WO2021102831A1 - 信号滤波方法、装置及数据处理设备 - Google Patents

信号滤波方法、装置及数据处理设备 Download PDF

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WO2021102831A1
WO2021102831A1 PCT/CN2019/121693 CN2019121693W WO2021102831A1 WO 2021102831 A1 WO2021102831 A1 WO 2021102831A1 CN 2019121693 W CN2019121693 W CN 2019121693W WO 2021102831 A1 WO2021102831 A1 WO 2021102831A1
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preset
signal
smoothness
difference
filtering
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PCT/CN2019/121693
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English (en)
French (fr)
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陈有生
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广州极飞科技有限公司
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Publication of WO2021102831A1 publication Critical patent/WO2021102831A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
    • G01R23/16—Spectrum analysis; Fourier analysis
    • G01R23/165—Spectrum analysis; Fourier analysis using filters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
    • H04B1/06—Receivers
    • H04B1/10—Means associated with receiver for limiting or suppressing noise or interference
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/10—Frequency-modulated carrier systems, i.e. using frequency-shift keying
    • H04L27/14—Demodulator circuits; Receiver circuits
    • H04L27/144—Demodulator circuits; Receiver circuits with demodulation using spectral properties of the received signal, e.g. by using frequency selective- or frequency sensitive elements
    • H04L27/148—Demodulator circuits; Receiver circuits with demodulation using spectral properties of the received signal, e.g. by using frequency selective- or frequency sensitive elements using filters, including PLL-type filters

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  • This application relates to the technical field of signal processing, and in particular to a signal filtering method, device and data processing equipment.
  • the filter is designed according to the characteristics of the system that generates the signal, so as to reproduce the real signal from the signal with noise; or the noise is suppressed or eliminated according to the characteristics of the noise.
  • the usual filtering method is difficult to balance the smoothness of the filtering and the phase deviation, that is, when the signal obtained after filtering tends to be smooth, the deviation between the phase of the filtering result and the real signal will increase. The larger is, but when the phase deviation is expected to be reduced, the smoothness of the signal obtained after filtering will be reduced.
  • one of the objectives of the present application is to provide a signal filtering method, which is characterized in that the method includes:
  • the step of filtering the signal to be processed using a preset filter function with differential constraints includes:
  • a preset filter function of the first preset multiple is used to process the signal value of the sampling point, and the second preset filter function is superimposed on the filtering result.
  • the sum of the i-th order differences of the preset multiples is used to obtain the filtered result signal value after the sampling point is filtered; wherein, i is greater than or equal to 1.
  • the method further includes:
  • the first preset multiple is adjusted so that the smoothness of the filtered signal is greater than a first preset smoothness threshold, and the phase difference between the filtered signal and the signal to be processed is less than a preset phase difference threshold ;
  • the second preset multiple is adjusted so that the smoothness of the filtered signal is greater than a second preset smoothness threshold, where the second preset smoothness threshold is greater than the first preset smoothness threshold.
  • the method before adjusting the first preset multiple, the method further includes:
  • the step of adjusting the second preset multiple includes:
  • the step of gradually increasing the second preset multiple includes:
  • the present application also provides a signal filtering device, which includes:
  • the signal acquisition module is configured to acquire the signal to be processed
  • the signal processing module is configured to filter the signal to be processed using a preset filter function with a difference constraint to obtain a filtered signal, wherein the difference constraint is to superimpose the filter result on the preset filter function The difference value of the preset multiple of the preset filter function.
  • the signal processing module is specifically configured to, for each sampling point of the signal to be processed, use a preset filter function of a first preset multiple to process the signal value of the sampling point, and The sum of the i-th order difference of the second preset multiple of the preset filter function at the sampling point is superimposed on the filtering result to obtain the filtered result signal value after the sampling point; where i is greater than or equal to 1.
