CN109410579B - Audio detection system and detection method for moving vehicle - Google Patents

Audio detection system and detection method for moving vehicle Download PDF

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CN109410579B
CN109410579B CN201811338515.7A CN201811338515A CN109410579B CN 109410579 B CN109410579 B CN 109410579B CN 201811338515 A CN201811338515 A CN 201811338515A CN 109410579 B CN109410579 B CN 109410579B
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vehicle
mems microphone
frequency
tire noise
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CN109410579A (en
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郑明德
徐韶华
黎恒
唐文娟
陈大华
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Guangxi Jiaoke Group Co Ltd
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Guangxi Transportation Research and Consulting Co Ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count

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Abstract

The invention provides a moving vehicle audio detection system, which comprises a plurality of MEMS microphone modules in an array, a plurality of sensors and a plurality of sensors, wherein the MEMS microphone modules are used for acquiring tire sound signals of a moving vehicle and converting the acquired sound signals into digital signals; the MEMS microphone modules are connected with the signal processing unit, the signal processing unit receives the digital signals of the MEMS microphone modules, processes the digital signals and calculates the positioning information of the vehicle, wherein the signal processing unit executes the following instructions to calculate the positioning information of the vehicle: the signal processing unit is connected with the communication module and outputs the calculated vehicle positioning information through the communication module. The invention is convenient to install and maintain and can effectively reduce the influence of environmental factors in the vehicle positioning process.

Description

Audio detection system and detection method for moving vehicle
Technical Field
The invention relates to the technical field of positioning detection, in particular to a moving vehicle audio detection system and a detection method.
Background
With the rapid development of highway construction, highway traffic has become an indispensable link for economic development. With the rapid development of computers and information technologies, traffic control is also developing in the direction of intellectualization and informatization. In the application of intelligent transportation, the most basic part is the real-time acquisition of vehicle information, and the key of the part is the detection technology of vehicles. As one of the key technologies of intelligent traffic, vehicle detection technology plays an important role in traffic guidance, traffic signal control, highway management, electronic toll collection, and other fields. Traffic development requires a large number of vehicle detectors along the road in order to obtain vehicle operation information, and the mainstream detection methods used at present have difficulty meeting the requirement of a large number of sensors along the road for various reasons. The current technologies for vehicle detection include ground induction coil, camera, microwave, infrared, etc., which are briefly described as follows:
(1) ground induction coil
Coil vehicle detectors, which appeared in the 60's of the 20 th century, are the most widely used traffic volume detectors in traffic control today. With the rapid development of electronic technology, control theory and computer technology, the coil-based vehicle detector is also continuously improved, and an overall design scheme of a vehicle detection system based on a toroidal coil is provided. The detector is a vehicle detector based on electromagnetic induction principle, and its sensor is a loop coil buried under road surface and passed through with a certain alternating current. When the vehicle passes through the coil or stops on the coil, the vehicle causes the inductance of the coil loop to change, and the detector detects the change so as to detect the existence of the vehicle.
The ground induction coils are embedded with 2 annular coils at a certain distance on a lane, the output of the annular coils can be in a square wave or pulse mode, and the vehicle can be detected in both modes as long as the microprocessor counts the rising edge or the falling edge of an output signal. The method of the geomagnetic induction coil needs to cut a road surface and even close a road section for construction, is difficult to install and maintain, and has great potential safety hazard due to congestion caused in the road sealing construction process. The electromagnetic induction detection technology is used for detecting the arrival and departure of vehicles according to the different changes of inductance of different vehicles when the different vehicles pass through an annular induction coil buried under a road, and classifying the vehicle types. The key part of the system is a coil detector which consists of an induction coil and a control part. When no vehicle passes through, the frequency of the oscillator is artificial, when a vehicle approaches and passes through the oscillator at a certain speed, the inductance parameter of the coil changes, and further the oscillation frequency of the oscillator slightly changes, and in the driving process of the vehicle, the oscillation frequency changes along with the chassis of the vehicle due to different shapes. When the vehicle leaves the toroidal coil, the frequency of the oscillator is restored, thus obtaining a value that varies with the time of passage of the vehicle, a threshold value is set, and when the value of the frequency variation exceeds the threshold value, it is considered that the vehicle passes.
(2) Another important method for vehicle detection is to capture the vehicle entering the camera sight range for identification by the camera device. The camera-based vehicle detection method has high requirements on illumination, and vehicles captured by the camera are required to be clearly visible. In the weather of night, dense fog and the like, the identification precision of the camera detection method is greatly reduced.
(3) Other detection methods such as ultrasonic detection, infrared detection, etc. have been used to varying degrees for vehicle detection, and although these methods are well established, they are either destructive to the road section, require high post-maintenance, or are not suitable for mass-production along the road.
