WO2022061596A1 - 一种信号噪声滤除方法、装置、存储介质及激光雷达 - Google Patents

一种信号噪声滤除方法、装置、存储介质及激光雷达 Download PDF

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WO2022061596A1
WO2022061596A1 PCT/CN2020/117177 CN2020117177W WO2022061596A1 WO 2022061596 A1 WO2022061596 A1 WO 2022061596A1 CN 2020117177 W CN2020117177 W CN 2020117177W WO 2022061596 A1 WO2022061596 A1 WO 2022061596A1
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noise
signal
frequency
component
frequency signal
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PCT/CN2020/117177
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English (en)
French (fr)
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朱琳
任亚林
汪敬
牛犇
篠原磊磊
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深圳市速腾聚创科技有限公司
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Priority to CN202080004328.4A priority Critical patent/CN114616487A/zh
Priority to PCT/CN2020/117177 priority patent/WO2022061596A1/zh
Publication of WO2022061596A1 publication Critical patent/WO2022061596A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves

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  • the present application relates to the field of computer technology, and in particular, to a signal noise filtering method, device, storage medium, and lidar.
  • Frequency Modulated Continuous Wave Lidar belongs to a continuous wave Lidar based on coherent detection, which transmits a continuous wave whose frequency changes linearly during the frequency sweep period as a transmit signal, and a part of the transmit signal is used as a local oscillator signal. The remaining part is emitted outward for detection, and the echo signal returned after being reflected by the object forms a difference frequency signal with the local oscillator signal. Since the signal is easily affected by the inherent noise of the lidar system and the environment during the actual detection process, the signal-to-noise ratio is low, and the effective difference frequency signal cannot be extracted well.
  • Embodiments of the present application provide a signal noise filtering method, device, storage medium, and lidar, which can improve the signal-to-noise ratio of the beat frequency signal and improve the success rate of effective beat frequency signal extraction.
  • an embodiment of the present application provides a signal noise filtering method, including:
  • a combined reconstruction process is performed on the denoised component set to obtain a denoised time-domain difference frequency signal.
  • each noise-containing component in the noise-containing component set respectively acquiring the noise position in each noise-containing component includes:
  • the noise positions whose instantaneous frequency values belong to the filtering frequency range are obtained in each of the noise-containing components.
  • the described noise-containing component set is subjected to Hilbert transform processing to obtain the instantaneous frequency value corresponding to each noise-containing component in the noise-containing component set at each moment, including:
  • the instantaneous frequency value corresponding to each noise-containing component at each moment is obtained from the Hilbert spectrum corresponding to the initial beat frequency signal.
  • the acquiring the noise position whose instantaneous frequency value belongs to the filtering frequency range in each of the noise-containing components includes:
  • Fast Fourier transform is performed on the time-domain beat-frequency signal to obtain a frequency-domain beat-frequency signal, and a beat-frequency frequency value corresponding to the maximum amplitude is obtained from the frequency-domain beat-frequency signal.
  • an embodiment of the present application provides a signal noise filtering device, including:
  • a noise-containing component acquiring unit configured to perform collective empirical modal decomposition on the initial beat frequency signal generated by the lidar to obtain a set of noise-containing components corresponding to the initial beat frequency signal;
  • a noise position obtaining unit configured to obtain the noise position of each noise-containing component according to the filtering frequency range and the instantaneous frequency value corresponding to each noise-containing component in the noise-containing component set;
  • a denoising component obtaining unit configured to respectively set the noise amplitude corresponding to the noise position in each of the noise-containing components to zero to obtain a denoising component set
  • a signal reconstruction unit configured to perform combined reconstruction processing on the denoised component set to obtain a time domain difference frequency signal after denoising.
  • the noise location acquisition unit includes:
  • a component frequency obtaining subunit configured to perform Hilbert transform processing on the set of noisy components to obtain the instantaneous frequency value corresponding to each noise component in the set of noisy components at each moment;
  • the noise position obtaining subunit is used for obtaining the noise position whose instantaneous frequency value belongs to the filtering frequency range in each of the noise-containing components.
  • the component frequency acquisition subunit is specifically used to perform Hilbert transform processing on each noise-containing component in the noise-containing component set, to obtain the corresponding Hilbert spectrum of each noise-containing component;
  • the instantaneous frequency value corresponding to each noise-containing component at each moment is obtained from the Hilbert spectrum corresponding to the initial beat frequency signal.
  • the noise position acquisition subunit is specifically used to traverse the instantaneous frequency values corresponding to the noise-containing components at each moment, and obtaining the instantaneous frequency values from the Hilbert spectrum corresponding to the initial difference frequency signal belongs to filtering
  • the position coordinates of the frequency range, and the position coordinates are determined as the noise positions in each of the noise-containing components.
  • a beat frequency acquisition unit configured to perform fast Fourier transform processing on the time domain beat frequency signal to obtain a frequency domain beat frequency signal, and obtain the beat frequency value corresponding to the maximum amplitude in the frequency domain beat frequency signal .
  • An aspect of an embodiment of the present application provides a computer storage medium, where the computer storage medium stores a computer program, the computer program includes program instructions, and the program instructions, when executed by a processor, execute the above method steps.
  • An aspect of the embodiments of the present application provides a lidar, including a processor, a memory, and an input and output interface;
  • the processor is respectively connected to the memory and the input and output interface, wherein the input and output interface is used for page interaction, the memory is used to store program codes, and the processor is used to call the program codes , to perform the above method steps.
  • the initial difference frequency signal of the lidar is decomposed into components, and the noise position in each noise-containing component is extracted according to the filtering frequency range and the instantaneous frequency value of each noise-containing component, and then the amplitude of the noise position is determined.
  • the value is uniformly set to obtain the denoising component set, which realizes the filtering of the noise signal in the initial beat frequency signal, and finally obtains the denoised time domain beat frequency by combining and reconstructing the denoising component set.
  • the signal-to-noise ratio of the beat frequency signal is improved, thereby improving the success rate of effective beat frequency frequency extraction.
  • 1 is a system architecture diagram of signal noise filtering provided by an embodiment of the present application.
  • FIG. 2 is a schematic flowchart of a signal noise filtering method provided by an embodiment of the present application
  • FIG. 3 is a schematic flowchart of a signal noise filtering method provided by an embodiment of the present application.
  • FIG. 4 is a schematic flowchart of a component frequency acquisition provided by an embodiment of the present application.
  • FIG. 5 is a schematic flowchart of a noise location acquisition provided by an embodiment of the present application.
  • FIG. 6 is an example schematic diagram of a Hilbert spectrum before filtering provided by an embodiment of the present application.
  • Fig. 7 is an example schematic diagram of a filtered Hilbert spectrum provided by an embodiment of the present application.
  • FIG. 8 is a schematic structural diagram of a signal noise filtering device provided by an embodiment of the present application.
  • FIG. 9 is a schematic structural diagram of a signal noise filtering device provided by an embodiment of the present application.
  • FIG. 10 is a schematic structural diagram of a noise component acquisition unit provided by an embodiment of the present application.
  • FIG. 11 is a schematic structural diagram of a laser radar according to an embodiment of the present application.
  • a system architecture diagram of a signal noise filtering is provided for an embodiment of the present application.
  • the embodiment of the present application can be applied to the detection scenarios of lidar, such as: environmental monitoring, aerospace, communication, automatic driving navigation, positioning and other detection scenarios.
  • Periodically change transmit the signal to the detection target, receive the echo signal returned by the detection target, and obtain the initial beat frequency signal formed by the transmitted signal and the echo signal.
  • the initial beat frequency signal can be processed by a signal processor for a series of
  • the signal processing process includes analog-to-digital conversion processing, signal filtering processing, signal data extraction, signal data calculation, etc., and then manages operations such as storage and display of the signal spectrum and data generated by the signal processor through the background management device.
  • the initial beat frequency signal with the noise signal is presented in the frequency spectrum.
  • the embodiment of the present application Signal specifically proposes a signal noise filtering device, the signal noise filtering device can be set in the signal processor, or can be used as an independent device to realize the noise filtering processing of the initial beat frequency signal, the signal noise
  • the filtering device can perform collective empirical modal decomposition on the initial beat frequency signal generated by the lidar to obtain a set of noise-containing components corresponding to the initial beat-frequency signal.
  • the instantaneous frequency value corresponding to each noise-containing component in the component set obtain the noise position in each noise-containing component, and the signal noise filtering device respectively Set to zero to obtain a denoising component set, and the signal noise filtering device performs a combined reconstruction process on the denoising component set to obtain a denoised time-domain difference frequency signal.
