CN103778288A - Ant colony optimization-based near field sound source localization method under non-uniform array noise condition - Google Patents

Ant colony optimization-based near field sound source localization method under non-uniform array noise condition Download PDF

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CN103778288A
CN103778288A CN201410016858.7A CN201410016858A CN103778288A CN 103778288 A CN103778288 A CN 103778288A CN 201410016858 A CN201410016858 A CN 201410016858A CN 103778288 A CN103778288 A CN 103778288A
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CN103778288B (en
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侯云山
翟红村
金勇�
吴景艳
冀保峰
汤艳红
翟蒲杰
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Henan University of Science and Technology
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Abstract

The invention relates to an ant colony optimization-based near field sound source localization method under a non-uniform array noise condition and belongs to the technical field of array signal processing. The method comprises the steps of firstly establishing a near field sound source signal model based on a plane array, further establishing the maximum likelihood estimation problem of direction and distance information of a sound source to be estimated under a space non-uniform noise condition, and finally estimating sound source parameters by using a continuous space ant colony optimization method, thus finishing the localization on the near field sound source under the space non-uniform noise condition. The method is higher in estimation precision, both direction and distance mean square errors of the sound source under low signal-to-noise are smaller than those of a conventional maximum likelihood method, and the direction and distance square mean errors of the sound source under the high signal-to-noise condition approach to a Cramer-Rao Boundary and are far superior than those of the normal maximum likelihood method.

Description

Near field sound localization method under non-homogeneous array element noise conditions based on ant group optimization
Technical field
The present invention relates near field sound localization method under a kind of non-homogeneous array element noise conditions based on ant group optimization, belong to Array Signal Processing field.
Background technology
Because auditory localization is in the significant application value of the aspects such as radar, sonar, radio communication, seismology and biomedicine, in the last few years, the auditory localization based on sensor array had become one of study hotspot of signal processing.But traditional auditory localization is mostly based on arrowband, far field hypothesis, so can only estimate the azimuth information of sound source.In the time that sound source is closer from array, when the near field in array, because the phase differential between different array element is the function of sound bearing and distance, need to do to arrowband, far field sound localization method the expansion of distance domain, derive thus near field sound localization method under some uniform Gaussian noise assumed conditions, as least variance method, MUSIC and maximum likelihood method etc.In said method, there is the advantages such as the high angle resolution characteristic of consistent nothing partially, under minimum variance and Low SNR although maximum likelihood method computational complexity is high, be often used as the standard of other method performance of assessment.
But the Gaussian noise of space uniform is supposed and is not met some Practical Project problems.For example, the type that compacts radar array is owing to existing correlation noise, array to be output as coloured stochastic process between array element.And for example, for the larger microphone array of array element distance because the reasons such as mechanical vibration, array calibration error cause the output power of array element noise not identical.In above-mentioned two situations, the performance of the near field sources algorithm based on space uniform Gaussian noise assumed condition is by degradation.And conventional maximum likelihood method under the non-homogeneous Gaussian noise condition in space to the location of near field sound source in, calculate due to determining of sound source parameter need to be carried out to multi-C parameter space search, thereby high being difficult to of complexity positions.
Summary of the invention
Object of the present invention is just to provide near field sound localization method under a kind of non-homogeneous array element noise conditions based on ant group optimization, thereby it has realized the location near field sound source by the method that adopts continuous space ant group optimization in maximum likelihood localization method.