  • the filtering device further includes:
  • the first adjustment module is configured to adjust the first preset multiple so that the smoothness of the filtered signal is greater than a first preset smoothness threshold, and the phase of the filtered signal and the signal to be processed is The difference is less than the preset phase difference threshold;
  • the second adjustment module is configured to adjust the second preset multiple so that the smoothness of the filtered signal is greater than a second preset smoothness threshold, wherein the second preset smoothness threshold is greater than the first preset Set the smoothness threshold.
  • the first adjustment module is further configured to set the second preset multiple to zero before adjusting the first preset multiple
  • the second adjustment module is specifically configured to gradually increase the second preset multiple so that the smoothness of the filtered signal is greater than the second preset smoothness threshold.
  • different order differences correspond to different second preset multiples; the second adjustment module is specifically configured to sequentially increase the first order difference corresponding to each order difference starting from the first order difference. Two preset multiples.
  • the present application also provides a data processing device, including a machine-readable storage medium and a processor, the machine-readable storage medium stores machine-executable instructions, and the machine-executable instructions are executed by the processor.
  • a data processing device including a machine-readable storage medium and a processor, the machine-readable storage medium stores machine-executable instructions, and the machine-executable instructions are executed by the processor.
  • Provide signal filtering method including a signal filtering method.
  • the present application also provides a machine-readable storage medium that stores machine-executable instructions that, when executed by a processor, implement the signal filtering method provided in the present application.
  • the signal filtering method, device and data processing equipment provided in this application from the perspective of studying the difference between a real smooth signal and a signal containing noise, filter the signal to be processed by using a preset filter function with differential constraints, The signal difference can be further reduced on the filtering result of the preset filtering function, so that the smoothness of the filtering result can be further improved without affecting the phase deviation.
  • Figures 1a and 1b are schematic diagrams of the smoothness and phase deviation of the filtering results
  • Figure 2 is a schematic diagram of the difference between a real signal and a signal with noise
  • Figure 3 is a schematic diagram of a data processing device provided by an embodiment of the application.
  • FIG. 4 is a schematic flowchart of a signal filtering method provided by an embodiment of the application.
  • FIG. 5 is a schematic diagram of comparing the waveforms of the filtering results of the signal filtering method provided by an embodiment of the application and other filters;
  • FIG. 6 is a schematic diagram of the phase deviation comparison between the signal filtering method provided by an embodiment of the application and the filtering results of other filters;
  • FIG. 7 is a schematic diagram of the difference comparison between the signal filtering method provided by an embodiment of the application and the filtering results of other filters;
  • FIG. 8 is one of schematic diagrams of functional modules of a signal filtering device provided by an embodiment of the application.
  • FIG. 9 is the second schematic diagram of the functional modules of the signal filtering device provided by the embodiment of the application.
  • Icon 100-data processing equipment; 110-signal filtering device; 111-signal acquisition module; 112-signal processing module; 113-first adjustment module; 114-second adjustment module; 120-machine-readable storage medium; 130- processor.
  • the characteristics of the real signal is usually determined by the physical characteristics of the system that generates the real signal. Although the real signal will be interfered by noise during transmission, if you know enough about the system that generates the real signal, The real signal can be reproduced from the noise-contaminated signal according to the physical characteristics of the system.
  • the design of the filter mainly considers the characteristics of the main systems involved in the signal generation to the collection process, such as sensors, tested machines, transmission media, etc., and based on their physical characteristics, it is constructed according to the process characteristics of the signal generation to the collection process.
  • Physical model such as differential equation, state space
  • obtain physical characteristic information such as bandwidth. Then perform parameter identification based on these physical models, and appropriately optimize the identified models to complete the filter design.
  • state observers such as extended state observers
  • band-pass filters such as low-pass filters, high-pass filters
  • the design of the filter mainly considers the relevant mathematical statistics and analysis of the noise to obtain the characteristics of the noise in the corresponding environment. Then, based on the characteristics of these noises, the relevant filters are constructed or optimized, and then the filter design is completed.