  • the denoising component set realizes the filtering of the noise signal in the initial beat frequency signal, and finally, by combining and reconstructing the denoising component set, the time domain beat frequency signal after denoising is obtained, and the beat frequency signal is improved. Therefore, the success rate of effective beat frequency extraction is improved.
  • FIG. 2 provides a schematic flowchart of a signal noise filtering method according to an embodiment of the present application.
  • the method in this embodiment of the present application may include the following steps S101 to S104.
  • the signal noise filtering device performs collective empirical mode decomposition (Ensemble Empirical Mode Decomposition, EEMD) on the initial beat frequency signal generated by the lidar
  • the initial beat frequency signal may specifically be a beat frequency signal including a noise signal
  • the laser radar can obtain a set of noise-containing components corresponding to the initial beat-frequency signal after EEMD processing.
  • the set of noise-containing components includes multiple noise-containing components
  • the The plurality of noisy components may include a plurality of eigenmode components and a residual component.
  • the signal noise filtering device may obtain the noise position in each noise-containing component according to the filtering frequency range and the instantaneous frequency value corresponding to each noise-containing component in the noise-containing component set, and the filtering frequency
  • the range may specifically be a preset frequency range used to indicate the noise position
  • the filtering frequency range may be set according to the frequency value of the initial difference frequency signal
  • the instantaneous frequency value of each noise-containing component may specifically be the frequency range for the noise-containing component.
  • the noise component set is subjected to Hilbert transform processing, and the instantaneous frequency value corresponding to each noise-containing component is obtained.
  • the signal noise filtering device may respectively match the instantaneous frequency value corresponding to each noise-containing component with the filtering frequency range, and determine the noise position indicated by the filtering frequency range in each noise-containing component.
  • the signal noise filtering device can respectively set the noise amplitude corresponding to the noise position in each noise-containing component to zero, and by uniformly setting the value of the noise amplitude of the noise position, the initial The filtering of the noise signal in the difference frequency signal, and further, after setting, a set of denoising components can be obtained, and the set of denoising components includes a plurality of denoising components.
  • the noise-containing component and the de-noising component have a one-to-one correspondence, and by setting the noise amplitude corresponding to the noise position in the noise-containing component to zero, the de-noising component corresponding to the noise-containing component can be generated.
  • the signal noise filtering device may perform the noise amplitude zeroing process on the noise position of each noise-containing component in the noise-containing component set, and obtain the de-noising component corresponding to each noise-containing component , to form a set of denoising components.
  • the signal noise filtering device may perform a combined reconstruction process on the denoised component set, and the combined reconstruction process is specifically a process of inversely deriving the components into a signal.
  • the multiple denoised components in the set are combined and reconstructed to obtain a denoised time-domain difference frequency signal.
  • both the time domain beat frequency signal and the initial beat frequency signal can be expressed as the beat frequency signal in the time domain
  • the initial beat frequency signal is the beat frequency signal in the time domain before denoising
  • the time domain beat frequency signal is The difference frequency signal in the time domain after denoising.
  • the initial difference frequency signal of the lidar is decomposed into components, and the noise position in each noise-containing component is extracted according to the filtering frequency range and the instantaneous frequency value of each noise-containing component, and then the amplitude of the noise position is determined.
  • the value is uniformly set to obtain the denoising component set, which realizes the filtering of the noise signal in the initial beat frequency signal, and finally obtains the denoised time domain beat frequency by combining and reconstructing the denoising component set.
  • the signal-to-noise ratio of the beat frequency signal is improved, thereby improving the success rate of effective beat frequency frequency extraction.
  • FIG. 3 provides a schematic flowchart of a signal noise filtering method according to an embodiment of the present application. As shown in FIG. 3 , the method in this embodiment of the present application may include the following steps S201 to S206.
  • the signal noise filtering device performs EEMD processing on the initial beat frequency signal generated by the lidar
  • the initial beat frequency signal may specifically be a beat frequency signal including a noise signal, that is, the lidar targets the detection target and does not perform signal processing.
  • EEMD processing a set of noise-containing components corresponding to the initial beat-frequency signal can be obtained, and the set of noise-containing components includes multiple noise-containing components, and the multiple noise-containing components may include multiple eigenvalues modal components and a residual component.
  • performing EEMD processing on x(t) can obtain m eigenmode components c i (t) and one residual component r(t).
  • m represents the number of eigenmode components
  • t represents the time of the component
  • i represents the ith component
  • i is less than or equal to m.
  • the signal noise filtering device may perform Hilbert transform processing on each noise-containing component in the set of noise-containing components, so as to obtain the Hilbert spectrum corresponding to each noise-containing component, that is, each The relationship spectrum of the instantaneous frequency value, time, and instantaneous amplitude of the noise-containing components, and then the Hilbert spectra corresponding to all the noise-containing components are aggregated and processed to obtain the complete signal corresponding to the initial difference frequency signal. Albert spectrum.
  • the summarization process can be expressed as a process of fusing the Hilbert spectra corresponding to each noise-containing component.
  • the obtained (m+1) Noise component Hilbert transform is performed on (m+1) noise-containing components to obtain (m+1) Hilbert spectrum, and then (m+1) Hilbert spectrum is aggregated into
  • the time spectrum is the Hilbert spectrum corresponding to the initial beat frequency signal.
  • a noise-containing component in the Hilbert spectrum corresponding to the initial beat frequency signal may include multiple signals. There are several component points, and the position of each component point represents the instantaneous frequency value and instantaneous amplitude value of the noisy component at the current moment.
  • the signal noise filtering device may further obtain the instantaneous frequency value corresponding to each noise component at each moment in the Hilbert spectrum corresponding to the initial beat frequency signal.
  • the signal noise filtering device may use the filtering frequency range to determine the noise position in the Hilbert spectrum corresponding to the initial beat frequency signal, that is, in the Hilbert spectrum corresponding to the initial beat frequency signal
  • the position coordinates of the instantaneous frequency values belonging to the filtering frequency range are marked, and the marked position coordinates correspond to the noise position.
  • the signal noise filtering device may traverse the instantaneous frequency value corresponding to each noise-containing component at each moment in the Hilbert spectrum corresponding to the initial beat frequency signal, and the filtering frequency range may specifically be preset. is used to indicate the frequency range of the noise signal, and the filtering frequency range can be specifically set according to the frequency value of the initial difference frequency signal.
  • the two can be matched to determine the target instantaneous frequency value belonging to the filtering frequency range, and record the corresponding instantaneous frequency value of the target. and obtain the target noise-containing component to which the target instantaneous frequency value belongs, and then determine the position coordinate as the noise position of the target noise-containing component in the Hilbert spectrum corresponding to the initial beat frequency signal.
  • the signal noise filtering device can respectively set the noise amplitude corresponding to the noise position in each noise-containing component to zero, and by uniformly setting the value of the noise amplitude of the noise position, the initial The filtering of the noise signal in the difference frequency signal, and further, after setting, a set of denoising components can be obtained, and the set of denoising components includes a plurality of denoising components.
  • the noise-containing component and the de-noising component have a one-to-one correspondence, and by setting the noise amplitude corresponding to the noise position in the noise-containing component to zero, the de-noising component corresponding to the noise-containing component can be generated.
  • the signal noise filtering device may perform the noise amplitude zeroing process on the noise position of each noise-containing component in the noise-containing component set, and obtain the de-noising component corresponding to each noise-containing component , to form a set of denoising components.
  • the signal noise filtering device may perform a combined reconstruction process on the denoised component set, and the combined reconstruction process is specifically a process of inversely deriving the components into a signal.
  • the multiple denoised components in the set are combined and reconstructed to obtain a denoised time-domain difference frequency signal.
  • both the time domain beat frequency signal and the initial beat frequency signal can be expressed as the beat frequency signal in the time domain
  • the initial beat frequency signal is the beat frequency signal in the time domain before denoising
  • the time domain beat frequency signal is The difference frequency signal in the time domain after denoising.
  • the signal noise filtering device may perform fast Fourier transform processing on the time-domain difference-frequency signal to obtain a frequency-domain difference-frequency signal, and obtain the difference corresponding to the maximum amplitude in the frequency-domain difference-frequency signal.
  • frequency value the frequency domain beat frequency signal can be specifically expressed as a beat frequency signal in the frequency domain after denoising, and the signal noise filtering device can obtain the maximum amplitude in the spectrogram formed by the frequency domain beat frequency signal
  • the frequency value corresponding to the position is determined as the beat frequency value of the effective beat frequency signal, and the effective beat frequency signal is specifically expressed as the real and effective signal returned by the transmitted signal through the detection target.
