Fast and High-Precision TDOA Sound Source Localization Method

Abstract

The present invention provides a fast and high-precision TDOA sound source localization method, which solves the problems of the solution speed and accuracy of TDOA localization in the case of no denoising means. The methods in this method include: detecting the time delay of the sound source signal propagating to two sensors through the TDOA localization algorithm to obtain the distance difference, and determining the sound source position through the propagation distance difference values of multiple groups of sensors; according to the maximum absolute distance criterion between the propagation distance difference elements, re-arranging the positions of the TDOA solution equations to reduce the influence of the equation sorting of the linear equations on the solution process; adopting a greedy strategy to determine the TDOA solution equations for solving the sound source coordinates; performing density clustering on the sound source coordinates; selecting the clustering center to obtain the localization point. Thus, the calculation speed and localization accuracy of the TDOA sound source localization algorithm can be effectively improved.

Description

Background Art

[0002] The main fault location methods of transformers include: inspection by removing the transformer cover, partial discharge (PD) location, and sound source location. Inspection by removing the transformer cover requires power outage for maintenance. Although the inspection is comprehensive, it is time-consuming and laborious, and cannot meet the increasing maintenance requirements. PD location locates the position of PD by receiving ultrasonic waves. This method has mature research means, but ultrasonic waves are only applicable to the detection of PD. Sound source location determines the fault position by collecting the sound signals of the transformer. The detected faults include not only general mechanical faults but also electrical faults such as winding short circuits. In recent years, due to the advantages of small interference and convenient operation in the detection and location of transformer sound signals, it has gradually attracted the attention of researchers.

[0003] A commonly used method for sound source location is: Time Difference of Arrival (TDOA) location. TDOA location requires solving a hyperbolic equation system, but directly solving it has a large computational difficulty. Therefore, many algorithms (such as Taylor series expansion, Chan algorithm) have been proposed successively to speed up the calculation. However, considering the requirements of accuracy and the complexity of the actual environment, the location effects of these algorithms are not very good. Specifically, the clock synchronization error of the sampling chip, time delay estimation error, signal propagation speed error, sensor position error, and Non-Line-Of-Sight (NLOS) environment will all affect the TDOA location result. Therefore, the current research mainly focuses on how to improve the location accuracy.

[0004] In view of the deficiencies of the Chan algorithm, an improved Chan algorithm based on an adaptive error model is proposed. The adaptive error model assumes a proportional relationship between the measured distance and the actual distance, but this is not necessarily a general case, and the Chan algorithm is generally only applicable to high signal-to-noise ratio environments. With the rise of intelligent optimization algorithms, many intelligent optimization algorithms have gradually been used in TDOA positioning. Some research uses an improved Harris Hawk Optimization (IHHO) algorithm to solve the TDOA positioning problem. IHHO uses an improved fitness function and the initial population solved by the Chan algorithm. The simulation results show that IHHO has a lower Root Mean Square Error (RMSE) under different numbers of base stations, but it does not consider the impact of measurement anomalies from different sources (such as abnormal sensor position measurement, abnormal delay estimation, etc.) on the positioning result. To address the impact of measurement anomalies from different sources on the positioning accuracy, some research considers reducing the impact of measurement anomalies on the positioning accuracy starting from the loss function, changing the commonly used RMSE to a mixed Huber loss (MHL), and using k-means to cluster and transform the positioning center of multiple positioning points obtained from the reference microphones. However, the threshold in MHL is inconvenient to obtain, and a large amount of calculation is required to obtain the optimal threshold. Summary of the Invention

[0005] Aiming at the defects in the prior art, the purpose of the present invention is to provide a fast and high-precision TDOA sound source positioning method.

