each second by using a generalized cross-correlation algorithm, finds time delay points corresponding to first several maximum peaks of a generalized cross-correlation function for each second, then substitutes time delays of multiple times into the dynamic programming model, finds a minimum fluctuation time delay line and TDOA, and then slides a time window to calculate subsequent TDOA.
Fig. 2 is a schematic diagram of ten-second data acquisition, extracting 5 delay points corresponding to the maximum peak from the cross power density distribution function of the second data per second, and drawing the delay points into a scatter diagram. It can be seen that the delay distribution at the same time has multiple values, and a general algorithm directly uses the delay corresponding to the maximum peak as the TDOA, but in this way, the requirement on the signal-to-noise ratio of the sound source signal is high, and once the TDOA does not appear on the TDOA corresponding to the maximum peak due to noise or multipath effect, the estimated TDOA has a large error.
Fig. 3 is a time delay point connecting line with the minimum fluctuation found in the ten-second time delay points in fig. 2 by using the invention, and a black connecting line is the time delay point connecting line with the minimum fluctuation, and it is seen from the figure that several points below the connecting line can be connected into a time delay point connecting line with smaller fluctuation, but because of the formula proposed by us:
Figure BDA0001952415660000042
in which P is addedhThis quantity gives us the preference to select lines that correspond to delay points on the cross power density distribution function where the peak is large, when selecting the lines.
FIGS. 4 and 5 show the results of our use of this method for initial TDOA estimation. FIG. 4 shows the effect of continuous TDOA tracking estimation using Gaussian function combined with GCC algorithm after setting the initial value of TDOA estimation to 0, which is implemented as follows:
calculating a Gaussian distribution prior probability density distribution function according to the time delay estimation result at the previous moment and the cross-power spectrum density distribution function:
Figure BDA0001952415660000043
where μ ═ tpast，
Figure BDA0001952415660000044
tpastRepresenting the final result of the time delay estimation at the last moment, B representing the cross-power spectral density distribution function at the last moment
Figure BDA0001952415660000045
The interval range limited by the zero points on the left and right sides of the corresponding value; according to
Figure BDA0001952415660000051
Calculating the cross-power spectrum density distribution function at the current moment, wherein t satisfies t e [ -d/c, d/c]The constraint condition is that,
Figure BDA0001952415660000052
the cross-power spectral density function at the current moment is shown, d is the distance between the two microphones, and c is the sound velocity. It can be seen that, since the initial value is wrong, the tracking effect is very poor when the TDOA estimate is subsequently tracked by using the gaussian algorithm, and although the TDOA estimate is subsequently wrong for a long time before the tracking algorithm is iterated to return the TDOA to the correct track. FIG. 5 shows the effect of subsequent continuous TDOA tracking estimation using the tracking algorithm after we set the initial value of TDOA as the TDOA extracted by the present invention.
The above-described embodiments are intended to illustrate rather than to limit the invention, and any modifications and variations of the present invention are within the spirit of the invention and the scope of the appended claims.

Claims (3)

1. A mobile sound source arrival time delay estimation method based on a dynamic programming algorithm is characterized by comprising the following steps:
(1) two groups of time domain acoustic signals x collected at t moment of microphone array are calculated according to generalized cross-correlation function1(t)、x2(t) cross-power spectral density distribution function
Figure FDA0002611001020000011
Figure FDA0002611001020000012
Wherein
Figure FDA0002611001020000013
Is x1(t) and x2(t) the product of the Fourier transform results,
Figure FDA0002611001020000014
is x1(t) and x2(t) a frequency domain filter;
(2) extracting to obtain
Figure FDA0002611001020000015
The function continuously processes m time delays corresponding to m maximum peak values of each time in n time moments;
(3) according to the consistency of the time delay estimation of the mobile sound source, finding a curve with the minimum fluctuation in time delay connected curves at n moments by using the following formula, and calculating the time delay TDOA of the microphone:
Figure FDA0002611001020000016
wherein T ishRepresenting the time delay at the h-th instant, ThTaken from the set [ t ]h,1,th,2,...,th,m]，th,iRepresenting the time delay corresponding to the ith maximum peak value at the h moment; phRepresents ThThe peak value of the cross-power spectral density distribution function corresponding to the obtained time delay; a is used for coming PhAnd ThThe size of the magnetic particles is adjusted to the same order of magnitude;
(4) solving the formula (2) through a dynamic programming algorithm to obtain the final TDOA at the nth moment;
(5) and obtaining the TDOA at each moment after the time by using the sliding time window.
2. The method for estimating arrival time delay of a mobile sound source based on dynamic programming algorithm as claimed in claim 1, wherein in step (1),
Figure FDA0002611001020000017
using phase-shift (PHAT) weighting functions
Figure FDA0002611001020000018
3. The method for estimating arrival delay of a mobile sound source based on dynamic programming algorithm as claimed in claim 1, wherein in the step (4), the following dynamic programming algorithm is used to solve equation (2):
Figure FDA0002611001020000019
wherein i, v ═ 1, …, m; h 2., n;
Figure FDA00026110010200000110
the ith intermediate variable representing the h-th time instant, here the initial value
Figure FDA00026110010200000111
Is set as the magnitude of the v-th peak of the cross-power spectral density distribution function at the first moment, and the final result is obtained by the iterative formula (3)
Figure FDA00026110010200000112
The final TDOA is calculated using the following formula:
Figure FDA0002611001020000021
CN201910055840.0A 2019-01-18 2019-01-18 Mobile sound source arrival time delay estimation method based on dynamic programming algorithm Active CN109633553B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910055840.0A CN109633553B (en) 2019-01-18 2019-01-18 Mobile sound source arrival time delay estimation method based on dynamic programming algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910055840.0A CN109633553B (en) 2019-01-18 2019-01-18 Mobile sound source arrival time delay estimation method based on dynamic programming algorithm

