Direction finding method and device for moving sound source in interference environment

Abstract

A direction finding method for a moving sound source in an interference environment is applied to outdoor environment monitoring, a microphone network, at least one noise source and the moving sound source in a moving state are deployed in the outdoor environment, the microphone network comprises a plurality of microphone nodes, each microphone node comprises a plurality of microphones, the microphones are used for acquiring sound waves in the environment, and the sound waves are transmitted to the sound source. The method comprises the following steps: processing sound wave signals collected at the moment k, and determining a covariance matrix of narrow-band signals in the sound wave signals; determining a plurality of possible directions of arrival of the moving sound source based on the covariance matrix; determining a state prediction value at the moment k based on the state at the moment k-1, and determining prior estimation of the direction of arrival at the moment k based on the state prediction value; based on the possible direction of arrival and the priori estimation, determining the posteriori estimation of the sound wave signal at the moment k; and adjusting the state prediction value based on the posteriori estimation. According to the method, high-precision DOA estimation can be carried out on the moving target in the presence of an interference sound source.

Description

Technical Field

[0001] The present invention relates to the technical field of audio signal processing, and in particular to a method and device for finding the direction of a moving sound source in an interference environment. Background Art

[0002] Direction of arrival (DOA) estimation is one of the core directions in the field of array signal processing, and plays an important role in both military and civil fields. By processing the received signal, DOA estimation can obtain the angle and position information of the target signal source. As a key technology for parameter estimation, it is widely used in tasks such as target positioning, monitoring, navigation, and interference suppression of communication systems. Therefore, high-precision DOA estimation is crucial for tracking and locating moving sound sources. In the presence of noise, it will interfere with the measurement of the sound source, making the measurement result inaccurate. Therefore, how to find the direction of the sound source in an interference environment has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] In order to solve the problems existing in the prior art, the embodiments of the present application provide a method, apparatus, computing device, computer storage medium and product including a computer program for direction finding of a moving sound source in an interference environment, which can perform high-precision DOA estimation of a moving target in the presence of an interfering sound source (in an interference environment), thereby improving the ability to locate the target in a complex environment.

[0004] In a first aspect, an embodiment of the present application provides a direction finding method for a moving sound source in an interference environment, which is applied to outdoor environment monitoring. The outdoor environment is deployed with a microphone network, at least one noise source, and a moving sound source in a moving state. The microphone network includes multiple microphone nodes, each microphone node includes multiple microphones, and the microphone is used to obtain sound waves in the environment. The method includes: processing the sound wave signal collected at time k to determine the covariance matrix of the narrowband signal in the sound wave signal; based on the covariance matrix, determining multiple possible arrival directions of the moving sound source; based on the state at time k-1, determining a state prediction value at time k, and based on the state prediction value, determining a priori estimate of the arrival direction at time k; the state is used to characterize the position and speed of the moving sound source in the coordinate system; based on the possible arrival directions and the priori estimate, determining a posteriori estimate of the sound wave signal at time k; based on the posteriori estimate, adjusting the state prediction value.

[0005] In some possible implementations, multiple possible arrival directions of a moving sound source are determined based on a covariance matrix, including: obtaining a noise subspace and a signal subspace of the covariance matrix; constructing a multiple signal classification MUSIC spectrum corresponding to each subband according to the orthogonality of the signal subspace and the noise subspace, performing weighted averaging on the MUSIC spectra of all subbands to determine a final MUSIC spectrum of a wideband signal; traversing the MUSIC spectrum of the wideband signal to obtain all the maximum values ​​therein, and taking the maximum values ​​as possible arrival directions.

[0006] In some possible implementations, the a posteriori estimate of the sound wave signal at time k is determined by comparing the a priori estimate at time k with the maximum value in the MUSIC spectrum of the broadband signal, and taking the closest maximum value as the a posteriori estimate.

