CN109375223B - Indoor space perception and mobile sound source self-positioning method based on sound wave particle duality - Google Patents
Indoor space perception and mobile sound source self-positioning method based on sound wave particle duality Download PDFInfo
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- 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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Abstract
The invention discloses an indoor space perception and mobile sound source self-positioning method based on sound wave particle duality, which is a method for realizing self-positioning of a mobile sound source by using a smart phone as sound receiving and transmitting integrated equipment, adopting a non-cooperative mode, designing and optimizing sound pulse test signals, analyzing the resonance characteristic of a sound field, perceiving the space size of an indoor sound field environment, establishing a space model of the mobile sound source and a mirror image thereof, constructing an Euclidean distance array between a space point source and a receiving point, accurately judging the flight time of a first-order sound echo by using the attribute of an EDM (enhanced direct memory) rank, and then solving the position information of the mobile sound source in real time by using a multi-dimensional scale algorithm. The method does not depend on the cooperation between auxiliary facilities and equipment of an application place, gets rid of the dependence of the traditional geometric acoustics space sensing method on line-of-sight signal signals, improves the universality of the space sensing and mobile sound source self-positioning scheme, and is not only suitable for the reconstruction of the geometric outline of the indoor space, but also suitable for the tracking and positioning of the mobile sound source in the indoor environment.
Description
Technical Field
The invention relates to the field of indoor positioning, in particular to a method for realizing space perception and continuous self-positioning of a mobile sound source in an indoor complex environment based on sound wave particle duality (volatility and particle) analysis, and has important application value in a position perception and position service scene.
Background
According to relevant statistics, more than 80% of life time of a person is indoor, 80% of mobile communication services occur indoors, and 80% of information is related to time and position, so that urgent needs are generated for indoor space map services and position information thereof by pedestrians for autonomous positioning navigation and path planning, or virtual reality, augmented reality and autonomous robots (including unmanned vehicles, unmanned planes and the like) in a mobile scene. In fact, a number of literature studies have shown that: spatial awareness helps to improve the positioning accuracy of moving sources in the room.
At present, common space perception technologies mainly comprise vision, laser/millimeter wave radar ranging, sound wave ranging and the like, the problem of reconstructing an environment map from a large amount of landmark measurement data is mainly solved, and possibility is provided for realizing indoor high-precision positioning. However, the vision-based spatial perception mode poses a great threat to indoor privacy protection; the space perception based on the laser radar is limited to special application scenes and special equipment; the space sensing technology based on the acoustic ranging mostly focuses on the category of geometric acoustics, ignores the fluctuation characteristic of sound, and is limited by the acquisition degree of an LOS signal in an indoor environment. As early as the early twentieth century, foreign scholars began to study indoor space perception and sound source localization based on the theory of geometric acoustics by using a single sound source + microphone array, or a single sound source + single microphone, or a multi-device cooperation mode integrating transmission and reception. Such as: estimating the indoor space occupancy rate by using sonar; using echo and multi-path signal to construct indoor geometric shape and analyze indoor space size; based on the indoor space size and indoor reverberation prior, blind source separation is realized, the TDOA identification accuracy of the target source is improved, and the tracking and positioning precision of the target source is improved; by utilizing the indoor reverberation, the robustness of the array on the positioning performance of the target source can be improved. However, highly accurate spatial perception results come at the cost of high computational complexity and often rely on specialized equipment. Therefore, a spatial perception technique based on the acoustic particle diphasic analysis is considered as a breakthrough to solve the problem of self-localization of an indoor moving sound source.
Recent research results show that: through cooperation among a plurality of smart phones, indoor space perception and sound source self-positioning can be better realized. Therefore, by means of the advantages of the sound receiving and transmitting integrated equipment of the smart phone, the fluctuation theory of a sound field and the indoor geometric acoustics theory are deeply researched, the wave particle duality of sound is discussed and inferred, the resonance characteristic of the indoor sound field is analyzed, and a space model of a mobile sound source and a mirror image of the mobile sound source is established.