To achieve these goals, technical scheme of the present invention is: near field sound localization method under the non-homogeneous array element noise conditions based on ant group optimization, comprises the steps:
Step 1, set up near field acoustic source array output model
The signal wave that arrives array under Near Field is spherical wave, establishes the now position coordinates of sound source to be
Figure 605006DEST_PATH_IMAGE001
, the position vector of individual array element is
Figure 739315DEST_PATH_IMAGE003
(
Figure 2014100168587100002DEST_PATH_IMAGE004
), so, sound source is expressed as to the steering vector of array
Figure 369011DEST_PATH_IMAGE005
, in formula
Figure 2014100168587100002DEST_PATH_IMAGE006
represent signal carrier frequency, work as existence
Figure 106023DEST_PATH_IMAGE007
when individual near field sound source, array output is expressed as
Figure 2014100168587100002DEST_PATH_IMAGE008
, in formula,
Figure 2014100168587100002DEST_PATH_IMAGE009
for array
Figure 2014100168587100002DEST_PATH_IMAGE010
the output of inferior sampling,
Figure 2014100168587100002DEST_PATH_IMAGE011
for array manifold,
Figure 2014100168587100002DEST_PATH_IMAGE012
for
Figure 255506DEST_PATH_IMAGE007
individual unknown determinacy sound source
Figure 560717DEST_PATH_IMAGE013
inferior output, for the irrelevant white complex gaussian noise in spatial domain, its covariance matrix is ;
Step 2, set up the maximal possibility estimation problem of solve for parameter
Definition treats that estimated parameter vector is
Figure 2014100168587100002DEST_PATH_IMAGE016
, in formula ,
Figure 2014100168587100002DEST_PATH_IMAGE018
, serve as reasons
Figure 2014100168587100002DEST_PATH_IMAGE020
diagonal element form vector,
Figure 682202DEST_PATH_IMAGE021
log-likelihood function be
Figure 2014100168587100002DEST_PATH_IMAGE022
, in formula
Figure 2014100168587100002DEST_PATH_IMAGE023
,
Figure 2014100168587100002DEST_PATH_IMAGE024
maximal possibility estimation be reduced to , sound source parameter
Figure 2014100168587100002DEST_PATH_IMAGE026
,
Figure 116037DEST_PATH_IMAGE027
maximal possibility estimation be
Figure 2014100168587100002DEST_PATH_IMAGE028
, parameter
Figure 489380DEST_PATH_IMAGE029
, maximal possibility estimation be
Figure 870814DEST_PATH_IMAGE031
;
Step 3, ant group optimization solve solve for parameter
1., exist
Figure DEST_PATH_IMAGE032
dimension
Figure 962398DEST_PATH_IMAGE033
in search volume, generate at random
Figure DEST_PATH_IMAGE034
individual ant, wherein
Figure 844903DEST_PATH_IMAGE035
, establish
Figure DEST_PATH_IMAGE036
individual ant
Figure 72754DEST_PATH_IMAGE037
for
Figure DEST_PATH_IMAGE038
, in formula,
Figure 890668DEST_PATH_IMAGE039
represent individual particle is
Figure DEST_PATH_IMAGE040
position in dimension;
2., objective definition function for
Figure 972522DEST_PATH_IMAGE031
, and use initial
Figure 820392DEST_PATH_IMAGE034
individual ant constructs archives table, each ant according to
Figure DEST_PATH_IMAGE042
descending sort; Described archives table is ;
3., each ant is divided
Figure 744101DEST_PATH_IMAGE032
step produces respectively each component of an ant, the
Figure 71177DEST_PATH_IMAGE040
step with probability
Figure 230894DEST_PATH_IMAGE045
choose one dimension Gaussian function
Figure DEST_PATH_IMAGE046
and it is once sampled, wherein
Figure 518787DEST_PATH_IMAGE047
,
Figure DEST_PATH_IMAGE048
, in formula
Figure 156573DEST_PATH_IMAGE049
,
Figure DEST_PATH_IMAGE050
, the
Figure 213521DEST_PATH_IMAGE051
individual one dimension Gaussian function
Figure 668774DEST_PATH_IMAGE046
for
Figure DEST_PATH_IMAGE052
, its average
Figure DEST_PATH_IMAGE053
and variance
Figure DEST_PATH_IMAGE054
be defined as respectively
Figure DEST_PATH_IMAGE055
,
Figure DEST_PATH_IMAGE056
, in formula
Figure DEST_PATH_IMAGE057
;
4., calculate the new target function value that produces ant, if the target function value of some ant is greater than in archives table the target function value of the most several ants of below in them, replace with these new ants the old solution that target function value is little, and archives table is resequenced;
5., record the ant of the first row after each archives table upgrades, if archives table upgrades after several times, the last standard deviation of the ant of the first row of several archives tables is continuously less than certain vector given in advance, obtained an optimum solution, each component of the average of the ant of the first row of these archives tables is required
Figure 194695DEST_PATH_IMAGE033
estimated value
Figure 636172DEST_PATH_IMAGE058
, otherwise turn to step 3.;
6., according to tried to achieve
Figure 672261DEST_PATH_IMAGE058
with hypothesis
Figure DEST_PATH_IMAGE059
initial value calculate
Figure DEST_PATH_IMAGE060
least-squares estimation
Figure 173781DEST_PATH_IMAGE061
, recycling
Figure 436266DEST_PATH_IMAGE058
,
Figure 540488DEST_PATH_IMAGE061
revise
Figure 572029DEST_PATH_IMAGE059
, double counting until
Figure DEST_PATH_IMAGE062
convergence.