In the existing method, the ground induction coil mode needs to cut the road surface for installation and maintenance; the scheme based on the camera has high cost, needs aiming point installation and has lower recognition effect in night, foggy days and rainy days; the method based on ultrasonic waves and infrared rays is low in detection precision, short in detection distance and easy to be influenced by environmental factors such as rain, snow and the like.
Therefore, in order to solve the problems of difficult installation and maintenance and easy influence of environmental factors existing in the existing vehicle detection technology, a moving vehicle audio detection system and a detection method are needed.
Disclosure of Invention
One aspect of the present invention is to provide a moving vehicle audio detection system, which includes a plurality of MEMS microphone modules arrayed to collect tire sound signals of a moving vehicle and convert the collected sound signals into digital signals;
the MEMS microphone modules are connected with the signal processing unit, the signal processing unit receives the digital signals of the MEMS microphone modules, processes the digital signals and calculates the positioning information of the vehicle, wherein the signal processing unit executes the following instructions to calculate the positioning information of the vehicle:
a) preprocessing digital signals input by each MEMS microphone module, performing time domain-frequency domain transformation and extracting tire noise;
b) in the tire noise frequency band, performing vehicle orientation estimation on all frequency bands of the tire noise by a beam forming scanning algorithm;
c) on the basis of vehicle orientation estimation, the number of vehicles is estimated by applying an audio frequency spectrum sparsity principle, and the frequency band occupied by each vehicle sound in the time is obtained;
d) on the basis of vehicle number estimation, vehicle positioning is carried out by applying an SRP-PHAT algorithm to obtain positioning information of the vehicle;
the signal processing unit is connected with the communication module and outputs the calculated vehicle positioning information through the communication module.
Further, in the step a), the preprocessing, the time-frequency domain transformation and the tire noise extraction of the digital signal input by each MEMS microphone module comprise the following method steps:
a1) the signal processing unit receives the obtained n-path digital signal xn(t) framing with each frame length of 25ms to obtain a framed signal x1(t)，x2(t)，…，xn(t)；
a2) For the framed signal x1(t)，x2(t)，…，xn(t) adding a Hamming window and performing fast Fourier transform to obtain a frequency domain signal X1(ω)，X2(ω)，…，Xn(ω)；
a3) Extracting the frequency range of the frequency domain signal belonging to the tire noise to form a narrow-band tire noise frequency domain signal Xt，k＝[X1(k)，X2(k)，...，Xn(k)]。
Further, in the step b), the vehicle orientation estimation is performed on all frequency bands of the tire noise at time t and frequency ω by using a beam forming scanning algorithm in the tire noise frequency band by the following method steps:
b1) calculating array steering vector d formed by all MEMS microphone modules and steering vector d of steering angle thetaθComprises the following steps:
Figure BDA0001862551920000031
wherein r is the radius of the microphone array,
Figure BDA0001862551920000032
the azimuth angle of the nth MEMS microphone module is n, which is 1, 2, …, M, M is the number of the MEMS microphone modules, and c is the propagation speed of sound in the air;
b2) selecting a directivity angle ρ of a microphone array directivity pattern0And M-1 null angles (0-2 pi) noted ρ ═ ρ1，ρ1，...，ρM-1]；
b3) Calculating steering vectors for the M-1 null angles in step b2), forming a matrix A:
Figure BDA0001862551920000041
b4) solving equation A*hρ0Get M-dimensional filter operator h ═ cρ0Wherein c is an M-dimensional column vector c ═ 1,0,0]；
b5) Beamforming is performed over an angle β ∈ (0-2 π):
Figure BDA0001862551920000042
b6) finding the maximum beamforming Bt，βThe corresponding angle β is the vehicle direction at time t, frequency ω:
Figure BDA0001862551920000043
an estimate of the orientation of the vehicle is obtained.
Further, in the step c), on the basis of the vehicle orientation estimation, the vehicle number estimation is performed by applying the audio frequency spectrum sparsity principle, and the frequency band occupied by each vehicle sound in the time is obtained, including the following steps:
c1) and setting an upper limit N of the positioning quantity of the vehicles.
c2) And the estimation of the vehicle direction under each frequency band is obtained through the vehicle orientation estimation and is recorded as: gamma rayt，ω＝[γt，ω1，γt，ω2，...]。
c3) Recording gamma according to the estimation of the vehicle direction under each frequency bandt，ω＝[γt，ω1，γt，ω2，...]Counting the occurrence frequency of the vehicles in all directions within the time t to form an angle-vehicle frequency statistical graph;
c4) finding the maximum value q of the vehicle times in the angle-vehicle times statistical chartt，maxSum peak value
Figure BDA0001862551920000044
c5) Retention
Figure BDA0001862551920000048
Middle qt，peak＞0.7qt，peaknTo obtain a component of
Figure BDA0001862551920000045
Wherein N is 1, 2, 3, …, N-1,
Figure BDA0001862551920000046
the dimension of (d), i.e. the number of vehicles present at time t;
c6) obtaining from a statistical map of angle-vehicle times
Figure BDA0001862551920000047
The corresponding frequency set of each dimension in the time-frequency spectrum outputs the frequency spectrum set omega corresponding to each dimension (single vehicle sound source) in the time tt，velnAnd n is 1, 2 and …, and the frequency band occupied by each vehicle sound in the time t is obtained.