[0006] In a first aspect, the present application provides a fast and high-precision TDOA sound source positioning method, including:

[0007] Step 1: Calculate the time delay of the sound source signal propagating to two microphones, and then obtain the distance difference. Combine the positions of the microphones to obtain the TDOA positioning equation system;

[0008] Step 2: Use a greedy strategy to select multiple groups of equation systems that meet the requirements from the TDOA positioning equation system, and rearrange the positions of the selected equation systems according to the maximum absolute distance criterion between the propagation distance difference elements;

[0009] Step 3: Convert the rearranged equation system into the form of a linear equation system and a non-linear equation, and solve the linear equation system to obtain an approximate positioning point;

[0010] Step 4: After all the selected equation systems are converted and solved, perform density-based clustering on all the approximate positioning points;

[0011] Step 5: Construct an objective function, and select the clustering center that can minimize the objective function as the target positioning point.

[0012] Optionally, step 1 includes:

[0013] Step 1.1: Estimate the time delay of the sound source signal propagating to two microphones using the generalized cross-correlation method. Assume that the sound sequences of length N received by the two microphones are x1(m) and x2(m), s respectively, is the correlation between the two microphones. Denote X1(k), X2(k), as the discrete Fourier transforms of x1(m), x2(m), respectively. According to the Wiener-Khinchin theorem, we have:

[0014]

[0015]

[0016] where: P(k) represents the power spectral density, R(n) represents the correlation of the sequence under different time delays, and the time delay of the sequence is obtained by searching for the maximum value of R(n). is represents the reciprocal of; represents the conjugate of X1(k), k represents a multiple of the fundamental frequency, and n represents the sample points by which the sound sequence leads or lags the reference sound sequence;

[0017] Step 1.2: Obtain the distance difference between the sound source and different microphones according to the time delay, and combine the microphone positions to obtain the TDOA positioning equation.

[0018] Optionally, step 2 includes:

[0019] Assume that the distance difference vector from the sound source to the reference microphone and the remaining microphones is denoted as d = [r 1，1 , r 2，1 ,..., r M，1 . Rearrange the vector d in ascending order to get d' = [r' 1，1 , r' 2，1 ,..., r' M，1 (r' 1，1 ≤ r' 2，1 ≤... ≤ r' M，1 ). Then insert the elements in d' between two numbers in d' or to the right of the rightmost number in descending order, and take out the elements of the same length as the vector d from left to right to form the rearranged vector; where: each element in d' corresponds to an equation in the TDOA positioning equation set, and the positioning equation set is rearranged according to d' and based on the maximum absolute distance.

[0020] Optionally, step 3 includes:

[0021] Step 3.1: Subtract adjacent equations of the rearranged system of equations to obtain the required system of linear equations;

[0022] Step 3.2: Classify and discuss the solution methods of the system of linear equations according to whether the coefficient matrix of the system of linear equations is invertible.

[0023] Optionally, the said Step 4 includes:

[0024] Step 4.1: Normalize the input data, set the values of ε and MinPts, and randomly assume a core object. Denote the remaining number of samples to be searched as the total number of samples N; where ε represents the sample neighborhood distance threshold, and MinPts represents the threshold of the number of samples in the ε-neighborhood of a certain sample;

[0025] Step 4.2: Judge whether N is greater than 0. If it is greater than 0, then execute Step 4.3; if it is not greater than 0, then end the program;

[0026] Step 4.3: Search for all density-reachable points of the core object, and denote the number of density-reachable points as m; if m < MinPts, then the corresponding assumed core object is noise; if m ≥ MinPts, then the corresponding assumed core object is a core object, and it is of the same class as the remaining m points;

[0027] Step 4.4: Remove the already classified m + 1 samples from the samples to be searched, randomly specify a core object, and then return to execute Step 4.2.

[0028] Optionally, the said Step 5 includes:

[0029] Construct the objective function:

[0030]

[0031] where: x, y, and z respectively represent the coordinates corresponding to the x-axis, y-axis, and z-axis of the sound source, and r represents the distance from the reference microphone to the sound source point; M represents the number of sensors, x i represents the x-axis coordinate of the i-th sensor, y i represents the y-axis coordinate of the i-th sensor, z i represents the z-axis coordinate of the i-th sensor, r i，1 represents the distance from the sound source to the i-th microphone minus the distance from the sound source to the reference microphone;

[0032] Substitute the four-dimensional vectors (x, y, z, r) of each class center into the objective function f(x, y, z, r). When the value of f(x, y, z, r) corresponding to a certain class center is the smallest, then take the class center of the corresponding class as the final sound source localization point.