[0007] In some possible implementations, the narrowband signal is expressed according to the following formula:

[0008]

[0009] Where m represents the mth microphone among M microphones, j represents the jth microphone among J microphone nodes, l represents the snapshot number, k represents the frame number, and f i represents the frequency of the i-th sub-band, S represents the frequency domain representation of the sound source, i represents the sub-band number, and N represents the frequency domain representation of the noise.

[0010] In some possible implementations, the covariance matrix is ​​calculated according to the following formula:

[0011]

[0012] In the formula, R represents the covariance matrix, L represents the number of snapshots, Y represents the entire received signal of node j at time k, H represents the conjugate transpose, and f i Characterizes the frequency of the i-th subband.

[0013] In some possible implementations, the a priori estimate of the direction of arrival at time k is calculated as follows:

[0014]

[0015] In the formula, Characterize the direction estimate, represents the coordinate of the position of the jth microphone network node on the x-axis, represents the coordinate of the jth microphone network node position on the y-axis, x k|k-1 Represents the coordinate of the state prediction value on the x-axis, y k|k-1 Represents the coordinate of the state prediction value on the y-axis.

[0016] In some possible implementations, the state prediction value is adjusted based on the posterior estimation, including: determining the new information result by comparing the state prediction value with the state measurement value; and correcting the state prediction value using the new information result.

[0017] In some possible implementations, the state prediction value is adjusted based on the posterior estimate, specifically including: generating a set of feature points based on the mean of the predicted state estimate at time k and the defense matrix; calculating the cross-covariance between the feature points propagated from time k-1 to time k and the observed feature points, and determining the Kalman gain at time k based on the cross-covariance; using the posterior estimate at time k as the actual observation value, and using the Kalman gain to correct the state estimate at time k.

[0018] In some possible implementations, the noise subspace and the signal subspace of the covariance matrix are obtained according to the following formula:

[0019]

[0020] In the formula, The eigenvector matrix representing the signal subspace, The eigenvalue matrix representing the signal subspace, The eigenvector matrix representing the noise subspace, The eigenvalue matrix that characterizes the noise subspace.

[0021] In a second aspect, an embodiment of the present application provides a direction-finding device for a moving sound source in an interference environment, which is deployed for outdoor environment monitoring. The outdoor environment is deployed with a microphone network, at least one noise source, and a moving sound source in a moving state. The microphone network includes multiple microphone nodes, each microphone node includes multiple microphones, and the microphone is used to acquire sound waves in the environment. The device includes: an acquisition module, which is used to collect sound wave signals in the outdoor environment; a processing module, which is used to process the sound wave signals collected at time k and determine the covariance matrix of the narrowband signal in the sound wave signal; the processing module is also used to determine multiple possible arrival directions of the moving sound source based on the covariance matrix; the processing module is also used to determine a state prediction value at time k based on the state at time k-1, and determine a priori estimate of the arrival direction at time k based on the state prediction value; the state is used to characterize the position and speed of the moving sound source in the coordinate system; the processing module is also used to determine a posteriori estimate of the sound wave signal at time k based on possible arrival directions and a priori estimates; the processing module is also used to adjust the state prediction value based on the posteriori estimate.

[0022] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, comprising computer-readable instructions. When a computer reads and executes the computer-readable instructions, the computer executes the method described in any one of the first aspects.

[0023] In a fourth aspect, an embodiment of the present application provides a computing device, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the method described in any one of the first aspects is executed.

[0024] In a fifth aspect, an embodiment of the present application provides a product comprising a computer program. When the computer program product runs on a processor, the processor executes the method as described in any one of the first aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0026] Figure 1 It is a flow chart of a method for finding the direction of a moving sound source in an interference environment provided by an embodiment of the present application;

[0027] Figure 2 is a schematic diagram of a microphone network structure provided in an embodiment of the present application;

[0028] Figure 3 It is a structural schematic diagram of a direction finding device for a moving sound source in an interference environment provided by an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] The term "and / or" in this article is a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The symbol " / " in this article indicates that the associated objects are in an or relationship, for example, A / B means A or B.

