Signal reconstruction method and device, electronic equipment, storage medium and product

Abstract

This application discloses a signal reconstruction method, apparatus, electronic device, storage medium, and product. The method includes: acquiring a mixed signal to be processed; performing M-level wavelet decomposition on the mixed signal based on orthogonal wavelets to generate M high-frequency components and one low-frequency component; determining an adaptive filtering threshold corresponding to each high-frequency component, and performing hierarchical threshold filtering on the high-frequency components based on the adaptive filtering threshold to generate corresponding high-frequency target components; determining a hard filtering threshold corresponding to the low-frequency component, and performing threshold filtering on the low-frequency components based on the hard filtering threshold to generate corresponding low-frequency target components; and reconstructing the M high-frequency target components and the low-frequency target component to generate a target signal. This solution can remove ultra-low frequency drift signals from the mixed signal while also performing hierarchical threshold filtering to remove high-frequency noise from the mixed signal, significantly improving the algorithm's tracking ability and anti-interference performance for non-stationary signals.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, and in particular to a signal reconstruction method, apparatus, electronic device, storage medium, and product. Background Technology

[0002] In the field of signal processing, baseline drift can occur due to devices such as accelerometers and gyroscopes. This baseline drift affects the data acquisition and processing. Therefore, it is necessary to filter out baseline drift signals from the mixed signal and reconstruct the signal from the filtered mixed signal.

[0003] In related technologies, adaptive baseline drift signal filtering techniques for mixed signals mainly include several schemes based on Least Mean Squares (LMS), Recursive Least Squares (RLS), and state estimation. The LMS algorithm adjusts the filter weights using gradient descent to filter out baseline drift signals from the mixed signal with the goal of minimizing the mean square error. This method is computationally simple, but there is an inherent contradiction between convergence speed and steady-state error, and it is quite sensitive to the correlation of the input signal. The RLS algorithm uses the recursive least squares criterion and updates the weights using the matrix inverse lemma, resulting in a faster convergence speed than LMS, but it has high computational complexity and is prone to numerical instability. State estimation based on Kalman filtering requires highly accurate system modeling and performs poorly in nonlinear or non-Gaussian scenarios.

[0004] More importantly, the above methods all struggle to achieve a good balance between computational complexity, convergence speed, and adaptability, lacking a general solution that can simultaneously meet real-time requirements and anti-interference capabilities. In particular, when dealing with non-stationary signals, the tracking ability and robustness of existing methods still need to be improved. Summary of the Invention

[0005] This application provides a signal reconstruction method, apparatus, electronic device, storage medium, and product. By utilizing the multi-resolution characteristics of orthogonal wavelet transform, it is possible to remove ultra-low frequency drift signals in mixed signals while also performing hierarchical threshold filtering to remove high-frequency noise in mixed signals.

[0006] According to one aspect of this application, a signal reconstruction method is provided, the method comprising: In response to a signal reconstruction event being triggered, acquire the mixed signal to be processed; The mixed signal is decomposed into M-level wavelet components based on orthogonal wavelets, generating M high-frequency components and one low-frequency component; where M is an integer greater than or equal to 1. An adaptive filtering threshold is determined for each of the high-frequency components, and the high-frequency components are subjected to hierarchical threshold filtering based on the adaptive filtering threshold to generate the corresponding high-frequency target components. Determine the hard filtering threshold corresponding to the low-frequency component, and perform threshold filtering on the low-frequency component based on the hard filtering threshold to generate the corresponding low-frequency target component; The target signal is generated by reconstructing the M high-frequency target components and the low-frequency target components.

[0007] According to one aspect of this application, a signal reconstruction apparatus is provided, the apparatus comprising: The mixed signal acquisition module is used to acquire the mixed signal to be processed in response to the signal reconstruction event being triggered. The wavelet decomposition module is used to perform M-level wavelet decomposition on the mixed signal based on orthogonal wavelets, generating M high-frequency components and one low-frequency component; where M is an integer greater than or equal to 1. A high-frequency target component generation module is used to determine the adaptive filtering threshold corresponding to each high-frequency component, and perform hierarchical threshold filtering on the high-frequency components based on the adaptive filtering threshold to generate the corresponding high-frequency target components. A low-frequency target component generation module is used to determine the hard filtering threshold corresponding to the low-frequency component, and perform threshold filtering on the low-frequency component based on the hard filtering threshold to generate the corresponding low-frequency target component. The signal reconstruction module is used to reconstruct the M high-frequency target components and the low-frequency target components to generate a target signal.