Further, in the step d), based on the vehicle number estimation, the vehicle positioning is performed by applying the SRP-phot algorithm, and obtaining the positioning information of the vehicle includes the following steps:
d1) the set of spectral frequencies ω corresponding at time t, and for each dimension at time tt，velnAnd an estimate gamma of the vehicle direction at each frequency bandt，ωIn this case, each vehicle corresponds to a frequency spectrum set ω corresponding to tt，veln，n＝1，2，…；
Assume that the sound source position is in three-dimensional coordinates (x, y, z) noted
Figure BDA0001862551920000057
d2) Calculating the time delay of the linear propagation from the sound source to the ith MEMS microphone module:
Figure BDA0001862551920000051
wherein m islThe coordinate of the first MEMS microphone module is shown, and c is the propagation speed of sound in the air;
d3) calculating a loss function
Figure BDA0001862551920000052
Figure BDA0001862551920000053
Wherein psikl(k) For the correlation coefficients of the kth MEMS microphone module and the l MEMS microphone module, "-" denotes the complex conjugate:
Figure BDA0001862551920000054
d4) finding the loss function
Figure BDA0001862551920000055
Maximum sound source estimation
Figure BDA0001862551920000058
Using it as the vehicle position xs，
Figure BDA0001862551920000056
Another aspect of the present invention is to provide a moving vehicle audio detection method, comprising the method steps of:
a) preprocessing digital signals input by each MEMS microphone module, performing time domain-frequency domain transformation and extracting tire noise;
b) in the tire noise frequency band, performing vehicle orientation estimation on all frequency bands of the tire noise by a beam forming scanning algorithm;
c) on the basis of vehicle orientation estimation, the number of vehicles is estimated by applying an audio frequency spectrum sparsity principle, and the frequency band occupied by each vehicle sound in the time is obtained;
d) and on the basis of vehicle number estimation, positioning the vehicle by applying an SRP-PHAT algorithm to obtain the positioning information of the vehicle.
Further, in the step a), the preprocessing, the time-frequency domain transformation and the tire noise extraction of the digital signal input by each MEMS microphone module comprise the following method steps:
a1) the signal processing unit receives the obtained n-path digital signal xn(t) framing with each frame length of 25ms to obtain a framed signal x1(t)，x2(t)，…，xn(t)；
a2) For the framed signal x1(t)，x2(t)，…，xn(t) adding a Hamming window and performing fast Fourier transform to obtain a frequency domain signal X1(ω)，X2(ω)，…，Xn(ω)；
a3) Extracting the frequency range of the frequency domain signal belonging to the tire noise to form a narrow-band tire noise frequency domain signal Xt，k＝[X1(k)，X2(k)，...，Xn(k)]。
Further, in the step b), the vehicle orientation estimation is performed on all frequency bands of the tire noise at time t and frequency ω by using a beam forming scanning algorithm in the tire noise frequency band by the following method steps:
b1) calculating array steering vector d formed by all MEMS microphone modules and steering vector d of steering angle thetaθComprises the following steps:
Figure BDA0001862551920000061
wherein r is the radius of the microphone array,
Figure BDA0001862551920000062
the azimuth angle of the nth MEMS microphone module is n equal to 1, 2, …, M is the number of the MEMS microphone modules, c is the sound spaceThe speed of propagation in the air;
b2) selecting a directivity angle ρ of a microphone array directivity pattern0And M-1 null angles (0-2 pi) noted ρ ═ ρ1，ρ1，...，ρM-1]；
b3) Calculating steering vectors for the M-1 null angles in step b2), forming a matrix A:
Figure BDA0001862551920000063
b4) solving equation A*hρ0Get M-dimensional filter operator h ═ cρ0Wherein c is an M-dimensional column vector c ═ 1,0,0]；
b5) Beamforming is performed over an angle β ∈ (0-2 π):
Figure BDA0001862551920000064
b6) finding the maximum beamforming Bt，βThe corresponding angle β is the vehicle direction at time t, frequency ω:
Figure BDA0001862551920000065
an estimate of the orientation of the vehicle is obtained.
