A target orientation estimation method based on weighted deconvolution

Abstract

The application particularly relates to a target direction estimation method based on weighted deconvolution, which comprises the following steps: receiving far-field signals of a plurality of sound sources by using a sensor array, and constructing array receiving signals according to the far-field signals received by sensors at different positions in the array; configuring an array receiving signal data matrix of a multi-shot condition based on the array receiving signals; determining a covariance matrix corresponding to the array receiving signal data matrix; configuring a weighting vector of a conventional beamformer, and scanning the covariance matrix in different directions in a two-dimensional space by using the conventional beamformer to obtain a corresponding spatial spectrum; calculating the spatial spectrum based on the relationship between a sound source intensity distribution function and a point spread function to obtain a point spread function corresponding to the spatial spectrum; and calling an improved R-L method to perform direction estimation according to the point spread function and the spatial spectrum, and determining the position information of the sound source. The method can be applied to sensor arrays of any shape, and the resolution capability for multiple targets is improved.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and more specifically to a target orientation estimation method based on weighted deconvolution. Background Technology

[0002] In related technologies, conventional beamforming (CBF) is a classic method for target azimuth estimation. However, CBF has limited azimuth resolution, resulting in poor multi-target resolution. The azimuth spectrum of CBF is the convolution result of the array's beammap function and the signal source function. Under ideal conditions, the signal source function can be represented as a Dirac function, meaning that deconvolution can obtain the signal source azimuth information, significantly improving direction finding accuracy and background interference suppression. Existing deconvolution methods for target azimuth estimation based on beamforming can be summarized into three categories based on the number of point spread functions (PSFs) that need to be calculated. The first type of deconvolution method requires the number of PSFs to be calculated to be related to the number of iterations. The second type of deconvolution method requires calculating the PSFs of all scan points on the focused sound source plane, with the Richardson-Lucy (RL) method being a typical example. The third type of deconvolution method calculates the PSF of the sound source at the center focal point and is only applicable to arrays where the PSF is time-invariant.

[0003] The RL method is a Bayesian iterative algorithm. Although the RL method has a significant improvement in azimuth estimation performance compared to conventional beamforming methods, its resolution is still limited.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a target orientation estimation method based on weighted deconvolution, a computer program product, and an electronic device, which can effectively overcome the defects existing in the prior art.

[0006] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0007] According to a first aspect of the present invention, a target orientation estimation method based on weighted deconvolution is provided, the method comprising:

[0008] The array of sensors is used to receive far-field signals from several sound sources, and the array received signal is constructed based on the far-field signals received by each sensor at different positions in the array.

[0009] Based on the array received signal configuration for multiple snapshot scenarios, the array received signal data matrix is ​​determined; and the covariance matrix corresponding to the array received signal data matrix is ​​determined.

[0010] Configure the weighting vector of the conventional beamformer, and use the weighting vector of the conventional beamformer to weight the covariance matrix in different directions in two-dimensional space to obtain the corresponding spatial spectrum;

[0011] The spatial spectrum is solved based on the relationship between the sound source intensity distribution function and the point spread function to obtain the point spread function corresponding to the spatial spectrum;

[0012] The RL method is called to estimate the location of the sound source based on the point spread function and spatial spectrum.

[0013] In some exemplary embodiments, the sensor array includes a polygonal sensor array constructed based on M sound field sensors; wherein the polygonal sensor array has a regular polygonal structure or an arbitrary array shape.

[0014] In some exemplary embodiments, constructing an array received signal based on far-field signals received by each sound field sensor at different locations in the array includes:

[0015] Configure the position coordinates of each sensor in the array coordinate system based on the position of each sensor in the two-dimensional plane;

[0016] Based on the signals received by each sensor at its corresponding coordinates, and combining the array manifold matrix, the incident signal vector, the set of signal incident azimuth angles, and noise, the array received signals are determined, including:

[0017]

[0018] in, Indicates that the array receives signals; Represents the incident signal vector; This represents Gaussian white noise with a mean of 0, which is uncorrelated with the incident signal. ; This represents the array manifold matrix.