  • Gaussian filters, mean filters, etc. are typical examples.
  • Figure 2 is a schematic diagram of the differential comparison between a signal with high frequency noise and a real signal without noise. It can be seen from Figure 2 that the real signal is usually a relatively smooth signal, and its difference value is usually small, and the higher the order of the difference, the smaller the difference value. But for noise, no matter what the difference order is, the difference value cannot be reduced, and even as the difference order increases, the difference value may become larger.
  • this embodiment provides a solution for reducing the difference between the noise signal and the real signal by using differential constraint control, thereby further improving the smoothness of the filtering result.
  • the solution provided in this embodiment will be explained in detail below.
  • This embodiment provides a data processing device, which can be any electronic device with data processing capabilities, the data processing device can receive signals and filter the signals, or the data processing device can receive signals from other devices. And filter the acquired signal.
  • the data processing device may be a server, a personal computer (PC), a tablet computer, a personal digital assistant (PDA), a mobile Internet device (MID), etc.
  • PC personal computer
  • PDA personal digital assistant
  • MID mobile Internet device
  • FIG. 3 is a block diagram of a data processing device 100 provided by an embodiment of the present application.
  • the data processing device 100 may include a signal filtering device 110, a machine-readable storage medium 120, and a processor 130.
  • the machine-readable storage medium 120 and the processor 130 are directly or indirectly electrically connected to realize data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines.
  • the signal filtering device 110 includes at least one piece of software that can be stored in the machine-readable storage medium 120 in the form of software or firmware or solidified in the operating system (OS) of the data processing device 100 functional module.
  • the processor 130 is configured to execute executable modules stored in the machine-readable storage medium 120, such as software function modules and computer programs included in the signal filtering device 110.
  • the machine-readable storage medium 120 may be, but is not limited to, random access memory (Random Access Memory, RAM), read-only memory (Read Only Memory, ROM), and programmable read-only memory (Programmable Read-Only). Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrical Erasable Programmable Read-Only Memory (EEPROM), etc.
  • the machine-readable storage medium 120 is configured to store a program, and the processor 130 executes the program after receiving the execution instruction.
  • this embodiment provides a signal filtering method applied to the above-mentioned data processing device, and each step of the method is described in detail below.
  • Step S110 Obtain a signal to be processed.
  • Step S120 filtering the signal to be processed using a preset filter function with a difference constraint to obtain a filtered signal, wherein the difference constraint is superimposing the preset filter on the filter result of the preset filter function The difference value of the function.
  • the preset filter function may be any known digital filter function, for example, a low-pass filter function.
  • a preset filter function of a first preset multiple may be used to process the signal value of the sampling point, and the filtering result may be superimposed on the
  • the preset filter function is the sum of the i-th order difference of the second preset multiple of the sampling point to obtain the filtered signal value of the sampling point.
  • the value of k may be greater than or equal to 1, that is, at least the first-order difference value of the preset filter function is superimposed on the filter result of the preset filter function.
  • x(n) is the signal value of the nth sampling point
  • X(n) is the correlation matrix or vector formed by the nth sampling point and the sampled signal values of several sampling points before the nth sampling point
  • Y (n) is the correlation matrix or vector formed by the filtering result signal values of several sampling points before the nth sampling point
  • f( ⁇ ) is the filter function
  • ⁇ (n) is the parameter of the filter function f( ⁇ )
  • h ( ⁇ ) are restrictive constraints related to practical applications.
  • the parameter of the filter function can be solved to obtain formula (2), where y(n) is the filtered result signal value after filtering x(n), It is the result of solving ⁇ (n) under the constraints of formula (1).
  • ⁇ i f[X(n), Y(n), ⁇ (n)] is the i-th difference of f( ⁇ )
  • c 0 is the first preset multiple to control the phase of the filtering result Poor approximation performance and smoothness of the filtering result
  • c i is the second preset multiple, c i ⁇ 0, used to control the smoothness of the filtering result.