Publications (2)

Publication Number Publication Date
CN109633553A CN109633553A (en) 2019-04-16
CN109633553B true CN109633553B (en) 2020-11-13

Family

ID=66062609

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910055840.0A Active CN109633553B (en) 2019-01-18 2019-01-18 Mobile sound source arrival time delay estimation method based on dynamic programming algorithm

Country Status (1)

Country Link
CN (1) CN109633553B (en)

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106131949A (en) * 2016-06-02 2016-11-16 上海物联网有限公司 A kind of method of estimation time of advent based on average energy value detection

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102103200B (en) * 2010-11-29 2012-12-05 清华大学 Acoustic source spatial positioning method for distributed asynchronous acoustic sensor
CN103850679B (en) * 2014-04-01 2019-12-17 北京师范大学 method for reconstructing sound wave time difference curve by using various logging curves
CN105277921B (en) * 2015-09-23 2017-12-26 浙江大学 A kind of passive acoustic localization method based on smart mobile phone
KR101869865B1 (en) * 2015-12-21 2018-06-22 서울대학교산학협력단 The method of indoor localization using pre-designed acoustic source data
KR101875273B1 (en) * 2016-07-11 2018-08-02 국방과학연구소 Apparatus and Method for estimating position of acoustic source using Time Difference Of Arrival scheme based on sound wave directly transmitted from acoustic source and sound wave measurement reflected from the ground
CN108241106A (en) * 2017-12-29 2018-07-03 国网北京市电力公司 Delay time estimation method and device

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106131949A (en) * 2016-06-02 2016-11-16 上海物联网有限公司 A kind of method of estimation time of advent based on average energy value detection

Also Published As

Publication number Publication date
CN109633553A (en) 2019-04-16

Similar Documents

Publication Publication Date Title
Zhong et al. Particle filtering approaches for multiple acoustic source detection and 2-D direction of arrival estimation using a single acoustic vector sensor
Wei et al. Multidimensional scaling analysis for passive moving target localization with TDOA and FDOA measurements
CN111474521B (en) Sound source positioning method based on microphone array in multipath environment
CN101509972B (en) Wideband radar detecting method for correcting correlation matrix based on high resolution target distance image
CN105467364A (en) Method and apparatus for localizing target sound source
CN109188362B (en) Microphone array sound source positioning signal processing method
CN110515038B (en) Self-adaptive passive positioning device based on unmanned aerial vehicle-array and implementation method
CN107015205B (en) False target elimination method for distributed MIMO radar detection
CN106707258A (en) Multi-parameter estimation method for micro-motion target under non-Gaussian background
CN110495185B (en) Voice signal processing method and device
CN112712557A (en) Super-resolution CIR indoor fingerprint positioning method based on convolutional neural network
CN109901112A (en) It is positioned simultaneously based on the acoustics that multiple channel acousto obtains and builds drawing method
CN110927660A (en) Mixed signal direction of arrival estimation method based on co-prime array
CN111965596A (en) Low-complexity single-anchor node positioning method and device based on joint parameter estimation
CN102243309B (en) GNSS cross-correlation interferences suppressing method and device
CN110488277B (en) Distributed active and passive radar combined positioning method based on external radiation source
CN103901400A (en) Binaural sound source positioning method based on delay compensation and binaural coincidence
CN108564962B (en) Unmanned aerial vehicle sound signal enhancement method based on tetrahedral microphone array
EP2362238B1 (en) Estimating the distance from a sensor to a sound source
CN109633554A (en) Moving sound based on probabilistic data association reaches delay time estimation method
CN109633553B (en) Mobile sound source arrival time delay estimation method based on dynamic programming algorithm
CN112114300A (en) Underwater weak target detection method based on image sparse representation
Shi et al. Neural network based localization using outdoor lte measurements
CN112731292B (en) Low-altitude flight target signal time delay estimation method with local IMF energy weighting
CN112731283B (en) High subsonic flight target acoustic direction finding method based on multistage wiener filter

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