Beneficial effect: first the present invention has set up near field sound-source signal model based on planar array, and then set up the maximal possibility estimation problem of sound bearing to be estimated and range information under spatial non-uniform noise condition, and by using continuous space ant colony optimization method finally to estimate sound source parameter value, thereby complete the location near field sound source under spatial non-uniform noise condition.The emulation experiment of double sound source shows that the inventive method estimated accuracy is higher, sound bearing and be all less than conventional maximum likelihood method apart from square error under low signal-to-noise ratio, and the square error of sound bearing and distance is all approached CramerRao circle under high s/n ratio condition, the estimated accuracy of the inventive method is much better than conventional maximum likelihood method.
Accompanying drawing explanation
Fig. 1 is that square error comparison diagram is estimated in sound source 1 orientation of the inventive method in emulation experiment, conventional maximum likelihood method and CramerRao circle.
Fig. 2 is that square error comparison diagram is estimated in sound source 2 orientation of the inventive method in emulation experiment, conventional maximum likelihood method and CramerRao circle.
Fig. 3 is the sound source 1 distance estimations square error comparison diagram of the inventive method in emulation experiment, conventional maximum likelihood method and CramerRao circle.
Fig. 4 is the sound source 2 distance estimations square error comparison diagrams of the inventive method in emulation experiment, conventional maximum likelihood method and CramerRao circle.
Fig. 5 is the near field Source Model figure that the present invention sets up.
Embodiment
Near field sound localization method under non-homogeneous array element noise conditions based on ant group optimization, comprises the steps:
Step 1, set up near field acoustic source array output model
The signal wave that arrives array under Near Field is spherical wave, as shown in Figure 5, establishes the now position coordinates of sound source and is
Figure 244450DEST_PATH_IMAGE063
(1)
Figure 853286DEST_PATH_IMAGE002
the position vector of individual array element is
Figure 26778DEST_PATH_IMAGE003
(
Figure 912826DEST_PATH_IMAGE004
), determined by array element coordinate, sound source to the
Figure DEST_PATH_IMAGE064
the distance of array element is
Figure DEST_PATH_IMAGE065
,(2)
In formula
Figure DEST_PATH_IMAGE066
represent Euclidean vector norm, the
Figure DEST_PATH_IMAGE067
array element is expressed as with respect to the time delay of reference array element
Figure DEST_PATH_IMAGE068
,(3)
In above formula
Figure DEST_PATH_IMAGE069
for the velocity of sound,
Figure 506880DEST_PATH_IMAGE070
for sound source is to the distance of reference array element.So, sound source is expressed as to the steering vector of array
Figure DEST_PATH_IMAGE071
(4)
In formula
Figure 478379DEST_PATH_IMAGE006
represent signal carrier frequency, so, existence worked as when individual near field sound source, array output can be expressed as
,(5)
In formula,
Figure 69394DEST_PATH_IMAGE009
for array
Figure 528188DEST_PATH_IMAGE013
the output of inferior sampling,
Figure 777904DEST_PATH_IMAGE011
for array manifold,
Figure 763178DEST_PATH_IMAGE012
for
Figure 948302DEST_PATH_IMAGE007
individual unknown determinacy sound source
Figure 19027DEST_PATH_IMAGE010
inferior output,
Figure 72433DEST_PATH_IMAGE072
for the irrelevant white complex gaussian noise in spatial domain, its covariance matrix is