  • the initial difference frequency signal of the lidar is decomposed into components, and the noise position in each noise-containing component is extracted according to the filtering frequency range and the instantaneous frequency value of each noise-containing component, and then the amplitude of the noise position is determined.
  • the value is uniformly set to obtain the denoising component set, which realizes the filtering of the noise signal in the initial beat frequency signal, and finally obtains the denoised time domain beat frequency by combining and reconstructing the denoising component set.
  • the signal-to-noise ratio of the beat frequency signal is improved, and by combining the Hilbert transform and the fast Fourier transform, the effective beat frequency signal extraction is ensured, and the success rate of the beat frequency frequency extraction is improved.
  • the component frequency acquisition process is the execution process of step S202 in the embodiment shown in FIG. 2 , and specifically includes:
  • the signal noise filtering device may perform Hilbert transform processing on each noise-containing component in the set of noise-containing components, so as to obtain the Hilbert spectrum corresponding to each noise-containing component, that is, each The relationship spectrum of the instantaneous frequency value, time, and instantaneous amplitude of the noise-containing components; and then the Hilbert spectra corresponding to all the noise-containing components are aggregated and processed to obtain the complete signal corresponding to the initial difference frequency signal. Albert spectrum.
  • the Hilbert spectrum corresponding to the initial beat frequency signal includes the time, the instantaneous frequency value, the instantaneous amplitude value of each noise-containing component, and the corresponding relationship of the three;
  • the (m+1) noise-containing components are obtained, and the (m+1) noise-containing components are obtained.
  • a noise-containing component may include multiple component points, and each component point of the noise-containing component is in the Hilbert spectrum corresponding to the initial beat frequency signal.
  • the position of represents the instantaneous frequency value and instantaneous amplitude value of the noisy component at the current moment.
  • the signal noise filtering device may further obtain the instantaneous frequency value corresponding to each noise component at each moment in the Hilbert spectrum corresponding to the initial beat frequency signal.
  • the Hilbert transform processing method to convert the noise-containing component in the time domain and the frequency domain, the corresponding relationship between the instantaneous frequency, the instantaneous amplitude and the time of the noise-containing component can be quickly and accurately located. , which ensures the accuracy of subsequent noise location acquisition and noise filtering of noisy components.
  • the noise location acquisition process is the execution process of step S203 in the embodiment shown in FIG. 2 , and specifically includes:
  • the signal noise filtering device may use the filtering frequency range to determine the noise position in the Hilbert spectrum corresponding to the initial beat frequency signal, that is, in the Hilbert spectrum corresponding to the initial beat frequency signal
  • the position coordinates of the instantaneous frequency values belonging to the filtering frequency range are marked, and the marked position coordinates correspond to the noise position.
  • the signal noise filtering device may traverse the instantaneous frequency value corresponding to each noise-containing component at each moment in the Hilbert spectrum corresponding to the initial beat frequency signal, and the filtering frequency range may specifically be preset. is used to indicate the frequency range of the noise signal, and the filtering frequency range can be set according to the frequency value of the initial beat frequency signal. For example, if the frequency value of the initial beat frequency signal is 266.7MHz, then 0 ⁇ f ⁇ 100MHz And the two frequency bands f>400MHz are the filtering frequency range in which the noise to be removed is located.
  • the two can be matched to determine the target instantaneous frequency value belonging to the filtering frequency range, and record the corresponding instantaneous frequency value of the target. and obtain the target noise-containing component to which the target instantaneous frequency value belongs, and then determine the position coordinate as the noise position of the target noise-containing component in the Hilbert spectrum corresponding to the initial beat frequency signal.
  • the instantaneous frequency value of each noise-containing component at time t is currently obtained from the Hilbert spectrum corresponding to the initial beat frequency signal, where t is any moment in the signal duration of the initial beat frequency signal, All the instantaneous frequency values at time t are matched with the filtering frequency range respectively, and when there is a target instantaneous frequency value belonging to the filtering frequency range, obtain the Hill corresponding to the initial difference frequency signal of the target instantaneous frequency value.
  • the coordinate position in the Bert spectrum and determine the target noise-containing component to which the target instantaneous frequency value belongs, and determine the obtained coordinate position as the noise position of the target noise-containing component at time t, when each noise-containing component is at t
  • the noise position of the moment can be transferred to the next moment (for example: time t+1, the specific time interval can be set according to the actual situation), and the noise position of each noise-containing component at the next moment can be obtained, and so on.
  • the signal noise filtering device may determine multiple noise positions of each noise-containing component in the above-mentioned manner.
  • the position coordinates of the noise signal can be located in the Hilbert spectrum of the signal, and then the noise position in each noise-containing component can be traced back to the instantaneous frequency of the noise-containing component.
  • FIG. 6 shows the Hilbert spectrogram before noise filtering.
  • each noise-containing component is The distribution in the Hilbert spectrum of the initial beat frequency signal is relatively scattered, and the frequency value of each noise component fluctuates irregularly.
  • Figure 7 shows the Hilbert spectrum after noise filtering. , as shown in Figure 7, the noise position is separated by filtering the frequency range, and after setting the noise amplitude of the noise position in each noise-containing component to zero, it is ensured that the frequency in the spectrogram floats around the frequency of the initial beat frequency signal.
  • the signal noise filtering apparatus provided by the embodiments of the present application will be described in detail below with reference to FIG. 8 to FIG. 10 .
  • the signal noise filtering device in FIG. 8-FIG. 10 is used to execute the method of the embodiment shown in FIG. 2-FIG. 7 of the present application.
  • FIGS. 2 to 7 of the present application For convenience of description, only the same embodiment as the embodiment of the present application is shown.
  • FIGS. 2 to 7 of the present application please refer to the embodiments shown in FIGS. 2 to 7 of the present application.
  • FIG. 8 is a schematic structural diagram of an apparatus for filtering signal noise according to an embodiment of the present application.
  • the signal noise filtering apparatus 1 in the embodiment of the present application may include: a noise-containing component obtaining unit 11 , a noise position obtaining unit 12 , a denoising component obtaining unit 13 , and a signal reconstruction unit 14 .
  • the noise-containing component obtaining unit 11 is configured to perform collective empirical mode decomposition on the initial beat frequency signal generated by the lidar to obtain a set of noise-containing components corresponding to the initial beat frequency signal;
  • the noise position obtaining unit 12 is configured to obtain the noise position in each noise-containing component according to the filtering frequency range and the instantaneous frequency value corresponding to each noise-containing component in the noise-containing component set;
  • a denoising component obtaining unit 13 configured to respectively set the noise amplitude corresponding to the noise position in each of the noise-containing components to zero to obtain a denoising component set;
  • the signal reconstruction unit 14 is configured to perform combined reconstruction processing on the denoised component set to obtain a time domain difference frequency signal after denoising.
  • the initial difference frequency signal of the lidar is decomposed into components, and the noise position in each noise-containing component is extracted according to the filtering frequency range and the instantaneous frequency value of each noise-containing component, and then the amplitude of the noise position is determined.
  • the value is uniformly set to obtain the denoising component set, which realizes the filtering of the noise signal in the initial beat frequency signal, and finally obtains the denoised time domain beat frequency by combining and reconstructing the denoising component set.
  • the signal-to-noise ratio of the beat frequency signal is improved, thereby improving the success rate of effective beat frequency frequency extraction.
  • FIG. 9 is a schematic structural diagram of an apparatus for filtering signal noise according to an embodiment of the present application.
  • the signal noise filtering apparatus 1 in the embodiment of the present application may include: a noise-containing component obtaining unit 11 , a noise position obtaining unit 12 , a denoising component obtaining unit 13 , a signal reconstruction unit 14 , and a difference frequency Frequency acquisition unit 15 .
  • the noise-containing component obtaining unit 11 is configured to perform collective empirical mode decomposition on the initial beat frequency signal generated by the lidar to obtain a set of noise-containing components corresponding to the initial beat frequency signal;
  • the noise position obtaining unit 12 is configured to obtain the noise position in each noise-containing component according to the filtering frequency range and the instantaneous frequency value corresponding to each noise-containing component in the noise-containing component set;
  • the noise frequency band acquisition unit 12 may include:
  • the component frequency obtaining subunit 121 is used to perform Hilbert transform processing on the set of noisy components to obtain the instantaneous frequency value corresponding to each noise component in the set of noisy components at each moment;
  • the component frequency obtaining subunit 121 is specifically configured to perform Hilbert transform processing on each noise-containing component in the noise-containing component set, to obtain a Hilbert spectrum corresponding to each noise-containing component;
  • the instantaneous frequency value corresponding to each noise-containing component at each moment is obtained from the Hilbert spectrum corresponding to the initial beat frequency signal.