[0019] In some exemplary embodiments, a weighting vector of a conventional beamformer is configured, and the covariance matrix is ​​weighted in different directions in a two-dimensional space using the weighting vector of the conventional beamformer to obtain the corresponding spatial spectrum, including:

[0020] Configure the weighting vectors for a conventional beamformer, including:

[0021]

[0022] in, Represents a weighted vector; express Array manifold vectors in the direction; This represents the number of elements in any sensor array;

[0023] Based on the weighted vector and the covariance matrix of the array received signal data matrix, the spatial spectrum of the conventional beamformer output is determined, including:

[0024]

[0025] in, Represents the spatial spectrum; Indicates the beam scanning angle; The covariance matrix represents the received signal data matrix; express Weighted vector in direction; This represents the conjugate transpose of the weighted vector.

[0026] In some exemplary embodiments, the spatial spectrum is solved based on the relationship between the sound source intensity distribution function and the point spread function to obtain the point spread function corresponding to the spatial spectrum, including:

[0027] Based on the sound source intensity distribution function and point spread function corresponding to each beam scanning angle, the spatial spectrum is analyzed, including:

[0028]

[0029] in, Represents the beam intensity function; A function representing the intensity distribution of a sound source at different angles; Indicates the beam scanning angle. Indicates the location of the sound source;

[0030] In the spatial domain, the beam intensity function of a conventional beamformer is determined based on a weighted vector when the beam principal axis points towards the azimuth of the sound source, including:

[0031]

[0032] in, express Array manifold vectors in the direction; This represents the conjugate transpose of the weighted vectors;

[0033] The beam intensity function Configured as a point spread function for a sensor array.

[0034] In some exemplary implementations, the RL method is invoked to perform orientation estimation based on the point spread function and spatial spectrum to determine the location information of the sound source, including:

[0035] The adaptive spatial weight coefficients are configured according to the gradient of the iterative sound source intensity distribution function.

[0036] By combining adaptive spatial weight coefficients, point spread function, and spatial spectrum, an iterative weighted deconvolution process based on an improved RL algorithm is performed to determine the spatial distribution of the sound source, including:

[0037]

[0038] in, Represents the spatial spectrum; Indicates adaptive spatial weights; Represents the point spread function; This represents the sound source intensity distribution function.

[0039] In some exemplary embodiments, adaptive spatial weight coefficients are configured according to the gradient of the iterative sound source intensity distribution function, including:

[0040]

[0041] in, β represents the gradient of the sound source intensity distribution function; β represents the coefficient used to control the changing trend of the weighting coefficient.

[0042] In some exemplary embodiments, the method further includes:

[0043] The gradient of the sound source intensity distribution function is smoothed using a Gaussian smoothing filter, and the smoothed gradient is then used for iteration.

[0044] According to a second aspect of the present invention, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described target orientation estimation method based on weighted deconvolution.

[0045] According to a third aspect of the present invention, a computer program product is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the above-described target orientation estimation method based on weighted deconvolution.

[0046] According to a fourth aspect of the present invention, an electronic device is provided, comprising:

[0047] Processor; and

[0048] Memory for storing the executable instructions of the processor;

[0049] The processor is configured to implement the above-described target orientation estimation method based on weighted deconvolution when executing the executable instructions.

[0050] The target azimuth estimation method based on weighted deconvolution provided in this invention utilizes a sensor array to receive far-field signals from a sound source and calculates the corresponding array received signal data matrix. A conventional beamformer scans the covariance matrix corresponding to the array received signal data matrix in different directions within a two-dimensional space to obtain the spatial spectrum. The spatial spectrum is solved to obtain the corresponding point spread function, and then a weighted deconvolution operation is performed using an improved RL algorithm to achieve target azimuth estimation. This method can deconvolve the conventional beamforming output of any array of sensor arrays using an improved RL algorithm, while also considering that the conventional beamforming output has different output intensities in signal-containing and signal-free regions, thus providing better spatial resolution.

[0051] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0052] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0053] Figure 1 The illustration shows a schematic diagram of a target orientation estimation method based on weighted deconvolution, an exemplary embodiment of the present invention.