[0008] According to another aspect of this application, an electronic device is provided, the electronic device comprising: At least one processor; and A memory that is communicatively connected to at least one processor; wherein, The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the signal reconstruction method of any embodiment of this application.

[0009] According to another aspect of this application, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the signal reconstruction method of any embodiment of this application.

[0010] According to another aspect of this application, a computer program product is provided, which includes a computer program that, when executed by a processor, implements the signal reconstruction method of any embodiment of this application.

[0011] The signal reconstruction scheme provided in this application embodiment, in response to a signal reconstruction event being triggered, acquires the mixed signal to be processed; performs M-level wavelet decomposition on the mixed signal based on orthogonal wavelets to generate M high-frequency components and one low-frequency component; where M is an integer greater than or equal to 1; determines an adaptive filtering threshold corresponding to each of the high-frequency components, and performs hierarchical threshold filtering on the high-frequency components based on the adaptive filtering threshold to generate the corresponding high-frequency target component; determines a hard filtering threshold corresponding to the low-frequency component, and performs threshold filtering on the low-frequency component based on the hard filtering threshold to generate the corresponding low-frequency target component; and reconstructs the signal from the M high-frequency target components and the low-frequency target component to generate the target signal. The technical solution provided in this application embodiment, through the multi-resolution characteristics of orthogonal wavelet transform, can remove ultra-low frequency drift signals from the mixed signal while also performing hierarchical threshold filtering to remove high-frequency noise from the mixed signal. This achieves significant improvement in the algorithm's tracking ability and anti-interference performance for non-stationary signals while maintaining low computational complexity, effectively solving the problems of the contradiction between convergence speed and steady-state error, and the balance between computational efficiency and filtering accuracy in traditional methods.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A flowchart illustrating a signal reconstruction method provided in this application embodiment; Figure 2 This is a schematic diagram of a signal reconstruction device provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0015] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0016] It should be noted that the terms "first," "second," "third," "fourth," "actual," "preset," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0017] Figure 1 This is a flowchart illustrating a signal reconstruction method provided in an embodiment of this application. This embodiment is applicable to situations where a mixed signal is reconstructed after removing drift signals and high-frequency noise signals. The method can be executed by a signal reconstruction device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes: S110. In response to a signal reconstruction event being triggered, acquire the mixed signal to be processed.

[0018] For example, a signal reconstruction event is determined to be triggered when a signal reconstruction command input by the user is received. Also for example, a signal reconstruction event is determined to be triggered when the inertial measurement unit (IMU) is detected to have started acquiring signals. In this embodiment, in response to the signal reconstruction event being triggered, a mixed signal to be processed is acquired. For example, the mixed signal to be processed can be a signal acquired by the IMU, wherein the mixed signal includes not only the valid signal (i.e., the valid measurement signal of the IMU), but also ultra-low frequency, slowly changing baseline drift signal and random high-frequency noise. For example, the mixed signal x(t) can be expressed as: x(t) = s(t) + d(t) + n(t), where s(t) represents the valid signal, d(t) represents the baseline drift signal, and n(t) represents random high-frequency noise. The IMU can be a measurement unit such as an accelerometer or gyroscope.

[0019] S120. Perform M-level wavelet decomposition on the mixed signal based on orthogonal wavelets to generate M high-frequency components and one low-frequency component; where M is an integer greater than or equal to 1.

[0020] In this embodiment, a pre-defined orthogonal wavelet (e.g., db4 wavelet, wavelet length L=8) is obtained. The orthogonal wavelet is an orthogonal wavelet function capable of supporting lossless reconstruction, ensuring no information loss during subsequent signal reconstruction. The decomposition level M of the orthogonal wavelet decomposition is determined, where M is an integer greater than or equal to 1. For example, the decomposition level M can be a pre-defined constant. Based on the orthogonal wavelet, the mixed signal undergoes M-level orthogonal wavelet decomposition, generating M high-frequency components (also called detail coefficients) and 1 low-frequency component (also called approximation coefficient). It should be noted that the low-frequency component is the lowest frequency component after M-level orthogonal wavelet decomposition of the mixed signal; the baseline drift signal d(t) is typically contained within the low-frequency component. The high-frequency component is the higher frequency component after M-level orthogonal wavelet decomposition of the mixed signal, and each high-frequency component contains an effective signal s(t) and random high-frequency noise n(t).

