CN109459745B - Method for estimating speed of moving sound source by using radiation noise - Google Patents

Method for estimating speed of moving sound source by using radiation noise Download PDF

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CN109459745B
CN109459745B CN201811264359.4A CN201811264359A CN109459745B CN 109459745 B CN109459745 B CN 109459745B CN 201811264359 A CN201811264359 A CN 201811264359A CN 109459745 B CN109459745 B CN 109459745B
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杨益新
梁宁宁
郭西京
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Northwestern Polytechnical University
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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
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    • G01S11/14—Systems for determining distance or velocity not using reflection or reradiation using ultrasonic, sonic, or infrasonic waves

Abstract

The invention provides a method for estimating the speed of a moving sound source by using radiation noise, which comprises the steps of acquiring and recording a noise signal by using a sensor, carrying out filtering pretreatment on the recorded signal, substituting undetermined parameters, obtaining the time-frequency distribution of the signal by using a Doppler-CT method, extracting an instantaneous frequency change curve from the time-frequency distribution, obtaining an estimated value of the undetermined parameters based on a least square criterion, and continuously iterating until Doppler-CT iterative convergence, thus obtaining an accurate speed estimated value. The method has the advantages that when the instantaneous frequency change curve caused by the Doppler effect is fitted, compared with the PCT which generally needs to estimate up to 10 polynomial coefficients, the method only needs to estimate 4 parameters such as speed and the like, and therefore the calculation efficiency is effectively improved.

Description

Method for estimating speed of moving sound source by using radiation noise
Technical Field
The invention relates to the field of signal processing and joint time-frequency analysis, in particular to a method for realizing speed estimation of a motion noise source by using a time-frequency analysis method.
Background
Moving objects such as automobiles and airplanes can be regarded as radiation noise sources, when the moving objects move relative to a receiving system, the amplitude and the frequency of signals output by the receiving system can change correspondingly due to the Doppler effect, and the Doppler phenomenon is the basis of a passive acoustic velocity measurement method. Typically, the radiated noise power spectrum of such noise sources consists of a broadband continuum with multiple line spectra that are harmonically related, with the line spectral components being more energetic and thus more easily detectable. When the target keeps constant linear motion and passes through a receiving sensor fixed at a certain point, the speed of the motion sound source can be estimated according to the instantaneous frequency change curve of a line spectrum extracted from a received signal by using a joint time-frequency analysis method.
Ferguson firstly establishes the relation between the instantaneous frequency and the parameters such as the speed of a target line spectrum under the condition of constant linear motion according to the Doppler effect (the relation is called as a frequency shift model for Short-Time Fourier Transform (STFT) in the invention), then extracts the instantaneous frequency change curve from the received signal through Short-Time Fourier Transform (STFT), and finally estimates the parameters such as the speed by combining the frequency shift model. Based on this method, Ferguson successfully estimates the flight speed of a uniform linear motion aircraft (Ferguson B G, Quinn B G. application of the short-time Fourier transform and the Wigner-video distribution to the acoustic localization of aircraft J. Journal of the acoustic Society of America,1994,96(2):821 and 827.). In the method, the key for accurately estimating the speed is to obtain an accurate instantaneous frequency change curve. The STFT has low time-frequency aggregation, so that the accuracy of the extracted instantaneous frequency change curve is limited, and the performance of speed estimation is influenced. Xulingji et al proposed to extract instantaneous frequency change curves using a PCT (polymeric chip transform) method with better time-frequency aggregation, and found that more accurate velocity estimates were obtained using the PCT method than using the STFT method in comparative analysis of measured data (Xu L, Yang Y, Yu s. analysis of moving sources using a polymeric chip transform [ J ]. Journal of the acoustic Society of America 2015, (137) (4) EL320-EL 326). However, the calculation process of the PCT method takes a long time. In the PCT method, a polynomial is used to fit the target line spectrum instantaneous frequency variation curve. In order to obtain a good fitting effect, the polynomial order generally needs to be about 10 th, that is, the same number of polynomial coefficients needs to be estimated, and the calculation is complex, which is a main reason that the method for estimating the speed of the motion noise source based on the PCT has low calculation efficiency. The invention uses the frequency shift model to fit the instantaneous frequency change curve, and only needs to estimate 4 parameters such as speed and the like, thereby effectively improving the calculation efficiency.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method for extracting an instantaneous frequency change curve by using a new time-frequency analysis method, namely Doppler Chirplet Transform (Doppler-CT), and estimating the speed of a sound source.