Disclosure of Invention
Aiming at the problem that the self-positioning capability of the existing indoor mobile sound source is still insufficient due to attenuation and loss of sound signal propagation in an indoor complex environment, the invention provides an indoor space sensing and mobile sound source self-positioning method based on sound wave particle duality, and the self-positioning of the mobile sound source in a non-equipment-dependent and non-cooperative mode is realized. The method takes the smart phone as sound receiving and transmitting integrated equipment, fully utilizes the wave-particle duality of sound to carry out space perception, space modeling and sound source self-positioning, gets rid of the dependence of the traditional geometric acoustics space perception method on LOS signals, improves the universality of the mobile sound source self-positioning method, and has lower complexity and higher universality.
The invention relates to an indoor space perception and mobile sound source self-positioning method based on sound wave particle duality, which takes a smart phone as sound receiving and transmitting integrated equipment and mainly comprises the following three steps:
(1) spatial perception of indoor sound field environment: the method comprises the steps that the distance between a smart phone loudspeaker and a main microphone is used as a priori, based on the fluctuation theory of an indoor sound field, an acoustic pulse test signal is designed and optimized, the resonance characteristic (namely the fluctuation analysis of sound) of the sound field is analyzed, and the space size of the indoor sound field environment is sensed;
(2) spatial modeling: establishing a space model of a mobile sound source and a mirror image thereof (namely, the particle analysis of sound) based on an indoor geometric acoustic theory by combining a space perception result;
(3) self-positioning of a sound source: by combining with a space model, an Euclidean Distance Matrix (EDM) between a space point source and a receiving point is constructed, the Time of Flight (TOF) of a first-order acoustic echo is accurately judged by utilizing the attribute of an EDM rank, and then the position information of a mobile sound source is solved in real Time by a multi-dimensional scaling (MDS) algorithm, so that the self-positioning and mapping of the mobile sound source in an indoor sound field environment are further completed.
In the research on waves in the physical field, the corresponding wave equation can be converted into a Helmholtz equation to be solved under the condition of a boundary.
The Helmholtz equation describes the input and output relationships in the propagation of acoustic waves as: the wave propagation of sound is analyzed from the angle of the signal and the system, an indoor sound field can be regarded as an acoustic system, the vibration of air medium particles caused by the vibration of a sound source can be regarded as system input, and the sound pressure value at the sound receiving end can be regarded as system output. From this input and output relationship, the Helmholtz equation can be described by an acoustic excitation function and a Green function.
The green's function expression for helmholtz is:
where G (x | ξ, k) represents the Green's function, x represents the microphone position, ξ represents the sound source position, L x The distance between two parallel wall surfaces in the indoor x-axis direction is to be obtained, k represents wave number, and k is ω/c, where ω is the angular frequency of the sound wave, and c is the sound propagation speed.
The standard solution form of the helmholtz equation is as follows:
L x is the distance between two parallel walls in the indoor x-axis direction to be calculated, x r The position coordinates representing the receiving microphone are an unknown quantity, but the distance d between it and the position coordinates xi of the sound source is known, so that xi is found, then x r The solution can be obtained.
Thus, in the real world of measurement and acquisition, the sound source is located ξ and the microphone is located x r A transfer function G between m (ω) so that G m (ω)≈G(x r And | ξ, k), combining with known distance prior d (obtained by directly measuring the size of the mobile phone), the spatial distance can be obtained.