[0021] Optionally, before performing M-level wavelet decomposition on the mixed signal based on orthogonal wavelets, the method further includes: determining the sampling frequency of the mixed signal; and determining the decomposition level M of the orthogonal wavelets based on the sampling frequency and a pre-set estimated drift frequency. This approach effectively avoids the omission of baseline drift signals during orthogonal wavelet decomposition of the mixed signal. In this embodiment, the sampling frequency of the mixed signal can be determined by analyzing the sampling parameters of the inertial measurement unit, or the sampling frequency of the manually input mixed signal can be directly obtained. A pre-set estimated drift frequency is obtained, which can be set according to the frequency range of the drift signal. For example, if the frequency range of the drift signal is 0.1~1 Hz, the estimated drift frequency can be set to 0.5 Hz. The decomposition level M of the orthogonal wavelets is determined based on the sampling frequency of the mixed signal and the pre-set estimated drift frequency. For example, the decomposition level M of the orthogonal wavelet can be calculated according to the following formula: M = floor( log2(f_s / (2 × f_d)) ), where f_s represents the sampling frequency of the mixed signal and f_d represents the estimated drift frequency. It should be noted that the embodiments of this application do not limit the method of determining the decomposition level M of the orthogonal wavelet based on the sampling frequency of the mixed signal and the preset estimated drift frequency.

[0022] S130. Determine the adaptive filtering threshold corresponding to each of the high-frequency components, and perform hierarchical threshold filtering on the high-frequency components based on the adaptive filtering threshold to generate the corresponding high-frequency target components.

[0023] In this embodiment, for each high-frequency component, the high-frequency component is analyzed to determine the corresponding adaptive filtering threshold. It is understood that an adaptive filtering threshold is adaptively determined for each high-frequency component; different high-frequency components will have different corresponding adaptive filtering thresholds. For example, the high-frequency component can be input into a pre-trained threshold determination model, and the adaptive filtering threshold corresponding to the high-frequency component can be determined based on the output of the threshold determination model. Optionally, each high-frequency component is a sampling point sequence containing multiple sampling points; determining the adaptive filtering threshold corresponding to each high-frequency component includes: for each high-frequency component, constructing an amplitude sequence based on the amplitude of each sampling point in the sampling point sequence constituting the high-frequency component, and determining the median amplitude in the amplitude sequence; determining the adaptive filtering threshold corresponding to the high-frequency component based on the median amplitude.

[0024] In this embodiment, if the duration of the mixed signal is T, then based on the sampling frequency f_s and the duration of the mixed signal, the number of sampling points contained in the mixed signal can be determined to be N = f_s×T. Therefore, the M high-frequency components and one low-frequency component generated after performing M-level orthogonal wavelet decomposition on the mixed signal based on the orthogonal wavelet function also contain multiple sampling points. It can be understood that the M high-frequency components and one low-frequency component are each composed of a sampling point sequence containing multiple sampling points. The number of sampling points contained in the sampling point sequence constituting each high-frequency component is related to N, and the number of sampling points contained in the sampling point sequence constituting the low-frequency component is also related to N.

[0025] In this embodiment, for each high-frequency component, the amplitude of each sampling point in the sampling point sequence constituting the high-frequency component is determined, and an amplitude sequence is constructed based on the amplitudes of each sampling point in the sampling point sequence constituting the high-frequency component. Then, the median amplitude in the amplitude sequence is determined, thereby determining the adaptive filtering threshold corresponding to the high-frequency component based on the median amplitude. For example, the adaptive filtering threshold corresponding to the j-th high-frequency component among M high-frequency components can be calculated according to the following formula. : σ = median(|D_j|) / 0.6745, where median(|D_j|) represents the median amplitude in the amplitude sequence corresponding to the j-th high-frequency component D_j. It should be noted that this application does not limit the method of determining the adaptive filtering threshold corresponding to the high-frequency component based on the median amplitude. Alternatively, the mean amplitude in the amplitude sequence corresponding to the high-frequency component can be determined, and then the adaptive filtering threshold corresponding to the high-frequency component can be determined based on the mean amplitude.