[0054] Figure 2 The illustration shows a schematic diagram of a high-order acoustic field sensor array distribution based on an arbitrary structure, as an exemplary embodiment of the present invention.

[0055] Figure 3 The diagram illustrates a sensor array distribution based on a uniform linear array, as exemplified by an embodiment of the present invention.

[0056] Figure 4 This illustration schematically shows a normalized spatial orientation spectrum of different methods for a single target, according to an exemplary embodiment of the present invention.

[0057] Figure 5 This schematic diagram illustrates a normalized spatial orientation spectrum for different methods targeting two targets, according to an exemplary embodiment of the present invention.

[0058] Figure 6This illustration schematically demonstrates an exemplary embodiment of the present invention regarding the target resolution success probability of different methods under different signal-to-noise ratio conditions in a dual-target scenario;

[0059] Figure 7 This schematic diagram illustrates another coordinate distribution of a high-order acoustic field sensor array based on an arbitrary structure, as per an exemplary embodiment of the present invention.

[0060] Figure 8 The illustration shows a normalized spatial orientation spectrum of different methods in a dual-target scenario, as an exemplary embodiment of the present invention.

[0061] Figure 9 The diagram illustrates the composition of an electronic device according to an exemplary embodiment of the present invention. Detailed Implementation

[0062] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0063] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0064] To address the shortcomings and deficiencies of existing technologies, this example implementation provides a target orientation estimation method based on weighted deconvolution, referencing... Figure 1 As shown, the method includes:

[0065] Step S11: Receive far-field signals from several sound sources using a sensor array, and construct the array received signal based on the far-field signals received by each sensor at different positions in the array.

[0066] Step S12: Configure the array received signal data matrix for multiple snapshot scenarios based on the array received signal; and determine the covariance matrix corresponding to the array received signal data matrix.

[0067] Step S13: Configure the weighting vector of the conventional beamformer, and use the weighting vector of the conventional beamformer to weight the covariance matrix in different directions in two-dimensional space to obtain the corresponding spatial spectrum;

[0068] Step S14: Solve the spatial spectrum based on the relationship between the sound source intensity distribution function and the point spread function to obtain the point spread function corresponding to the spatial spectrum;

[0069] Step S15: Invoke the improved RL method to perform orientation estimation based on the point spread function and spatial spectrum to determine the location information of the sound source.

[0070] The following will describe in more detail each step of the weighted deconvolution orientation estimation method for a high-order acoustic field sensor array in this exemplary embodiment, with reference to the accompanying drawings and embodiments.

[0071] In step S11, a sensor array is used to receive far-field signals from several sound sources, and an array receiving signal is constructed based on the far-field signals received by each sensor at different positions in the array.

[0072] For example, the sensor array includes a polygonal sensor array constructed based on M sound field sensors; wherein the polygonal sensor array has a regular polygonal structure or an arbitrary array shape.

[0073] Specifically, the sensor array can be a high-order sound field sensor array, comprising M array elements. Each array element can be a high-order sound field sensor; each high-order sound field sensor can include multiple sound pressure sensors. (Reference) Figure 2 The image shows a sensor array with an irregular polygonal structure. Alternatively, in some exemplary embodiments, other types of sensors may be used.

[0074] For example, in step S11, constructing the array received signal based on the far-field signals received by each sound field sensor at different locations in the array includes:

[0075] Step S21: Configure the position coordinates of each sensor in the array coordinate system according to the position of each sensor in the two-dimensional plane;

[0076] Step S22: Based on the signals received by each sensor at the corresponding position coordinates, and combined with the array manifold matrix, the incident signal vector, the set of signal incident azimuth angles and noise, determine the array received signal.

[0077] Specifically, refer to Figure 2 The sensor array shown is an arbitrary-shaped sensor array with M elements in a two-dimensional plane. The rectangular coordinates of the m-th element can be expressed as:

[0078] (1)

[0079] in, The line connecting the m-th element and the origin is... x The angle between the positive and negative axes. Indicates transpose. Let m be the distance between the m-th array element and the origin.