The technical scheme adopted by the invention for solving the technical problem specifically comprises the following steps:
step 1: acquiring and recording a noise signal by using a sensor;
fixing a receiving sensor, enabling the target to keep uniform linear motion to pass through the sensor, enabling the sensor to continuously receive radiation noise of the target and convert the radiation noise into an electric signal, recording the electric signal as s through a data acquisition instrument after the electric signal is filtered and amplifiedr(t)；
Step 2: for recorded signal sr(t) performing a filtering pretreatment
Determining spectral lines to be analyzed from STFTIn the frequency range of the signal s by means of a band-pass filterr(t) processing, and filtering interference outside the frequency range to obtain a signal s (t) after preprocessing;
and step 3: setting initial values of the parameters to be determined.
Frequency f in initial undetermined parameter0 0Set to the center frequency, velocity v, of the bandpass filter in step 20CPA time τc 0And CPA distance Rc 0Setting the value to be greater than 0 according to the prior knowledge, and if the value is not the prior knowledge, setting the value to be greater than 0;
and 4, step 4: substituting the undetermined parameters, and obtaining the time-frequency distribution of s (t) by a Doppler-CT method;
according to the definition of Doppler linear frequency-modulated wavelet transform
Figure GDA0003461285710000021
Obtaining the time-frequency distribution of the preprocessed signal s (t), wherein tau represents a window function wσ(t), ω represents the signal frequency, wσ(t) represents a Gaussian window with a standard deviation of sigma,
Figure GDA0003461285710000022
is the rotated operator of the signal s (t)
Figure GDA0003461285710000023
And frequency shift operator
Figure GDA0003461285710000024
Processed signal, D ═ f0,v,τc,Rc) 4 undetermined parameters, f, representing constant updating during the iteration process0A constant frequency f, which is essentially not time-varying, being the true frequency of the spectral line0The frequency of the received signals is changed from big to small along with the time, and v is the motion speed of the uniform linear motion of the motion sound source; the point closest to the sensor in the sound source motion trajectory is called CPA (the close point of ap), taucThe time when the sound source passes the CPA is referred to as the CPA time; rcMeaning the distance of the sensor from the CPA,referred to as CPA distance;
and 5: extracting an instantaneous frequency change curve from time-frequency distribution;
extracting spectral peaks from the s (t) time frequency distribution obtained in the step 4 to obtain a group of spectral peak frequencies at different moments, namely instantaneous frequency change curves, and selecting N according to the truncation effect at two ends of the signalw2 to N-NwInstantaneous frequency variation between/2, where N represents the discrete signal length, NwRepresents the windowing length;
step 6: obtaining an estimation value of a parameter to be determined based on a least square criterion;
based on the least square criterion, fitting the instantaneous frequency change curve by using a frequency shift model, and solving the undetermined parameter phase by adopting a Gauss-Newton iterative algorithm, namely solving the problem that D is equal to (f)0,v,τc,Rc) When gauss and newton iteration converges, obtaining an estimated value of the undetermined parameter;
and 7: taking the undetermined parameter estimation value solved in the step 6 as an undetermined parameter of the next iteration, and repeating the steps 4 to 6 until the Doppler-CT iteration is converged, wherein the convergence condition is as follows:
Figure GDA0003461285710000031
wherein, δ is a threshold value, and the threshold value range is between one hundred thousandth and one ten thousandth, so that an accurate speed estimation value can be obtained.
The method has the advantages that when the instantaneous frequency change curve caused by the Doppler effect is fitted, compared with the PCT which generally needs to estimate up to 10 polynomial coefficients, the method only needs to estimate 4 parameters such as speed and the like, and therefore the calculation efficiency is effectively improved.
Drawings
Fig. 1 is a schematic diagram of the propagation process of sound emitted from a sound source to a sensor.
Fig. 2 is a flow chart for estimating a moving noise source velocity using a sensor receive signal.
FIG. 3 is an iterative flow chart of the Doppler-CT method.
Fig. 4 shows the result of processing a certain signal STFT in the embodiment.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
Step 1: acquiring and recording a noise signal by using a sensor;
fixing a receiving sensor, enabling the target to keep moving at a constant speed in a straight line to pass through the sensor, continuously receiving radiation noise of the target by the sensor and converting the radiation noise into an electric signal in the process (as shown in figure 1), filtering and amplifying the electric signal, recording the electric signal by a data acquisition instrument, and recording the electric signal as sr(t)；
Step 2: for recorded signal sr(t) performing a filtering pretreatment
Determining the frequency range of the spectral line to be analyzed according to the STFT, and using a band-pass filter to carry out signal sr(t) processing, and filtering interference outside the frequency range to obtain a signal s (t) after preprocessing;
and step 3: setting initial values of the parameters to be determined.