In summary, the spatial perception process of the indoor sound field environment in step (1) is divided into two steps:
(1.1) transfer function G m (ω) measurement;
in order to measure and acquire indoor sound field environment, a sound source is positioned xi, and a microphone is positioned x r A transfer function G between m (ω), the acoustic emission signal needs to be properly waveform-designed. Because chirp has good autocorrelation and cross-correlation characteristics, the chirp acoustic signal is used as an acoustic pulse transmitting waveform, can be well compatible with a smart phone, and can simplify G m (ω) measurement calculation process. Let S (t) be a chirp sound signal emitted by a mobile phone loudspeaker (sound source) S, and R be an autocorrelation function of S (t) ss (τ), then:
R ss (τ)＝δ(τ) (2)
wherein f is 0 Is the upper limit frequency, f 1 The lower limit frequency is T, the duration of the chirp sound signal is T, and the delta (tau) is a Dirac function, which shows that the chirp signal has better autocorrelation characteristics; with R (tau) s,r Representing the cross-correlation function of S (t) with r (t), h (t, S, M) representing the room impulse response, then:
R(τ) s,r ＝R ss (τ)*h(t,S,M)＝h(t,S,M) (3)
therefore, when using a chirp acoustic signal as an acoustic source signal, h (t, S, M) can be obtained by calculating a cross-correlation function between the acoustic transmission and reception signals, and further:
(1.2) indoor space resonance analysis:
the room space can be generally regarded as a resonant cavity, where N denotes the form of resonance, N denotes a positive integer, and the distance L between the reflectors x With resonant frequency omega n The relationship between them is as follows:
therefore, as long as ω in the n form is obtained n Then L can be obtained x In combination with G (x) r |ξ,k)≈G m (ω) and
constructing an objective optimization function according to a Helmholtz equation standard solution:
due to | G (x) r |ξ,k)ksin(kL x )|<1, is bounded by the product function, so f (l) must have a minimum value:
adjusting the direction of the speaker (sound source) S of the smart phone, and obtaining L in the same way y Then the spatial distance [ L ] x ,L y ]The method can be used for solving the problems.
Step (2) space modeling, wherein the specific method is as follows:
based on the geometric acoustic principle, based on the particle characteristics of sound, the acoustic mirror image model can know the sound source S and a first-order mirror image sound source relative to the ith wall of the indoor space
Should satisfy the following relations:
wherein p is i Represents any point on the ith wall of the indoor space, and can be separated from the space by a distance [ L ] x ,L y ]K represents the number of reflecting walls in the room space, and S is the sum of
Inter-dot distance therebetween
And M and
inter-dot distance therebetween
Respectively expressed as:
wherein the function norm (·) represents the Euclidean distance solution, tof i Representing an acoustic signal from
TOF value of direct M.
Step (3) sound source self-positioning, which comprises the following steps:
(3.1) utilization of
And
construction of the Euclidean matrix E aug ：
Wherein, the upper label
Representing a transpose operation; when considering the case where the indoor space is three-dimensional, there is E aug Rank constraint of (2): rank (E) aug ) Less than or equal to 5, thus, from R (tau) s,r Accurately judge each tof i Value of E aug Rank constraint of EDM is satisfied, at which time E aug Is a real symmetric matrix of m x m.
The principle of MDS is to construct a suitable low-dimensional space by using the similarity between pairs of samples, so that the distance of the samples in this space and the similarity between the samples in the high-dimensional space are kept as consistent as possible. When the mutual distances between a plurality of sample points are known, but the specific coordinates of each sample point are not known, the MDS analysis requires solving the original coordinates of each sample point, and then ensuring that the original coordinates of the sample points conform to the distance matrix relationship as much as possible.
(3.2) solving the position information of the mobile sound source in real time by a multi-dimensional scale algorithm (MDS):
first, let D (2) ＝E aug .^2 Even if E aug Each array element in the array is squared, D (2) Each array element of (a) represents a square value of the inter-dot distance;
then, at D (2) Are multiplied by the central matrix J, i.e. pair D, simultaneously (2) Performing double centralization to obtain a double-center form matrix B:
wherein E is an m-order unit matrix,
i is m-order full 1 matrix; and (3) carrying out singular value decomposition on the B to obtain three maximum eigenvalues of the B to form a unit matrix Lambda, and forming an eigenvector matrix V by using three corresponding eigenvectors to obtain the position coordinate of the sound source S:
and obtaining the position coordinate of S, namely realizing the self-positioning of the sound source.