[0080] Assuming there are K far-field signals in space, the th... k An incident signal The horizontal azimuth is The unit vector of its propagation direction is defined as:

[0081] (2)

[0082] Define the signal to be received at the origin. The parsing form is Then the signal received at the position of the m-th array element can be represented as:

[0083]

[0084] in, The imaginary unit; ; Indicates signal The frequency; This represents the time delay relative to the origin when the k-th sound source signal reaches the m-th array element. c Let T be the speed of sound propagation and T be the number of snapshots. This represents the noise received by the m-th array element.

[0085] Define the array receive signal vector as:

[0086]

[0087] The incident signal vector is:

[0088]

[0089] The noise vector is represented as:

[0090]

[0091] According to formula (3), the array received signal can be re-expressed as:

[0092] (4)

[0093] in, Indicates that the array receives signals; Represents the incident signal vector; This represents Gaussian white noise with a mean of 0, which is uncorrelated with the incident signal. ; This represents the array manifold matrix. The set of incident azimuth angles of the signal, i.e. .

[0094] The array manifold matrix is ​​specifically represented as:

[0095] (5)

[0096] In step S12, the array received signal data matrix for multiple snapshot scenarios is configured based on the array received signal; and the covariance matrix corresponding to the array received signal data matrix is ​​determined.

[0097] For example, after calculating the array received signal matrix, the array received signal data matrix for multi-snapshot scenarios can be defined, specifically as follows:

[0098]

[0099] Correspondingly, the covariance matrix of the array received signal can be calculated based on the array received signal data matrix under multi-shot conditions, specifically expressed as:

[0100] (6)

[0101] in, It indicates a desire for the expected value.

[0102] In step S13, the weighting vector of the conventional beamformer is configured, and the covariance matrix is ​​weighted in different directions in two-dimensional space using the weighting vector of the conventional beamformer to obtain the corresponding spatial spectrum.

[0103] For example, step S13 described above may include:

[0104] Step S31, configure the weighting vector of the conventional beamformer, including:

[0105] (7)

[0106] in, Represents a weighted vector; express Array manifold vectors in the direction; This represents the number of elements in any sensor array;

[0107] Step S32: Determine the spatial spectrum of the conventional beamformer output based on the weighted vector and the covariance matrix of the array received signal data matrix.

[0108] Specifically, by scanning in different directions within a two-dimensional space, the spatial spectrum of a conventional beam output is obtained, including:

[0109] (8)

[0110] in, Represents the spatial spectrum; Indicates the beam scanning angle; The covariance matrix represents the received signal data matrix; express Weighted vector in direction; express The conjugate transpose of the weighted vectors in the direction.

[0111] In step S14, the spatial spectrum is solved based on the relationship between the sound source intensity distribution function and the point spread function to obtain the point spread function corresponding to the spatial spectrum.

[0112] For example, step S14 described above may include:

[0113] Step S41: Analyze the spatial spectrum based on the sound source intensity distribution function and point spread function corresponding to each beam scanning angle;

[0114] Step S42: In the spatial domain, determine the beam intensity function of the conventional beamformer when the beam principal axis points to the azimuth of the sound source based on the weighted vector;

[0115] Step S43: Configure the beam intensity function as the point spread function of the sensor array.

[0116] Specifically, the spatial spectrum output of conventional beamforming can be viewed as the sum of the product of the directivity function (also known as the point spread function of the array, i.e., the PSF function) at each angle and the source intensity at that angle, expressed as follows:

[0117] (9)

[0118] in, Represents the beam intensity function (the directivity function of the array); A function representing the intensity distribution of a sound source at different angles; Indicates the beam scanning angle. Indicates the location of the sound source.

[0119] In spatial domain processing, many arrays exhibit a PSF (Principal Directivity Function) shift characteristic. The shift is defined as the beam output pointing at a specific angle being angle-dependent. Therefore, calculating the beam's principal axis direction... In this case, the beam intensity function is specifically expressed as:

[0120] (10)

[0121] in, express Array manifold vectors in the direction; This represents the conjugate transpose of the weighted vector.