[0031] The terms "first" and "second" in the specification and claims herein are used to distinguish different objects rather than to describe a specific order of the objects. For example, a first response message and a second response message are used to distinguish different response messages rather than to describe a specific order of the response messages.

[0032] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0033] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more than two. For example, multiple processing units refer to two or more processing units, etc.; multiple elements refer to two or more elements, etc.

[0034] To facilitate the understanding of the embodiments of the present application, the following will further explain and illustrate specific embodiments in conjunction with the accompanying drawings. The embodiments do not constitute a limitation on the embodiments of the present invention.

[0035] First, the technical terms involved in the embodiments of the present application are introduced:

[0036] 1. Multiple signal classification (MUSIC) is a high-resolution spectral estimation technology based on the subspace method.

[0037] 2. New information, also known as the new information sequence, is used to reflect the deviation between the test results and the estimated results.

[0038] Next, the technical solution provided by the embodiments of the present application is introduced.

[0039] High-precision DOA estimation methods mainly include traditional algorithms, beamforming algorithms, subspace algorithms and compressed sensing algorithms.

[0040] Among the traditional DOA estimation methods, the main one is based on the time difference of arrival method. The traditional algorithm can only estimate a single target and has a high sampling frequency requirement, resulting in relatively low accuracy.

[0041] The beamforming algorithm is essentially a spatial filtering technology that applies a spatial filter composed of weighted vectors to the data received by the array element to enhance the signal in the specified direction while suppressing interference in other directions, thereby achieving the purpose of DOA estimation. Although its principle is simple, it is limited by spatial resolution and it is difficult to distinguish signal sources with close angles.

[0042] With the emergence of subspace algorithms, DOA estimation methods have become more mature and entered the era of high-resolution applications. The core idea of ​​subspace algorithms is to use the covariance matrix of the received signal data, perform eigenvalue decomposition or singular value decomposition on it, and use the geometric orthogonality of the signal space and the noise space to accurately estimate the direction of arrival of the signal. Common subspace algorithms include multiple signal classification (MUSIC) and estimation by rotational invariance technique (ESPRI T). The MUSIC algorithm has high resolution and can distinguish multiple signal sources with similar angles, but it has high computational complexity and requires searching all possible directions. The ESPRI T algorithm avoids the spectral search process by utilizing the rotational invariance of the array structure and has high computational efficiency, but requires the array to have specific structural characteristics. In addition, many variant algorithms have been proposed to further improve the accuracy and computational efficiency of DOA estimation, including G-MUSIC, ED-MUSIC, Root-MUSIC, TF-ESPRIT, Union ESPRI T, and ESPRI T-Root MUSIC.

[0043] The essence of compressed sensing algorithms is to use the sparsity and compressibility of signals to accurately reconstruct high-dimensional signals through a small amount of random linear observation data. It breaks the limitations of the traditional Nyquist sampling theorem, allowing the original signal to be reconstructed at a sampling rate far lower than the signal bandwidth, so that the direction of arrival of the signal source can be accurately estimated through a small amount of observation data. Among them, the sparse Bayesian learning (SBL) method uses a Bayesian framework to combine the sparsity of the signal with the prior information of the observation data, providing high-precision DOA estimation, and has become a research hotspot in this field.

[0044] However, existing DOA estimation research focuses on low signal-to-noise ratio and low snapshot scenarios, and research on interference source processing is relatively limited. Once there is an interference source in the microphone network, the accuracy of DOA estimation may be significantly reduced, resulting in the inability to accurately locate and track the moving sound source.

[0045] In view of this, an embodiment of the present application provides a method for finding the direction of a moving sound source in an interference environment, which is applied to outdoor environmental monitoring. When performing environmental acoustic monitoring outdoors, the sound field information of the surrounding environment is complex, and there may be multiple noise sources. In order to further prevent and control noise, it is necessary to monitor a specific noise source, but multiple noise sources around will affect the monitoring of the target noise source.