The invention provides a self-positioning method of an indoor environment mobile sound source independent of an application place infrastructure assistance and equipment-free cooperation mode. The method takes a smart phone as an acoustic transceiver, effectively utilizes the wave-particle diphasic property of sound based on the fluctuation theory of an indoor sound field and indoor geometric acoustics, realizes the rapid self-positioning of an indoor mobile sound source through universal and effective space perception and space modeling, and achieves the tracking and positioning effects of the indoor environment mobile sound source. The dependence of the traditional geometric acoustic spatial perception method on LOS signals is eliminated, the universality of the mobile sound source self-positioning method is improved, and the problem that the self-positioning capability of the existing indoor mobile sound source is still insufficient is solved.
Drawings
FIG. 1 is a block diagram of an indoor space sensing and mobile source self-positioning method based on acoustic particle duality;
FIG. 2 is a diagram of the positional relationship between a sound source, a microphone, and a reflecting wall in an indoor environment;
fig. 3 is a graph of input versus output in acoustic wave propagation based on helmholtz equation.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
The present invention will be further described with reference to the following examples and drawings, but the present invention is not limited thereto.
Examples
Referring to fig. 1, the method for indoor space sensing and self-positioning of a mobile sound source based on acoustic particle duality mainly comprises three steps: (1) spatial perception of an indoor sound field environment; (2) performing spatial modeling; (3) the sound source is self-locating.
Referring to fig. 2, in an indoor environment, a speaker (sound source) S is located ξ and a microphone M is located x r ，L x Is the distance L between two parallel walls in the indoor X-axis direction Y Is the distance, x, between two parallel walls in the direction of the indoor Y axis r The position coordinates representing the receiving microphone are an unknown quantity, but the distance d between it and the position coordinates xi of the sound source S is known, so that xi is found, then x r The solution can be obtained.
Referring to fig. 1, the spatial perception process of the indoor sound field environment in step (1) is divided into two steps:
(1.1) transfer function G m (ω) measurement;
in the indoor environment of measurement, sound source S is located xi, and microphone M is located x r A transfer function G between m (ω) as shown in FIG. 3, the acoustic emission signal needs to be properly wave-shaped. Because chirp signals have good autocorrelation and cross-correlation characteristics, chirp sound signals are used as sound pulse emission waveforms, and can be well compatible with smart phones and can simplify G m (ω) measurement calculation process. Let S (t) be a chirp sound signal emitted by a mobile phone loudspeaker (sound source) S, and R be an autocorrelation function of S (t) ss (τ), then:
R ss (τ)＝δ(τ) (2)
wherein f is 0 Is the upper limit frequency, f 1 The lower limit frequency is T, the duration of the chirp sound signal is T, and the delta (tau) is a Dirac function, which shows that the chirp signal has better autocorrelation characteristics; with R (tau) s,r Representing the cross-correlation function of S (t) with r (t), h (t, S, M) representing the room impulse response, then:
R(τ) s,r ＝R ss (τ)*h(t,S,M)＝h(t,S,M) (3)
therefore, when using a chirp acoustic signal as the acoustic source signal, h (t, S, M) can be obtained by calculating the cross-correlation function between the acoustic transmission and reception signals, and further, the following can be obtained:
(1.2) indoor space resonance analysis
The room space can be generally regarded as a resonant cavity, where N denotes the form of resonance, N denotes a positive integer, and the distance L between the reflectors x With resonant frequency omega n The relationship between them is as follows:
therefore, as long as ω in the n form is obtained n Then L can be obtained x . Binding to G (x) r |ξ,k)≈G m (ω) and
an objective optimization function is constructed according to equation (2):
due to | G (x) r |ξ,k)ksin(kL x )|<1, is bounded by the product function, so f (l) must have a minimum value:
the direction of the loudspeaker of the smart phone is adjusted, and L can be obtained in the same way y Then the spatial distance [ L ] x ,L y ]The method can be used for solving the problems.