Frequency f in initial undetermined parameter0 0Set to the center frequency, velocity v, of the bandpass filter in step 20CPA time τc 0And CPA distance Rc 0Setting the value to be greater than 0 according to the prior knowledge, and if the value is not the prior knowledge, setting the value to be greater than 0;
and 4, step 4: substituting the undetermined parameters, and obtaining the time-frequency distribution of s (t) by a Doppler-CT method;
according to the definition of Doppler linear frequency-modulated wavelet transform
Figure GDA0003461285710000041
Obtaining the time-frequency distribution of the preprocessed signal s (t), wherein tau represents a window function wσ(t), ω represents the signal frequency, wσ(t) represents a Gaussian window with a standard deviation of sigma,
Figure GDA0003461285710000042
is the rotated operator of the signal s (t)
Figure GDA0003461285710000043
And frequency shift operator
Figure GDA0003461285710000044
Processed signal, D ═ f0,v,τc,Rc) 4 undetermined parameters, f, representing constant updating during the iteration process0A constant frequency f, which is the true frequency of the spectral line and which is not inherently time-varying due to the Doppler effect0The frequency appears in the received signal as decreasing from large to small with time, as shown in fig. 1; v is the motion speed of the uniform linear motion of the motion sound source; the point closest to the sensor in the sound source motion trajectory is called CPA (the close point of ap), taucThe time when the sound source passes the CPA is referred to as the CPA time; rcMeaning the distance of the sensor from the CPA, referred to as the CPA distance;
and 5: extracting an instantaneous frequency change curve from time-frequency distribution;
extracting spectral peaks from the s (t) time frequency distribution obtained in the step 4 to obtain a group of spectral peak frequencies at different moments, namely instantaneous frequency change curves, and selecting N according to the truncation effect at two ends of the signalw2 to N-NwInstantaneous frequency variation between/2, where N represents the discrete signal length, NwRepresents the windowing length;
step 6: obtaining an estimation value of a parameter to be determined based on a least square criterion;
based on the least square criterion, fitting the instantaneous frequency change curve by using a frequency shift model, and solving the undetermined parameter phase by adopting a Gauss-Newton iterative algorithm, namely solving the problem that D is equal to (f)0,v,τc,Rc) When gauss and newton iteration converges, obtaining an estimated value of the undetermined parameter;
and 7: taking the undetermined parameter estimation value solved in the step 6 as an undetermined parameter of the next iteration, and repeating the steps 4 to 6 until the Doppler-CT iteration is converged, wherein the convergence condition is as follows:
Figure GDA0003461285710000051
wherein, δ is a threshold value, and the threshold value range is between one hundred thousandth and one ten thousandth, so that an accurate speed estimation value can be obtained.
As shown in fig. 1, a point at which the moving sound source is closest to the receiving sensor is referred to as a CPA, a CPA time is a time at which the sound source passes through the CPA, and a CPA distance is a distance between the CPA and the receiving sensor. The undetermined parameters are 4 in total and are respectively the frequency f of the original line spectrum0Velocity v of noise source and CPA time taucAnd CPA distance Rc. The Doppler-CT method firstly sets an initial value of a undetermined parameter, then obtains time-frequency distribution of a received signal by using the Doppler-CT method, extracts an instantaneous frequency change curve from the time-frequency distribution, obtains an estimated value of the undetermined parameter of a moving noise source based on a least square criterion, then takes the estimated value as an updated value of the undetermined parameter, and repeats the steps until iteration converges to obtain the speed of the moving noise source.
The general flow of the technical scheme of the invention is shown in fig. 2, and can be divided into the following steps:
1) noise signals are acquired and recorded by sensors, and are recorded as sr(t)。
2) For recorded signal sr(t) performing a filtering pretreatment to obtain s (t).
3) Setting initial values of the parameters to be determined.
4) Substituting the undetermined parameters, and obtaining the time-frequency distribution of s (t) by a Doppler-CT method.
5) And extracting an instantaneous frequency change curve from the time-frequency distribution.
6) And obtaining an estimated value of the undetermined parameter based on a least square criterion.
7) And repeating the steps 4) -6) until convergence, and obtaining the speed of the motion noise source.
Each step of the present invention is described in detail below:
the step 1) is specifically realized as follows:
and (3) fixing the receiving sensor, enabling the target to keep moving at a constant speed and pass through the sensor, continuously receiving the radiation noise of the target by the sensor and converting the radiation noise into an electric signal in the process, and recording the electric signal by a data acquisition instrument after filtering and amplifying the electric signal. A schematic diagram of the motion process is shown in fig. 1.
