CN109683134B - High-resolution positioning method for rotary sound source - Google Patents

High-resolution positioning method for rotary sound source Download PDF

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CN109683134B
CN109683134B CN201910014966.3A CN201910014966A CN109683134B CN 109683134 B CN109683134 B CN 109683134B CN 201910014966 A CN201910014966 A CN 201910014966A CN 109683134 B CN109683134 B CN 109683134B
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初宁
黄乾
汪琳琳
宁岳
吴大转
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Abstract

The invention discloses a high-resolution positioning method facing a rotary sound source, which comprises the following steps: setting a sound source scanning grid plane and a microphone array, acquiring an acoustic signal of a sound source to be detected to obtain an original acoustic signal, and performing frequency spectrum and time-frequency spectrum analysis to obtain a signal frequency of the original acoustic signal; dividing the obtained original sound signal into a plurality of sub-signals, wherein the number of sampling points contained in each sub-signal is not less than the minimum number of sampling points required by positioning the sound source position; substituting the sub-signals into a Capon beam forming algorithm for operation, scanning all grids of a sound source scanning plane one by one, and determining the position of a sound source to be detected in a time period corresponding to the sub-signals; and repeatedly substituting and scanning to further determine the position of the sound source to be detected in the time period corresponding to the original sound signal. The method carries out segmentation processing on the time domain sampling points, can position the position of the rotary sound source at a certain instant, and can still keep better positioning accuracy under the condition of low signal-to-noise ratio.

Description

High-resolution positioning method for rotary sound source
Technical Field
The invention relates to the technical field of acoustic signal processing, in particular to a high-resolution positioning method for a rotary sound source.
Background
In the sound source positioning, sound signal sensors such as a hydrophone and a microphone are adopted to collect sound signals, and the position and energy information of a target sound source are positioned through a series of processing algorithms. Currently, the commonly used positioning algorithms in the field of sound source positioning are mainly classified into beam forming algorithms and spectrum estimation methods.
The spectral estimation method adopts eigenvalue decomposition or subarray translation invariance to carry out spectral peak estimation. Although the spectral estimation algorithm has good spatial resolution, the good performance can be obtained only under ideal conditions, and the number of sound sources needs to be known in advance, so the algorithm can not necessarily maintain extremely high resolution in actual positioning.
The beam forming technique is actually an algorithm based on maximum likelihood estimation, namely, the delay and weighted summation processing is carried out on the output of each array element, so that the output of the array has the maximum response to the signal incident in a certain specified direction. The conventional beam forming algorithm has high resolution only for a sound source in a certain direction, and sound source signals in other directions influence the direction of a target sound source to generate side lobes, grating lobes and the like, so that the identification accuracy is limited.
The Capon beam forming algorithm is based on conventional beam forming, and uses the minimum variance distortionless response azimuth estimation beam former provided by Capon to make the response of non-output signals such as noise and interference signals reduced to the minimum, improve the sound source positioning resolution, and simultaneously make more accurate estimation on the absolute sound pressure level, and keep higher energy resolution and position resolution.
The localization of a rotating sound source is different from that of a normal stationary sound source, because the position of the rotating sound source changes every time the sensor collects an acoustic signal; when a sound source is positioned, a certain number of time domain sampling points are needed to accurately position the position of a target sound source, so that contradiction exists between the rotation of the sound source and the time domain sampling.
Most of the existing technologies for positioning a rotary sound source have the problems of low resolution and poor real-time performance, and particularly, the position of each instantaneous sound source cannot be accurately positioned for low-frequency signals. Therefore, the development of the high-resolution positioning method for the rotary sound source has important significance and practical value.
Disclosure of Invention
Aiming at the defects in the field, the invention provides a high-resolution positioning method facing a rotary sound source, which has higher resolution, carries out segmentation processing on time domain sampling points and can position the rotary sound source at a certain instantaneous position.
