A signal noise filtering method, device, storage medium and laser radar

Abstract

A signal noise filtering method, apparatus, storage medium, and lidar are disclosed. The method includes: performing ensemble empirical mode decomposition on the initial difference frequency signal generated by the lidar to obtain a set of noisy components corresponding to the initial difference frequency signal (S101); obtaining the noise location in each noisy component according to the filtering frequency range and the instantaneous frequency value corresponding to each noisy component in the set of noisy components (S102); setting the noise amplitude corresponding to the noise location in each noisy component to zero to obtain a set of denoised components (S103); and performing combination reconstruction processing on the set of denoised components to obtain the denoised time-domain difference frequency signal (S104). This method can improve the signal-to-noise ratio of the difference frequency signal and increase the success rate of effective difference frequency signal extraction.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a signal noise filtering method, apparatus, storage medium, and lidar. Background Technology

[0002] Frequency Modulated Continuous Wave (FMCW) lidar is a type of continuous wave lidar based on coherent detection. It transmits a linearly varying frequency continuous wave as the transmitted signal during a frequency sweep period. A portion of the transmitted signal serves as the local oscillator signal, while the remainder is emitted for detection. The echo signal reflected from the object and returned forms a difference frequency signal with the local oscillator signal. However, in actual detection, the signal is easily affected by inherent noise from the lidar system and the environment, resulting in a low signal-to-noise ratio and making it difficult to extract the effective difference frequency signal. Summary of the Invention

[0003] This application provides a signal noise filtering method, apparatus, storage medium, and lidar, which can improve the signal-to-noise ratio of difference frequency signals and increase the success rate of effective difference frequency signal extraction.

[0004] One embodiment of this application provides a signal noise filtering method, including:

[0005] The initial difference frequency signal generated by the lidar is subjected to ensemble empirical mode decomposition to obtain the set of noisy components corresponding to the initial difference frequency signal;

[0006] Based on the filtering frequency range and the instantaneous frequency value corresponding to each noise component in the set of noise components, the noise location in each noise component is obtained respectively;

[0007] The noise amplitude corresponding to the noise position in each of the noisy components is set to zero to obtain a set of denoised components.

[0008] The denoised component set is combined and reconstructed to obtain the denoised time-domain difference frequency signal.

[0009] The step of obtaining the noise location in each noise component based on the filtering frequency range and the instantaneous frequency value corresponding to each noise component in the noise component set includes:

[0010] The Hilbert transform is applied to the set of noisy components to obtain the instantaneous frequency value of each noisy component in the set at each time step.

[0011] The noise location whose instantaneous frequency value belongs to the filtered frequency range is obtained from each of the noisy components.

[0012] The step of performing Hilbert transform on the set of noisy components to obtain the instantaneous frequency value of each noisy component at each moment includes:

[0013] Perform Hilbert transform on each noisy component in the set of noisy components to obtain the Hilbert spectrum corresponding to each noisy component;

[0014] The Hilbert spectra corresponding to each noisy component are summarized to obtain the Hilbert spectrum corresponding to the initial difference frequency signal.

[0015] The instantaneous frequency value of each noisy component at each moment is obtained from the Hilbert spectrum corresponding to the initial difference frequency signal.

[0016] Wherein, obtaining the noise location in each noisy component whose instantaneous frequency value belongs to the filtered frequency range includes:

[0017] The instantaneous frequency values ​​corresponding to each noisy component at each time moment are traversed, and the position coordinates of the instantaneous frequency values ​​belonging to the filter frequency range are obtained in the Hilbert spectrum corresponding to the initial difference frequency signal. The position coordinates are determined as the noise positions in each noisy component.

[0018] This also includes:

[0019] The time-domain difference frequency signal is processed by a fast Fourier transform to obtain a frequency-domain difference frequency signal, and the difference frequency value corresponding to the maximum amplitude value is obtained from the frequency-domain difference frequency signal.

[0020] One embodiment of this application provides a signal noise filtering device, including:

[0021] The noisy component acquisition unit is used to perform ensemble empirical mode decomposition on the initial difference frequency signal generated by the lidar to obtain the set of noisy components corresponding to the initial difference frequency signal.

[0022] The noise location acquisition unit is used to acquire the noise location in each noise component according to the filter frequency range and the instantaneous frequency value corresponding to each noise component in the set of noise components.

[0023] A noise reduction component acquisition unit is used to set the noise amplitude corresponding to the noise position in each noise component to zero, thereby obtaining a set of noise reduction components.

[0024] The signal reconstruction unit is used to perform combined reconstruction processing on the set of denoised components to obtain the denoised time-domain difference frequency signal.

[0025] The noise location acquisition unit includes:

[0026] The component frequency acquisition subunit is used to perform Hilbert transform processing on the noisy component set to obtain the instantaneous frequency value of each noisy component in the noisy component set at each time moment.

[0027] The noise location acquisition subunit is used to acquire the noise location in each noisy component whose instantaneous frequency value belongs to the filter frequency range.