The step 2) is specifically realized as follows:
determining the frequency range of the spectral line to be analyzed according to the STFT, and using a band-pass filter to carry out signal sr(t) processing, filtering the interference outside the frequency range to obtain a signal s (t) after preprocessing.
The step 3) is specifically realized as follows:
as shown in fig. 1, a point at which the moving sound source is closest to the receiving sensor is referred to as a CPA, a CPA time is a time at which the sound source passes through the CPA, and a CPA distance is a distance between the CPA and the receiving sensor. The undetermined parameters are 4 in total and are respectively the frequency f of the original line spectrum0Velocity v of noise source and CPA time taucAnd CPA distance Rc. Typically, the frequency f in the initial pending parameter0 0Can be set as the center frequency and the speed v of the band-pass filtering in the step 2)0CPA time τc 0And CPA distance Rc 0The value may be set to some suitable value greater than 0 based on a priori knowledge, such as no a priori knowledge, or may be set to any value greater than 0.
The step 4) is specifically realized as follows:
definition fDAs follows
Figure GDA0003461285710000061
Wherein f isDRepresenting the transformation kernel function of Doppler-CT, which is essentially a model of frequency shift at a known speed of sound c, Doppler-CT can be expressed as follows:
Figure GDA0003461285710000062
wherein
Figure GDA0003461285710000063
In the formulae (2) and (3), τ represents a window function wσ(t), ω represents the signal frequency, wσ(t) denotes a Gaussian window with standard deviation σ and discrete form wσ(n)＝exp(-0.5(n/σ)2) In which is- (N)w-1)/2≤n≤(Nw-1)/2，σ＝(Nw-1)/5，NwThe length of the windowing is indicated,
Figure GDA0003461285710000071
is the rotated operator of the signal s (t)
Figure GDA0003461285710000072
And frequency shift operator
Figure GDA0003461285710000073
Processed signal, D ═ f0,v,τc,Rc) Representing a continuously updated kernel function f in an iterative processD4 parameters of (1).
After the initial undetermined parameters are set, the time-frequency distribution of the preprocessed signals s (t) can be obtained according to the formula (2). In subsequent iterations, the parameters to be determined will be given by the result of an estimation based on the least squares criterion, as shown in fig. 3.
The step 5) is specifically realized as follows:
extracting spectral peaks from the s (t) time frequency distribution obtained in step 4) to obtain a group of spectral peak frequencies at different moments, namely an instantaneous frequency change curve fi. Considering that there is a truncation effect at both ends of the signal, only N is actually selectedw2 to N-NwInstantaneous frequency variation between/2, where N represents the discrete signal length, NwIndicating the windowing length.
The step 6) is realized as follows:
fitting the instantaneous frequency change curve observations of step 5) with a frequency shift model based on least squares criterion can be generalized to solve a minimization problem of the form
Figure GDA0003461285710000074
Wherein f isD(τi) Is τiFitting value of time instant frequency change curve, fiRepresents tauiThe observed value of the instantaneous frequency change at the moment is given by step 5).
The problem of the formula (4) can be solved by adopting a Gaussian Newton iteration method, and the iteration formula is as follows
Figure GDA0003461285710000075
Where s represents the number of iterations,
Figure GDA0003461285710000076
representing a jacobian matrix.
And obtaining the estimation value of the undetermined parameter after the Gauss-Newton iteration converges (generally more than 200 iterations are needed).
The step 7) is specifically realized as follows:
and (4) taking the parameter estimation value of the motion noise source solved in the step 6) as an updated value of the undetermined parameter, and repeating the steps 4) to 6) until the D-C-CZT iteration converges, so that an accurate speed estimation value can be obtained. The decision condition for terminating the iteration may be set as follows:
Figure GDA0003461285710000081
wherein, δ is a threshold value, and the threshold value range is between one hundred thousandth and one ten thousandth.