The design idea of the invention comprises:
(1) and increasing the sampling frequency to ensure that the same sound source moves a very small distance in a sampling time period, and blurring all positions of the sound source in the sampling time period into a point sound source for positioning.
(2) The precision of the positioning algorithm is improved, and the positioning precision is obtained as high as possible by adopting the least time domain sampling points.
A method for high resolution localization of a rotating sound source, comprising:
(1) taking a plane where a sound source to be detected is positioned when rotating as a sound source scanning plane, and dividing the plane into a plurality of grids;
(2) the method comprises the steps that a microphone array is arranged to collect sound signals of a sound source to be detected to obtain original sound signals, frequency spectrum analysis and time-frequency spectrum analysis are carried out to obtain signal frequencies of the original sound signals, and the plane of the arranged microphone array is parallel to a sound source scanning plane;
(3) dividing the obtained original sound signal into a plurality of sub-signals, wherein the number of sampling points contained in each sub-signal is not less than the minimum number N of sampling points required by positioning the sound source position;
(4) substituting the sub-signals into a Capon beam forming algorithm for operation, scanning all grids of a sound source scanning plane one by one, and determining the position of a sound source to be detected in a time period corresponding to the sub-signals;
(5) and (5) repeating the step (4) until the operation of the original sound signal is completed, and determining the position of the sound source to be detected in the time period corresponding to the original sound signal.
In step (1), the sound source scanning plane refers to an imaginary plane, i.e. a plane region defined in space. Dividing a sound source scanning plane into a plurality of grids, wherein each grid is a position point where a sound source possibly exists, and traversing each grid in a grid-by-grid scanning mode to calculate to find out the position of the sound source.
In the step (2), preferably, the microphone array plane and the sound source scanning plane are coaxially arranged, so that the position coordinate of the sound source is conveniently calibrated.
Preferably, the distance between adjacent microphone elements in the microphone array is not greater than half the wavelength of the sound signal of the sound source to be measured. If the distance between adjacent microphone elements exceeds the half wavelength of the sound signal, obvious side lobes and grating lobes can be generated in the positioning result, the energy of the main lobe is reduced, and the resolution is reduced.
The spectral analysis may employ a Fast Fourier Transform (FFT).
The fast Fourier transform is a fast operation method of discrete Fourier transform, and the operation speed can be greatly improved. The discrete Fourier transform is realized on a computer through the following steps:
a. setting a sampling frequency, and changing a continuous signal into discrete data points according to a certain sampling frequency;
b. limiting the calculation range within a given interval;
c. and performing Fourier transform to obtain a spectrogram of the signal.
The present invention may invoke the FFT algorithm in MAT L AB to process the original acoustic signal.
The time-frequency spectrum analysis may employ a short-time fourier transform (STFT).
The short-time fourier transform solves the problem of the fast fourier transform having no time resolution and is typically used to analyze non-stationary signals. The principle of the algorithm is to window a section of signal, divide it into multiple sections, perform Fourier transform in each section to obtain the local frequency spectrum in the section, and finally splice the sections according to the time sequence to obtain the time frequency spectrum of the signal.
The present invention may invoke the STFT algorithm in MAT L AB to process the original acoustic signal.
In the step (3), the minimum number of sampling points N is the minimum number of sampling points required for positioning a sound source, and may be determined according to sampling frequency, sound source rotation speed, grid side length, and rotation radius of the sound source, and may be calculated according to the following formula:
Figure RE-GDA0001941369190000031
wherein f isrRepresenting the rotational speed of the sound source, fsDenotes the sampling frequency, R denotes the radius of rotation of the sound source, and div denotes the grid side length.
As can be seen from the formula (1), when the side length div of the grid and the rotation speed f of the sound source are equalrAfter determination, the sampling frequency fsThe higher the value of N may be.
Generally, the more sampling points, the higher the positioning accuracy, so the value of N in actual calculation may be larger than the theoretical value in equation (1).
Considering that a certain number of sampling points are needed for better positioning effect when a Capon beam forming algorithm is subsequently adopted for positioning, the value of N is preferably not less than 50.