  • a noise position obtaining subunit 122 configured to obtain the noise position whose instantaneous frequency value belongs to the filtering frequency range in each of the noise-containing components
  • the noise position acquisition subunit 122 is specifically configured to traverse the instantaneous frequency values corresponding to the noise-containing components at each moment, and acquire the instantaneous frequency from the Hilbert spectrum corresponding to the initial beat frequency signal
  • the value belongs to the position coordinates of the filter frequency range, and the position coordinates are determined as the noise positions in the respective noise-containing components.
  • a denoising component obtaining unit 13 configured to respectively set the noise amplitude corresponding to the noise position in each of the noise-containing components to zero to obtain a denoising component set;
  • a signal reconstruction unit 14 configured to perform a combined reconstruction process on the denoised component set to obtain a denoised time-domain difference frequency signal
  • the beat frequency acquisition unit 15 is configured to perform fast Fourier transform processing on the time domain beat frequency signal to obtain a frequency domain beat frequency signal, and obtain the beat frequency frequency corresponding to the maximum amplitude in the frequency domain beat frequency signal value.
  • the initial difference frequency signal of the lidar is decomposed into components, and the noise position in each noise-containing component is extracted according to the filtering frequency range and the instantaneous frequency value of each noise-containing component, and then the amplitude of the noise position is determined.
  • the value is uniformly set to obtain the denoising component set, which realizes the filtering of the noise signal in the initial beat frequency signal, and finally obtains the denoised time domain beat frequency by combining and reconstructing the denoising component set.
  • the signal-to-noise ratio of the beat frequency signal is improved, and by combining the Hilbert transform and the fast Fourier transform, the effective beat frequency signal extraction is ensured, and the success rate of the beat frequency frequency extraction is improved.
  • An embodiment of the present application further provides a computer storage medium, where the computer storage medium can store a plurality of program instructions, and the program instructions are suitable for being loaded by a processor and executing the above-mentioned embodiments shown in FIG. 2 to FIG. 7 .
  • the computer storage medium can store a plurality of program instructions, and the program instructions are suitable for being loaded by a processor and executing the above-mentioned embodiments shown in FIG. 2 to FIG. 7 .
  • the computer storage medium can store a plurality of program instructions, and the program instructions are suitable for being loaded by a processor and executing the above-mentioned embodiments shown in FIG. 2 to FIG. 7 .
  • the lidar 1000 may include: at least one processor 1001 , such as a CPU, at least one network interface 1004 , input and output interface 1003 , memory 1005 , and at least one communication bus 1002 .
  • the communication bus 1002 is used to realize the connection and communication between these components.
  • the network interface 1004 may optionally include a standard wired interface and a wireless interface (eg, a WI-FI interface).
  • the memory 1005 may be high-speed RAM memory or non-volatile memory, such as at least one disk memory.
  • the memory 1005 may also be at least one storage device located away from the aforementioned processor 1001 .
  • the memory 1005 as a computer storage medium may include an operating system, a network communication module, an input/output interface module, and a noise filtering application program.
  • the input and output interface 1003 is mainly used to provide an input interface for the user and the access device, and obtain data input by the user and the access device.
  • the processor 1001 may be configured to invoke the noise filtering application program stored in the memory 1005, and specifically perform the following operations:
  • a combined reconstruction process is performed on the denoised component set to obtain a denoised time-domain difference frequency signal.