[0033] In a second aspect, an embodiment of the present application provides a fast and high-precision TDOA sound source localization device, including: a processor and a memory. An executable program instruction is stored in the memory. When the processor calls the program instruction in the memory, the processor is configured to:

[0034] Execute the steps of the fast and high-precision TDOA sound source localization method described in any one of the first aspects.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] In the present invention, the time delay of the sound source signal propagating to two sensors is detected by the TDOA localization algorithm to obtain the distance difference, and the sound source position is determined by the propagation distance difference values of multiple groups of sensors; according to the maximum absolute distance criterion between the propagation distance difference elements, the position rearrangement of the TDOA solution equations is performed; the greedy strategy is adopted to determine the TDOA solution equations for solving the sound source coordinates; density clustering is performed on the sound source coordinates; the clustering center is selected to obtain the localization point. Thus, the calculation speed and localization accuracy of the TDOA sound source localization algorithm can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts. By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objects, and advantages of the present invention will become more obvious:

[0038] Figure 1 It is a schematic flowchart of a fast and high-precision TDOA sound source localization method provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The present invention will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can be made. These all belong to the protection scope of the present invention.

[0040] It should be noted that when an element is referred to as "fixed to" or "disposed on" another element, it can be directly on the other element or indirectly on the other element. When an element is referred to as "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element. In addition, the connection can be for fixing or for circuit connection.

[0041] It should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0042] In addition, the terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0043] The embodiments of the present application provide a fast and high-precision TDOA sound source localization method, aiming to solve the problems of the solution speed and accuracy of TDOA localization in the case of no denoising means. Specifically, in the embodiments of the present application, first, the time delay of the sound source signal propagating to two sensors is calculated, and then the distance difference is obtained. Then, the TDOA localization equation set is obtained by combining the positions of the microphones. Secondly, a greedy strategy is used to select multiple groups of equation sets that meet the requirements from the TDOA localization equation set, and the untransformed equation sets selected are rearranged in position according to the maximum absolute distance criterion between the propagation distance difference elements. Then, the rearranged equation sets are transformed into the form of a linear equation set and a non-linear equation, and the linear equation set is solved to obtain an approximate localization point. Then, after all the selected equation sets are transformed and solved, density-based clustering is performed on all the approximate localization points. Finally, an objective function is constructed, and the clustering center that can minimize the objective function is selected as the localization point.

[0044] Figure 1 It is a schematic flow chart of a fast and high-precision TDOA sound source localization method provided by the embodiments of the present application, as Figure 1 shown, the method in this embodiment may include:

[0045] Step S1: Calculate the time delay of the sound source signal propagating to two microphones, and then obtain the distance difference, and obtain the TDOA localization equation set by combining the positions of the microphones;

[0046] In this embodiment, step S1 includes:

[0047] Step 1.1: Estimate the time delay of the sound source signal propagating to two microphones by using the generalized cross-correlation method. Assume that the lengths of the sound sequences received by the two microphones are N s are x1(m) and x2(m) respectively, is the correlation between the two microphones, and X1(k), X2(k), are the discrete Fourier transforms of x1(m), x2(m), respectively. According to the Wiener-Khinchin theorem, we have:

[0048]

[0049]

[0050] where: P(k) represents the power spectral density, R(n) represents the correlation of the sequence under different time delays, and the time delay of the sequence is obtained by searching for the maximum value of R(n). is represents the reciprocal of; represents the conjugate of X1(k), k represents a multiple of the fundamental frequency, and n represents the sample points by which the sound sequence leads or lags the reference sound sequence;

[0051] Step 1.2: Obtain the distance difference from the sound source to different microphones according to the time delay, and combine the microphone positions to obtain the TDOA positioning equation.

[0052] In this embodiment, first, determine one of the multiple sensors as the reference sensor, calculate the time delay of the sound received by other sensors relative to the reference microphone, multiply the time delay by the sound propagation speed to obtain the distance difference, and then combine the positions of all microphones to obtain the TDOA positioning equation set.