Figure 787579DEST_PATH_IMAGE015
.(6)
Step 2, set up the maximal possibility estimation problem of solve for parameter
Definition treats that estimated parameter vector is
Figure 268239DEST_PATH_IMAGE016
, in formula
Figure 436047DEST_PATH_IMAGE017
,
Figure 293144DEST_PATH_IMAGE018
,
Figure DEST_PATH_IMAGE073
serve as reasons
Figure 862797DEST_PATH_IMAGE020
diagonal element form vector, so
Figure 514358DEST_PATH_IMAGE024
likelihood function can be expressed as
Figure 435041DEST_PATH_IMAGE074
(7)
In formula , (7) formula is taken the logarithm and ignored constant term, obtain
Figure 830250DEST_PATH_IMAGE021
log-likelihood function be
,(8)
In formula
Figure 342451DEST_PATH_IMAGE076
(9)
So
Figure 484850DEST_PATH_IMAGE021
maximal possibility estimation can be reduced to
Figure 418171DEST_PATH_IMAGE077
(10)
Formula (10) is one
Figure DEST_PATH_IMAGE078
dimension multiparameter associating estimation problem, sound source parameter to be estimated ,
Figure 221359DEST_PATH_IMAGE027
with the non-homogeneous Gaussian noise of array element
Figure 585476DEST_PATH_IMAGE079
be coupled, the computational complexity of global search is very high.In order to address this problem, first fixing
Figure 588067DEST_PATH_IMAGE029
,
Figure 111452DEST_PATH_IMAGE027
, ask (8) formula about vector
Figure 151083DEST_PATH_IMAGE079
gradient, and to make it be 0, so we obtain
Figure 392709DEST_PATH_IMAGE067
array element noise power
Figure DEST_PATH_IMAGE080
estimation
Figure DEST_PATH_IMAGE081
,(11)
In formula,
Figure 808778DEST_PATH_IMAGE082
represent residual vector
Figure DEST_PATH_IMAGE083
?
Figure 38598DEST_PATH_IMAGE067
element
(12)
Order
Figure 514710DEST_PATH_IMAGE085
and substitute in (8) formula
Figure 853419DEST_PATH_IMAGE079
, obtain sound source parameter to be estimated
Figure 197812DEST_PATH_IMAGE029
,
Figure 695790DEST_PATH_IMAGE027
approximate log-likelihood function
(13)
So sound source parameter , maximal possibility estimation be
Figure 51313DEST_PATH_IMAGE087
(14)
On the other hand, can fix
Figure 403797DEST_PATH_IMAGE029
, , ask
Figure 394067DEST_PATH_IMAGE060
least-squares estimation be
Figure DEST_PATH_IMAGE088
,(15)
In formula
Figure 955629DEST_PATH_IMAGE089
represent that Moore-Penrose is contrary, will (8) formula of bringing into obtains
Figure 37986DEST_PATH_IMAGE029
,
Figure 151435DEST_PATH_IMAGE030
approximate Likelihood Function
(16)
In formula, so, parameter
Figure 176340DEST_PATH_IMAGE029
,
Figure 378782DEST_PATH_IMAGE093
maximal possibility estimation be
Figure 663133DEST_PATH_IMAGE031
.(17)
Step 3, ant group optimization solve solve for parameter
1., exist
Figure 685447DEST_PATH_IMAGE032
dimension
Figure 713446DEST_PATH_IMAGE033
in search volume, generate at random
Figure 895029DEST_PATH_IMAGE034
individual initial value (
Figure DEST_PATH_IMAGE094
individual ant), wherein
Figure 225647DEST_PATH_IMAGE035
, establish individual ant
Figure 566947DEST_PATH_IMAGE095
for
Figure 603036DEST_PATH_IMAGE038
(18)
In formula,
Figure DEST_PATH_IMAGE096
represent
Figure 104555DEST_PATH_IMAGE036
individual particle is
Figure 226095DEST_PATH_IMAGE040
position in dimension;
2., objective definition function
Figure 471263DEST_PATH_IMAGE041
for
Figure 627438DEST_PATH_IMAGE031
, and use initial
Figure 299858DEST_PATH_IMAGE034
individual ant constructs archives table, each ant according to
Figure DEST_PATH_IMAGE097
descending sort; Described archives table is
Figure 784061DEST_PATH_IMAGE043
;
3., each ant is divided
Figure 691974DEST_PATH_IMAGE032
step produces respectively each component of an ant, the
Figure 702655DEST_PATH_IMAGE040
step
Figure DEST_PATH_IMAGE098
with probability
Figure 545977DEST_PATH_IMAGE045