  • the processor 1001 executes to obtain the noise position in each of the noise-containing components according to the filtering frequency range and the instantaneous frequency value corresponding to each of the noise-containing components in the set of noise-containing components, the processor 1001 specifically executes the following: operate:
  • the noise positions whose instantaneous frequency values belong to the filtering frequency range are obtained in each of the noise-containing components.
  • the processor 1001 performs Hilbert transform processing on the set of noise-containing components to obtain the instantaneous frequency value corresponding to each noise-containing component in the set of noise-containing components at each moment, specifically: Do the following:
  • the instantaneous frequency value corresponding to each noise-containing component at each moment is obtained from the Hilbert spectrum corresponding to the initial beat frequency signal.
  • the processor 1001 executes the acquisition of the noise position whose instantaneous frequency value belongs to the filtering frequency range from the noise-containing components, the processor 1001 specifically performs the following operations:
  • processor 1001 further performs the following operations:
  • Fast Fourier transform is performed on the time-domain beat-frequency signal to obtain a frequency-domain beat-frequency signal, and a beat-frequency frequency value corresponding to the maximum amplitude is obtained from the frequency-domain beat-frequency signal.
  • the initial difference frequency signal of the lidar is decomposed into components, and the noise position in each noise-containing component is extracted according to the filtering frequency range and the instantaneous frequency value of each noise-containing component, and then the amplitude of the noise position is determined.
  • the value is uniformly set to obtain the denoising component set, which realizes the filtering of the noise signal in the initial beat frequency signal, and finally obtains the denoised time domain beat frequency by combining and reconstructing the denoising component set.
  • the signal-to-noise ratio of the beat frequency signal is improved, and by combining the Hilbert transform and the fast Fourier transform, the effective beat frequency signal extraction is ensured, and the success rate of the beat frequency frequency extraction is improved.
  • the storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), or a random access memory (Random Access Memory, RAM) or the like.

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Abstract

一种信号噪声滤除方法、装置、存储介质及激光雷达，其中方法包括：对激光雷达产生的初始差频信号进行集合经验模态分解，得到初始差频信号对应的含噪分量集合（S101）；根据过滤频率范围和含噪分量集合中各含噪分量对应的瞬时频率值，分别获取各含噪分量中的噪声位置（S102）；分别将各含噪分量中的噪声位置对应的噪声幅值设置为零，得到去噪分量集合（S103）；对去噪分量集合进行组合重构处理，得到去噪后的时域差频信号（S104）。该方法可以提高差频信号的信噪比，提高有效的差频信号提取的成功率。

Description

一种信号噪声滤除方法、装置、存储介质及激光雷达 技术领域
本申请涉及计算机技术领域，尤其涉及一种信号噪声滤除方法、装置、存储介质及激光雷达。
背景技术
调频连续波激光雷达(Frequency Modulated Continuous Wave，FMCW)属于一种基于相干探测的连续波激光雷达，在扫频周期内发射频率线性变化的连续波作为发射信号，发射信号的一部分作为本振信号，其余部分向外出射进行探测，被物体反射后返回的回波信号与本振信号形成差频信号。由于信号在实际探测过程中容易受到激光雷达系统、环境等固有噪声的影响，导致信噪比较低，无法较好的提取有效的差频信号。
发明内容
本申请实施例提供一种信号噪声滤除方法、装置、存储介质及激光雷达，可以提高差频信号的信噪比，提高有效的差频信号提取的成功率。
本申请实施例一方面提供了一种信号噪声滤除方法，包括：
对激光雷达产生的初始差频信号进行集合经验模态分解，得到所述初始差频信号对应的含噪分量集合；
根据过滤频率范围和所述含噪分量集合中各含噪分量对应的瞬时频率值，分别获取所述各含噪分量中的噪声位置；
分别将所述各含噪分量中的噪声位置对应的噪声幅值设置为零，得到去噪分量集合；
对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号。
其中，所述根据过滤频率范围和所述含噪分量集合中各含噪分量对应的瞬时频率值，分别获取所述各含噪分量中的噪声位置，包括：
对所述含噪分量集合进行希尔伯特变换处理，得到所述含噪分量集合中各含噪分量在每个时刻对应的瞬时频率值；
在所述各含噪分量中获取瞬时频率值属于过滤频率范围的噪声位置。
其中，所述对所述含噪分量集合进行希尔伯特变换处理，得到所述含噪分量集合中各 含噪分量在每个时刻对应的瞬时频率值，包括：
对所述含噪分量集合中各含噪分量进行希尔伯特变换处理，得到所述各含噪分量对应的希尔伯特谱；
将所述各含噪分量对应的希尔伯特谱进行汇总处理，得到所述初始差频信号对应的希尔伯特谱；
在所述初始差频信号对应的希尔伯特谱中获取所述各含噪分量在每个时刻对应的瞬时频率值。
其中，所述在所述各含噪分量中获取瞬时频率值属于过滤频率范围的噪声位置，包括：
遍历所述各含噪分量在每个时刻对应的瞬时频率值，在所述初始差频信号对应的希尔伯特谱中获取瞬时频率值属于过滤频率范围的位置坐标，将所述位置坐标确定为所述各含噪分量中的噪声位置。
其中，还包括：
对所述时域差频信号进行快速傅里叶变换处理，得到频域差频信号，在所述频域差频信号中获取最大幅值对应的差频频率值。
本申请实施例一方面提供了一种信号噪声滤除装置，包括：
含噪分量获取单元，用于对激光雷达产生的初始差频信号进行集合经验模态分解，得到所述初始差频信号对应的含噪分量集合；
噪声位置获取单元，用于根据过滤频率范围和所述含噪分量集合中各含噪分量对应的瞬时频率值，分别获取所述各含噪分量中的噪声位置；
去噪分量获取单元，用于分别将所述各含噪分量中的噪声位置对应的噪声幅值设置为零，得到去噪分量集合；
信号重构单元，用于对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号。
其中，所述噪声位置获取单元包括：
分量频率获取子单元，用于对所述含噪分量集合进行希尔伯特变换处理，得到所述含噪分量集合中各含噪分量在每个时刻对应的瞬时频率值；
噪声位置获取子单元，用于在所述各含噪分量中获取瞬时频率值属于过滤频率范围的噪声位置。
其中，所述分量频率获取子单元具体用于对所述含噪分量集合中各含噪分量进行希尔 伯特变换处理，得到所述各含噪分量对应的希尔伯特谱；
将所述各含噪分量对应的希尔伯特谱进行汇总处理，得到所述初始差频信号对应的希尔伯特谱；