  • Equation (3) can represent a convex optimization model, so the parameter ⁇ (n) can be iteratively optimized in real time through gradient descent, and Obtain the optimal solution and realize adaptive filtering.
  • the phase difference of the filtering result can be as small as possible and the smoothness as high as possible by adjusting the first preset multiple and the second preset multiple.
  • the first preset multiple may be adjusted first, so that the smoothness of the filtered signal is greater than the first preset multiple.
  • a smoothness threshold is set and the phase difference between the filtered signal and the signal to be processed is smaller than a preset phase difference threshold.
  • the second preset multiple may be set to zero, that is, the phase difference and smoothness of the filtering result of the preset filter function itself are adjusted first, and then the second preset multiple is adjusted to zero. Gradually increase the second preset multiple to further adjust the smoothness of the filtering result.
  • the second preset multiple c i can be different.
  • the difference of each order can be increased from the first order difference in order.
  • the corresponding second preset multiple For example, the second preset multiple c 1 corresponding to the first-order difference can be adjusted first, until the smoothness is no longer improved, then the second preset multiple c 2 corresponding to the second-order difference can be adjusted, and so on, and the corresponding difference of each order can be adjusted in turn The second preset multiple of.
  • y(n) ⁇ 1 (n)y(n-1)+ ⁇ 2 (n)y(n-2)+ ⁇ 3 (n)x(n-2) as an example, Among them, x(n) is the sampled signal value of the nth sampling point, and y(n) is the filtered result signal value after filtering.
  • the corresponding adaptive filter with higher performance can be obtained through the following parameter adaptation process:
  • the filtered filtering result signal value corresponding to the nth sampling point is:
  • the first-order difference constraint is adopted to improve the above-mentioned low-pass filtering function to obtain the filtering model shown in equation (8):
  • the filtered filtering result signal value corresponding to the nth sampling point is:
  • the parameter ⁇ should satisfy 0 ⁇ T, and T is the sampling period of the signal.
  • ⁇ [ ⁇ 1 ⁇ 2 ... ⁇ m ] T
  • X [x(1) x(2)... x(n)] T
  • ⁇ Y is the first-order difference matrix of Y.
  • Figure 5 shows the use of a low-pass filter (shown in the "Lowpass Filter” curve in Figure 5), a low-pass filter with adaptive parameters (shown in the "Adaptive Filter” curve in Figure 5) and the filter provided in this embodiment
  • the method shown in the "LDC” curve in Fig. 5) is a comparative schematic diagram of the filtering result waveforms of filtering the noisy signal (shown in the "Withnoise” curve in Fig. 5), where the real signal is shown in the "Real” curve. It can be seen that the smoothness of the filtering result obtained by using the filtering method provided in this embodiment is significantly better than the low-pass filter and the low-pass filter with adaptive parameters.
  • Figure 6 shows the use of a low-pass filter (shown in the "Lowpass Filter” curve in Figure 6), a low-pass filter with adaptive parameters (shown in the "Adaptive Filter” curve in Figure 6) and this implementation
  • the example provides a filtering method (shown in the "LDC” curve in Figure 6) of the phase offset comparison diagram of the results of filtering a noisy signal, where "Zero Line” is the zero offset baseline.
  • the phase shift of the filtering result obtained by using the filtering method provided in this embodiment is significantly smaller than that of the ordinary low-pass filter, and is almost the same as the low-pass filter described in self-adaptation.
  • Figure 7 shows the use of a low-pass filter (shown in the "Lowpass Filter” curve in Figure 7), a low-pass filter with adaptive parameters (shown in the "Adaptive Filter” curve in Figure 7) and this implementation
  • the example provides a schematic diagram of the signal difference comparison of the result of filtering the noisy signal by the filtering method (shown in the "LDC" curve in Fig. 7). It can be seen that the signal difference of the filtering result obtained by the filtering method provided in this embodiment is significantly smaller than that of the low-pass filter and the low-pass filter with adaptive parameters.