choose one dimension Gaussian function and it is once sampled,
Figure DEST_PATH_IMAGE099
value determined by following formula
Figure 229079DEST_PATH_IMAGE047
，
Figure 235213DEST_PATH_IMAGE048
(19)
In formula value provided by following formula
Figure 374070DEST_PATH_IMAGE049
(20)
Obviously have , here
Figure 567285DEST_PATH_IMAGE050
an adjustable parameter, when hour, algorithm can be with the larger probability selection forward solution that sorts for value, when
Figure 957946DEST_PATH_IMAGE102
be worth when larger, algorithm selects the probability of separating each solution in archives table to be just more or less the same;
The individual one dimension Gaussian function for
Figure 933490DEST_PATH_IMAGE103
(21)
Its average
Figure DEST_PATH_IMAGE104
and variance
Figure 862262DEST_PATH_IMAGE054
be defined as respectively
Figure 702043DEST_PATH_IMAGE055
(22)
(23)
In formula
Figure 616089DEST_PATH_IMAGE057
be an adjustable parameter, be similar to the rate of volatilization in discrete space ant group algorithm;
4., calculate the new target function value that produces ant, if the target function value of some ant is greater than in archives table the target function value of the most several ants of below in them, replace with these new ants the old solution that target function value is little, and archives table is resequenced;
5., record the ant of the first row after each archives table upgrades, if archives table upgrades after several times, the last standard deviation of the ant of the first row of several archives tables is continuously less than certain vector given in advance, obtained an optimum solution, each component of the average of the ant of the first row of these archives tables is required
Figure 82973DEST_PATH_IMAGE033
estimated value
Figure 42839DEST_PATH_IMAGE058
, otherwise turn to step 3.;
6., according to tried to achieve
Figure 694400DEST_PATH_IMAGE058
with hypothesis
Figure 349504DEST_PATH_IMAGE059
initial value calculate least-squares estimation
Figure 434452DEST_PATH_IMAGE061
, recycling
Figure 256914DEST_PATH_IMAGE058
,
Figure 523947DEST_PATH_IMAGE061
revise
Figure 598214DEST_PATH_IMAGE059
, double counting until
Figure 267092DEST_PATH_IMAGE062
convergence.
Emulation experiment
Emulation experiment adopts arrowband sound source, establishes the velocity of sound and is
Figure DEST_PATH_IMAGE105
, frequency is 1000 , for convenience of the hypothetical target angle of pitch be
Figure DEST_PATH_IMAGE107
, using even 8 yuan of linear arrays, array element distance is the half of sound source wavelength, normalization noise covariance matrix is
Figure DEST_PATH_IMAGE108
, all experiment sampling numbers are 100 times, and two sound bearings, distance parameters are respectively
Figure DEST_PATH_IMAGE109
, , signal to noise ratio (S/N ratio)
Figure DEST_PATH_IMAGE111
change 100 Monte Carlo Experiments.The estimation square error (MSE) that the azimuth-range of the inventive method is estimated and conventional maximum likelihood method (conventional ML) and CramerRao circle (CRB) to such as shown in accompanying drawing 1～4.
Can find out from accompanying drawing 1～4, the estimated accuracy of the inventive method will be much better than conventional ML method, and in the time that signal to noise ratio (S/N ratio) is less than 10 dB, the estimation square error (MSE) of the azimuth-range of this paper method is all less than conventional ML method; In the time that signal to noise ratio (S/N ratio) is greater than 10 dB, the estimation square error (MSE) of the azimuth-range that this paper method obtains has all been approached CramerRao circle (CRB) well.