[0046] In this method, the signal collected by the microphone network is preprocessed, the time domain signal is converted to the frequency domain signal, and the frequency subband of interest is extracted to achieve the decomposition of the broadband signal. The covariance matrix of the frequency subband is calculated using the narrowband signal of the kth frame, and the final covariance matrix reflecting the statistical characteristics of multiple snapshots is obtained by averaging the number of snapshots. The corresponding MUS IC spectra are calculated using the data covariance matrices of the multiple frequency points obtained, and the spatial spectrum estimation of the broadband signal at time k is obtained by averaging all subband MUS IC spectra, and the maximum value in the MUS IC spectrum is extracted as the possible direction of arrival. The state filter value at time k is predicted using the unscented Kalman filter, and the prior estimate is obtained by performing an inverse tangent operation based on the state prediction value at time k and the position of each node of the microphone network. By comparing the prior estimate at time k with the maximum value in the MUS IC spectrum, the closest maximum value is selected as the final a posteriori estimate, thereby determining the direction of arrival of the signal at time k. The new information is calculated from the measured value and the predicted value, and the state prediction value is corrected using the new information to obtain the state filter value at time k, which provides a basis for subsequent DOA estimation.

[0047] For example, Figure 1 The embodiment of the present application provides a method for finding the direction of a moving sound source in an interference environment. The method can be applied to a scene with multiple noise sources. Figure 1 As shown, the method may include the following steps:

[0048] S11: Processing the sound wave signal collected at time k to determine the covariance matrix of the narrowband signal in the sound wave signal.

[0049] In this embodiment, the sound wave signal of the sound source can be received by a sensor, which can also be called a microphone. Unless otherwise specified, the sensor and microphone in the following text are also used to describe a microphone. The sound wave signal received by the sensor is a broadband signal, which is processed and decomposed into multiple narrowband signals. Please refer to Figure 2 , Figure 2 Schematic diagram of a microphone network structure provided by an embodiment of the present application. Figure 2As shown in FIG. 1 , a microphone network includes J microphone nodes (4 are used as an example in the figure), and each microphone node includes M microphones (4 are used as an example in the figure). Since the microphone network is composed of multiple microphones, each microphone can also be called an array element. The signal received by each microphone is expressed as have Where m represents the mth microphone among M microphones, j represents the jth microphone node among J microphone nodes, t represents the time, D represents the total number of sound sources, d represents the dth sound source, s d represents the sound source signal, τ represents the time delay when the sound source is projected to the mth array element, and n represents the additive noise received by the array element.

[0050] The microphone nodes are evenly distributed to form an array. The moving sound source in the environment is represented by a solid square, the interference source is represented by a cross-broken line, and the moving sound source moves along the x-axis. The microphone is used to receive the sound wave signal in the environment, which includes the sound wave signal of the moving sound source and the sound wave signal of the interference source. The sound wave signal received by the microphone network is framed and snapshot-divided to generate multiple short-term signal segments. The generated multiple short-term signal segments are fast Fourier transformed to decompose the received signal into narrowband signals. The narrowband signal with a frequency of f is processed by the fast Fourier transform. i The narrowband signal is calculated to obtain the covariance matrix of the subband. It is worth noting that the subband can also be called "frequency point", "frequency cabinet", "frequency band", etc.

[0051] In this embodiment, the operation is performed in the short-time Fourier transform domain, and the broadband signal is decomposed into narrowband signals through frame processing and snapshot division, and the covariance matrix of the narrowband signal of a specific frequency is solved. In the process of frame division and snapshot division, a non-overlapping division method is adopted, and a Hanning window is used for processing during the short-time Fourier transform to extract the frequency point signal with concentrated energy. The covariance matrix is ​​calculated for these frequency point signals, and the final covariance matrix reflecting the statistical characteristics of multiple snapshots can be obtained by averaging the snapshot book.