在所述初始差频信号对应的希尔伯特谱中获取所述各含噪分量在每个时刻对应的瞬时频率值。
其中，所述噪声位置获取子单元具体用于遍历所述各含噪分量在每个时刻对应的瞬时频率值，在所述初始差频信号对应的希尔伯特谱中获取瞬时频率值属于过滤频率范围的位置坐标，将所述位置坐标确定为所述各含噪分量中的噪声位置。
其中，还包括：
差频频率获取单元，用于对所述时域差频信号进行快速傅里叶变换处理，得到频域差频信号，在所述频域差频信号中获取最大幅值对应的差频频率值。
本申请实施例一方面提供了一种计算机存储介质，所述计算机存储介质存储有计算机程序，所述计算机程序包括程序指令，所述程序指令当被处理器执行时，执行上述的方法步骤。
本申请实施例一方面提供了一种激光雷达，包括处理器、存储器、输入输出接口；
其中，所述处理器分别与所述存储器和所述输入输出接口相连，其中，所述输入输出接口用于页面交互，所述存储器用于存储程序代码，所述处理器用于调用所述程序代码，以执行上述的方法步骤。
在本申请实施例中，通过对激光雷达的初始差频信号进行分量分解，并依据过滤频率范围以及各含噪分量的瞬时频率值提取各含噪分量中的噪声位置，进而对噪声位置的幅值进行统一设置，以得到去噪分量集合，实现了对初始差频信号中的噪声信号进行过滤，最终通过对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号，提高了差频信号的信噪比，进而提高了有效的差频频率提取的成功率。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根 据这些附图获得其他的附图。
图1是本申请实施例提供的信号噪声滤除的系统架构图；
图2是本申请实施例提供的一种信号噪声滤除方法的流程示意图；
图3是本申请实施例提供的一种信号噪声滤除方法的流程示意图；
图4是本申请实施例提供的一种分量频率获取的流程示意图；
图5是本申请实施例提供的一种噪声位置获取的流程示意图；
图6是本申请实施例提供的一种过滤前的希尔伯特谱的举例示意图；
图7是本申请实施例提供的一种过滤后的希尔伯特谱的举例示意图；
图8是本申请实施例提供的一种信号噪声滤除装置的结构示意图；
图9是本申请实施例提供的一种信号噪声滤除装置的结构示意图；
图10是本申请实施例提供的噪声分量获取单元的结构示意图；
图11是本申请实施例提供的一种激光雷达的结构示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
请结合图1-图7所示实施例，对本申请实施例提供的信号噪声滤除方法进行详细介绍。
请参见图1，为本申请实施例提供了一种信号噪声滤除的系统架构图。如图1所示，本申请实施例可以应用于激光雷达探测的场景，例如：环境监测、航天、通信、自动驾驶导航、定位等探测场景，激光雷达的发射信号按连续波，例如三角波，规律周期性变化，对探测目标进行信号发射，并接收由探测目标返回的回波信号，并获取发射信号与回波信号形成的初始差频信号，初始差频信号可以通过信号处理器进行一系列的信号处理过程，包括模数转换处理、信号滤波处理、信号数据提取、信号数据计算等，进而通过后台管理设备对信号处理器生成的信号谱、数据等进行存储、展示等管理操作。
由于发射信号和回波信号容易受到激光雷达系统、环境等固有噪声的影响，因而在频谱中呈现的是带有噪声信号的初始差频信号，本申请实施例为了去除初始差频信号中的噪声信号，具体提出了一种信号噪声滤除装置，所述信号噪声滤除装置可以设置于所述信号 处理器中，也可以作为独立设备，实现对初始差频信号的噪声滤除处理，信号噪声滤除装置可以对激光雷达产生的初始差频信号进行集合经验模态分解，得到所述初始差频信号对应的含噪分量集合，所述信号噪声滤除装置根据过滤频率范围和所述含噪分量集合中各含噪分量对应的瞬时频率值，分别获取所述各含噪分量中的噪声位置，所述信号噪声滤除装置分别将所述各含噪分量中的噪声位置对应的噪声幅值设置为零，得到去噪分量集合，所述信号噪声滤除装置对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号。通过对激光雷达的初始差频信号进行分量分解，并依据过滤频率范围以及各含噪分量的瞬时频率值提取各含噪分量中的噪声位置，进而对噪声位置的幅值进行统一设置，以得到去噪分量集合，实现了对初始差频信号中的噪声信号进行过滤，最终通过对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号，提高了差频信号的信噪比，进而提高了有效的差频频率提取的成功率。
基于图1的系统架构，请一并参见图2，为本申请实施例提供了一种信号噪声滤除方法的流程示意图。如图2所示，本申请实施例的所述方法可以包括以下步骤S101-步骤S104。
S101，对激光雷达产生的初始差频信号进行集合经验模态分解，得到所述初始差频信号对应的含噪分量集合；
具体的，信号噪声滤除装置对激光雷达产生的初始差频信号进行集合经验模态分解(Ensemble Empirical Mode Decomposition，EEMD)，所述初始差频信号具体可以为包含有噪声信号的差频信号，即激光雷达针对探测目标且未进行信号处理的差频信号，通过EEMD处理后可以得到所述初始差频信号对应的含噪分量集合，所述含噪分量集合包括多个含噪分量，所述多个含噪分量可以包括多个本征模态分量和一个残余分量。
S102，根据过滤频率范围和所述含噪分量集合中各含噪分量对应的瞬时频率值，分别获取所述各含噪分量中的噪声位置；
具体的，所述信号噪声滤除装置可以根据过滤频率范围和所述含噪分量集合中各含噪分量对应的瞬时频率值，分别获取所述各含噪分量中的噪声位置，所述过滤频率范围具体可以为预先设置的用于指示噪声位置的频率范围，所述过滤频率范围可以依据初始差频信号的频率值进行设置，所述各含噪分量的瞬时频率值具体可以为对所述含噪分量集合进行希尔伯特变换处理，得到的各含噪分量对应的瞬时频率值，可以理解的是，所述瞬时频率值为所述各含噪分量在每个时刻上对应的频率值，所述信号噪声滤除装置可以分别将各含 噪分量对应的瞬时频率值与所述过滤频率范围进行匹配，在所述各含噪分量中确定所述过滤频率范围指示的噪声位置。
S103，分别将所述各含噪分量中的噪声位置对应的噪声幅值设置为零，得到去噪分量集合；
具体的，所述信号噪声滤除装置可以分别将所述各含噪分量中的噪声位置对应的噪声幅值设置为零，通过对噪声位置的噪声幅值进行数值的统一设置，可以实现对初始差频信号中的噪声信号的过滤，进而在设置后可以得到去噪分量集合，所述去噪分量集合包括多个去噪分量。可以理解的是，所述含噪分量和所述去噪分量为一一对应关系，通过将含噪分量中噪声位置对应的噪声幅值设置为零，可以生成该含噪分量对应的去噪分量，以此类推，所述信号噪声滤除装置可以对所述含噪分量集合中的每个含噪分量的噪声位置均进行噪声幅值置零处理，得到每个含噪分量对应的去噪分量，以形成去噪分量集合。
S104，对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号；
具体的，所述信号噪声滤除装置可以对所述去噪分量集合进行组合重构处理，所述组合重构处理具体为对分量进行反向推导为信号的过程，通过对所述去噪分量集合中的多个去噪分量进行组合重构处理可以得到去噪后的时域差频信号。
可以理解的是，上述由初始差频信号进行分量分解的过程为由信号转换为分量的正向推导过程，因此由分量转换为信号的过程称为反向推导过程，即分量的组合重构过程，所述时域差频信号和所述初始差频信号均可以表示为时域上的差频信号，初始差频信号为去噪前的时域上的差频信号，时域差频信号为去噪后的时域上的差频信号。
在本申请实施例中，通过对激光雷达的初始差频信号进行分量分解，并依据过滤频率范围以及各含噪分量的瞬时频率值提取各含噪分量中的噪声位置，进而对噪声位置的幅值进行统一设置，以得到去噪分量集合，实现了对初始差频信号中的噪声信号进行过滤，最终通过对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号，提高了差频信号的信噪比，进而提高了有效的差频频率提取的成功率。
基于图1的系统架构，请一并参见图3，为本申请实施例提供了一种信号噪声滤除方法的流程示意图。如图3所示，本申请实施例的所述方法可以包括以下步骤S201-步骤S206。
S201，对激光雷达产生的初始差频信号进行集合经验模态分解，得到所述初始差频信号对应的含噪分量集合；
具体的，信号噪声滤除装置对激光雷达产生的初始差频信号进行EEMD处理，所述初始差频信号具体可以为包含有噪声信号的差频信号，即激光雷达针对探测目标且未进行信号处理的差频信号，通过EEMD处理后可以得到所述初始差频信号对应的含噪分量集合，所述含噪分量集合包括多个含噪分量，所述多个含噪分量可以包括多个本征模态分量和一个残余分量。
可选的，假设初始差频信号为x(t)，则对x(t)进行EEMD处理可以得到m个本征模态分量c i(t)和一个残余分量r(t)。
Figure PCTCN2020117177-appb-000001
其中，m表示本征模态分量的个数，t表示该分量的时间，i表示第i个分量，i小于等于m。
S202，对所述含噪分量集合进行希尔伯特变换处理，得到所述含噪分量集合中各含噪分量在每个时刻对应的瞬时频率值；
具体的，所述信号噪声滤除装置可以对所述含噪分量集合中的各含噪分量都分别进行希尔伯特变换处理，以得到各含噪分量对应的希尔伯特谱，即各含噪分量的瞬时频率值、时间、瞬时幅值三者的关系谱，进而再将所有含噪分量各自对应的希尔伯特谱进行汇总处理，得到完整的所述初始差频信号对应的希尔伯特谱。
所述汇总处理可以表示为将各含噪分量对应的希尔伯特谱进行融合的过程，依据上述举例，在对初始差频信号进行集合经验模态分解，得到的(m+1)个含噪分量，对(m+1)个含噪分量分别进行希尔伯特变换处理，得到(m+1)个希尔伯特谱，再将(m+1)个希尔伯特谱汇总到同一个时频谱中，该时频谱即为所述初始差频信号对应的希尔伯特谱，可以理解的是，一个含噪分量在初始差频信号对应的希尔伯特谱中可以包括多个分量点，每个分量点的位置表示的是该含噪分量在当前时刻的瞬时频率值和瞬时幅值。
所述信号噪声滤除装置可以进一步在所述初始差频信号对应的希尔伯特谱中获取所述各含噪分量在每个时刻对应的瞬时频率值。
S203，在所述各含噪分量中获取瞬时频率值属于过滤频率范围的噪声位置；