Further, in the step c), on the basis of the vehicle orientation estimation, the vehicle number estimation is performed by applying the audio frequency spectrum sparsity principle, and the frequency band occupied by each vehicle sound in the time is obtained, including the following steps:
c1) and setting an upper limit N of the positioning quantity of the vehicles.
c2) And the estimation of the vehicle direction under each frequency band is obtained through the vehicle orientation estimation and is recorded as: gamma rayt，ω＝[γt，ω1，γt，ω2，...]。
c3) Recording gamma according to the estimation of the vehicle direction under each frequency bandt，ω＝[γt，ω1，γt，ω2，...]Counting the occurrence frequency of the vehicles in all directions within the time t to form an angle-vehicle frequency statistical graph;
c4) finding vehicles in angle-vehicle times statistical chartMaximum value q of degreet，maxSum peak value
Figure BDA0001862551920000071
c5) Retention
Figure BDA0001862551920000072
Middle qt，peak＞0.7qt，peaknTo obtain a component of
Figure BDA0001862551920000073
Wherein N is 1, 2, 3, …, N-1,
Figure BDA0001862551920000074
the dimension of (d), i.e. the number of vehicles present at time t;
c6) obtaining from a statistical map of angle-vehicle times
Figure BDA0001862551920000075
The corresponding frequency set of each dimension in the time-frequency spectrum outputs the frequency spectrum set omega corresponding to each dimension (single vehicle sound source) in the time tt，velnAnd n is 1, 2 and …, and the frequency band occupied by each vehicle sound in the time t is obtained.
Further, in the step d), based on the vehicle number estimation, the vehicle positioning is performed by applying the SRP-phot algorithm, and obtaining the positioning information of the vehicle includes the following steps:
d1) the set of spectral frequencies ω corresponding at time t, and for each dimension at time tt，velnAnd an estimate gamma of the vehicle direction at each frequency bandt，ωIn this case, each vehicle corresponds to a frequency spectrum set ω corresponding to tt，veln，n＝1，2，…；
Assume that the sound source position is in three-dimensional coordinates (x, y, z) noted
Figure BDA0001862551920000079
d2) Calculating the time delay of the linear propagation from the sound source to the ith MEMS microphone module:
Figure BDA0001862551920000076
wherein m islThe coordinate of the first MEMS microphone module is shown, and c is the propagation speed of sound in the air;
d3) calculating a loss function
Figure BDA0001862551920000077
Figure BDA0001862551920000078
Wherein psikl(k) For the correlation coefficients of the kth MEMS microphone module and the l MEMS microphone module, "-" denotes the complex conjugate:
Figure BDA0001862551920000081
d4) finding the loss function
Figure BDA0001862551920000084
Maximum sound source estimation
Figure BDA0001862551920000082
Using it as the vehicle position xs，
Figure BDA0001862551920000083
The moving vehicle audio detection system and the detection method provided by the invention utilize the audio signals of the vehicle movement to carry out detection, have low equipment price and simple maintenance, can work all the day and can be laid in large quantities.
According to the moving vehicle audio detection system and the detection method, the audio vehicle detection technology is integrated with other videos, radars and the like, so that a better detection effect can be obtained. The invention takes the sound generated when the vehicle runs, in particular the tire noise generated by the friction between the tire and the ground in the running process of the vehicle as an object, analyzes the audio signal of the vehicle, and applies the microphone array signal processing technology to realize the orientation and the positioning of the vehicle so as to achieve the aim of detecting the vehicle.
The invention takes audio as an input object, realizes audio vehicle inspection by a microphone array signal processing technology, and has the following beneficial effects compared with the prior art:
(1) simple installation and maintenance
The device receives the sound generated by the friction between the tire and the ground in the moving process of the automobile, can be arranged on a portal frame and a road side, and only needs to simply replace equipment in the installation and maintenance process without the processes of cutting the road surface, restoring the road surface and the like.
(2) Low cost
The audio sensor is a microphone, and compared with sensors such as a camera and a coil, the cost is lower. The audio signal frequency after digital processing is low, generally about 16kHz, and compared with the image signal, the audio signal can realize the signal processing algorithm only by a simple and cheap processor.
(3) Without aiming point mounting
The direction of the detection exists by means of cameras, ultrasonic waves, infrared rays and the like, and the detection needs to be carried out aiming at a road detection point/direction. The audio mode has wide receiving range, does not need aiming point installation, and only needs to be installed on the side/upper part of a road section needing to be detected.
(4) Is not easily influenced by environmental factors
Compared with a camera, an ultrasonic wave method and an infrared method, the method is easily influenced by weather. The audio mode can work normally in all weather, and the performance can be guaranteed even if the sensor is polluted.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
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Further objects, features and advantages of the present invention will become apparent from the following description of embodiments of the invention, with reference to the accompanying drawings, in which:
FIG. 1 is a block diagram of the audio detection system for a moving vehicle according to the present invention.
FIG. 2 is a block flow diagram of a method for audio detection of a moving vehicle according to the present invention.
Detailed Description
The objects and functions of the present invention and methods for accomplishing the same will be apparent by reference to the exemplary embodiments. However, the present invention is not limited to the exemplary embodiments disclosed below; it can be implemented in different forms. The nature of the description is merely to assist those skilled in the relevant art in a comprehensive understanding of the specific details of the invention.
Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the drawings, the same reference numerals denote the same or similar parts, or the same or similar steps. The following is a description of the present invention by means of specific embodiments, and as shown in fig. 1, the present invention provides a structural block diagram of a moving vehicle audio detection system, which includes a plurality of MEMS microphone modules arrayed to form a MEMS microphone array 102 for collecting tire sound signals of a moving vehicle and converting the collected sound signals into digital signals.
The plurality of MEMS microphone modules are connected with the signal processing unit 101, and the signal processing unit 101 receives the digital signals of the plurality of MEMS microphone modules, processes the digital signals and calculates the positioning information of the vehicle.
The signal processing unit 101 is connected to the communication module 103, and outputs the calculated vehicle positioning information through the communication module 103.
The moving vehicle audio detection system of the present invention further comprises a power module 104 for powering the MEMS microphone array 102, the signal processing unit 101 and the communication module 103.
According to one embodiment of the invention, the MEMS microphone module is a core-sensitive MSM261S4030H0R digital microphone, the signal processing unit 101 is an ST company STM32F765ZG microprocessor, and the communication module 103 is a UART control chip based on max 3232. In some embodiments, the signal processing unit 101 may use an FPGA of EP3C120F484C 8N.
The MEMS microphone modules that make up the MEMS microphone array 102 are located in different spatial locations, in some embodiments linearly, and in other embodiments annularly. Each MEMS microphone module is connected to the signal processing unit 101 through an I2S interface. In the present embodiment, four MEMS microphone modules are used as an example, and in some embodiments, there may be a plurality of MEMS microphone modules, but at least more than two MEMS microphone modules.
According to the embodiment of the invention, the MEMS microphone array 102 collects sound signals of friction between an automobile tire and a road surface, and sends the sound signals to the signal processing unit 101 for signal processing, wherein the signal processing unit 101 executes the following instructions to calculate positioning information of a vehicle:
a) the method comprises the following steps of preprocessing digital signals input by each MEMS microphone module, performing time domain-frequency domain transformation and extracting tire noise:
a1) the signal processing unit receives the obtained n-path digital signal xn(t) framing with each frame length of 25ms to obtain a framed signal x1(t)，x2(t)，…，xn(t)。
a2) For the framed signal x1(t)，x2(t)，…，xn(t) adding a Hamming window and performing fast Fourier transform to obtain a frequency domain signal X1(ω)，X2(ω)，…，Xn(ω)。
a3) Extracting the frequency range of the frequency domain signal belonging to the tire noise to form a narrow-band tire noise frequency domain signal Xt，k＝[X1(k)，X2(k)，...，Xn(k)]。
In the above embodiments, the digital signal of each MEMS microphone module is framed for 25ms and the extracted tire noise is 800 to 1500kHz, and in some embodiments, the digital signal of each MEMS microphone module is framed for 20ms and the extracted tire noise is 1000 to 2000 kHz.
b) And in the tire noise frequency band, performing vehicle orientation estimation on all frequency bands of the tire noise by a beam forming scanning algorithm.
In the embodiment, in the tire noise frequency band (800 to 1500kHz), the vehicle orientation estimation of all frequency bands of the tire noise at time t and frequency ω by the beam forming scanning algorithm comprises the following method steps:
b1) calculating array steering vector d formed by all MEMS microphone modules and steering vector d of steering angle thetaθComprises the following steps:
Figure BDA0001862551920000111
wherein r is the radius of the microphone array,
Figure BDA0001862551920000112
the azimuth angle of the nth MEMS microphone module is n, 1, 2, …, M is the number of MEMS microphone modules, and c is the speed of sound traveling in air.
b2) Selecting a directivity angle ρ of a microphone array directivity pattern0And M-1 null angles (0-2 pi) noted ρ ═ ρ1，ρ1，...，ρM-1]。
b3) Calculating steering vectors for the M-1 null angles in step b2), forming a matrix A:
Figure BDA0001862551920000113
b4) solving equation A*hρ0Get M-dimensional filter operator h ═ cρ0Wherein c is an M-dimensional column vector c ═ 1,0,0]。
b5) Beamforming is performed over an angle β ∈ (0-2 π):
Figure BDA0001862551920000114
b6) finding the maximum beamforming Bt，βThe corresponding angle β is the vehicle direction at time t, frequency ω:
Figure BDA0001862551920000115
an estimate of the orientation of the vehicle is obtained.
c) On the basis of vehicle orientation estimation, the method for estimating the number of vehicles by applying an audio frequency spectrum sparsity principle and obtaining the frequency band occupied by each vehicle sound in the time comprises the following steps:
c1) and setting an upper limit N of the positioning quantity of the vehicles.
c2) And the estimation of the vehicle direction under each frequency band is obtained through the vehicle orientation estimation and is recorded as: gamma rayt，ω＝[γt，ω1，γt，ω2，...]。
c3) Recording gamma according to the estimation of the vehicle direction under each frequency bandt，ω＝[γt，ω1，γt，ω2，...]And counting the times of the vehicles appearing in each direction in the time t to form an angle-vehicle time statistical chart.