[0052] In some possible embodiments, the narrowband signal is expressed according to the following formula:

[0053]

[0054] Where m represents the mth microphone among M microphones, j represents the jth microphone among J microphone nodes, l represents the snapshot number, k represents the frame number, and f i represents the frequency of the i-th sub-band, S represents the frequency domain representation of the sound source, i represents the sub-band number, and N represents the frequency domain representation of the noise.

[0055] In some possible embodiments, the covariance matrix of the subband is calculated according to the following formula:

[0056]

[0057] Where R represents the covariance matrix, L represents the number of snapshots, Y represents the entire received signal of node j at time k, and H represents the conjugate transpose.

[0058] S12: Determine possible directions of arrival based on the covariance matrix.

[0059] In this embodiment, after determining the covariance matrix of the sub-band, the covariance matrix can be estimated by multiple signal classification (MUSIC) of multiple frequency points in the frequency domain, and the spatial spectrum estimation of the broadband signal is obtained by averaging the MUSIC spectra of each sub-band. All maximum points are extracted from the spatial spectrum, and the maximum points are used to characterize the possible wave arrival direction. Specifically, the following steps may be included:

[0060] S121: Obtain the noise subspace and signal subspace of the covariance matrix.

[0061] In this embodiment, the frequency f i The subband covariance matrix of is subjected to eigenvalue decomposition to obtain the noise subspace and signal subspace of the covariance matrix. Specifically, the formula is as follows:

[0062]

[0063] In the formula, The eigenvector matrix representing the signal subspace, The eigenvalue matrix representing the signal subspace, The eigenvector matrix representing the noise subspace, The eigenvalue matrix that characterizes the noise subspace.

[0064] S122: construct a MUSIC spectrum corresponding to the subband according to the orthogonality of the signal subspace and the noise subspace, and perform weighted averaging on the MUSIC spectra of all subbands to determine the final MUSIC spectrum of the broadband signal.

[0065] Specifically, the MUSIC spectrum corresponding to the sub-band is calculated according to the following formula:

[0066]

[0067] Where θ represents the DOA variable, a represents the array manifold vector, The eigenvector matrix representing the noise subspace of node j at time k.

[0068] The final MUS IC spectrum of the broadband signal is calculated according to the following formula:

[0069]

[0070] Where I represents the total number of subbands.

[0071] S123: Traverse the MUSIC spectrum of the broadband signal, obtain all the maximum values ​​therein, and take the maximum value as the possible direction of arrival, expressed as Q is an integer greater than 1, 1≤q≤Q.

[0072] S13: Based on the state at time k-1, determine the state prediction value at time k, and determine the prior estimate of the direction of arrival at time k based on the state prediction value.

[0073] In this embodiment, the state refers to the variable of the object (i.e., the moving sound source), including its position and velocity information in the plane coordinates. The state at the next moment is predicted by the state at the current moment, and the state prediction value at the next moment can be obtained. The predicted value is corrected, which is the state filter value. Specifically, an unscented Kalman filter can be used to obtain the state prediction value at time k from the state filter value at time k-1. Then, the state prediction value at time k is obtained. Specifically, a set of feature points can be generated based on the state estimate at time k-1 and the covariance matrix. Since the distribution state of the moving sound source at the next moment is an uncertain state, its state distribution is uncertain. The main features of the state distribution are represented by feature points, and the nonlinear characteristics of the entire system (i.e., including the moving sound source and the microphone network) are captured. The feature points are represented as follows:

[0074]

[0075]

[0076] In the formula, represents the feature point, x represents the coordinate on the x-axis, u represents the dimension of the state vector, and λ represents the adjustment parameter.

[0077] Each feature point is propagated through the state transfer function to predict the state of these feature points at time k. It is expressed as f(·) represents the state transfer function. The weighted average of the propagated feature points is performed to obtain the predicted state mean at time k, which is expressed as the following formula:

[0078]

[0079] Where W i (b) Characterizes the mean weighting coefficient, x k|k-1 The x-axis coordinate representing the predicted state.