具体的，所述信号噪声滤除装置可以采用过滤频率范围在所述初始差频信号对应的希尔伯特谱中确定噪声位置，即在所述初始差频信号对应的希尔伯特谱中将属于所述过滤频率范围的瞬时频率值的位置坐标进行标记，被标记的位置坐标对应的即为噪声位置。
可选的，所述信号噪声滤除装置可以在初始差频信号对应的希尔伯特谱中遍历各含噪分量在每个时刻对应的瞬时频率值，所述过滤频率范围具体可以为预先设置的用于指示噪声信号的频率段，所述过滤频率范围具体可以依据初始差频信号的频率值进行设置。通过获取的各含噪分量在每个时刻对应的瞬时频率值以及预先设置的过滤频率范围，可以对两者进行匹配，确定出属于过滤频率范围的目标瞬时频率值，记录该目标瞬时频率值对应的位置坐标，并获取该目标瞬时频率值所属的目标含噪分量，进而可以将该位置坐标确定为该目标含噪分量在初始差频信号对应的希尔伯特谱中的噪声位置。
S204，分别将所述各含噪分量中的噪声位置对应的噪声幅值设置为零，得到去噪分量集合；
具体的，所述信号噪声滤除装置可以分别将所述各含噪分量中的噪声位置对应的噪声幅值设置为零，通过对噪声位置的噪声幅值进行数值的统一设置，可以实现对初始差频信号中的噪声信号的过滤，进而在设置后可以得到去噪分量集合，所述去噪分量集合包括多个去噪分量。可以理解的是，所述含噪分量和所述去噪分量为一一对应关系，通过将含噪分量中噪声位置对应的噪声幅值设置为零，可以生成该含噪分量对应的去噪分量，以此类推，所述信号噪声滤除装置可以对所述含噪分量集合中的每个含噪分量的噪声位置均进行噪声幅值置零处理，得到每个含噪分量对应的去噪分量，以形成去噪分量集合。
S205，对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号；
具体的，所述信号噪声滤除装置可以对所述去噪分量集合进行组合重构处理，所述组合重构处理具体为对分量进行反向推导为信号的过程，通过对所述去噪分量集合中的多个去噪分量进行组合重构处理可以得到去噪后的时域差频信号。
可以理解的是，上述由初始差频信号进行分量分解的过程为由信号转换为分量的正向推导过程，因此由分量转换为信号的过程称为反向推导过程，即分量的组合重构过程，所述时域差频信号和所述初始差频信号均可以表示为时域上的差频信号，初始差频信号为去噪前的时域上的差频信号，时域差频信号为去噪后的时域上的差频信号。
S206，对所述时域差频信号进行快速傅里叶变换处理，得到频域差频信号，在所述频域差频信号中获取最大幅值对应的差频频率值；
具体的，所述信号噪声滤除装置可以对所述时域差频信号进行快速傅里叶变换处理，得到频域差频信号，在所述频域差频信号中获取最大幅值对应的差频频率值，所述频域差频信号具体可以表示为去噪后的频域上的差频信号，所述信号噪声滤除装置可以在频域差 频信号所形成的频谱图中获取最大幅值的位置，并将该位置对应的频率值确定为有效的差频信号的差频频率值，有效的差频信号具体表示为发射信号经探测目标返回的真实有效的信号。
在本申请实施例中，通过对激光雷达的初始差频信号进行分量分解，并依据过滤频率范围以及各含噪分量的瞬时频率值提取各含噪分量中的噪声位置，进而对噪声位置的幅值进行统一设置，以得到去噪分量集合，实现了对初始差频信号中的噪声信号进行过滤，最终通过对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号，提高了差频信号的信噪比，并通过结合希尔伯特变换以及快速傅里叶变换的方式，保证了有效的差频信号的提取，提升了差频频率提取的成功率。
请参见图4，为本申请实施例提供了分量频率获取的流程示意图。如图4所示，所述分量频率获取过程为图2所示实施例中的步骤S202的执行过程，具体包括：
S301，对所述含噪分量集合中各含噪分量进行希尔伯特变换处理，得到所述各含噪分量对应的希尔伯特谱；
S302，将所述各含噪分量对应的希尔伯特谱进行汇总处理，得到所述初始差频信号对应的希尔伯特谱；
S303，在所述初始差频信号对应的希尔伯特谱中获取所述各含噪分量对应的瞬时频率值；
具体的，所述信号噪声滤除装置可以对所述含噪分量集合中的各含噪分量都分别进行希尔伯特变换处理，以得到各含噪分量对应的希尔伯特谱，即各含噪分量的瞬时频率值、时间、瞬时幅值三者的关系谱；进而再将所有含噪分量各自对应的希尔伯特谱进行汇总处理，得到完整的所述初始差频信号对应的希尔伯特谱。
所述初始差频信号对应的希尔伯特谱中包含了各含噪分量的时间、瞬时频率值、瞬时幅值以及三者的对应关系；所述汇总处理可以表示为将各含噪分量对应的希尔伯特谱进行融合的过程，依据上述举例，在对初始差频信号进行集合经验模态分解，得到的(m+1)个含噪分量，对(m+1)个含噪分量分别进行希尔伯特变换处理，得到(m+1)个希尔伯特谱，再将(m+1)个希尔伯特谱汇总到同一个时频谱中，该时频谱即为所述初始差频信号对应的希尔伯特谱，可以理解的是，一个含噪分量可以包括多个分量点，该含噪分量的每个分量点在初始差频信号对应的希尔伯特谱中的位置表示的是该含噪分量在当前时刻的瞬时频率 值和瞬时幅值。
所述信号噪声滤除装置可以进一步在所述初始差频信号对应的希尔伯特谱中获取所述各含噪分量在每个时刻对应的瞬时频率值。
在本申请实施例中，通过采用希尔伯特变换的处理方式对含噪分量进行时域和频域的转换，可以快速准确的定位含噪分量的瞬时频率、瞬时幅值以及时间的对应关系，保证了后续进行含噪分量的噪声位置获取以及噪声过滤的准确性。
请参见图5，为本申请实施例提供了噪声位置获取的流程示意图。如图5所示，所述噪声位置获取过程为图2所示实施例中的步骤S203的执行过程，具体包括：
S401，遍历所述各含噪分量在每个时刻对应的瞬时频率值，在所述初始差频信号对应的希尔伯特谱中获取瞬时频率值属于过滤频率范围的位置坐标，将所述位置坐标确定为所述各含噪分量中的噪声位置；
具体的，所述信号噪声滤除装置可以采用过滤频率范围在所述初始差频信号对应的希尔伯特谱中确定噪声位置，即在所述初始差频信号对应的希尔伯特谱中将属于所述过滤频率范围的瞬时频率值的位置坐标进行标记，被标记的位置坐标对应的即为噪声位置。
可选的，所述信号噪声滤除装置可以在初始差频信号对应的希尔伯特谱中遍历各含噪分量在每个时刻对应的瞬时频率值，所述过滤频率范围具体可以为预先设置的用于指示噪声信号的频率段，所述过滤频率范围具体可以依据初始差频信号的频率值进行设置，例如：假设初始差频信号的频率值为266.7MHz，则可以将0<f<100MHz以及f>400MHz这两个频率段作为需要去除的噪声所处的过滤频率范围。通过获取的各含噪分量在每个时刻对应的瞬时频率值以及预先设置的过滤频率范围，可以对两者进行匹配，确定出属于过滤频率范围的目标瞬时频率值，记录该目标瞬时频率值对应的位置坐标，并获取该目标瞬时频率值所属的目标含噪分量，进而可以将该位置坐标确定为该目标含噪分量在初始差频信号对应的希尔伯特谱中的噪声位置。
可选的，假设当前在初始差频信号对应的希尔伯特谱中获取t时刻上每个含噪分量的瞬时频率值，t为所述初始差频信号的信号时长中的任一时刻，将t时刻的所有瞬时频率值分别与所述过滤频率范围进行匹配，当存在属于所述过滤频率范围的目标瞬时频率值时，获取该目标瞬时频率值在所述初始差频信号对应的希尔伯特谱中的坐标位置，并确定该目标瞬时频率值所属的目标含噪分量，将获取到的坐标位置确定为所述目标含噪分量在t时 刻的噪声位置，当各含噪分量在t时刻的噪声位置获取之后，可以转入下一时刻(例如：t+1时刻，具体时间间隔可以依据实际情况进行设置)，对各含噪分量在下一时刻的噪声位置进行获取，以此类推，遍历所述信号时长中各含噪分量在每个时刻对应的瞬时频率值，所述信号噪声滤除装置均可以采用上述方式对各含噪分量的多个噪声位置进行确定。
在本申请实施例中，通过采用过滤频率范围具体实现了在信号的希尔伯特谱中定位噪声信号的位置坐标，进而可以结合含噪分量的瞬时频率追溯到各含噪分量中的噪声位置。
在本申请实施例中，请参见图6，图6示出了噪声过滤前的希尔伯特谱图，如图6所示，由于含噪分量集合中存在噪声频率，因此各含噪分量在初始差频信号的希尔伯特谱中分布的较为散乱，各含噪分量的频率值浮动无规律，再请一并参见图7，图7示出了噪声过滤后的希尔伯特谱图，如图7所示，通过过滤频率范围分离出噪声位置，并将各含噪分量中噪声位置的噪声幅值置零后，保证了谱图中的频率在初始差频信号的频率附近浮动。
基于图1的系统架构，下面将结合附图8-附图10，对本申请实施例提供的信号噪声滤除装置进行详细介绍。需要说明的是，附图8-附图10中的信号噪声滤除装置，用于执行本申请图2-图7所示实施例的方法，为了便于说明，仅示出了与本申请实施例相关的部分，具体技术细节未揭示的，请参照本申请图2-图7所示的实施例。
请参见图8，为本申请实施例提供了一种信号噪声滤除装置的结构示意图。如图8所示，本申请实施例的所述信号噪声滤除装置1可以包括：含噪分量获取单元11、噪声位置获取单元12、去噪分量获取单元13和信号重构单元14。
含噪分量获取单元11，用于对激光雷达产生的初始差频信号进行集合经验模态分解，得到所述初始差频信号对应的含噪分量集合；
噪声位置获取单元12，用于根据过滤频率范围和所述含噪分量集合中各含噪分量对应的瞬时频率值，分别获取所述各含噪分量中的噪声位置；
去噪分量获取单元13，用于分别将所述各含噪分量中的噪声位置对应的噪声幅值设置为零，得到去噪分量集合；
信号重构单元14，用于对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号。
在本申请实施例中，通过对激光雷达的初始差频信号进行分量分解，并依据过滤频率 范围以及各含噪分量的瞬时频率值提取各含噪分量中的噪声位置，进而对噪声位置的幅值进行统一设置，以得到去噪分量集合，实现了对初始差频信号中的噪声信号进行过滤，最终通过对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号，提高了差频信号的信噪比，进而提高了有效的差频频率提取的成功率。
请参见图9，为本申请实施例提供了一种信号噪声滤除装置的结构示意图。如图9所示，本申请实施例的所述信号噪声滤除装置1可以包括：含噪分量获取单元11、噪声位置获取单元12、去噪分量获取单元13、信号重构单元14和差频频率获取单元15。
含噪分量获取单元11，用于对激光雷达产生的初始差频信号进行集合经验模态分解，得到所述初始差频信号对应的含噪分量集合；
噪声位置获取单元12，用于根据过滤频率范围和所述含噪分量集合中各含噪分量对应的瞬时频率值，分别获取所述各含噪分量中的噪声位置；