c4) Finding the maximum value of the number of vehicles in the angle-vehicle number statistical chartqt，maxSum peak value
Figure BDA0001862551920000116
c5) Retention
Figure BDA0001862551920000117
Middle qt，peak＞0.7qt，peaknTo obtain a component of
Figure BDA0001862551920000118
Wherein N is 1, 2, 3, …, N-1,
Figure BDA0001862551920000119
i.e. the number of vehicles present at time t.
c6) Obtaining from a statistical map of angle-vehicle times
Figure BDA0001862551920000121
The corresponding frequency set of each dimension (namely each vehicle) in the time-frequency spectrum is output, and the frequency set of the frequency spectrum of each dimension (single vehicle sound source) corresponding to the time t is outputωt，velnAnd n is 1, 2 and …, and the frequency band occupied by each vehicle sound in the time t is obtained.
d) On the basis of vehicle number estimation, the SRP-PHAT algorithm is applied to carry out vehicle positioning to obtain the positioning information of the vehicle, and the method comprises the following steps:
d1) the set of spectral frequencies ω corresponding at time t, and for each dimension at time tt，velnAnd an estimate gamma of the vehicle direction at each frequency bandt，ωIn this case, each vehicle corresponds to a frequency spectrum set ω corresponding to tt，veln，n＝1，2，…；
Assume that the sound source position is in three-dimensional coordinates (x, y, z) noted
Figure BDA0001862551920000122
d2) Calculating the time delay of the linear propagation from the sound source to the ith MEMS microphone module:
Figure BDA0001862551920000123
wherein m islThe coordinate of the first MEMS microphone module is shown, and c is the propagation speed of sound in the air;
d3) calculating a loss function
Figure BDA0001862551920000124
Figure BDA0001862551920000125
Wherein psikl(k) For the correlation coefficients of the kth MEMS microphone module and the l MEMS microphone module, "-" denotes the complex conjugate:
Figure BDA0001862551920000126
d4) finding the loss function
Figure BDA0001862551920000127
Maximum sound source estimation
Figure BDA0001862551920000128
Using it as the vehicle position xs，
Figure BDA0001862551920000129
As shown in fig. 2, a flow chart of the moving vehicle audio detection method of the present invention is a moving vehicle audio detection method according to an embodiment of the present invention, which includes the following steps:
step S101, preprocessing, time domain-frequency domain conversion and tire noise extraction are carried out on the digital signals input by each MEMS microphone module.
The method comprises the following steps of preprocessing digital signals input by each MEMS microphone module, performing time domain-frequency domain transformation and extracting tire noise:
a1) the signal processing unit receives the obtained n-path digital signal xn(t) framing with each frame length of 25ms to obtain a framed signal x1(t)，x2(t)，…，xn(t)。
a2) For the framed signal x1(t)，x2(t)，…，xn(t) adding a Hamming window and performing fast Fourier transform to obtain a frequency domain signal X1(ω)，X2(ω)，…，Xn(ω)。
a3) Extracting the frequency range of the frequency domain signal belonging to the tire noise to form a narrow-band tire noise frequency domain signal Xt，k＝[X1(k)，X2(k)，...，Xn(k)]。
In the above embodiments, the digital signal of each MEMS microphone module is framed for 25ms and the extracted tire noise is 800 to 1500kHz, and in some embodiments, the digital signal of each MEMS microphone module is framed for 20ms and the extracted tire noise is 1000 to 2000 kHz.
And S102, performing vehicle orientation estimation on all frequency bands of the tire noise through a beam forming scanning algorithm in the tire noise frequency band.
In the embodiment, in the tire noise frequency band (800 to 1500kHz), the vehicle orientation estimation of all frequency bands of the tire noise at time t and frequency ω by the beam forming scanning algorithm comprises the following method steps:
b1) calculating array steering vector d formed by all MEMS microphone modules and steering vector d of steering angle thetaθComprises the following steps:
Figure BDA0001862551920000131
wherein r is the radius of the microphone array,
Figure BDA0001862551920000132
the azimuth angle of the nth MEMS microphone module is n, 1, 2, …, M is the number of MEMS microphone modules, and c is the speed of sound traveling in air.
b2) Selecting a directivity angle ρ of a microphone array directivity pattern0And M-1 null angles (0-2 pi) noted ρ ═ ρ1，ρ1，...，ρM-1]。
b3) Calculating steering vectors for the M-1 null angles in step b2), forming a matrix A:
Figure BDA0001862551920000133
b4) solving equation A*hρ0Get M-dimensional filter operator h ═ cρ0Wherein c is an M-dimensional column vector c ═ 1,0,0]。
b5) Beamforming is performed over an angle β ∈ (0-2 π):
Figure BDA0001862551920000134
b6) finding the maximum beamforming Bt，βThe corresponding angle β is the vehicle direction at time t, frequency ω:
Figure BDA0001862551920000141
an estimate of the orientation of the vehicle is obtained.