[0053] Exemplarily, assume there are N sensors, take one of the sensors as the reference, the distance from the sound source to this sensor is r1, the propagation time is denoted as t1, and the time from the sound source to the remaining i (2 < i < N) sensors is t i , the sound source position s = [x, y, z] T , the position matrix of N microphones Let Denote ui,j = ui - uju as x or y or z or t or K, and the sound propagation speed is v, then we have

[0054]

[0055] In Equation (1), let the distance difference \(r\) from the sound source to the \(i\)-th microphone relative to the first microphone i，1 = \(t\ i，1 v (i = 1, 2,..., N)\), then Equation (1) can be converted to

[0056]

[0057] Step S2: Use the greedy strategy to select multiple groups of equations that meet the requirements from the TDOA positioning equation set, and rearrange the positions of the selected equations according to the maximum absolute distance criterion between the elements of the propagation distance difference;

[0058] In this embodiment, different numbers or the same number but different combinations of equations are selected from the original TDOA positioning equation set, and the selected equations are rearranged according to the maximum absolute distance criterion.

[0059] Exemplarily, in order to improve the positioning accuracy and make full use of the parallel computing ability of the processor, a TDOA positioning method based on the greedy strategy is proposed. Considering that at least four microphones can determine the position of the sound source, but too many microphone combinations will increase the computational complexity. Therefore, the minimum number of microphones included in each group of microphones is limited according to the total number of microphones. Take \(M\) (\(\max(4, N - 2)\leq M\leq N\) and \(M\in Z\)) microphones as a group, and there are a total of kinds of possibilities. Among them

[0060] Since the time delay estimation is proportional to the distance difference, the larger the distance difference, the smaller the influence of the time delay estimation error on the distance difference under the same signal-to-noise ratio. To reduce the influence of the time delay difference estimation and the microphone array position on the positioning accuracy in the TDOA algorithm, it is considered to first rearrange the original equation according to the maximum absolute distance criterion. The maximum distance difference can make the distance between two adjacent microphones as far as possible, so that the relative errors of the time delay difference and the microphone coordinate difference estimation are smaller, which is beneficial to obtaining more accurate results. The maximum distance difference criterion is introduced below.

[0061] Without loss of generality, the numbers of the \(M\) microphones are 1 - \(M\). Let the distance difference vector \(d\) from the sound source to the first microphone and all microphones be \(d = [r 1，1 , r 2，1 ,..., r M，1 \). The vector \(d\) is rearranged in ascending order to get \(d' = [r' 1，1 , r' 2，1 ,..., r' M，1 \) (\(r' 1，1 \leq r' 2，1 \leq \cdots \leq r' M，1 \)). Then, the elements in \(d'\) are inserted between two numbers in \(d'\) or to the right of the last number from largest to smallest in turn, and the elements of the same length as the vector \(d\) are taken from left to right to form the rearranged vector.

[0062] The maximum value in d′ can be inserted to the left or right of the first value. When the elements in d′ are equidistant, the distance differences obtained by these two methods are equal. When the elements in d′ are not equidistant, the interpolation method with the maximum absolute distance can be obtained by comparison. Without loss of generality, assume that the maximum value in d′ is inserted to the right of the first value. At this time, we can get:

[0063] When M is even, the vector d with the maximum absolute distance is d = [r′ 1，1 , r′ M，1 , r′ 2，1 , r′ (M-1)，1 ,..., r′ M / 2，1 ;

[0064] When M is odd, the vector d with the maximum absolute distance is d = [r′ 1，1 , r′ M，1 , r′ 2，1 , r′ (M-1)，1 ,..., r′ (M+1) / 2，1 .

[0065] Step S3: Transform the rearranged system of equations into the form of a linear equation system and a non-linear equation, and solve the linear equation system to obtain an approximate positioning point.

[0066] In this embodiment, step S3 includes:

[0067] Step 3.1: Subtract adjacent equations of the rearranged system of equations to obtain the required linear equation system;

[0068] Step 3.2: Classify and discuss the solution method of the linear equation system according to whether the coefficient matrix of the linear equation system is invertible.