具体的，请一并参见图10，为本申请实施例提供了噪声频段获取单元的结构示意图。如图10所示，所述噪声频段获取单元12可以包括：
分量频率获取子单元121，用于对所述含噪分量集合进行希尔伯特变换处理，得到所述含噪分量集合中各含噪分量在每个时刻对应的瞬时频率值；
具体实现中，所述分量频率获取子单元121具体用于对所述含噪分量集合中各含噪分量进行希尔伯特变换处理，得到所述各含噪分量对应的希尔伯特谱；
将所述各含噪分量对应的希尔伯特谱进行汇总处理，得到所述初始差频信号对应的希尔伯特谱；
在所述初始差频信号对应的希尔伯特谱中获取所述各含噪分量在每个时刻对应的瞬时频率值。
噪声位置获取子单元122，用于在所述各含噪分量中获取瞬时频率值属于过滤频率范围的噪声位置；
具体实现中，所述噪声位置获取子单元122具体用于遍历所述各含噪分量在每个时刻对应的瞬时频率值，在所述初始差频信号对应的希尔伯特谱中获取瞬时频率值属于过滤频率范围的位置坐标，将所述位置坐标确定为所述各含噪分量中的噪声位置。
去噪分量获取单元13，用于分别将所述各含噪分量中的噪声位置对应的噪声幅值设置为零，得到去噪分量集合；
信号重构单元14，用于对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号；
差频频率获取单元15，用于对所述时域差频信号进行快速傅里叶变换处理，得到频域差频信号，在所述频域差频信号中获取最大幅值对应的差频频率值。
在本申请实施例中，通过对激光雷达的初始差频信号进行分量分解，并依据过滤频率范围以及各含噪分量的瞬时频率值提取各含噪分量中的噪声位置，进而对噪声位置的幅值进行统一设置，以得到去噪分量集合，实现了对初始差频信号中的噪声信号进行过滤，最终通过对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号，提高了差频信号的信噪比，并通过结合希尔伯特变换以及快速傅里叶变换的方式，保证了有效的差频信号的提取，提升了差频频率提取的成功率。
本申请实施例还提供了一种计算机存储介质，所述计算机存储介质可以存储有多条程序指令，所述程序指令适于由处理器加载并执行如上述图2-图7所示实施例的方法步骤，具体执行过程可以参见图2-图7所示实施例的具体说明，在此不进行赘述。
请参见图11，为本申请实施例提供了一种激光雷达的结构示意图。如图11所示，所述激光雷达1000可以包括：至少一个处理器1001，例如CPU，至少一个网络接口1004，输入输出接口1003，存储器1005，至少一个通信总线1002。其中，通信总线1002用于实现这些组件之间的连接通信。其中，网络接口1004可选的可以包括标准的有线接口、无线接口(如WI-FI接口)。存储器1005可以是高速RAM存储器，也可以是非不稳定的存储器(non-volatile memory)，例如至少一个磁盘存储器。存储器1005可选的还可以是至少一个位于远离前述处理器1001的存储装置。如图11所示，作为一种计算机存储介质的存储器1005中可以包括操作系统、网络通信模块、输入输出接口模块以及噪声滤除应用程序。
在图11所示的激光雷达1000中，输入输出接口1003主要用于为用户以及接入设备提供输入的接口，获取用户以及接入设备输入的数据。
在一个实施例中，处理器1001可以用于调用存储器1005中存储的噪声滤除应用程序，并具体执行以下操作：
对激光雷达产生的初始差频信号进行集合经验模态分解，得到所述初始差频信号对应 的含噪分量集合；
根据过滤频率范围和所述含噪分量集合中各含噪分量对应的瞬时频率值，分别获取所述各含噪分量中的噪声位置；
分别将所述各含噪分量中的噪声位置对应的噪声幅值设置为零，得到去噪分量集合；
对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号。
可选的，所述处理器1001在执行根据过滤频率范围和所述含噪分量集合中各含噪分量对应的瞬时频率值，分别获取所述各含噪分量中的噪声位置时，具体执行以下操作：
对所述含噪分量集合进行希尔伯特变换处理，得到所述含噪分量集合中各含噪分量在每个时刻对应的瞬时频率值；
在所述各含噪分量中获取瞬时频率值属于过滤频率范围的噪声位置。
可选的，所述处理器1001在执行对所述含噪分量集合进行希尔伯特变换处理，得到所述含噪分量集合中各含噪分量在每个时刻对应的瞬时频率值时，具体执行以下操作：
对所述含噪分量集合中各含噪分量进行希尔伯特变换处理，得到所述各含噪分量对应的希尔伯特谱；
将所述各含噪分量对应的希尔伯特谱进行汇总处理，得到所述初始差频信号对应的希尔伯特谱；
在所述初始差频信号对应的希尔伯特谱中获取所述各含噪分量在每个时刻对应的瞬时频率值。
可选的，所述处理器1001在执行在所述各含噪分量中获取瞬时频率值属于过滤频率范围的噪声位置时，具体执行以下操作：
遍历所述各含噪分量在每个时刻对应的瞬时频率值，在所述初始差频信号对应的希尔伯特谱中获取瞬时频率值属于过滤频率范围的位置坐标，将所述位置坐标确定为所述各含噪分量中的噪声位置。
可选的，所述处理器1001还执行以下操作：
对所述时域差频信号进行快速傅里叶变换处理，得到频域差频信号，在所述频域差频信号中获取最大幅值对应的差频频率值。
在本申请实施例中，通过对激光雷达的初始差频信号进行分量分解，并依据过滤频率范围以及各含噪分量的瞬时频率值提取各含噪分量中的噪声位置，进而对噪声位置的幅值进行统一设置，以得到去噪分量集合，实现了对初始差频信号中的噪声信号进行过滤，最 终通过对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号，提高了差频信号的信噪比，并通过结合希尔伯特变换以及快速傅里叶变换的方式，保证了有效的差频信号的提取，提升了差频频率提取的成功率。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的程序可存储于一计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，所述的存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory，ROM)或随机存储记忆体(Random Access Memory，RAM)等。
以上所揭露的仅为本申请较佳实施例而已，当然不能以此来限定本申请之权利范围，因此依本申请权利要求所作的等同变化，仍属本申请所涵盖的范围。

Claims (10)

  1. 一种信号噪声滤除方法，其特征在于，包括：
    对激光雷达产生的初始差频信号进行集合经验模态分解，得到所述初始差频信号对应的含噪分量集合；
    根据过滤频率范围和所述含噪分量集合中各含噪分量对应的瞬时频率值，分别获取所述各含噪分量中的噪声位置；
    分别将所述各含噪分量中的噪声位置对应的噪声幅值设置为零，得到去噪分量集合；
    对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号。
  2. 根据权利要求1所述的方法，其特征在于，所述根据过滤频率范围和所述含噪分量集合中各含噪分量对应的瞬时频率值，分别获取所述各含噪分量中的噪声位置，包括：
    对所述含噪分量集合进行希尔伯特变换处理，得到所述含噪分量集合中各含噪分量在每个时刻对应的瞬时频率值；
    在所述各含噪分量中获取瞬时频率值属于过滤频率范围的噪声位置。
  3. 根据权利要求2所述的方法，其特征在于，所述对所述含噪分量集合进行希尔伯特变换处理，得到所述含噪分量集合中各含噪分量在每个时刻对应的瞬时频率值，包括：
    对所述含噪分量集合中各含噪分量进行希尔伯特变换处理，得到所述各含噪分量对应的希尔伯特谱；
    将所述各含噪分量对应的希尔伯特谱进行汇总处理，得到所述初始差频信号对应的希尔伯特谱；
    在所述初始差频信号对应的希尔伯特谱中获取所述各含噪分量在每个时刻对应的瞬时频率值。
  4. 根据权利要求2所述的方法，其特征在于，所述在所述各含噪分量中获取瞬时频率值属于过滤频率范围的噪声位置，包括：
    遍历所述各含噪分量在每个时刻对应的瞬时频率值，在所述初始差频信号对应的希尔伯特谱中获取瞬时频率值属于过滤频率范围的位置坐标，将所述位置坐标确定为所述各含 噪分量中的噪声位置。
  5. 根据权利要求1所述的方法，其特征在于，还包括：
    对所述时域差频信号进行快速傅里叶变换处理，得到频域差频信号，在所述频域差频信号中获取最大幅值对应的差频频率值。
  6. 一种信号噪声滤除装置，其特征在于，包括：
    含噪分量获取单元，用于对激光雷达产生的初始差频信号进行集合经验模态分解，得到所述初始差频信号对应的含噪分量集合；
    噪声位置获取单元，用于根据过滤频率范围和所述含噪分量集合中各含噪分量对应的瞬时频率值，分别获取所述各含噪分量中的噪声位置；
    去噪分量获取单元，用于分别将所述各含噪分量中的噪声位置对应的噪声幅值设置为零，得到去噪分量集合；
    信号重构单元，用于对所述去噪分量集合进行组合重构处理，得到去噪后的时域差频信号。
  7. 根据权利要求6所述的装置，其特征在于，所述噪声位置获取单元包括：
    分量频率获取子单元，用于对所述含噪分量集合进行希尔伯特变换处理，得到所述含噪分量集合中各含噪分量在每个时刻对应的瞬时频率值；
    噪声位置获取子单元，用于在所述各含噪分量中获取瞬时频率值属于过滤频率范围的噪声位置。
  8. 根据权利要求7所述的装置，其特征在于，所述分量频率获取子单元具体用于对所述含噪分量集合中各含噪分量进行希尔伯特变换处理，得到所述各含噪分量对应的希尔伯特谱；
    将所述各含噪分量对应的希尔伯特谱进行汇总处理，得到所述初始差频信号对应的希尔伯特谱；
    在所述初始差频信号对应的希尔伯特谱中获取所述各含噪分量在每个时刻对应的瞬时频率值。
  9. 一种激光雷达，其特征在于，包括处理器、存储器、输入输出接口；
    所述处理器分别与所述存储器和所述输入输出接口相连，其中，所述输入输出接口用于页面交互，所述存储器用于存储程序代码，所述处理器用于调用所述程序代码，以执行如权利要求1-5任一项所述的方法。
  10. 一种计算机存储介质，其特征在于，所述计算机存储介质存储有计算机程序，所述计算机程序包括程序指令，所述程序指令当被处理器执行时，执行如权利要求1-5任一项所述的方法。
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