[0122] Then, the beam intensity function of conventional beamforming can be... PSF configured as an arbitrary array.

[0123] In step S15, the improved RL method is invoked to perform orientation estimation based on the point spread function and spatial spectrum to determine the location information of the sound source.

[0124] For example, step S15 described above may include:

[0125] Step S51: Configure the corresponding adaptive spatial weight coefficients according to the gradient of the iterative sound source intensity distribution function;

[0126] Step S52: Combining adaptive spatial weight coefficients, point spread function, and spatial spectrum, iterative weighted deconvolution processing is performed based on the improved RL algorithm to determine the spatial distribution of the sound source.

[0127] For example, the method further includes: smoothing the gradient of the sound source intensity distribution function using a Gaussian smoothing filter, and then using the smoothed gradient for iteration.

[0128] Specifically, the iterative relationship of the extended RL algorithm can be expressed as:

[0129] (11)

[0130] in, , represents the normalized directional function (i.e., point spread function) of any array; , indicating about the scanning angle θ The function.

[0131] In practical applications, considering that the scanning angle is a finite number of discretized values, equation (11) is discretized and re-expressed as follows:

[0132] (12)

[0133] Considering that the spatial spectrum of the output of the conventional beamforming array has a peak in the target signal incident region, while the value is smaller in other directions; therefore, this invention adds an adaptive spatial weight term to the iterative equation (12) of the extended RL algorithm and proposes a target orientation estimation method based on weighted deconvolution, which can effectively suppress background noise in non-target incident directions and improve the ability of existing deconvolution methods to distinguish multiple nearby targets.

[0134] Specifically, a spatial weight vector is introduced into the iterative formula of the extended RL algorithm, based on the sound source intensity distribution function obtained through iterative calculation. This is achieved by adaptively adjusting the weighting coefficients in different directions, thereby effectively preserving the azimuthal characteristics of the signal incident angle and suppressing the influence of noise and interference from other directions. (Sound source intensity distribution function) The signal exhibits a high peak value and significant variation along the incident direction, while the output is smaller and flatter in other directions. This invention utilizes the gradient value of the sound source intensity distribution function. This is used to characterize whether it is the direction of signal incidence. When it is close to the actual direction of signal incidence... The gradient value is relatively large when it is far from the direction of signal incidence. Smaller.

[0135] In a preferred embodiment, a Gaussian smoothing filter can also be used to smooth the gradient of the sound source intensity distribution function. Smoothing is performed to avoid abrupt changes in gradient values ​​that could cause discontinuities. The Gaussian smoothing process first requires generating a Gaussian kernel, the formula of which includes:

[0136] (13)

[0137] Will Convolving with the Gaussian kernel in equation (13) yields the gradient of the sound source intensity distribution function after Gaussian smoothing. The formula includes:

[0138] (14)

[0139] To effectively preserve the azimuth feature information at the signal incident angle, it is desired that the weight coefficients near the signal incident angle tend to 1, while the weight coefficients in other directions tend to 0. An iterative calculation formula for adaptive spatial weight coefficients is proposed, including:

[0140] (15)

[0141] in, β represents the gradient of the sound source intensity distribution function; β represents the coefficient used to control the changing trend of the weighting coefficient, which is a positive number and generally takes the value (0, 1).

[0142] Based on equations (12) and (15) above, the iterative formula for the weighted deconvolution super-resolution orientation estimation method applicable to arbitrary arrays is obtained, specifically including:

[0143] (16)

[0144] After multiple iterative calculations, the spatial distribution function of the signal is finally obtained. Based on the spatial distribution function, the target's location can be obtained through spectral peak search.

[0145] For example, in one embodiment, reference is made to Figure 3 The uniform linear array shown has 14 elements, a center frequency of 1 kHz, and a spacing between elements equal to half the wavelength of the signal corresponding to the center frequency. For a single-target scenario, considering a single complex Gaussian sound source incident on the array in space, with a signal incidence direction of 90° and a signal-to-noise ratio of 5 dB, normalized spatial orientation spectra are obtained using the CBF method, MVDR method, extended RL method, and the method proposed in this invention, respectively. Figure 4 As shown. The method proposed in this invention has the narrowest main lobe width and the lowest side lobe order.