[0080] Calculate the distribution of feature points and obtain the predicted state covariance matrix at time k. The formula is as follows:

[0081]

[0082] In the formula, P represents the state covariance matrix, um represents the state vector dimension, and W i (c) represents the covariance weighting coefficient, and Q represents the noise covariance matrix.

[0083] Next, the inverse tangent operation is performed on the state prediction value at time k and the position of each node in the microphone network to obtain the prior estimate of the DOA estimate at time k, which is as follows:

[0084]

[0085] In the formula, represents the direction estimate based on the predicted direction, represents the coordinate of the position of the jth microphone network node on the x-axis, represents the coordinate of the jth microphone network node position on the y-axis, x k|k-1 Represents the coordinate of the state prediction value on the x-axis, y k|k-1 Represents the coordinate of the state prediction value on the y-axis.

[0086] In this embodiment, a constant velocity (CV) model is used to describe the state change of the system, and the state transfer equation is constructed based on this model. This model assumes that the speed of the target remains constant at every moment, thereby simplifying the dynamic description of the system.

[0087] S14: Based on the possible directions of arrival and the priori estimate at time k, determine the a posteriori estimate of the sound wave signal at time k.

[0088] In this embodiment, the a priori estimate at time k is compared with the maximum value in the MUSIC spectrum, and the closest maximum value is selected as the final a posteriori estimate, thereby determining the arrival direction of the signal at time k. That is, the value with the largest a posteriori probability at time k is the maximum value of the MUSIC spectrum closest to the a priori value, and the maximum value is used as the arrival direction at time k. Specifically, the a posteriori estimate is determined by the direction with the smallest square norm of the difference between the a priori estimate and all possible candidate directions, which is expressed as: The set of posterior estimates is denoted as

[0089] In this embodiment, the a posteriori estimate at time k is used as the actual measurement value to further correct the system state, thereby improving the accuracy of the estimate and the stability of the system.

[0090] S15: Adjust the state prediction value based on the posterior estimation.

[0091] In this embodiment, in order to achieve DOA estimation of the moving sound source, it is necessary to accurately obtain the motion state of the target. By comparing the state prediction value with the state measurement value, the new information result is determined. Using the new information result, the state prediction value of the system is corrected, and finally the state filter value at the moment is obtained to improve the accuracy of the state estimation.

[0092] Specifically, the mean and covariance matrix of the predicted state at time k are estimated to generate a set of new feature points, which are used to capture the state distribution and nonlinear characteristics of the system at the current moment. The feature points are expressed as

[0093]

[0094]

[0095] According to the observation model function, the feature points predicted at time k-1 are mapped from the state space to the observation space at time k, which is expressed as Where h represents the observation model function, Characterize the mapping position. Perform weighted averaging on the feature points mapped to the observation space to determine the predicted observation average at time k, expressed as Where μ represents the predicted observed mean.

[0096] Next, calculate the cross-covariance between the feature points propagated from time k-1 to time k and the observed feature points, and use the cross-covariance between the state and the observation and the observation covariance to calculate the Kalman gain at time k. The calculation formula is as follows:

[0097]

[0098] In the formula, S k Represents the observation covariance matrix, W i (c) Characterizes the covariance weighting coefficient, C k Characterizes the cross-covariance matrix between states and observations, x k|k-1 Characterize the state prediction value, K k Characterize the Kalman gain.

[0099] Next, the posterior estimate at time k is used as the actual observation value; the state estimate is corrected using the Kalman gain to obtain the updated state and state covariance matrix at time k. The calculation formula is as follows:

[0100] y k =Θ k ,

[0101] x k|k =xk|k-1 +K k [y k -μ k ]

[0102]

[0103] In the formula, y k Represents the observed value at time k, x k|k Represents the state filter value at time k.