[0028] Specifically, the component frequency acquisition subunit is used to perform Hilbert transform processing on each noisy component in the noisy component set to obtain the Hilbert spectrum corresponding to each noisy component.

[0029] The Hilbert spectra corresponding to each noisy component are summarized to obtain the Hilbert spectrum corresponding to the initial difference frequency signal.

[0030] The instantaneous frequency value of each noisy component at each moment is obtained from the Hilbert spectrum corresponding to the initial difference frequency signal.

[0031] Specifically, the noise location acquisition subunit is used to traverse the instantaneous frequency value corresponding to each noisy component at each moment, obtain the location coordinates of the instantaneous frequency value belonging to the filter frequency range in the Hilbert spectrum corresponding to the initial difference frequency signal, and determine the location coordinates as the noise location in each noisy component.

[0032] This also includes:

[0033] The difference frequency acquisition unit is used to perform fast Fourier transform processing on the time-domain difference frequency signal to obtain the frequency-domain difference frequency signal, and to obtain the difference frequency value corresponding to the maximum amplitude value in the frequency-domain difference frequency signal.

[0034] One embodiment of this application provides a computer storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, perform the above-described method steps.

[0035] One embodiment of this application provides a lidar, including a processor, a memory, and an input / output interface;

[0036] The processor is connected to the memory and the input / output interface, respectively. The input / output interface is used for page interaction, the memory is used to store program code, and the processor is used to call the program code to execute the above-described method steps.

[0037] In this embodiment, the initial difference frequency signal of the lidar is decomposed into components, and the noise position in each noisy component is extracted according to the filtering frequency range and the instantaneous frequency value of each noisy component. Then, the amplitude of the noise position is uniformly set to obtain a set of denoised components. This achieves the filtering of noise signals in the initial difference frequency signal. Finally, the denoised time-domain difference frequency signal is obtained by combining and reconstructing the set of denoised components, which improves the signal-to-noise ratio of the difference frequency signal and thus improves the success rate of effective difference frequency extraction. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0039] Figure 1 This is a system architecture diagram of signal and noise filtering provided in the embodiments of this application;

[0040] Figure 2 This is a schematic flowchart of a signal noise filtering method provided in an embodiment of this application;

[0041] Figure 3 This is a schematic flowchart of a signal noise filtering method provided in an embodiment of this application;

[0042] Figure 4 This is a schematic diagram of a component frequency acquisition process provided in an embodiment of this application;

[0043] Figure 5 This is a schematic diagram of a noise location acquisition process provided in an embodiment of this application;

[0044] Figure 6 This is an example schematic diagram of a Hilbert spectrum before filtering provided in an embodiment of this application;

[0045] Figure 7 This is an example schematic diagram of a filtered Hilbert spectrum provided in an embodiment of this application;

[0046] Figure 8 This is a schematic diagram of the structure of a signal noise filtering device provided in an embodiment of this application;

[0047] Figure 9 This is a schematic diagram of the structure of a signal noise filtering device provided in an embodiment of this application;

[0048] Figure 10This is a schematic diagram of the structure of the noise component acquisition unit provided in the embodiments of this application;

[0049] Figure 11 This is a schematic diagram of the structure of a lidar provided in an embodiment of this application. Detailed Implementation

[0050] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] Please combine Figures 1-7 The illustrated embodiment provides a detailed description of the signal and noise filtering method provided in this application.

[0052] Please see Figure 1 This application provides a system architecture diagram for signal and noise filtering. For example... Figure 1 As shown, the embodiments of this application can be applied to scenarios of lidar detection, such as environmental monitoring, aerospace, communication, autonomous driving navigation, positioning and other detection scenarios. The lidar's transmitted signal changes periodically in a continuous wave, such as a triangular wave, to transmit a signal to the target and receive the echo signal returned by the target. The initial difference frequency signal formed by the transmitted signal and the echo signal is obtained. The initial difference frequency signal can be processed by a signal processor through a series of signal processing processes, including analog-to-digital conversion, signal filtering, signal data extraction, signal data calculation, etc. Then, the signal spectrum and data generated by the signal processor can be stored, displayed and managed by the background management device.

[0053] Because the transmitted and echo signals are easily affected by inherent noise from the lidar system and the environment, they present an initial difference frequency signal with noise in the spectrum. To remove noise from this initial difference frequency signal, this application proposes a signal noise filtering device. This device can be integrated into the signal processor or used as a standalone device to filter noise from the initial difference frequency signal. The device performs ensemble empirical mode decomposition on the initial difference frequency signal generated by the lidar to obtain a set of noisy components corresponding to the initial difference frequency signal. Based on the filtering frequency range and the instantaneous frequency value corresponding to each noisy component in the set of noisy components, the device obtains the noise location in each noisy component. The device sets the noise amplitude corresponding to the noise location in each noisy component to zero, obtaining a denoised component set. The device then performs a combination reconstruction process on the denoised component set to obtain the denoised time-domain difference frequency signal. By decomposing the initial difference frequency signal of the lidar into components and extracting the noise location in each noisy component based on the filtering frequency range and the instantaneous frequency value of each noisy component, and then uniformly setting the amplitude of the noise location to obtain a set of denoised components, the noise signal in the initial difference frequency signal is filtered out. Finally, by combining and reconstructing the set of denoised components, the denoised time-domain difference frequency signal is obtained, which improves the signal-to-noise ratio of the difference frequency signal and thus improves the success rate of effective difference frequency extraction.