Claims (1)

1. near field sound localization method under the non-homogeneous array element noise conditions based on ant group optimization, is characterized in that: comprise the steps:
Step 1, set up near field acoustic source array output model
The signal wave that arrives array under Near Field is spherical wave, establishes the now position coordinates of sound source to be
Figure 817349DEST_PATH_IMAGE001
,
Figure 609855DEST_PATH_IMAGE002
the position vector of individual array element is
Figure 389592DEST_PATH_IMAGE003
(
Figure 660168DEST_PATH_IMAGE004
), so, sound source is expressed as to the steering vector of array
Figure 349906DEST_PATH_IMAGE005
, in formula
Figure 172369DEST_PATH_IMAGE006
represent signal carrier frequency, work as existence
Figure 314768DEST_PATH_IMAGE007
when individual near field sound source, array output is expressed as
Figure 513669DEST_PATH_IMAGE008
, in formula,
Figure 57913DEST_PATH_IMAGE009
for array
Figure 51277DEST_PATH_IMAGE010
the output of inferior sampling,
Figure 805607DEST_PATH_IMAGE011
for array manifold,
Figure 417985DEST_PATH_IMAGE012
for individual unknown determinacy sound source
Figure 246580DEST_PATH_IMAGE010
inferior output, for the irrelevant white complex gaussian noise in spatial domain, its covariance matrix is ;
Step 2, set up the maximal possibility estimation problem of solve for parameter
Definition treats that estimated parameter vector is
Figure 282167DEST_PATH_IMAGE015
, in formula
Figure 492699DEST_PATH_IMAGE016
,
Figure 96987DEST_PATH_IMAGE017
,
Figure 175802DEST_PATH_IMAGE018
serve as reasons
Figure 549145DEST_PATH_IMAGE019
diagonal element form vector,
Figure 320792DEST_PATH_IMAGE020
log-likelihood function be
Figure 146797DEST_PATH_IMAGE021
, in formula
Figure 29302DEST_PATH_IMAGE022
,
Figure 257152DEST_PATH_IMAGE020
maximal possibility estimation be reduced to
Figure 199700DEST_PATH_IMAGE023
, sound source parameter ,
Figure 933618DEST_PATH_IMAGE025
maximal possibility estimation be
Figure 281554DEST_PATH_IMAGE026
, parameter
Figure 395004DEST_PATH_IMAGE024
, maximal possibility estimation be
Figure 295275DEST_PATH_IMAGE027
;
Step 3, ant group optimization solve solve for parameter
1., exist
Figure 356772DEST_PATH_IMAGE028
dimension
Figure 516489DEST_PATH_IMAGE016
in search volume, generate at random
Figure 804382DEST_PATH_IMAGE029
individual ant, wherein
Figure 707747DEST_PATH_IMAGE030
, establish individual ant
Figure 764696DEST_PATH_IMAGE032
for
Figure 954368DEST_PATH_IMAGE033
, in formula,
Figure 729558DEST_PATH_IMAGE034
represent
Figure 171034DEST_PATH_IMAGE031
individual particle is
Figure 472703DEST_PATH_IMAGE035
position in dimension;
2., objective definition function
Figure 708643DEST_PATH_IMAGE036
for
Figure 971128DEST_PATH_IMAGE027
, and use initial
Figure 340930DEST_PATH_IMAGE029
individual ant constructs archives table, each ant according to
Figure 83454DEST_PATH_IMAGE037
descending sort; Described archives table is
Figure 755875DEST_PATH_IMAGE038
;
3., each ant is divided
Figure 364711DEST_PATH_IMAGE028
step produces respectively each component of an ant, the step
Figure 424251DEST_PATH_IMAGE039
with probability
Figure 392207DEST_PATH_IMAGE040
choose one dimension Gaussian function
Figure 363705DEST_PATH_IMAGE041
and it is once sampled, wherein
Figure 75309DEST_PATH_IMAGE042
,
Figure 815863DEST_PATH_IMAGE043
, in formula
Figure 954720DEST_PATH_IMAGE044
,
Figure 413515DEST_PATH_IMAGE045
, the
Figure 663230DEST_PATH_IMAGE046
individual one dimension Gaussian function
Figure 523870DEST_PATH_IMAGE041
for
Figure DEST_PATH_IMAGE047
, its average
Figure 708995DEST_PATH_IMAGE048
and variance
Figure 779719DEST_PATH_IMAGE049
be defined as respectively
Figure 833126DEST_PATH_IMAGE050
, , in formula
Figure 28932DEST_PATH_IMAGE052
;