[0146] Furthermore, for the multi-target scenario, consider two complex Gaussian sound sources incident on the array at angles of 85° and 90°. The sampling frequency is 10 times the center frequency, and the number of snapshots is 200. First, the signal-to-noise ratio of the incident signal is set to 5dB. Normalized spatial orientation spectra are obtained using the CBF method, MVDR method, extended RL method, and the method proposed in this invention, respectively, as follows: Figure 5 As shown. Further, the success rate of dual-target resolution using different methods under different signal-to-noise ratios is analyzed, such as... Figure 6 As shown.

[0147] Compared to the normalized azimuth spectrum results of other methods, the method proposed in this invention has a lower sidelobe order and a narrower main lobe width. Since the CBF method cannot distinguish between two adjacent dual targets under all signal-to-noise ratio conditions, therefore... Figure 6 The results are not shown in the paper. Compared with the MVDR method and the extended RL method, the method proposed in this invention has the highest resolution probability under low signal-to-noise ratio conditions and has better multi-target resolution capability.

[0148] Exemplary, in another embodiment, reference is made to Figure 7 The sensor array shown has an arbitrary shape, 14 array elements, and a center frequency of 1kHz. The array element position coordinates are distributed as shown in Table 1 below.

[0149] Table 1

[0150]

[0151] Considering multi-target azimuth estimation, two complex Gaussian sound sources are assumed to be incident on the array at angles of 85° and 90°. The sampling frequency is 10 times the center frequency, and the number of snapshots is 200. First, the signal-to-noise ratio of the incident signal is set to 5dB. Normalized spatial azimuth spectra are obtained using the CBF method, MVDR method, extended RL method, and the method proposed in this invention, respectively, as follows: Figure 8 As shown. Based on the comparison with the normalized azimuth spectrum results of other methods, the method proposed in this invention has lower sidelobe order and narrower main lobe width for any array.

[0152] The method provided by this invention can be applied to intelligent terminal devices connected to sensor arrays. The far-field signals collected by the sensor array are sent to the terminal device, which then executes the method described above and outputs an estimation result of the sound source. This method can be applied to sensor arrays of arbitrary shapes. It utilizes an improved RL algorithm to deconvolve the conventional beamforming output of any array. By adding adaptive spatial weight coefficients and updating these weights in each iteration, it effectively suppresses background noise in non-target incident directions, improving the resolution capability of existing deconvolution methods for multiple nearby targets. Furthermore, by using the gradient of the sound source intensity distribution function calculated in each iteration, it adaptively adjusts the weight coefficients in different directions, thereby effectively preserving the azimuth feature information at the signal incident angle and suppressing the influence of noise and interference in other directions.

[0153] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0154] It should be noted that although several modules or units of the device for performing actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0155] Figure 9 A schematic diagram of an electronic device suitable for implementing embodiments of the present invention is shown.

[0156] It should be noted that, Figure 9The electronic device 1000 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0157] like Figure 9 As shown, the electronic device 1000 includes a Central Processing Unit (CPU) 1001, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 1002 or programs loaded from storage section 1008 into Random Access Memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An Input / Output (I / O) interface 1005 is also connected to the bus 1004. Furthermore, the electronic device 1000 also includes an FPGA device and a System-on-a-Chip (SoC) device.

[0158] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1010 as needed so that computer programs read from them can be installed into storage section 1008 as needed.

[0159] In particular, according to embodiments of the present invention, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit (CPU) 1001, it performs various functions defined in the system of this application.

[0160] It should be noted that the storage medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein computer-readable program code is carried. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0161] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0162] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0163] It should be noted that, as another aspect, this application also provides a storage medium, which may be included in an electronic device or may exist independently without being assembled into the electronic device. The aforementioned storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to perform the methods described in the following embodiments. For example, the electronic device may perform... Figure 1 The steps of the method shown.