[0069] In this embodiment, when M microphones numbered 1 - M are used for detection, let the distance difference vector sorted based on the maximum absolute distance be [r 1，1 , r 2，1 ,..., r M，1 . From this, we can get

[0070]

[0071] Subtract the upper and lower equations of formula (3) to get

[0072]

[0073] Denote s1 = [x, y, z, r] T , then formula (4) becomes:

[0074]

[0075] Where: r 2，1 represents the difference in the distance from the second microphone to the sound source relative to the first microphone (reference microphone), r M，1 represents the difference in the distance from the Mth microphone to the sound source relative to the reference microphone, K 2，1 represents the difference in the square of the distance from the second microphone to the origin relative to the reference microphone, K M，(M-1) represents the difference in the square of the distance from the Mth microphone to the origin relative to the (M - 1)th microphone, K 2，1 represents the difference in the square of the distance from the second microphone to the origin relative to the reference microphone, x 2，1 represents the difference in the x-axis coordinate of the second microphone relative to the reference microphone, x M，(M-1) represents the difference in the x-axis coordinate of the Mth microphone relative to the (M - 1)th microphone, y M，(M-1) represents the difference in the y-axis coordinate of the Mth microphone relative to the (M - 1)th microphone, z M，(M-1) represents the difference in the z-axis coordinate of the Mth microphone relative to the (M - 1)th microphone, r M，(M-1) represents the difference in the distance from the Mth microphone to the sound source relative to the (M - 1)th microphone.

[0076] Ignore the non-linear equations in Equation (5) and only solve the linear equations to obtain an approximate positioning point. The solution method of the linear equations will vary depending on the coefficient matrix A. The matrix A is classified and discussed as follows:

[0077] 1) When the matrix A is full rank, s1 = A -1 b.

[0078] 2) When the matrix A is not full rank and does not contain a zero column, there is

[0079]

[0080] In Equation (4), represents the generalized inverse, A = U∑V T is called the singular value decomposition (SVD) of the matrix A.

[0081] 3) When the matrix A is not full rank and contains a zero column, without loss of generality, let the third column be the zero column,

[0082] Let Then

[0083]

[0084]

[0085] In Equation (8), the plus or minus sign in the z expression is selected according to the actual orientation of the sound source and the microphone array. Generally, it is assumed that the sound source is above the microphone, and only the plus sign is taken in this case. When the value of z is a complex number, the absolute value of the complex number is taken for z.

[0086] Step S4: After all the selected equations are transformed and solved, density-based clustering is performed on all approximate positioning points.

[0087] In this embodiment, step S4 includes:

[0088] Step S4.1: Normalize the input data, set the values of ε and MinPts, and randomly assume a core object. The remaining number of samples to be searched is denoted as the total number of samples N. Among them, ε represents the sample neighborhood distance threshold, and MinPts represents a certain sample

[0089]

[0090] In Equation (9), x, y, and z respectively represent the x-axis, y-axis, and z-axis coordinates of the sound source, r represents the distance from the reference microphone to the sound source point, and r i,1 represents the difference in the distance from the i-th microphone to the sound source relative to the first microphone.

[0091] Substitute the four-dimensional vectors (x, y, z, r) of each class center into the objective function f(x, y, z, r). When the value of f(x, y, z, r) corresponding to a certain class center is the smallest, the class center of this class is taken as the final sound source positioning point.

[0092] Many studies adopt the method of using the quadratic equation corresponding to the reference microphone as the reference equation, and then subtracting the reference equation from other quadratic equations to obtain a system of linear equations. Adding all the linear equations to a reference equation constitutes the solution condition for TDOA positioning. However, considering that when the measurement data in the reference equation is severely distorted, it will have a huge impact on other equations. In order to reduce the influence of the reference microphone on the positioning result, the maximum absolute distance sorting criterion is proposed. This criterion first sorts the system of equations and then obtains a system of linear equations by subtracting adjacent equations. In this way, it can be ensured that adjacent equations come from microphones that are relatively far apart as much as possible, which is beneficial to reducing the interference of noise and the influence of calculation errors. The simulation results show that the sorted system of equations has a higher probability of better positioning accuracy than the unsorted case in different signal-to-noise ratio environments.