  • the filtering method provided by this embodiment can further improve the smoothness of the filtering result under the same phase shift degree.
  • the signal filtering device 110 may include a signal acquisition module 111 and a signal processing module 112.
  • the signal acquisition module 111 is configured to acquire signals to be processed.
  • the signal acquisition module 111 may be configured to perform step S110 shown in FIG. 4, and for a specific description of the signal acquisition module 111, refer to the description of step S110.
  • the signal processing module 112 is configured to filter the signal to be processed by using a preset filter function with a difference constraint to obtain a filtered signal, wherein the difference constraint is a filtering result of the preset filter function The difference value of the preset multiple of the preset filter function is superimposed on it.
  • the signal processing module 112 may be configured to perform step S120 shown in FIG. 4, and for a specific description of the signal processing module 112, refer to the description of step S120.
  • the signal processing module 112 is specifically configured to process the signal value of the sampling point by using a preset filter function of a first preset multiple for each sampling point of the signal to be processed. And superimpose the sum of the i-th order difference of the second preset multiple of the preset filter function at the sampling point on the filtering result to obtain the filtered result signal value after filtering at the sampling point; where i is greater than or equal to 1.
  • the filtering device further includes a first adjustment module 113 and a second adjustment module 114.
  • the first adjustment module 113 is configured to adjust the first preset multiple so that the smoothness of the filtered signal is greater than a first preset smoothness threshold, and the filtered signal is the same as the signal to be processed The phase difference of is less than the preset phase difference threshold.
  • the second adjustment module 114 is configured to adjust the second preset multiple so that the smoothness of the filtered signal is greater than a second preset smoothness threshold, wherein the second preset smoothness threshold is greater than the first A preset smoothness threshold.
  • the first adjustment module 113 is further configured to set the second preset multiple to zero before adjusting the first preset multiple;
  • the second adjustment module 114 is specifically configured to gradually increase the second preset multiple so that the smoothness of the filtered signal is greater than the second preset smoothness threshold.
  • different order differences correspond to different second preset multiples; the second adjustment module 114 is specifically configured to sequentially increase the corresponding order differences from the first order difference.
  • the second preset multiple is specifically configured to sequentially increase the corresponding order differences from the first order difference.
  • each block in the flowchart or block diagram may represent a module, program segment, or part of the code, and the module, program segment, or part of the code contains one or more functions for realizing the specified logical function. Executable instructions. It should also be noted that in some alternative implementations, the functions marked in the block may also occur in a different order from the order marked in the drawings.
  • each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart can be implemented by a dedicated hardware-based system that performs the specified functions or actions Or it can be realized by a combination of dedicated hardware and computer instructions.
  • the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.
  • the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer readable storage medium.
  • the technical solution of this application essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks and other media that can store program codes. .
  • the signal filtering method, device and data processing equipment provided in this application from the perspective of studying the difference between a real smooth signal and a signal containing noise, filter the signal to be processed by using a preset filter function with differential constraints, The signal difference can be further reduced on the filtering result of the preset filtering function, so that the smoothness of the filtering result can be further improved without affecting the phase deviation.