[0054] based on Figure 1 For the system architecture, please refer to [link / reference]. Figure 2 This is a flowchart illustrating a signal-noise filtering method provided in an embodiment of this application. Figure 2 As shown, the method described in this application embodiment may include the following steps S101-S104.

[0055] S101, Perform ensemble empirical mode decomposition on the initial difference frequency signal generated by the lidar to obtain the set of noisy components corresponding to the initial difference frequency signal;

[0056] Specifically, the signal noise filtering device performs ensemble empirical mode decomposition (EEMD) on the initial difference frequency signal generated by the lidar. The initial difference frequency signal can be a difference frequency signal containing noise, that is, the difference frequency signal of the lidar for detecting the target without signal processing. After EEMD processing, the set of noisy components corresponding to the initial difference frequency signal can be obtained. The set of noisy components includes multiple noisy components, which may include multiple intrinsic mode components and a residual component.

[0057] S102, based on the filtering frequency range and the instantaneous frequency value corresponding to each noise component in the set of noise components, obtain the noise location in each noise component respectively;

[0058] Specifically, the signal noise filtering device can obtain the noise location in each noisy component based on the filtering frequency range and the instantaneous frequency value corresponding to each noisy component in the noisy component set. The filtering frequency range can be a pre-set frequency range for indicating the noise location, and the filtering frequency range can be set based on the frequency value of the initial difference frequency signal. The instantaneous frequency value of each noisy component can be the instantaneous frequency value corresponding to each noisy component obtained by performing Hilbert transform processing on the noisy component set. It can be understood that the instantaneous frequency value is the frequency value corresponding to each noisy component at each moment. The signal noise filtering device can match the instantaneous frequency value corresponding to each noisy component with the filtering frequency range to determine the noise location indicated by the filtering frequency range in each noisy component.

[0059] S103, set the noise amplitude corresponding to the noise position in each noisy component to zero to obtain a set of denoised components;

[0060] Specifically, the signal noise filtering device can set the noise amplitude corresponding to the noise position in each noisy component to zero. By uniformly setting the noise amplitude at the noise position, noise signals in the initial difference frequency signal can be filtered out, resulting in a set of denoised components, which includes multiple denoised components. It is understood that there is a one-to-one correspondence between the noisy components and the denoised components. By setting the noise amplitude corresponding to the noise position in a noisy component to zero, a denoised component corresponding to that noisy component can be generated. Similarly, the signal noise filtering device can set the noise amplitude to zero at the noise position of each noisy component in the set of noisy components to obtain a denoised component corresponding to each noisy component, thus forming a set of denoised components.

[0061] S104, Perform combination and reconstruction processing on the denoised component set to obtain the denoised time-domain difference frequency signal;

[0062] Specifically, the signal noise filtering device can perform combined reconstruction processing on the set of denoised components. The combined reconstruction processing is specifically the process of reverse deducing the components into signals. By performing combined reconstruction processing on multiple denoised components in the set of denoised components, a denoised time-domain difference frequency signal can be obtained.

[0063] It is understandable that the above process of component decomposition from the initial difference frequency signal is a forward derivation process of converting the signal into components. Therefore, the process of converting the components into signals is called the reverse derivation process, that is, the component combination and reconstruction process. The time-domain difference frequency signal and the initial difference frequency signal can both be represented as difference frequency signals in the time domain. The initial difference frequency signal is the difference frequency signal in the time domain before denoising, and the time-domain difference frequency signal is the difference frequency signal in the time domain after denoising.

[0064] In this embodiment, the initial difference frequency signal of the lidar is decomposed into components, and the noise position in each noisy component is extracted according to the filtering frequency range and the instantaneous frequency value of each noisy component. Then, the amplitude of the noise position is uniformly set to obtain a set of denoised components. This achieves the filtering of noise signals in the initial difference frequency signal. Finally, the denoised time-domain difference frequency signal is obtained by combining and reconstructing the set of denoised components, which improves the signal-to-noise ratio of the difference frequency signal and thus improves the success rate of effective difference frequency extraction.

[0065] based on Figure 1 For the system architecture, please refer to [link / reference]. Figure 3 This is a flowchart illustrating a signal-noise filtering method provided in an embodiment of this application. Figure 3 As shown, the method described in this application embodiment may include the following steps S201-S206.

[0066] S201, Perform ensemble empirical mode decomposition on the initial difference frequency signal generated by the lidar to obtain the set of noisy components corresponding to the initial difference frequency signal;

[0067] Specifically, the signal noise filtering device performs EEMD processing on the initial difference frequency signal generated by the lidar. The initial difference frequency signal can be a difference frequency signal containing noise, that is, the difference frequency signal of the lidar for detecting the target without signal processing. After EEMD processing, the set of noisy components corresponding to the initial difference frequency signal can be obtained. The set of noisy components includes multiple noisy components, which may include multiple intrinsic mode components and a residual component.