Therefore, when the theoretical value calculated by the formula (1) is less than 50, the value of N is 50.
In the step (4), the Capon beamforming algorithm adds a constraint condition for the weight vector on the basis of the conventional beamforming algorithm, that is, determines the weight vector based on the Minimum Variance principle (MVDR), so that the output power of the microphone array for the position of the target sound source is a constant coefficient, and the output power for other positions reaches the Minimum value.
The conventional beamforming algorithm generally requires a microphone array signal acquisition model to be established first. If M sound sources are provided and the microphone array comprises Q microphone elements, the signal output of the ith microphone element is xi() Can be expressed as:
Figure RE-GDA0001941369190000041
wherein S isk(t) represents a source signal from the kth sound source,. DELTA.ri() Representing the distance from the kth sound source to the ith array element and representing amplitude attenuation;
Figure RE-GDA0001941369190000042
representing phase information in the process of propagating from the kth sound source to the ith array element; n isi(t) represents a noise signal; t represents time; j is an imaginary unit, j2-1; e is the base of the natural logarithm.
Phase information
Figure RE-GDA0001941369190000043
Can be represented by the following formula:
Figure RE-GDA0001941369190000044
wherein, T represents the period of the acoustic signal, and T is 1/f; f is the signal frequency obtained by performing spectral analysis and time-frequency spectral analysis on the acoustic signal; c represents the sound propagation speed; tau isikRepresenting the propagation time of the kth sound source to the ith microphone element.
For convenience of presentation, let:
Figure RE-GDA0001941369190000045
thus, equation (2) can be expressed as:
Figure RE-GDA0001941369190000046
in matrix form, can be expressed as:
X＝AS+N (6)
wherein X ═ X1(t),x2(t),…,xQ(t)]T，
Figure RE-GDA0001941369190000047
A is the directional matrix of the microphone array, a1(θ)，a2(θ)，···，aM(theta) is a direction vector, which can be expressed as ai(θ)，i＝1，2，···，M，
S＝[S1(t),S2(t),…,SM(t)]T，
N＝[n1(t),n2(t),…,nQ(t)]T。
The principle of the conventional beamforming algorithm is to output x the signal of each microphone element of the microphone arrayi(t) weighted summation is performed, and thus the signal output p (k, t) of the microphone array can be expressed as:
Figure RE-GDA0001941369190000051
wherein k isiThe weights representing the output signals of the i-th microphone element, i.e. the i-th element in the weight vector K.
The spatial power spectrum of the signal output of the microphone array of the conventional beamforming algorithm is:
P＝E[|p(k,t)|2]＝KHRK (8)
where K denotes the weight vector of the microphone array and R is the covariance matrix of the signal outputs of the microphone array, representing the following:
R＝E[XXH](9)。
as can be seen from the principle of mathematical statistics, when the samples are large enough, the mean value of the samples can be approximately regarded as the overall mathematical expectation, so that the covariance matrix of the signals output by the array can be estimated by using N array output samples at equal time intervals, where the ith row and the jth column of the matrix are represented as follows:
Figure RE-GDA0001941369190000052
wherein x isi(k) K element, x, representing the output signal of the i microphone elementj H(k) Representing the complex conjugate of the kth element of the jth microphone element output signal.
Based on the conventional beamforming algorithm, the optimization constraint of Capon beamforming algorithm can be expressed as formulas (11) and (12):
K＝argmin{KHRK} (11)
KHai(θ)＝1 (12)
wherein, aiAnd (θ) is a direction vector, i is 1, 2, …, M.
Equation (12) is also a constraint of the conventional beamforming algorithm, i.e., the weight vector and the direction vector are orthogonal.
When the Capon beam forming algorithm of each grid is operated, the condition that the direction vector corresponding to the grid is orthogonal to the weight vector is only required to be met.
The lagrange multiplier method is used to establish the following equation:
fcapon＝KHRK+λ[KHai(θ)-1](13)
where λ is the regularization parameter.