[0093] Considering the complexity of the application scenarios of the positioning algorithm, in order to avoid the computational burden brought by the intelligent optimization search for the global solution, an equation system transformation and a greedy strategy are proposed to solve the TDOA positioning equation system. The equation system transformation is used to transform the original equation system into multiple linear equations and a non-linear equation. The greedy strategy is to select different numbers or the same number but different sources of linear equations from the linear equation system to solve and obtain multiple sets of positioning points, and then use density clustering on the multiple sets of positioning points to obtain the clustering centers, and finally obtain the final positioning points after screening the clustering centers.

[0094] An embodiment of the present application also provides a fast and high-precision TDOA sound source positioning device, which may include: a processor and a memory.

[0095] The memory is used to store programs; the memory may include volatile memory (English: volatile memory), such as random access memory (English: random-access memory, abbreviation: RAM), such as static random access memory (English: static random-access memory, abbreviation: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic Random Access Memory, abbreviation: DDR SDRAM), etc.; the memory may also include non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory). The memory is used to store computer programs (such as application programs and functional modules for implementing the above methods), computer instructions, etc. The above computer programs, computer instructions, etc. can be stored in partitions in one or more memories. And the above computer programs, computer instructions, data, etc. can be called by the processor.

[0096] The above computer programs, computer instructions, etc. can be stored in partitions in one or more memories. And the above computer programs, computer instructions, data, etc. can be called by the processor.

[0097] The processor is used to execute the computer programs stored in the memory to implement each step in the methods involved in the above embodiments.

[0098] Specifically, reference can be made to the relevant descriptions in the foregoing method embodiments.

[0099] The processor and the memory can be of an independent structure or an integrated structure integrated together. When the processor and the memory are of an independent structure, the memory and the processor can be coupled and connected through a bus.

[0100] The fast and high-precision TDOA sound source localization device of this embodiment can execute the technical solutions in the above method. For the specific implementation process and technical principle, please refer to the relevant descriptions in the above method and will not be elaborated here.

[0101] Those skilled in the art can understand that various aspects of the present invention can be implemented as a system, method, or program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "platform" here.

[0102] In addition, the embodiments of the present application further provide a computer-readable storage medium. Computer-executable instructions are stored in the computer-readable storage medium. When at least one processor of the user device executes the computer-executable instructions, the user device executes the above various possible methods. Among them, the computer-readable medium includes computer storage media and communication media, where the communication media includes any medium facilitating the transmission of a computer program from one place to another. The storage medium can be any available medium accessible by a general or special-purpose computer. An exemplary storage medium is coupled to the processor, enabling the processor to read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. Additionally, the ASIC can be located in the user device. Of course, the processor and the storage medium can also exist as discrete components in the communication device.

[0103] The above is the core idea of the present invention. To make the above objects, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0104] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other. The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

[0105] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various deformations or modifications within the scope of the claims, which does not affect the essence of the present invention.

Claims

1. A fast and high-precision TDOA sound source localization method, characterized in that, Including: Step 1: Calculate the time delay of the sound source signal propagating to two microphones, and then obtain the distance difference. Combine the positions of the microphones to obtain the TDOA positioning equation set. Step 2: Use the greedy strategy to select multiple sets of equation sets that meet the requirements from the TDOA positioning equation set, and rearrange the positions of the selected equation sets according to the maximum absolute distance criterion between the propagation distance difference elements. Step 3: Transform the rearranged equation set into the form of a linear equation set and a non-linear equation, and solve the linear equation set to obtain an approximate positioning point. Step 4: After all the selected equation sets are transformed and solved, perform density-based clustering on all the approximate positioning points. Step 5: Construct an objective function, and select the clustering center that can minimize the objective function as the target positioning point.