(2) Spatial modeling
Based on the geometric acoustic principle, based on the particle characteristics of sound, the acoustic mirror image model can know the sound source S and a first-order mirror image sound source relative to the ith wall of the indoor space
Should satisfy the following relations:
wherein p is i Represents any point on the ith wall of the indoor space, and can be separated from the space by a distance [ L ] x ,L y ]And K represents the number of indoor space reflecting wall surfaces. Thus, S and
dot spacing therebetweenSeparation device
And M and
inter-dot distance therebetween
Respectively expressed as:
wherein the function norm (-) represents the Euclidean distance solution, tof i Representing an acoustic signal from
TOF value of direct M.
(3) Sound source self-positioning
By using
And
construction of the Euclidean matrix E aug ：
Wherein, the upper label
Representing a transpose operation. When considering the case where the indoor space is three-dimensional, there is E aug Rank constraint of (2): rank (E) aug ) 5 or less, and thus can be selected from R (τ) s,r Accurately determine each tof i Value of E aug The rank constraint of the EDM is satisfied. At this time, E aug Is a real symmetric matrix of m x m.
The principle of MDS is to construct a suitable low-dimensional space by using the similarity between pairs of samples, so that the distance of the samples in this space and the similarity between the samples in the high-dimensional space are kept as consistent as possible. When the mutual distances between many sample points are known, but the specific coordinates of each sample point are not known, the MDS analysis requires solving the original coordinates of each sample point and then ensuring that the original coordinates of the sample points conform to the distance matrix relationship as much as possible.
First, let D (2) ＝E aug .^2 Even if E aug Each array element in the array is squared, D (2) Each array element of (a) represents the square of the inter-dot distance.
Then, at D (2) Are multiplied by the central matrix J, i.e. pair D, simultaneously (2) Performing double centralization to obtain a double-center form matrix B:
wherein E is an m-order unit matrix,
i is m-order full 1 matrix. And (3) carrying out singular value decomposition on the B to obtain three maximum eigenvalues of the B to form a unit matrix Lambda, and obtaining the position coordinate of the S by forming an eigenvector matrix V by using three corresponding eigenvectors:
and obtaining the position coordinate of S, namely realizing the self-positioning of the sound source.
Claims (2)
1. An indoor space perception and mobile sound source self-positioning method based on sound wave particle duality takes a smart phone as sound receiving and transmitting integrated equipment, and is characterized in that the positioning method is mainly divided into three steps:
(1) spatial perception of indoor sound field environment: the method comprises the steps of designing and optimizing an acoustic pulse test signal based on the fluctuation theory of an indoor sound field by using the distance between a smart phone loudspeaker and a main microphone as a priori, analyzing the resonance characteristic of the sound field, namely analyzing the fluctuation of sound, and sensing the space size of the indoor sound field environment;
(2) spatial modeling: establishing a space model of a mobile sound source and a mirror image thereof based on an indoor geometric acoustic theory by combining a space perception result, namely, particle analysis of sound;
(3) self-positioning of a sound source: by combining a space model, an Euclidean distance matrix EDM between a space point source and a receiving point is constructed, the flight time of a first-order acoustic echo is accurately judged by utilizing the attribute of an EDM rank, and the position information of a mobile sound source is solved in real time by a multi-dimensional scale algorithm, so that the self-positioning and mapping of the mobile sound source in an indoor sound field environment are completed in one step;
the spatial perception process of the indoor sound field environment in the step (1) is divided into two steps:
(1.1) in the indoor sound field environment, the sound source is positioned in xi, and the microphone is positioned in x r A transfer function G between m (ω)；
Let S (t) be a chirp sound signal emitted by a mobile phone sound source S, and R be an autocorrelation function of S (t) ss (τ), then:
R ss (τ)＝δ(τ) (2)