From equation (13), the weight vector K can be found as:
Figure RE-GDA0001941369190000061
substituting the formula (14) for the formula (8) to obtain the spatial power spectrum P of the signal output of the microphone array of the Capon beam forming algorithmMVDRThe method comprises the following steps:
Figure RE-GDA0001941369190000062
the method for determining the position of the sound source to be detected in the time period corresponding to the sub-signal of the segment may be to calculate the signal output power of the microphone array for each mesh by using a Capon beam forming algorithm, and take the mesh corresponding to the maximum value of the signal output power as the position of the sound source to be detected in the time period corresponding to the sub-signal of the segment.
Preferably, after step (5), the positioning result can be displayed on the sound source scanning plane in an imaging mode, and intuitive sound source position and amplitude information of the sound signal can be obtained. The amplitude of the acoustic signal is understood to be the size of the acoustic signal, representing the energy information of the sound.
The invention divides the whole sampling signal into multiple sub-signals for respective positioning according to the relation among sampling frequency, sound source rotating speed, sound source rotating radius and grid side length, thereby blurring the sound source motion track in each small segment into a point sound source and solving the problem that the sound source can not be accurately positioned in the rotating process.
Compared with the prior art, the invention has the main advantages that:
(1) the method has higher resolution, carries out sectional processing on time domain sampling points, and can position the position of the rotary sound source at a certain moment.
(2) And the better positioning precision can still be kept under the condition of low signal-to-noise ratio. The Capon beamforming algorithm shows excellent performance in the middle and high frequency band, and the algorithm still keeps better precision in the low frequency band.
Drawings
FIG. 1 is a flow chart of an embodiment of a high resolution localization method for a rotational sound source;
FIG. 2 is a schematic diagram of a simulated sound source and a scan plane of an embodiment;
FIG. 3 is a schematic diagram of an exemplary simulated microphone array;
FIG. 4 is a diagram illustrating the positioning result of the 1 st segment of sub-signals by the high resolution positioning method for the rotational sound source according to the embodiment;
FIG. 5 is a diagram illustrating the positioning result of the 20 th segment of sub-signal by the high resolution positioning method for the rotational sound source according to the embodiment;
FIG. 6 is a diagram illustrating the positioning result of the high resolution positioning method for a rotational sound source on the 38 th segment of sub-signal according to the embodiment;
fig. 7 is a diagram illustrating the positioning result of the high resolution positioning method for a rotational sound source according to the embodiment on the 50 th segment of sub-signal.
Detailed Description
The invention is further described with reference to the following drawings and specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. The experimental procedures, in which specific conditions are not noted in the following examples, are generally carried out under conventional conditions or conditions recommended by the manufacturers.
The present embodiment adopts MAT L AB simulation, and the flow of the adopted high resolution localization method facing to the rotational sound source is shown in fig. 1, and the specific steps include:
(1) as shown in FIG. 2, the setting is 1.2 × 1.2.2 m2Wherein each small grid is 0.02 × 0.02.02 m2(not shown in the figure).
As shown in fig. 2, three artificial sound sources are provided, the initial coordinates are (0.4,0), (0.4,0.3), (0,0.5), the color scale on the right side shows the energy level of the sound source, and the histograms on the left and lower sides show the superposition of the energy of all the sound sources in the direction of the coordinate axis.
(2) Leading in a microphone array, and collecting acoustic signals to obtain original acoustic signals; and performing spectral analysis and time-frequency spectral analysis to obtain the signal frequency of the original acoustic signal.
A schematic diagram of a simulated microphone array is shown in FIG. 3, which is an array of 7 × 5, with an array area of 2.4 × 1.6m2The array plane and the plane of the sound source are parallel, and the distance is 1 m. The signal-to-noise ratio of the acoustic signal is set to be 10dB, the signal and the noise are both in Gaussian distribution, and the center frequency of the signal is 1000 Hz. The rotating speed of a sound source is set to be 20Hz, the sound source rotates anticlockwise, and random frequency fluctuation of-5 Hz is added on the basis of the basic rotating speed of 20Hz in order to simulate the real running condition of a rotating machine.