[0068] Optionally, assuming the initial difference frequency signal is x(t), then performing EEMD processing on x(t) can yield m eigenmode components c. i (t) and a residual component r(t).

[0069]

[0070] Where m represents the number of intrinsic mode components, t represents the time of the component, and i represents the i-th component, i being less than or equal to m.

[0071] S202, Perform Hilbert transform on the set of noisy components to obtain the instantaneous frequency value of each noisy component in the set of noisy components at each time.

[0072] Specifically, the signal noise filtering device can perform Hilbert transform processing on each noisy component in the set of noisy components to obtain the Hilbert spectrum corresponding to each noisy component, that is, the relationship spectrum of the instantaneous frequency value, time, and instantaneous amplitude of each noisy component. Then, the Hilbert spectra corresponding to each of the noisy components are summarized to obtain the complete Hilbert spectrum corresponding to the initial difference frequency signal.

[0073] The summarization process can be represented as the process of fusing the Hilbert spectra corresponding to each noisy component. Based on the example above, after performing ensemble empirical mode decomposition on the initial difference frequency signal to obtain (m+1) noisy components, Hilbert transform is performed on each of the (m+1) noisy components to obtain (m+1) Hilbert spectra. Then, the (m+1) Hilbert spectra are summed into the same time spectrum, which is the Hilbert spectrum corresponding to the initial difference frequency signal. It can be understood that a noisy component may include multiple component points in the Hilbert spectrum corresponding to the initial difference frequency signal. The position of each component point represents the instantaneous frequency value and instantaneous amplitude value of the noisy component at the current moment.

[0074] The signal noise filtering device can further obtain the instantaneous frequency value of each noisy component at each moment from the Hilbert spectrum corresponding to the initial difference frequency signal.

[0075] S203, Obtain the noise location in each noisy component whose instantaneous frequency value belongs to the filter frequency range;

[0076] Specifically, the signal noise filtering device can determine the noise location in the Hilbert spectrum corresponding to the initial difference frequency signal by using the filtering frequency range. That is, the position coordinates of the instantaneous frequency values ​​belonging to the filtering frequency range are marked in the Hilbert spectrum corresponding to the initial difference frequency signal, and the marked position coordinates correspond to the noise location.

[0077] Optionally, the signal noise filtering device can traverse the Hilbert spectrum corresponding to each noisy component at each moment in the initial difference frequency signal. The filtering frequency range can specifically be a pre-set frequency band used to indicate the noise signal, and can be set according to the frequency value of the initial difference frequency signal. By obtaining the instantaneous frequency values ​​corresponding to each noisy component at each moment and the pre-set filtering frequency range, the two can be matched to determine the target instantaneous frequency value belonging to the filtering frequency range. The position coordinates corresponding to the target instantaneous frequency value are recorded, and the target noisy component to which the target instantaneous frequency value belongs are obtained. Furthermore, the position coordinates can be determined as the noise position of the target noisy component in the Hilbert spectrum corresponding to the initial difference frequency signal.

[0078] S204, set the noise amplitude corresponding to the noise position in each noisy component to zero to obtain a set of denoised components;

[0079] Specifically, the signal noise filtering device can set the noise amplitude corresponding to the noise position in each noisy component to zero. By uniformly setting the noise amplitude at the noise position, noise signals in the initial difference frequency signal can be filtered out, resulting in a set of denoised components, which includes multiple denoised components. It is understood that there is a one-to-one correspondence between the noisy components and the denoised components. By setting the noise amplitude corresponding to the noise position in a noisy component to zero, a denoised component corresponding to that noisy component can be generated. Similarly, the signal noise filtering device can set the noise amplitude to zero at the noise position of each noisy component in the set of noisy components to obtain a denoised component corresponding to each noisy component, thus forming a set of denoised components.

[0080] S205, Perform combination and reconstruction processing on the denoised component set to obtain the denoised time-domain difference frequency signal;

[0081] Specifically, the signal noise filtering device can perform combined reconstruction processing on the set of denoised components. The combined reconstruction processing is specifically the process of reverse deducing the components into signals. By performing combined reconstruction processing on multiple denoised components in the set of denoised components, a denoised time-domain difference frequency signal can be obtained.

[0082] It is understandable that the above process of component decomposition from the initial difference frequency signal is a forward derivation process of converting the signal into components. Therefore, the process of converting the components into signals is called the reverse derivation process, that is, the component combination and reconstruction process. The time-domain difference frequency signal and the initial difference frequency signal can both be represented as difference frequency signals in the time domain. The initial difference frequency signal is the difference frequency signal in the time domain before denoising, and the time-domain difference frequency signal is the difference frequency signal in the time domain after denoising.

[0083] S206, Perform a fast Fourier transform on the time-domain difference frequency signal to obtain a frequency-domain difference frequency signal, and obtain the difference frequency value corresponding to the maximum amplitude value in the frequency-domain difference frequency signal.