4., calculate the new target function value that produces ant, if the target function value of some ant is greater than in archives table the target function value of the most several ants of below in them, replace with these new ants the old solution that target function value is little, and archives table is resequenced;
5., record the ant of the first row after each archives table upgrades, if archives table upgrades after several times, the last standard deviation of the ant of the first row of several archives tables is continuously less than certain vector given in advance, obtained an optimum solution, each component of the average of the ant of the first row of these archives tables is required
Figure 196739DEST_PATH_IMAGE016
estimated value
Figure 53837DEST_PATH_IMAGE053
, otherwise turn to step 3.;
6., according to tried to achieve
Figure 623490DEST_PATH_IMAGE053
with hypothesis
Figure 275051DEST_PATH_IMAGE054
initial value calculate
Figure 195733DEST_PATH_IMAGE025
least-squares estimation
Figure 590943DEST_PATH_IMAGE055
, recycling
Figure 15102DEST_PATH_IMAGE053
,
Figure 103144DEST_PATH_IMAGE055
revise
Figure 245543DEST_PATH_IMAGE054
, double counting until
Figure 444443DEST_PATH_IMAGE056
convergence.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104330787A (en) * 2013-12-30 2015-02-04 河南科技大学 Underwater motion array multi-target detection and position estimation integrated method
CN105548957A (en) * 2016-01-18 2016-05-04 吉林大学 Multi-target far and near field mixed source positioning method under unknown colored noise
CN107255796A (en) * 2017-07-25 2017-10-17 西安交通大学 Arrowband near-field signals source localization method under a kind of non-uniform noise
CN111383440A (en) * 2018-12-29 2020-07-07 北京骑胜科技有限公司 Standard parking method and device for shared vehicles and electronic equipment

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090052689A1 (en) * 2005-05-10 2009-02-26 U.S.A. As Represented By The Administrator Of The National Aeronautics And Space Administration Deconvolution Methods and Systems for the Mapping of Acoustic Sources from Phased Microphone Arrays
CN101595739A (en) * 2007-01-26 2009-12-02 微软公司 The multisensor auditory localization

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090052689A1 (en) * 2005-05-10 2009-02-26 U.S.A. As Represented By The Administrator Of The National Aeronautics And Space Administration Deconvolution Methods and Systems for the Mapping of Acoustic Sources from Phased Microphone Arrays
CN101595739A (en) * 2007-01-26 2009-12-02 微软公司 The multisensor auditory localization

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
KRZYSZTOF SOCHA等: "Ant colony optimization for continuous domains", 《EUROPEAN JOURNAL OF OPERATIONAL RESEARCH》 *
MOHAMMED NABILELKORSO等: "Statistical analysis of achievable resolution limit in the near field source locatization context", 《SIGNALPROCESSING》 *
刘先省等: "阵元非均匀高斯白噪声背景下的近场声源定位研究", 《河南大学学报（自然科学版）》 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104330787A (en) * 2013-12-30 2015-02-04 河南科技大学 Underwater motion array multi-target detection and position estimation integrated method
CN105548957A (en) * 2016-01-18 2016-05-04 吉林大学 Multi-target far and near field mixed source positioning method under unknown colored noise
CN105548957B (en) * 2016-01-18 2017-11-17 吉林大学 Multiple target distance field mixing source localization method under a kind of unknown coloured noise
CN107255796A (en) * 2017-07-25 2017-10-17 西安交通大学 Arrowband near-field signals source localization method under a kind of non-uniform noise
CN107255796B (en) * 2017-07-25 2020-03-13 西安交通大学 Method for positioning narrow-band near-field signal source under non-uniform noise
CN111383440A (en) * 2018-12-29 2020-07-07 北京骑胜科技有限公司 Standard parking method and device for shared vehicles and electronic equipment

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