[0104] The above is a method for direction finding of a moving sound source in an interference environment provided by an embodiment of the present application. By processing the collected sound wave signals, the direction of the moving sound source can be found in the interference environment. In this method, by utilizing the iterative characteristics of the unscented Kalman filter, only the initial state and the data received by the microphone need to be obtained to achieve accurate direction finding of the moving sound source; the multi-channel post-filtering method based on accurate estimation of the prior signal-to-noise ratio has a better enhancement effect; the execution of the method is carried out in the short-time Fourier transform domain, which is easy to be integrated with other signal processing algorithms in a unified framework. This method can perform high-precision DOA estimation on moving targets in the presence of interference sound sources (in an interference environment), thereby improving the ability to locate targets in complex environments.

[0105] It is understandable that the size of the sequence number of each step in the above-mentioned embodiments does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. In addition, in some possible implementations, the steps in the above-mentioned embodiments can be selectively executed according to actual conditions, and can be partially executed or fully executed, which is not limited here. All or part of any feature of any embodiment of the present application can be freely and arbitrarily combined without contradiction. The combined technical solution is also within the scope of the present application.

[0106] Based on the method in the above embodiment, the embodiment of the present application also provides a device for finding the direction of a moving sound source in an interference environment. Figure 3 The schematic diagram of the structure of a direction finding device for a moving sound source in an interference environment provided by an embodiment of the present application is shown. The direction finding device 300 is deployed for outdoor environment monitoring. The outdoor environment is deployed with a microphone network, at least one noise source, and a moving sound source in a moving state. The microphone network includes multiple microphone nodes, each microphone node includes multiple microphones, and the microphones are used to obtain sound waves in the environment. Figure 3 As shown, the direction finding device includes an acquisition module 301 and a processing module 302 .

[0107] The acquisition module 301 is used to collect sound wave signals in an outdoor environment.

[0108] The processing module 302 is used to process the sound wave signal collected at time k and determine the covariance matrix of the narrowband signal in the sound wave signal.

[0109] The processing module 302 is further configured to determine a plurality of possible directions of arrival of the moving sound source based on the covariance matrix.

[0110] The processing module 302 is also used to determine the state prediction value at time k based on the state at time k-1, and determine the prior estimate of the wave arrival direction at time k based on the state prediction value; the state is used to characterize the position and speed of the moving sound source in the coordinate system.

[0111] The processing module 302 is further configured to determine a posteriori estimate of the acoustic wave signal at time k based on possible directions of arrival and a priori estimates.

[0112] The processing module 302 is further configured to adjust the state prediction value based on the posterior estimation.

[0113] Based on the methods in the above embodiments, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the methods in the above embodiments.

[0114] Based on the methods in the above embodiments, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the methods in the above embodiments.

[0115] It is understandable that the processor in the embodiments of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0116] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can 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.

[0117] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0118] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

Claims

1. A method for finding the direction of a moving sound source in an interference environment, characterized in that: Applied to outdoor environment monitoring, the outdoor environment is deployed with a microphone network, at least one noise source, and a moving sound source in a moving state, the microphone network includes a plurality of microphone nodes, each of the microphone nodes includes a plurality of microphones, and the microphones are used to obtain sound waves in the environment. The method includes: Processing the sound wave signal collected at time k to determine the covariance matrix of the narrowband signal in the sound wave signal; Determining a plurality of possible directions of arrival of the moving sound source based on the covariance matrix; Based on the state at time k-1, determine the state prediction value at time k, and based on the state prediction value, determine the prior estimate of the wave arrival direction at time k; the state is used to characterize the position and speed of the moving sound source in the coordinate system; Determining a posterior estimate of the acoustic wave signal at time k based on the possible directions of arrival and the prior estimate; Based on the a posteriori estimate, the state prediction value is adjusted.