[0084] Specifically, the signal noise filtering device can perform a fast Fourier transform on the time-domain difference frequency signal to obtain a frequency-domain difference frequency signal. The difference frequency value corresponding to the maximum amplitude value is obtained in the frequency-domain difference frequency signal. The frequency-domain difference frequency signal can be specifically represented as the difference frequency signal in the frequency domain after noise reduction. The signal noise filtering device can obtain the position of the maximum amplitude value in the spectrum diagram formed by the frequency-domain difference frequency signal and determine the frequency value corresponding to the position as the difference frequency value of the effective difference frequency signal. The effective difference frequency signal is specifically represented as the real and effective signal returned by the detected target after the transmitted signal passes through it.

[0085] In this embodiment, the initial difference frequency signal of the lidar is decomposed into components, and the noise location in each noisy component is extracted based on the filtering frequency range and the instantaneous frequency value of each noisy component. Then, the amplitude of the noise location is uniformly set to obtain a set of denoised components. This achieves the filtering of noise signals in the initial difference frequency signal. Finally, the denoised time-domain difference frequency signal is obtained by combining and reconstructing the set of denoised components, which improves the signal-to-noise ratio of the difference frequency signal. Furthermore, by combining Hilbert transform and fast Fourier transform, effective extraction of the difference frequency signal is ensured, and the success rate of difference frequency extraction is improved.

[0086] Please see Figure 4 This document provides a flowchart illustrating the process of acquiring component frequencies in an embodiment of this application. Figure 4 As shown, the component frequency acquisition process is as follows: Figure 2 The execution process of step S202 in the illustrated embodiment specifically includes:

[0087] S301, Perform Hilbert transform on each noisy component in the set of noisy components to obtain the Hilbert spectrum corresponding to each noisy component;

[0088] S302, the Hilbert spectra corresponding to each noisy component are summarized to obtain the Hilbert spectrum corresponding to the initial difference frequency signal;

[0089] S303, obtain the instantaneous frequency value corresponding to each noisy component from the Hilbert spectrum corresponding to the initial difference frequency signal;

[0090] Specifically, the signal noise filtering device can perform Hilbert transform processing on each noisy component in the set of noisy components to obtain the Hilbert spectrum corresponding to each noisy component, that is, the relationship spectrum of the instantaneous frequency value, time, and instantaneous amplitude of each noisy component; then, the Hilbert spectra corresponding to each of the noisy components are summarized to obtain the complete Hilbert spectrum corresponding to the initial difference frequency signal.

[0091] The Hilbert spectrum corresponding to the initial difference frequency signal contains the time, instantaneous frequency value, instantaneous amplitude value, and the correspondence among the three of each noisy component. The summarization process can be represented as the process of fusing the Hilbert spectra corresponding to each noisy component. Based on the above example, after performing ensemble empirical mode decomposition on the initial difference frequency signal to obtain (m+1) noisy components, Hilbert transform is performed on each of the (m+1) noisy components to obtain (m+1) Hilbert spectra. Then, the (m+1) Hilbert spectra are summarized into the same time spectrum, which is the Hilbert spectrum corresponding to the initial difference frequency signal. It can be understood that a noisy component may include multiple component points. The position of each component point of the noisy component in the Hilbert spectrum corresponding to the initial difference frequency signal represents the instantaneous frequency value and instantaneous amplitude value of the noisy component at the current moment.

[0092] The signal noise filtering device can further obtain the instantaneous frequency value of each noisy component at each moment from the Hilbert spectrum corresponding to the initial difference frequency signal.

[0093] In this embodiment, by using Hilbert transform to convert the noisy component between the time and frequency domains, the instantaneous frequency, instantaneous amplitude, and time correspondence of the noisy component can be quickly and accurately located, ensuring the accuracy of subsequent noise location acquisition and noise filtering.

[0094] Please see Figure 5 This provides a flowchart illustrating the noise location acquisition process in an embodiment of this application. For example... Figure 5 As shown, the noise location acquisition process is as follows: Figure 2 The execution process of step S203 in the illustrated embodiment specifically includes:

[0095] S401, traverse the instantaneous frequency values ​​corresponding to each noisy component at each moment, obtain the position coordinates of the instantaneous frequency value belonging to the filter frequency range in the Hilbert spectrum corresponding to the initial difference frequency signal, and determine the position coordinates as the noise position in each noisy component;

[0096] Specifically, the signal noise filtering device can determine the noise position in the Hilbert spectrum corresponding to the initial difference frequency signal by using a filtering frequency range, that is, mark the position coordinates of the instantaneous frequency values belonging to the filtering frequency range in the Hilbert spectrum corresponding to the initial difference frequency signal, and the position coordinates corresponding to the marked ones are the noise positions.