And S103, on the basis of vehicle orientation estimation, estimating the number of vehicles by applying an audio frequency spectrum sparsity principle, and obtaining the frequency band occupied by each vehicle sound in the time.
On the basis of vehicle orientation estimation, the method for estimating the number of vehicles by applying an audio frequency spectrum sparsity principle and obtaining the frequency band occupied by each vehicle sound in the time comprises the following steps:
c1) and setting an upper limit N of the positioning quantity of the vehicles.
c2) And the estimation of the vehicle direction under each frequency band is obtained through the vehicle orientation estimation and is recorded as: gamma rayt，ω＝[γt，ω1，γt，ω2，...]。
c3) Recording gamma according to the estimation of the vehicle direction under each frequency bandt，ω＝[γt，ω1，γt，ω2，...]And counting the times of the vehicles appearing in each direction in the time t to form an angle-vehicle time statistical chart.
c4) Finding the maximum value q of the vehicle times in the angle-vehicle times statistical chartt，maxSum peak value
Figure BDA0001862551920000142
c5) Retention
Figure BDA0001862551920000143
Middle qt，peak＞0.7qt，peaknTo obtain a component of
Figure BDA0001862551920000144
Wherein N is 1, 2, 3, …, N-1,
Figure BDA0001862551920000145
i.e. the number of vehicles present at time t.
c6) Obtaining from a statistical map of angle-vehicle times
Figure BDA0001862551920000146
Each dimension (i.e. each vehicle) outputs a corresponding set of frequencies in the time-frequency spectrum for each dimensionSpectral frequency set omega corresponding to time t (single vehicle sound source)t，velnAnd n is 1, 2 and …, and the frequency band occupied by each vehicle sound in the time t is obtained.
And S104, on the basis of vehicle number estimation, positioning the vehicle by applying an SRP-PHAT algorithm to obtain positioning information of the vehicle.
On the basis of vehicle number estimation, the SRP-PHAT algorithm is applied to carry out vehicle positioning to obtain the positioning information of the vehicle, and the method comprises the following steps:
d1) the set of spectral frequencies ω corresponding at time t, and for each dimension at time tt，velnAnd an estimate gamma of the vehicle direction at each frequency bandt，ωIn this case, each vehicle corresponds to a frequency spectrum set ω corresponding to tt，veln，n＝1，2，…；
Assume that the sound source position is in three-dimensional coordinates (x, y, z) noted
Figure BDA0001862551920000147
d2) Calculating the time delay of the linear propagation from the sound source to the ith MEMS microphone module:
Figure BDA0001862551920000148
wherein m islThe coordinate of the first MEMS microphone module is shown, and c is the propagation speed of sound in the air;
d3) calculating a loss function
Figure BDA0001862551920000151
Figure BDA0001862551920000152
Wherein psikl(k) For the correlation coefficients of the kth MEMS microphone module and the l MEMS microphone module, "-" denotes the complex conjugate:
Figure BDA0001862551920000153
d4) finding the loss function
Figure BDA0001862551920000154
Maximum sound source estimation
Figure BDA0001862551920000155
Using it as the vehicle position xs，
Figure BDA0001862551920000156
The invention takes the sound generated when the vehicle runs, in particular the tire noise generated by the friction between the tire and the ground in the running process of the vehicle as an object, analyzes the audio signal of the vehicle, and applies the microphone array signal processing technology to realize the orientation and the positioning of the vehicle so as to achieve the aim of vehicle detection.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.

Claims (4)

1. A moving vehicle audio detection system, characterized in that the system comprises a plurality of MEMS microphone modules arrayed for collecting tire sound signals of a moving vehicle and converting the collected sound signals into digital signals;
the MEMS microphone modules are connected with the signal processing unit, the signal processing unit receives the digital signals of the MEMS microphone modules, processes the digital signals and calculates the positioning information of the vehicle, wherein the signal processing unit executes the following instructions to calculate the positioning information of the vehicle:
a) preprocessing digital signals input by each MEMS microphone module, performing time domain-frequency domain transformation and extracting tire noise;
b) in the tire noise frequency band, performing vehicle orientation estimation on all frequency bands of the tire noise by a beam forming scanning algorithm;
in the tire noise frequency band, the vehicle orientation estimation is carried out on all frequency bands of the tire noise at time t and frequency omega by a beam forming scanning algorithm, and the method comprises the following steps:
b1) calculating array steering vector d formed by all MEMS microphone modules and steering vector d of steering angle thetaθComprises the following steps:
Figure FDA0003012094640000011
wherein r is the radius of the microphone array,
Figure FDA0003012094640000013
the azimuth angle of the nth MEMS microphone module is n, which is 1, 2, …, M, M is the number of the MEMS microphone modules, and c is the propagation speed of sound in the air;
b2) selecting a directivity angle ρ of a microphone array directivity pattern0And M-1 null angles rho, 0 < rho < 2 pi, and is recorded as rho ═ rho1，ρ2，…，ρM-1]；
b3) Calculating the angle p with respect to the pointing in step b2)0And M-1 null angles form a matrix A:
Figure FDA0003012094640000012
b4) solving equation A*hρ0Get M-dimensional filter operator h ═ cρ0Wherein c is M-dimensional column vector c ═ 1,0,0, …,0]；
b5) Beam forming is carried out in the range of the angle 0 < beta < 2 pi:
Figure FDA0003012094640000021
b6) finding the maximum beamforming BtβSolving the vehicle direction under the angle beta corresponding to the time t and the frequency omega
Figure FDA0003012094640000022
Obtaining a vehicle orientation estimate;
c) on the basis of vehicle orientation estimation, the number of vehicles is estimated by applying an audio frequency spectrum sparsity principle, and a frequency band occupied by each vehicle sound within a period of time is obtained;
d) on the basis of vehicle number estimation, vehicle positioning is carried out by applying an SRP-PHAT algorithm to obtain positioning information of the vehicle;
the signal processing unit is connected with the communication module and outputs the calculated vehicle positioning information through the communication module.