[0026] For each high-frequency component, a hierarchical threshold filter is performed on the high-frequency component based on the adaptive filtering threshold corresponding to that high-frequency component to filter out random high-frequency noise and generate the corresponding high-frequency target component. For example, for each sampling point in the sampling point sequence constituting the high-frequency component, it is determined whether the amplitude corresponding to the sampling point is less than the adaptive filtering threshold corresponding to the high-frequency component. If so, the sampling point is determined to be high-frequency random noise, and therefore, the amplitude of the sampling point is set to 0, thereby filtering the sampling point out of the sampling point sequence constituting the high-frequency component. If not, the sampling point is determined to be a high-frequency valid signal, and therefore, the sampling point is retained, and its amplitude remains unchanged or is adjusted, thereby generating the high-frequency target component in the above manner. It can be understood that M high-frequency components can be represented as D_M, D_{M-1}, ..., D_j, ..., D_1, and the high-frequency target component generated after filtering the M high-frequency components can be correspondingly represented as D_M', D_{M-1}', ..., D_j', ..., D_1'. Optionally, performing hierarchical threshold filtering on the high-frequency components based on the adaptive filtering threshold includes: using a soft threshold filtering method to perform hierarchical threshold filtering on the high-frequency components based on the adaptive filtering threshold.

[0027] S140. Determine the hard filtering threshold corresponding to the low-frequency component, and perform threshold filtering on the low-frequency component based on the hard filtering threshold to generate the corresponding low-frequency target component.

[0028] In this embodiment, low-frequency components are analyzed to determine the corresponding hard-filtering threshold. For example, the low-frequency components can be input into a pre-trained threshold determination model, and the hard-filtering threshold corresponding to the low-frequency components can be determined based on the output of the threshold determination model. Optionally, the low-frequency components are a sequence of sampling points containing multiple sampling points; determining the hard-filtering threshold corresponding to the low-frequency components includes: determining the amplitude of each sampling point in the sampling point sequence constituting the low-frequency components, and determining the maximum amplitude among all the amplitudes of the sampling points corresponding to the low-frequency components; and determining the hard-filtering threshold corresponding to the low-frequency components based on the maximum amplitude.

[0029] In this embodiment, the amplitude of each sampling point in the sampling point sequence constituting the low-frequency component is determined, and the maximum amplitude among all sampling point amplitudes corresponding to the low-frequency component is determined. The hard filtering threshold corresponding to the low-frequency component is then determined based on the maximum amplitude. For example, the hard filtering threshold corresponding to the low-frequency component can be calculated using the following formula. : ,in, Represents low-frequency components The maximum amplitude among all corresponding sampling points. It should be noted that the embodiments of this application do not limit the method of determining the hard filter threshold corresponding to the low-frequency component based on the maximum amplitude.

[0030] For the low-frequency component, threshold filtering is performed on the low-frequency component based on the hard filtering threshold corresponding to it to filter out the baseline drift signal and generate the low-frequency target component. For example, for each sampling point in the sampling point sequence constituting the low-frequency component, it is determined whether the amplitude corresponding to the sampling point is less than the hard filtering threshold corresponding to the low-frequency component. If so, the sampling point is determined to be a baseline drift signal, and therefore, the amplitude of the sampling point is set to 0, thereby filtering the sampling point out of the sampling point sequence constituting the low-frequency component. If not, the sampling point is determined to be a valid low-frequency signal, and therefore, the sampling point is retained, and its amplitude remains unchanged, thereby generating the low-frequency target component in the above manner. It can be understood that the low-frequency component can be represented as... Then, the low-frequency target component generated after filtering the low-frequency component can be represented as C_M'. Optionally, threshold filtering of the low-frequency component based on the hard filtering threshold includes: using a hard threshold filtering method to perform threshold filtering of the low-frequency component based on the hard filtering threshold.

[0031] S150. Reconstruct the M high-frequency target components and the low-frequency target components to generate a target signal.

[0032] In this embodiment, M high-frequency target components and 1 low-frequency target component constitute M+1 target components. Signal reconstruction is performed on the M+1 target components to generate a target signal. The target components are effective signals that have been filtered out for high-frequency random noise and baseline drift. Optionally, signal reconstruction of the M high-frequency and low-frequency target components to generate the target signal includes: constructing a target component set based on the M high-frequency and low-frequency target components; and performing an inverse orthogonal wavelet transform on the target component set to generate the target signal. For example, the target component set constructed from the M high-frequency and 1 low-frequency target components can be represented as: (C_M', D_M', D_(M-1)', ..., D_1'). Because orthogonal wavelets satisfy the "biorthogonality," the components generated by orthogonal wavelet decomposition can be reconstructed using inverse wavelet transform without information loss. Therefore, the target component set (C_M', D_M', D_(M-1)', ..., D_1') is subjected to inverse orthogonal wavelet transform to generate the target signal. For example, the target component set (C_M', D_M', D_(M-1)', ..., D_1') is input into the inverse orthogonal wavelet transform operator to obtain the filtered and reconstructed target signal s'(t). It can be understood that the target signal s'(t) is a useful signal for practical applications and can be considered as the original signal x(t) after filtering out the baseline drift signal d(t) and random high-frequency noise n(t).