[0097] Optionally, the signal noise filtering device can traverse the instantaneous frequency values corresponding to each noisy component at each moment in the Hilbert spectrum corresponding to the initial difference frequency signal. The filtering frequency range can specifically be a preset frequency band for indicating the noise signal, and the filtering frequency range can specifically be set according to the frequency value of the initial difference frequency signal. For example, assuming the frequency value of the initial difference frequency signal is 266.7 MHz, then the two frequency bands of 0 < f < 100 MHz and f > 400 MHz can be used as the filtering frequency ranges where the noise to be removed is located. By obtaining the instantaneous frequency values corresponding to each noisy component at each moment and the preset filtering frequency range, the two can be matched to determine the target instantaneous frequency values belonging to the filtering frequency range, record the position coordinates corresponding to the target instantaneous frequency values, and obtain the target noisy component to which the target instantaneous frequency value belongs. Furthermore, the position coordinates can be determined as the noise positions of the target noisy component in the Hilbert spectrum corresponding to the initial difference frequency signal.

[0098] Optionally, assume that currently the instantaneous frequency values of each noisy component at time t are obtained in the Hilbert spectrum corresponding to the initial difference frequency signal, where t is any moment in the signal duration of the initial difference frequency signal. Match the instantaneous frequency values at time t with the filtering frequency range respectively. When there are target instantaneous frequency values belonging to the filtering frequency range, obtain the coordinate position of the target instantaneous frequency value in the Hilbert spectrum corresponding to the initial difference frequency signal, and determine the target noisy component to which the target instantaneous frequency value belongs. Determine the obtained coordinate position as the noise position of the target noisy component at time t. After the noise positions of each noisy component at time t are obtained, it can turn to the next moment (for example: time t + 1, and the specific time interval can be set according to the actual situation) to obtain the noise positions of each noisy component at the next moment, and so on, traverse the instantaneous frequency values corresponding to each noisy component at each moment in the signal duration, and the signal noise filtering device can use the above method to determine the multiple noise positions of each noisy component.

[0099] In the embodiments of the present application, by using the filtering frequency range, the position coordinates of the noise signal are specifically located in the Hilbert spectrum of the signal, and then the noise positions in each noisy component can be traced back in combination with the instantaneous frequency of the noisy component.

[0100] In the embodiments of the present application, please refer to Figure 6, Figure 6 The Hilbert spectrum before noise filtering is shown, as follows: Figure 6 As shown, due to the presence of noise frequencies in the set of noisy components, the distribution of each noisy component in the Hilbert spectrum of the initial difference frequency signal is rather scattered, and the frequency values ​​of each noisy component fluctuate irregularly. Please refer to [further details omitted]. Figure 7 , Figure 7 The noise-filtered Hilbert spectrum is shown, as follows: Figure 7 As shown, by filtering the frequency range to separate the noise location, and setting the noise amplitude of each noise location in the noisy component to zero, the frequency in the spectrum is ensured to float near the frequency of the initial difference frequency signal.

[0101] based on Figure 1 The system architecture will be discussed below in conjunction with the appendix. Figure 8 -Appendix Figure 10 This paper provides a detailed description of the signal and noise filtering device provided in the embodiments of this application. It should be noted that the appendix... Figure 8 -Appendix Figure 10 The signal and noise filtering device in the present application is used to perform the present application. Figures 2-7 The methods shown in the embodiments are for illustrative purposes only, illustrating the parts relevant to the embodiments of this application. For specific technical details not disclosed, please refer to this application. Figures 2-7 The example shown.

[0102] Please see Figure 8 This is a schematic diagram of a signal noise filtering device provided in an embodiment of this application. Figure 8 As shown, the signal noise filtering device 1 in this application embodiment may include: a noisy component acquisition unit 11, a noise location acquisition unit 12, a denoised component acquisition unit 13, and a signal reconstruction unit 14.

[0103] The noisy component acquisition unit 11 is used to perform ensemble empirical mode decomposition on the initial difference frequency signal generated by the lidar to obtain the set of noisy components corresponding to the initial difference frequency signal.

[0104] The noise location acquisition unit 12 is used to acquire the noise location in each noise component according to the filter frequency range and the instantaneous frequency value corresponding to each noise component in the set of noise components.

[0105] The noise reduction component acquisition unit 13 is used to set the noise amplitude corresponding to the noise position in each noise component to zero, so as to obtain a set of noise reduction components.

[0106] The signal reconstruction unit 14 is used to perform combined reconstruction processing on the set of denoised components to obtain the denoised time-domain difference frequency signal.

[0107] In this embodiment, the initial difference frequency signal of the lidar is decomposed into components, and the noise position in each noisy component is extracted according to the filtering frequency range and the instantaneous frequency value of each noisy component. Then, the amplitude of the noise position is uniformly set to obtain a set of denoised components. This achieves the filtering of noise signals in the initial difference frequency signal. Finally, the denoised time-domain difference frequency signal is obtained by combining and reconstructing the set of denoised components, which improves the signal-to-noise ratio of the difference frequency signal and thus improves the success rate of effective difference frequency extraction.

[0108] Please see Figure 9 This is a schematic diagram of a signal noise filtering device provided in an embodiment of this application. Figure 9 As shown, the signal noise filtering device 1 in this application embodiment may include: a noisy component acquisition unit 11, a noise location acquisition unit 12, a denoised component acquisition unit 13, a signal reconstruction unit 14, and a difference frequency acquisition unit 15.