[0164] In one embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0165] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0166] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0167] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A target orientation estimation method based on weighted deconvolution, characterized in that, The method includes: The array of sensors is used to receive far-field signals from several sound sources, and the array received signal is constructed based on the far-field signals received by each sensor at different positions in the array. Based on the array received signal configuration for multiple snapshot scenarios, the array received signal data matrix is ​​determined; and the covariance matrix corresponding to the array received signal data matrix is ​​determined. Configure the weighting vector of the conventional beamformer, and use the weighting vector of the conventional beamformer to weight the covariance matrix in different directions in two-dimensional space to obtain the corresponding spatial spectrum; The spatial spectrum is solved based on the relationship between the sound source intensity distribution function and the point spread function to obtain the point spread function corresponding to the spatial spectrum; The improved RL method is invoked to perform orientation estimation based on the point spread function and spatial spectrum to determine the location information of the sound source, including: The adaptive spatial weight coefficients are configured according to the gradient of the iterative sound source intensity distribution function, including: in, The gradient of the sound source intensity distribution function is represented by β; β represents the coefficient used to control the changing trend of the weighting coefficients. By combining adaptive spatial weight coefficients, point spread function, and spatial spectrum, an iterative weighted deconvolution process based on an improved RL algorithm is performed to determine the spatial distribution of the sound source, including: in, Represents the spatial spectrum; Indicates adaptive spatial weights; Represents the point spread function; This represents the sound source intensity distribution function.

2. The method according to claim 1, characterized in that, The sensor array includes a polygonal sensor array constructed based on M sound field sensors; wherein the polygonal sensor array has a regular polygonal structure.

3. The method according to claim 1, characterized in that, The array received signal is constructed based on the far-field signals received by each sound field sensor at different locations in the array, including: Configure the position coordinates of each sensor in the array coordinate system based on the position of each sensor in the two-dimensional plane; Based on the signals received by each sensor at its corresponding coordinates, and combining the array manifold matrix, the incident signal vector, the set of signal incident azimuth angles, and noise, the array received signals are determined, including: in, This indicates that the array is receiving signals; Represents the incident signal vector; This represents Gaussian white noise with a mean of 0, which is uncorrelated with the incident signal. ; This represents the array manifold matrix.

4. The method according to claim 1, characterized in that, Configure the weighting vector of a conventional beamformer, and use the weighting vector of the conventional beamformer to weight the covariance matrix in different directions in two-dimensional space to obtain the corresponding spatial spectrum, including: Configure the weighting vectors for a conventional beamformer, including: in, Represents a weighted vector; express Array manifold vectors in the direction; This represents the number of elements in any sensor array; Based on the weighted vector and the covariance matrix of the array received signal data matrix, the spatial spectrum of the conventional beamformer output is determined, including: in, Represents the spatial spectrum; Indicates the beam scanning angle; The covariance matrix represents the received signal data matrix; express Weighted vector in direction; express The conjugate transpose of the weighted vectors in the direction.

5. The method according to claim 1, characterized in that, The spatial spectrum is solved based on the relationship between the sound source intensity distribution function and the point spread function to obtain the corresponding point spread function, including: Based on the sound source intensity distribution function and point spread function corresponding to each beam scanning angle, the spatial spectrum is analyzed, including: in, Represents the beam intensity function; A function representing the intensity distribution of a sound source at different angles; Indicates the beam scanning angle. Indicates the location of the sound source; In the spatial domain, the beam intensity function of a conventional beamformer is determined based on a weighted vector when the beam principal axis points towards the azimuth of the sound source, including: in, express Array manifold vectors in the direction; This represents the conjugate transpose of the weighted vectors; The beam intensity function Configured as a point spread function for a sensor array.

6. The method according to claim 1, characterized in that, The method further includes: The gradient of the sound source intensity distribution function is smoothed using a Gaussian smoothing filter, and the smoothed gradient is then used for iteration.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the target orientation estimation method based on weighted deconvolution as described in any one of claims 1 to 6.

8. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to implement the target orientation estimation method based on weighted deconvolution as described in any one of claims 1 to 6 by executing the executable instructions.

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