wherein f is 0 Is the upper limit frequency, f 1 The lower limit frequency is T, the duration of the chirp sound signal is T, and the delta (tau) is a Dirac function, which shows that the chirp signal has better autocorrelation characteristics; with R (tau) s，r Denotes the cross-correlation function of S (t) with r (t), h (t, S)And M) represents the room impulse response, then there are:
R(τ) s，r ＝R ss (τ)*h(t，S，M)＝h(t，S，M) (3)
therefore, when using a chirp acoustic signal as an acoustic source signal, h (t, S, M) can be obtained by calculating a cross-correlation function between the acoustic transmission and reception signals, and further:
(1.2) indoor space resonance analysis:
the room space can be generally regarded as a resonant cavity, where N denotes the form of resonance, N denotes a positive integer, and the distance L between the reflectors x With resonant frequency omega n The relationship between them is as follows:
therefore, as long as ω in the n form is obtained n Then L can be obtained x In combination with G (x) r |ξ，k)≈G m (ω) and
establishing an objective optimization function according to a Helmholtz equation standard solution:
due to | G (x) r |ξ，k)ksin(kL x ) If | is < 1, it is bounded by the product function, so f (l) must have a minimum value:
adjusting the direction of the sound source S of the smart phone to obtain L in the same way y Then spatial distance [ L ] x ，L y ]Calculating;
step (2) space modeling, wherein the specific method is as follows:
based on the geometric acoustic principle, based on the particle characteristics of sound, the acoustic mirror image model can know the sound source S and a first-order mirror image sound source relative to the ith wall of the indoor space
Should satisfy the following relations:
wherein p is i Represents any point on the ith wall of the indoor space, and can be separated from the space by a distance [ L ] x ，L y ]K represents the number of reflecting walls in the room space, and S is the sum of
Inter-dot distance therebetween
And M and
inter-dot distance therebetween
Respectively expressed as:
wherein the function norm (·) represents the Euclidean distance solution, tof i Representing an acoustic signal from
TOF value of direct M.
2. The method for self-positioning of an indoor space sensing and moving sound source based on sonic particle duality as claimed in claim 1, wherein: step (3) self-positioning of the sound source, which comprises the following specific steps:
(3.1) utilization of
And
construction of the Euclidean matrix E aug ：
Wherein, the upper label
Representing a transpose operation; when considering the case where the indoor space is three-dimensional, there is E aug Rank constraint of (2):
rank(E aug ) 5 or less, and thus can be selected from R (τ) s，r Accurately judge each tof i Value of E aug The rank constraint of EDM is satisfied, at this time, E aug Is a real symmetric matrix of m x m;
(3.2) solving the position information of the mobile sound source in real time by a multi-dimensional scale algorithm:
first, let D (2) ＝E aug ·^ 2 Even if E aug Each array element in the array is squared, D (2) Each array element of (a) represents a square value of the inter-dot distance;
then, at D (2) Are multiplied by the central matrix J, i.e. pair D, simultaneously (2) Performing double centralization to obtain a double-center form matrix B:
wherein E is an m-order unit matrix,
i is m-order full 1 matrix; performing singular value decomposition on the B to obtain three maximum eigenvalues of the B to form a unit matrix Lambda, and forming a eigenvector matrix V by using the corresponding three eigenvectors to obtain the position coordinates of the sound source S:
and obtaining the position coordinate of S, namely realizing the self-positioning of the sound source.
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| Acoustic echoes reveal room shape;Ivan Dokmanic 等;《Proceedings of the National Academy of Sciences》;20130723;第110卷(第30期);第12186-12191页 * |
| Localization of environmental reflectors from a single measured transfer function;Ladan Zamaninezhad 等;《2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP)》;20140814;第157-160页 * |
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Application publication date: 20190222 Assignee: Wuhan xingeno Technology Co.,Ltd. Assignor: GUILIN University OF ELECTRONIC TECHNOLOGY Contract record no.: X2022450000387 Denomination of invention: Indoor space perception and mobile sound source location based on acoustic wave particle duality Granted publication date: 20220816 License type: Common License Record date: 20221226 |