[0109] The noisy component acquisition unit 11 is used to perform ensemble empirical mode decomposition on the initial difference frequency signal generated by the lidar to obtain the set of noisy components corresponding to the initial difference frequency signal.

[0110] The noise location acquisition unit 12 is used to acquire the noise location in each noise component according to the filter frequency range and the instantaneous frequency value corresponding to each noise component in the set of noise components.

[0111] For details, please refer to the following: Figure 10 The present application provides a schematic diagram of the structure of a noise frequency band acquisition unit. Figure 10 As shown, the noise frequency band acquisition unit 12 may include:

[0112] The component frequency acquisition subunit 121 is used to perform Hilbert transform processing on the noisy component set to obtain the instantaneous frequency value of each noisy component in the noisy component set at each time moment.

[0113] In a specific implementation, the component frequency acquisition subunit 121 is specifically used to perform Hilbert transform processing on each noisy component in the noisy component set to obtain the Hilbert spectrum corresponding to each noisy component.

[0114] The Hilbert spectra corresponding to each noisy component are summarized to obtain the Hilbert spectrum corresponding to the initial difference frequency signal.

[0115] The instantaneous frequency value of each noisy component at each moment is obtained from the Hilbert spectrum corresponding to the initial difference frequency signal.

[0116] Noise location acquisition subunit 122 is used to acquire the noise location whose instantaneous frequency value belongs to the filter frequency range in each of the noise components;

[0117] In a specific implementation, the noise location acquisition subunit 122 is specifically used to traverse the instantaneous frequency value corresponding to each noisy component at each moment, obtain the location coordinates of the instantaneous frequency value belonging to the filter frequency range in the Hilbert spectrum corresponding to the initial difference frequency signal, and determine the location coordinates as the noise location in each noisy component.

[0118] The noise reduction component acquisition unit 13 is used to set the noise amplitude corresponding to the noise position in each noise component to zero, so as to obtain a set of noise reduction components.

[0119] Signal reconstruction unit 14 is used to perform combined reconstruction processing on the denoised component set to obtain the denoised time-domain difference frequency signal;

[0120] The difference frequency acquisition unit 15 is used to perform fast Fourier transform processing on the time-domain difference frequency signal to obtain the frequency-domain difference frequency signal, and to obtain the difference frequency value corresponding to the maximum amplitude value in the frequency-domain difference frequency signal.

[0121] In this embodiment, the initial difference frequency signal of the lidar is decomposed into components, and the noise location in each noisy component is extracted based on the filtering frequency range and the instantaneous frequency value of each noisy component. Then, the amplitude of the noise location is uniformly set to obtain a set of denoised components. This achieves the filtering of noise signals in the initial difference frequency signal. Finally, the denoised time-domain difference frequency signal is obtained by combining and reconstructing the set of denoised components, which improves the signal-to-noise ratio of the difference frequency signal. Furthermore, by combining Hilbert transform and fast Fourier transform, effective extraction of the difference frequency signal is ensured, and the success rate of difference frequency extraction is improved.

[0122] This application also provides a computer storage medium that can store multiple program instructions, which are adapted to be loaded and executed by a processor as described above. Figures 2-7 The method steps of the illustrated embodiment can be found in the following documentation for detailed execution. Figures 2-7 The specific details of the illustrated embodiments will not be elaborated here.

[0123] Please see Figure 11 The diagram below provides a structural schematic of a lidar according to an embodiment of this application. Figure 11As shown, the lidar 1000 may include: at least one processor 1001, such as a CPU; at least one network interface 1004; an input / output interface 1003; a memory 1005; and at least one communication bus 1002. The communication bus 1002 is used to enable communication between these components. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as at least one disk storage device. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 11 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, an input / output interface module, and a noise filtering application.

[0124] exist Figure 11 In the lidar 1000 shown, the input / output interface 1003 is mainly used to provide an input interface for users and access devices to acquire data input by users and access devices.

[0125] In one embodiment, processor 1001 can be used to invoke a noise filtering application stored in memory 1005 and specifically perform the following operations:

[0126] The initial difference frequency signal generated by the lidar is subjected to ensemble empirical mode decomposition to obtain the set of noisy components corresponding to the initial difference frequency signal;

[0127] Based on the filtering frequency range and the instantaneous frequency value corresponding to each noise component in the set of noise components, the noise location in each noise component is obtained respectively;

[0128] The noise amplitude corresponding to the noise position in each of the noisy components is set to zero to obtain a set of denoised components.

[0129] The denoised component set is combined and reconstructed to obtain the denoised time-domain difference frequency signal.

[0130] Optionally, when the processor 1001 executes the operation of obtaining the noise location in each noisy component based on the filtering frequency range and the instantaneous frequency value corresponding to each noisy component in the set of noisy components, it specifically performs the following operations:

[0131] The Hilbert transform is applied to the set of noisy components to obtain the instantaneous frequency value of each noisy component in the set at each time step.

[0132] The noise location whose instantaneous frequency value belongs to the filtered frequency range is obtained from each of the noisy components.