[0033] The signal reconstruction method provided in this application embodiment, in response to a signal reconstruction event being triggered, acquires the mixed signal to be processed; performs M-level wavelet decomposition on the mixed signal based on orthogonal wavelets to generate M high-frequency components and one low-frequency component; where M is an integer greater than or equal to 1; determines an adaptive filtering threshold corresponding to each of the high-frequency components, and performs hierarchical threshold filtering on the high-frequency components based on the adaptive filtering threshold to generate the corresponding high-frequency target component; determines a hard filtering threshold corresponding to the low-frequency component, and performs threshold filtering on the low-frequency component based on the hard filtering threshold to generate the corresponding low-frequency target component; and reconstructs the signal from the M high-frequency target components and the low-frequency target component to generate the target signal. The technical solution provided in this application embodiment, through the multi-resolution characteristics of orthogonal wavelet transform, can remove ultra-low frequency drift signals from the mixed signal while also performing hierarchical threshold filtering to remove high-frequency noise from the mixed signal. This achieves significant improvement in the algorithm's tracking ability and anti-interference performance for non-stationary signals while maintaining low computational complexity, effectively solving the problems of the contradiction between convergence speed and steady-state error, and the balance between computational efficiency and filtering accuracy in traditional methods.

[0034] Figure 2This is a schematic diagram of a signal reconstruction device provided in an embodiment of this application. This device can execute the signal reconstruction method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects for executing the method. Figure 2 As shown, the device includes: The mixed signal acquisition module 210 is used to acquire the mixed signal to be processed in response to the signal reconstruction event being triggered; Wavelet decomposition module 220 is used to perform M-level wavelet decomposition on the mixed signal based on orthogonal wavelets to generate M high-frequency components and one low-frequency component; where M is an integer greater than or equal to 1. The high-frequency target component generation module 230 is used to determine the adaptive filtering threshold corresponding to each high-frequency component, and perform hierarchical threshold filtering on the high-frequency components based on the adaptive filtering threshold to generate the corresponding high-frequency target components. The low-frequency target component generation module 240 is used to determine the hard filtering threshold corresponding to the low-frequency component, and perform threshold filtering on the low-frequency component based on the hard filtering threshold to generate the corresponding low-frequency target component. The signal reconstruction module 250 is used to reconstruct the M high-frequency target components and the low-frequency target components to generate a target signal.

[0035] Optionally, the device further includes: The sampling frequency determination module is used to determine the sampling frequency of the mixed signal before performing M-level wavelet decomposition on the mixed signal based on orthogonal wavelets; The decomposition level determination module is used to determine the decomposition level M of the orthogonal wavelet based on the sampling frequency and the preset estimated drift frequency.

[0036] Optionally, the high-frequency target component generation module is used for: A soft thresholding method is used to perform hierarchical thresholding filtering on the high-frequency components based on the adaptive filtering threshold. The low-frequency target component generation module is used for: The low-frequency component is threshold filtered using a hard threshold method based on the hard filtering threshold.

[0037] Optionally, each of the high-frequency components is a sequence of sampling points containing multiple sampling points; High-frequency target component generation unit, used for: For each of the high-frequency components, an amplitude sequence is constructed based on the amplitude of each sampling point in the sampling point sequence constituting the high-frequency component, and the median amplitude in the amplitude sequence is determined. The adaptive filtering threshold corresponding to the high-frequency component is determined based on the median amplitude.

[0038] Optionally, a low-frequency target component generation unit is used for: Determining the corresponding hard filter threshold for the low-frequency component includes: Determine the amplitude of each sampling point in the sampling point sequence that constitutes the low-frequency component, and determine the maximum amplitude among all sampling point amplitudes corresponding to the low-frequency component; The hard filter threshold corresponding to the low-frequency component is determined based on the maximum amplitude.

[0039] Optional, a signal reconstruction module, used for: A target component set is constructed based on the M high-frequency target components and the low-frequency target components; The target component set is subjected to an inverse orthogonal wavelet transform to generate the target signal.