In order to verify the effectiveness of the method for estimating the motion speed of the noise source, the simulation experiment is designed as follows: setting parameters (f)0,v,τc,RcAnd c) is (88,25,5,50,340), i.e. the line spectrum radiation noise frequency f of the assumed noise source0Making uniform linear motion with speed v 25m/s and transverse time tau 88HzcPositive lateral distance R from the noise source to the receiving sensor, 5sc50m, sound velocity in air c 340 m/s. Observation time was set to 10s, sampling rate fsThe iteration number is 3 times at 1024Hz, the gaussian window length is 1024, the signal-to-noise ratio is SNR 0dB, which represents the relative magnitude of the total energy of the signal and the noise in the observation time, and the noise is white gaussian noise. The simulation signal is processed by the method provided by the invention, so that the processing result of the signal STFT shown in figure 4 can be obtained, the spectral line is shown in figure 4, and the frequency variation range in 30 seconds is approximately 116 Hz-121 Hz. The bright lines can be seen in fig. 4, and the filtering range of the band pass filter can be determined according to the bright lines. The resulting velocity estimates are shown in table 1. From table 1, it can be seen that the method provided by the present invention can accurately estimate parameters such as speed of an airborne sound source, and the like, and the effectiveness of the method is proved.
TABLE 1 parameter estimation results of Doppler-CT method
Figure GDA0003461285710000082

Claims (1)

1. A method for estimating a velocity of a moving sound source using radiated noise, comprising the steps of:
step 1: acquiring and recording a noise signal by using a sensor;
fixing a receiving sensor, enabling the target to keep uniform linear motion to pass through the sensor, enabling the sensor to continuously receive radiation noise of the target and convert the radiation noise into an electric signal, recording the electric signal as s through a data acquisition instrument after the electric signal is filtered and amplifiedr(t)；
Step 2: for recorded signal sr(t) performing a filtering pretreatment
Determining the frequency range of the spectral line to be analyzed according to the STFT, and using a band-pass filter to carry out signal sr(t) processing, and filtering interference outside the frequency range to obtain a signal s (t) after preprocessing;
and step 3: setting an initial value of a parameter to be determined;
frequency f in initial undetermined parameter0 0Set to the center frequency, speed of bandpass filtering in step 2Degree v0CPA time τc 0And CPA distance Rc 0Setting the value to be greater than 0 according to the prior knowledge, and if the value is not the prior knowledge, setting the value to be greater than 0;
and 4, step 4: substituting the undetermined parameters, and obtaining the time-frequency distribution of s (t) by a Doppler-CT method;
according to the definition of Doppler linear frequency-modulated wavelet transform
Figure FDA0003419511200000011
Obtaining the time-frequency distribution of the preprocessed signal s (t), wherein tau represents a window function wσ(t), ω represents the signal frequency, wσ(t) represents a Gaussian window with a standard deviation of sigma,
Figure FDA0003419511200000012
is the rotated operator of the signal s (t)
Figure FDA0003419511200000013
And frequency shift operator
Figure FDA0003419511200000014
Processed signal, D ═ f0,v,τc,Rc) 4 undetermined parameters, f, representing constant updating during the iteration process0A constant frequency f, which is essentially not time-varying, being the true frequency of the spectral line0The frequency of the received signals is changed from big to small along with the time, and v is the motion speed of the uniform linear motion of the motion sound source; the point closest to the sensor in the sound source motion trajectory is called CPA, taucThe time when the sound source passes the CPA is referred to as the CPA time; rcMeaning the distance of the sensor from the CPA, referred to as the CPA distance;
and 5: extracting an instantaneous frequency change curve from time-frequency distribution;
extracting spectral peaks from the s (t) time frequency distribution obtained in the step 4 to obtain a group of spectral peak frequencies at different moments, namely instantaneous frequency change curves, and selecting N according to the truncation effect at two ends of the signalw2 to N-Nw/2 ofWith N representing the length of the discrete signal, NwRepresents the windowing length;
step 6: obtaining an estimation value of a parameter to be determined based on a least square criterion;
based on the least square criterion, fitting the instantaneous frequency change curve by using a frequency shift model, and solving the undetermined parameter phase by adopting a Gauss-Newton iterative algorithm, namely solving the problem that D is equal to (f)0,v,τc,Rc) When gauss and newton iteration converges, obtaining an estimated value of the undetermined parameter;
and 7: taking the undetermined parameter estimation value solved in the step 6 as an undetermined parameter of the next iteration, and repeating the steps 4 to 6 until the Doppler-CT iteration is converged, wherein the convergence condition is as follows:
Figure FDA0003419511200000021
wherein f isi (s+1)Represents the estimated value of the ith instantaneous frequency in the instantaneous frequency change curve extracted after the (s + 1) th iteration, fi (s)And representing the estimated value of the ith instantaneous frequency in the instantaneous frequency change curve extracted after the s-th iteration, wherein delta is a threshold value, and the value range of the threshold value is between one hundred thousandth and one ten thousandth, so that the accurate speed estimated value can be obtained.
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