(3) In the simulation, the sampling frequency is set to be 50kHz, the length L of an original sound signal is 2500 acquisition points, the number N of the minimum sampling points is 50 acquisition points, the original sound signal is divided into 50 sections of sub-signals to be respectively positioned, and the length of each section of sub-signals is 50 acquisition points.
(4) Substituting each segment of sub-signal in the step (3) into a Capon beam forming algorithm for operation, and determining the position of the sound source to be detected in the time period corresponding to the segment of sub-signal;
(5) and (5) repeating the step (4) until the operation of 50 segments of sub-signals is completed, and determining the position of the sound source to be detected in the time period corresponding to the original sound signal.
Positioning results of the high-resolution positioning method for the rotary sound source in this embodiment are shown in fig. 4 to 7, which respectively show positioning results of sub-signals of segments 1, 20, 38, and 50, and in order to visually reflect positioning accuracy of the algorithm, the actual position of the sound source at the time corresponding to the sub-signal of the segment is marked with black in the positioning result image.
Therefore, the high-resolution positioning method facing the rotary sound source can effectively position the rotary sound source and can keep better positioning resolution.
Furthermore, it should be understood that various changes and modifications can be made by one skilled in the art after reading the above description of the present invention, and equivalents also fall within the scope of the invention as defined by the appended claims.

Claims (7)

1. A method for high resolution localization of a rotating sound source, comprising:
(1) taking a plane where a sound source to be detected is positioned when rotating as a sound source scanning plane, and dividing the plane into a plurality of grids;
(2) the method comprises the steps that a microphone array is arranged to collect sound signals of a sound source to be detected to obtain original sound signals, frequency spectrum analysis and time-frequency spectrum analysis are carried out to obtain signal frequencies of the original sound signals, and the plane of the arranged microphone array is parallel to a sound source scanning plane;
(3) dividing the obtained original sound signal into a plurality of sub-signals, wherein the number of sampling points contained in each sub-signal is not less than the minimum number N of sampling points required by positioning the sound source position; the minimum sampling point number N is determined according to the sampling frequency, the rotating speed of the sound source, the side length of the grid and the rotating radius of the sound source, and the calculation mode is as follows:
Figure FDA0002459333300000011
wherein f isrRepresenting the rotational speed of the sound source, fsRepresenting the sampling frequency, R representing the rotation radius of the sound source, div representing the grid side length;
(4) substituting the sub-signals into a Capon beam forming algorithm for operation, scanning all grids of a sound source scanning plane one by one, and determining the position of a sound source to be detected in a time period corresponding to the sub-signals;
(5) and (5) repeating the step (4) until the operation of the original sound signal is completed, and determining the position of the sound source to be detected in the time period corresponding to the original sound signal.
2. The method of claim 1, wherein the microphone array plane and the sound source scanning plane are coaxially arranged.
3. The method for positioning sound source facing rotating with high resolution according to claim 1 or 2, wherein the distance between adjacent microphone elements in the microphone array is not greater than half wavelength of the sound signal of the sound source to be measured.
4. The method for high-resolution localization of a rotational sound source according to claim 1, wherein the spectral analysis uses fast fourier transform and the time-frequency spectral analysis uses short-time fourier transform.
5. The method for high-resolution localization of a rotating sound source according to claim 1, wherein the minimum number of sampling points N is 50.
6. The method according to claim 1, wherein in step (4), the method for determining the position of the sound source to be measured in the time segment corresponding to the sub-signal is to calculate the signal output power of the microphone array for each mesh by using a Capon beam forming algorithm, and the mesh corresponding to the maximum value of the signal output power is taken as the position of the sound source to be measured in the time segment corresponding to the sub-signal.
7. The method for high-resolution localization of a rotary sound source according to claim 1, wherein after step (5), the localization result is displayed in an imaged form on the sound source scanning plane.
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