[0040] The signal reconstruction apparatus provided in this application can execute a signal reconstruction method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of executing the method.

[0041] Figure 3 A schematic diagram of an electronic device 10, which can be used to implement embodiments of this application, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0042] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0043] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0044] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as signal reconstruction methods.

[0045] In some embodiments, the signal reconstruction method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the signal reconstruction method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the signal reconstruction method by any other suitable means (e.g., by means of firmware).

[0046] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0047] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable signal reconfiguration device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0048] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0049] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0050] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0051] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0052] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the signal reconstruction method provided in any embodiment of this application.

[0053] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or it can be connected to an external computer (e.g., via the Internet using an Internet service provider). It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired information of the technical solution of this application can be achieved, and this is not limited herein.

[0054] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A signal reconstruction method, characterized in that, The method includes: In response to a signal reconstruction event being triggered, acquire the mixed signal to be processed; The mixed signal is decomposed into M-level wavelet components based on orthogonal wavelets, generating M high-frequency components and one low-frequency component; where M is an integer greater than or equal to 1. An adaptive filtering threshold is determined for each of the high-frequency components, and the high-frequency components are subjected to hierarchical threshold filtering based on the adaptive filtering threshold to generate the corresponding high-frequency target components. Determine the hard filtering threshold corresponding to the low-frequency component, and perform threshold filtering on the low-frequency component based on the hard filtering threshold to generate the corresponding low-frequency target component; The target signal is generated by reconstructing the M high-frequency target components and the low-frequency target components.

2. The method according to claim 1, characterized in that, Before performing M-level wavelet decomposition on the mixed signal based on orthogonal wavelets, the following steps are also included: Determine the sampling frequency of the mixed signal; The decomposition level M of the orthogonal wavelet is determined based on the sampling frequency and the pre-set estimated drift frequency.

3. The method according to claim 1, characterized in that, Layered threshold filtering of the high-frequency components based on the adaptive filtering threshold includes: A soft thresholding method is used to perform hierarchical thresholding filtering on the high-frequency components based on the adaptive filtering threshold. Threshold filtering of the low-frequency components based on the hard filtering threshold includes: The low-frequency component is threshold filtered using a hard threshold method based on the hard filtering threshold.

4. The method according to claim 1, characterized in that, Each of the high-frequency components is a sequence of sampling points containing multiple sampling points; Determine the adaptive filtering threshold for each of the high-frequency components, including: For each of the high-frequency components, an amplitude sequence is constructed based on the amplitude of each sampling point in the sampling point sequence constituting the high-frequency component, and the median amplitude in the amplitude sequence is determined. The adaptive filtering threshold corresponding to the high-frequency component is determined based on the median amplitude.

5. The method according to claim 1, characterized in that, The low-frequency component is a sequence of sampling points containing multiple sampling points; Determining the corresponding hard filter threshold for the low-frequency component includes: Determine the amplitude of each sampling point in the sampling point sequence that constitutes the low-frequency component, and determine the maximum amplitude among all sampling point amplitudes corresponding to the low-frequency component; The hard filter threshold corresponding to the low-frequency component is determined based on the maximum amplitude.

6. The method according to claim 1, characterized in that, The target signal is generated by reconstructing the M high-frequency target components and the low-frequency target components, including: A target component set is constructed based on the M high-frequency target components and the low-frequency target components; The target component set is subjected to an inverse orthogonal wavelet transform to generate the target signal.

7. A signal reconstruction device, characterized in that, include: The mixed signal acquisition module is used to acquire the mixed signal to be processed in response to the signal reconstruction event being triggered. The wavelet decomposition module is used to perform M-level wavelet decomposition on the mixed signal based on orthogonal wavelets, generating M high-frequency components and one low-frequency component; where M is an integer greater than or equal to 1. A high-frequency target component generation module is used to determine the adaptive filtering threshold corresponding to each high-frequency component, and perform hierarchical threshold filtering on the high-frequency components based on the adaptive filtering threshold to generate the corresponding high-frequency target components. A low-frequency target component generation module is used to determine the hard filtering threshold corresponding to the low-frequency component, and perform threshold filtering on the low-frequency component based on the hard filtering threshold to generate the corresponding low-frequency target component. The signal reconstruction module is used to reconstruct the M high-frequency target components and the low-frequency target components to generate a target signal.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the signal reconstruction method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the signal reconstruction method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the signal reconstruction method according to any one of claims 1-6.

Images