[0133] Optionally, when the processor 1001 performs Hilbert transform processing on the noisy component set to obtain the instantaneous frequency value of each noisy component in the noisy component set at each time moment, it specifically performs the following operations:

[0134] Perform Hilbert transform on each noisy component in the set of noisy components to obtain the Hilbert spectrum corresponding to each noisy component;

[0135] The Hilbert spectra corresponding to each noisy component are summarized to obtain the Hilbert spectrum corresponding to the initial difference frequency signal.

[0136] The instantaneous frequency value of each noisy component at each moment is obtained from the Hilbert spectrum corresponding to the initial difference frequency signal.

[0137] Optionally, when the processor 1001 executes the operation of obtaining the noise location in each noisy component whose instantaneous frequency value belongs to the filtered frequency range, it specifically performs the following operations:

[0138] The instantaneous frequency values ​​corresponding to each noisy component at each time moment are traversed, and the position coordinates of the instantaneous frequency values ​​belonging to the filter frequency range are obtained in the Hilbert spectrum corresponding to the initial difference frequency signal. The position coordinates are determined as the noise positions in each noisy component.

[0139] Optionally, the processor 1001 also performs the following operations:

[0140] The time-domain difference frequency signal is processed by a fast Fourier transform to obtain a frequency-domain difference frequency signal, and the difference frequency value corresponding to the maximum amplitude value is obtained from the frequency-domain difference frequency signal.

[0141] In this embodiment, the initial difference frequency signal of the lidar is decomposed into components, and the noise location in each noisy component is extracted based on the filtering frequency range and the instantaneous frequency value of each noisy component. Then, the amplitude of the noise location is uniformly set to obtain a set of denoised components. This achieves the filtering of noise signals in the initial difference frequency signal. Finally, the denoised time-domain difference frequency signal is obtained by combining and reconstructing the set of denoised components, which improves the signal-to-noise ratio of the difference frequency signal. Furthermore, by combining Hilbert transform and fast Fourier transform, effective extraction of the difference frequency signal is ensured, and the success rate of difference frequency extraction is improved.

[0142] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0143] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A method for signal noise filtering, characterized in that, include: The initial difference frequency signal generated by the lidar is subjected to ensemble empirical mode decomposition to obtain the set of noisy components corresponding to the initial difference frequency signal; Perform Hilbert transform on each noisy component in the set of noisy components to obtain the Hilbert spectrum corresponding to each noisy component; The Hilbert spectra corresponding to each noisy component are summarized to obtain the Hilbert spectrum corresponding to the initial difference frequency signal. Obtain the instantaneous frequency value of each noisy component at each moment from the Hilbert spectrum corresponding to the initial difference frequency signal; The instantaneous frequency values ​​corresponding to each noisy component at each time are traversed, and the position coordinates of the instantaneous frequency values ​​belonging to the filter frequency range are obtained in the Hilbert spectrum corresponding to the initial difference frequency signal. The position coordinates are determined as the noise positions in each noisy component. The noise amplitude corresponding to the noise position in each of the noisy components is set to zero to obtain a set of denoised components. The denoised component set is combined and reconstructed to obtain the denoised time-domain difference frequency signal.

2. The method according to claim 1, characterized in that, Also includes: The time-domain difference frequency signal is processed by a fast Fourier transform to obtain a frequency-domain difference frequency signal, and the difference frequency value corresponding to the maximum amplitude value is obtained from the frequency-domain difference frequency signal.

3. A signal noise filtering device, characterized in that, include: The noisy component acquisition unit is used to perform ensemble empirical mode decomposition on the initial difference frequency signal generated by the lidar to obtain the set of noisy components corresponding to the initial difference frequency signal. The component frequency acquisition subunit is used to perform Hilbert transform processing on each noisy component in the noisy component set to obtain the Hilbert spectrum corresponding to each noisy component; and to summarize the Hilbert spectra corresponding to each noisy component to obtain the Hilbert spectrum corresponding to the initial difference frequency signal. Obtain the instantaneous frequency value of each noisy component at each moment from the Hilbert spectrum corresponding to the initial difference frequency signal; The noise location acquisition subunit is used to traverse the instantaneous frequency values ​​corresponding to each noisy component at each time moment, acquire the location coordinates of the instantaneous frequency value belonging to the filter frequency range in the Hilbert spectrum corresponding to the initial difference frequency signal, and determine the location coordinates as the noise location in each noisy component. A noise reduction component acquisition unit is used to set the noise amplitude corresponding to the noise position in each noise component to zero, thereby obtaining a set of noise reduction components. The signal reconstruction unit is used to perform combined reconstruction processing on the set of denoised components to obtain the denoised time-domain difference frequency signal.

4. A lidar, characterized in that, Includes processor, memory, and input / output interfaces; The processor is connected to the memory and the input / output interface respectively, wherein the input / output interface is used for page interaction, the memory is used to store program code, and the processor is used to call the program code to execute the method as described in any one of claims 1 or 2.

5. A computer storage medium, characterized in that, The computer storage medium stores a computer program, the computer program including program instructions, which, when executed by a processor, perform the method as described in any one of claims 1 or 2.

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