2. The method according to claim 1, characterized in that: The determining, based on the covariance matrix, a plurality of possible directions of arrival of the moving sound source comprises: Obtaining a noise subspace and a signal subspace of the covariance matrix; According to the orthogonality of the signal subspace and the noise subspace, a multiple signal classification MUSIC spectrum corresponding to each subband is constructed, and the MUSIC spectrum of all subbands is weighted averaged to determine the MUSIC spectrum of the final broadband signal; The MUSIC spectrum of the broadband signal is traversed to obtain all the maximum values ​​therein, and the maximum values ​​are used as possible directions of arrival.

3. The method according to claim 2, characterized in that The a posteriori estimation of the sound wave signal at time k is specifically determined as follows: The a priori estimate at time k is compared with the maximum value in the MUSIC spectrum of the broadband signal, and the closest maximum value is used as the a posteriori estimate.

4. The method according to claim 1, characterized in that: The narrowband signal is expressed according to the following formula Where m represents the mth microphone among M microphones, j represents the jth microphone among J microphone nodes, l represents the snapshot number, k represents the frame number, and f i represents the frequency of the i-th sub-band, S represents the frequency domain representation of the sound source, i represents the sub-band number, and N represents the frequency domain representation of the noise.

5. The method according to claim 1, characterized in that The covariance matrix is ​​calculated according to the following formula In the formula, R represents the covariance matrix, L represents the number of snapshots, Y represents the entire received signal of node j at time k, H represents the conjugate transpose, and f i Characterizes the frequency of the i-th subband.

6. The method according to claim 1, characterized in that The a priori estimate of the direction of arrival at time k is calculated as follows: In the formula, Characterize the direction estimate, represents the coordinate of the position of the jth microphone network node on the x-axis, represents the coordinate of the jth microphone network node position on the y-axis, x k|k-1 Represents the coordinate of the state prediction value on the x-axis, y k|k-1 Represents the coordinate of the state prediction value on the y-axis.

7. The method according to claim 1, characterized in that The adjusting the state prediction value based on the posterior estimation includes: By comparing the state prediction value and the state measurement value, the new information result is determined; The state prediction value is corrected using the new information result.

8. The method according to claim 1, characterized in that: The adjusting the state prediction value based on the posterior estimation specifically includes: Generate a set of feature points based on the estimated mean of the predicted state at time k and the defense matrix; Calculate the cross-covariance between the feature points propagated from time k-1 to time k and the observed feature points, and determine the Kalman gain at time k based on the cross-covariance; The posterior estimate at time k is used as the actual observation value, and the Kalman gain is used to correct the state estimate at time k.

9. The method according to claim 2, characterized in that: The noise subspace and signal subspace of the covariance matrix are obtained according to the following formula: In the formula, The eigenvector matrix representing the signal subspace, The eigenvalue matrix representing the signal subspace, The eigenvector matrix representing the noise subspace, The eigenvalue matrix that characterizes the noise subspace.

10. A device for finding the direction of a moving sound source in an interference environment, characterized in that: Deployed for outdoor environment monitoring, the outdoor environment is deployed with a microphone network, at least one noise source, and a moving sound source in motion, the microphone network includes a plurality of microphone nodes, each of the microphone nodes includes a plurality of microphones, and the microphones are used to obtain sound waves in the environment. The device includes: An acquisition module is used to collect sound wave signals in an outdoor environment; A processing module, used to process the sound wave signal collected at time k and determine the covariance matrix of the narrowband signal in the sound wave signal; The processing module is further used to determine a plurality of possible directions of arrival of the moving sound source based on the covariance matrix; The processing module is further used to determine a state prediction value at time k based on the state at time k-1, and to determine a priori estimation of the direction of arrival at time k based on the state prediction value; the state is used to characterize the position and speed of the moving sound source in the coordinate system; The processing module is further used to determine a posterior estimate of the sound wave signal at time k based on the possible wave arrival direction and the prior estimate; The processing module is further used to adjust the state prediction value based on the posterior estimation.

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