2. The fast and high-precision TDOA sound source localization method according to claim 1, characterized in that The said Step 1 includes: Step 1.1: Estimate the time delay of the sound source signal propagating to two microphones using the generalized cross-correlation method. Assume that the lengths of the sound sequences received by the two microphones are N s are x1(m) and x2(m), respectively, is the correlation between the two microphones. Denote X1(k), X2(k), as the discrete Fourier transforms of x1(m), x2(m), respectively. According to the Wiener-Khinchin theorem, we have: Where: P(k) represents the power spectral density, R(n) represents the correlation of the sequence under different delay conditions, and the time delay of the sequence is obtained by searching for the maximum value of R(n). is denoted as the reciprocal of; represents the conjugate of X1(k), k represents the multiple of the fundamental frequency, and n represents the sample points by which the sound sequence leads or lags the reference sound sequence. Step 1.2: Obtain the distance difference between the sound source and different microphones according to the time delay, and combine the microphone positions to obtain the TDOA positioning equation.

3. The fast and high-precision TDOA sound source localization method according to claim 1, characterized in that The said Step 2 includes: Suppose the distance difference vector from the sound source to the reference microphone and the remaining microphones is denoted as d = [r 1,1 , r 2,1 , …, r M,1 . After ascending re - sorting the vector d, we get d ′ = [r1 ′ ,1 , r2 ′ ,1 , …, r ′ M,1 (r1 ′ ,1 ≤ r2 ′ ,1 ≤ … ≤ r ′ M,1 ). Then, insert the elements in d ′ from largest to smallest between the elements of d ′ or to the right of the right - most number, and take out the elements of the same length as the vector d from left to right to form the re - sorted vector; where: each element in d ′ corresponds to an equation in the TDOA positioning equation set, and re - sort the positioning equation set according to d ′ and based on the maximum absolute distance.

4. The fast and high-precision TDOA sound source localization method according to claim 1, wherein The said Step 3 includes: Step 3.1: Subtract adjacent equations of the rearranged equation set to obtain the required linear equation set. Step 3.2: Classify and discuss the solution method of the linear equation set according to whether the coefficient matrix of the linear equation set is invertible.

5. The fast and high-precision TDOA sound source localization method according to claim 1, characterized in that The said Step 4 includes: Step 4.1: Normalize the input data, set the values of ε and MinPts, and randomly assume a core object. Denote the remaining number of samples to be searched as the total number of samples N; where, ε represents the sample neighborhood distance threshold, and MinPts represents the threshold of the number of samples in the ε-neighborhood of a certain sample. Step 4.2: Judge whether N is greater than 0. If it is greater than 0, then execute Step 4.3; if it is not greater than 0, then end the program. Step 4.3: Search for all density-reachable points of the core object, and denote the number of density-reachable points as m; if m < MinPts, then the corresponding assumed core object is noise; if m ≥ MinPts, then the corresponding assumed core object is a core object, and it is of the same class as the remaining m points. Step 4.4: Remove the m + 1 classified samples from the samples to be searched, randomly specify a core object, and then return to execute Step 4.

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6. The fast and high-precision TDOA sound source localization method according to claim 1, characterized in that The said Step 5 includes: Construct an objective function: Where: x, y, and z respectively represent the coordinates corresponding to the x-axis, y-axis, and z-axis of the sound source, and r represents the distance from the reference microphone to the sound source point; M represents the number of sensors, x i represents the x-axis coordinate of the i-th sensor, y i represents the y-axis coordinate of the i-th sensor, z i represents the z-axis coordinate of the i-th sensor, r i,1 represents the distance from the sound source to the i-th microphone minus the distance from the sound source to the reference microphone; Substitute the four-dimensional vectors (x, y, z, r) of each class center into the objective function f(x, y, z, r). When the value of f(x, y, z, r) corresponding to a certain class center is the smallest, then take the class center of the corresponding class as the final sound source positioning point.

7. A fast and high-precision TDOA sound source localization device, characterized in that, Including: A processor and a memory. Executable program instructions are stored in the memory. When the processor calls the program instructions in the memory, the processor is used to: Execute the steps of the fast and high-precision TDOA sound source positioning method as described in any one of claims 1-6.

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