2. The audio detection system according to claim 1, wherein the step a) of preprocessing, time-frequency domain transformation and tire noise extraction of the digital signal inputted by each MEMS microphone module comprises the following method steps:
a1) the signal processing unit receives the obtained n-path digital signal xn(t) framing with each frame length of 25ms to obtain a framed signal x1(t)，x2(t)，…，xn(t)；
a2) For the framed signal x1(t)，x2(t)，…，xn(t) adding a Hamming window and performing fast Fourier transform to obtain a frequency domain signal X1(ω)，X2(ω)，…，Xn(ω)；
a3) Extracting the frequency range of the frequency domain signal belonging to the tire noise to form a narrow-band tire noise frequency domain signal Xt,k＝[X1(k),X2(k),…,Xn(k)]。
3. A method for audio detection of a moving vehicle, characterized in that the method comprises the following method steps:
a) preprocessing digital signals input by each MEMS microphone module, performing time domain-frequency domain transformation and extracting tire noise;
b) in the tire noise frequency band, performing vehicle orientation estimation on all frequency bands of the tire noise by a beam forming scanning algorithm;
in the tire noise frequency band, the vehicle orientation estimation is carried out on all frequency bands of the tire noise at time t and frequency omega by a beam forming scanning algorithm, and the method comprises the following steps:
b1) calculating array steering vector d formed by all MEMS microphone modules and steering vector d of steering angle thetaθComprises the following steps:
Figure FDA0003012094640000023
wherein r is the radius of the microphone array,
Figure FDA0003012094640000031
the azimuth angle of the nth MEMS microphone module is n, which is 1, 2, …, M, M is the number of the MEMS microphone modules, and c is the propagation speed of sound in the air;
b2) selecting a directivity angle ρ of a microphone array directivity pattern0And M-1 nulling angles ρ,0 < ρ < 2 π π, denoted ρ ═ ρ1，ρ2，…，ρM-1]；
b3) Calculating the angle p with respect to the pointing in step b2)0And M-1 null angles form a matrix A:
Figure FDA0003012094640000032
b4) solving equation A*hρ0Get M-dimensional filter operator h ═ cρ0Wherein c is M-dimensional column vector c ═ 1,0,0, …,0]；
b5) Beam forming is carried out in the range of the angle 0 < beta < 2 pi:
Figure FDA0003012094640000033
b6) finding the maximum beamforming Bt,βSolving the vehicle direction under the angle beta corresponding to the time t and the frequency omega
Figure FDA0003012094640000034
Obtaining a vehicle orientation estimate;
c) on the basis of vehicle orientation estimation, the number of vehicles is estimated by applying an audio frequency spectrum sparsity principle, and a frequency band occupied by each vehicle sound within a period of time is obtained;
d) and on the basis of vehicle number estimation, positioning the vehicle by applying an SRP-PHAT algorithm to obtain the positioning information of the vehicle.
4. The audio detection method according to claim 3, wherein the step a) of preprocessing, time-frequency domain transformation and tire noise extraction of the digital signal input by each MEMS microphone module comprises the following method steps:
a1) the signal processing unit receives the obtained n-path digital signal xn(t) framing with each frame length of 25ms to obtain a framed signal x1(t)，x2(t)，…，xn(t)；
a2) For the framed signal x1(t)，x2(t)，…，xn(t) adding a Hamming window and performing fast Fourier transform to obtain a frequency domain signal X1(ω)，X2(ω)，…，Xn(ω)；
a3) Extracting the frequency range of the frequency domain signal belonging to the tire noise to form a narrow-band tire noise frequency domain signal Xt,k＝[X1(k),X2(k),…,Xn(k)]。
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