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Abstract

本申请提供一种信号滤波方法、装置及数据处理设备，所述方法包括：获取待处理信号；使用具有差分约束的预设滤波函数对所述待处理信号进行滤波，获得滤波后的信号，其中，所述差分约束为在所述预设滤波函数的滤波结果上叠加该预设滤波函数的预设倍数的差分值。本申请提供的方案从研究真实光滑信号与含噪音的信号之间的差异角度出发，通过使用具有差分约束的预设滤波函数对所述待处理信号进行滤波，可以在预设滤波函数的滤波结果上进一步减小信号差分，从而可以在不影响相位偏差的情况下进一步提高滤波结果的平滑性。

Description

信号滤波方法、装置及数据处理设备 技术领域
本申请涉及信号处理技术领域，具体而言，涉及一种信号滤波方法、装置及数据处理设备。
背景技术
信号从产生到被接收设备获取的过程中，可能会受到各种各样的干扰，导致接收到的信号相对于原本真实的信号出现信号值的波动，即接收到的信号带有噪声。因此需要对接收的信号进行滤波处理以去除噪声，还原出真实信号。
现有的滤波技术中，或根据产生信号的系统的特性设计滤波器，从而从具有噪声的信号中复现出真实信号；或根据噪声的特征对噪声进行抑制或剔除。但通常的滤波方式难以在滤波的平滑性和相位偏差之间的难调平衡，即当希望滤波后获得的信号趋于平滑的时候，滤波结果的相位与真实信号之间的偏差就会越来越大，但当希望相位偏差减小时，滤波后获得的信号平滑度会降低。
发明内容
为了至少克服现有技术中的上述不足，本申请的目的之一在于提供一种信号滤波方法，其特征在于，所述方法包括：
获取待处理信号；
使用具有差分约束的预设滤波函数对所述待处理信号进行滤波，获得滤波后的信号，其中，所述差分约束为在所述预设滤波函数的滤波结果上叠加该预设滤波函数的预设倍数的差分值。
在本申请的一个可能的例子中，使用具有差分约束的预设滤波函数对所述待处理信号进行滤波的步骤，包括：
针对待处理信号的每个采样点，使用第一预设倍数的预设滤波函数对该采样点的信号值进行处理，并在滤波结果上叠加所述预设滤波函数在该采样点的第二预设倍数的i阶差分的和，获得该采样点滤波后的滤波结果信号值；其中，i大于等于1。
在本申请的一个可能的例子中，所述方法还包括：
调整所述第一预设倍数，使所述滤波后的信号的平滑度大于第一预设平滑度阈值，且所述滤波后的信号与所述待处理信号的相位差小于预设相位差阈值；
调整所述第二预设倍数，使所述滤波后的信号的平滑度大于第二预设平滑度阈值，其中，所述第二预设平滑度阈值大于第一预设平滑度阈值。
在本申请的一个可能的例子中，在调整所述第一预设倍数之前，所述方法还包括：
将所述第二预设倍数置零；
调整所述第二预设倍数的步骤，包括：
逐渐增大所述第二预设倍数，使所述滤波后的信号的平滑度大于所述第二预设平滑度阈值。
在本申请的一个可能的例子中，不同阶数差分对应有不同的所述第二预设倍数；所述逐渐增大所述第二预设倍数的步骤，包括：
从1阶差分起，依次增大各阶差分对应的第二预设倍数。
本申请还提供一种信号滤波装置，所述装置包括：
信号获取模块，配置成获取待处理信号；
信号处理模块，配置成使用具有差分约束的预设滤波函数对所述待处理信号进行滤波，获得滤波后的信号，其中，所述差分约束为在所述预设滤波函数的滤波结果上叠加该预设滤波函数的预设倍数的差分值。
在本申请的一个可能的例子中，所述信号处理模块具体配置成针对待处理信号的每个采样点，使用第一预设倍数的预设滤波函数对该采样点的信号值进行处理，并在滤波结果上叠加所述预设滤波函数在该采样点的第二预设倍数的i阶差分的和，获得该采样点滤波后的滤波结果信号值；其中，i大于等于1。
在本申请的一个可能的例子中，所述滤波装置还包括：
第一调整模块，配置成调整所述第一预设倍数，使所述滤波后的信号的平滑度大于第一预设平滑度阈值，且所述滤波后的信号与所述待处理信号的相位差小于预设相位差阈值；
第二调整模块，配置成调整所述第二预设倍数，使所述滤波后的信号的平滑度大于第二预设平滑度阈值，其中，所述第二预设平滑度阈值大于第一预设平滑度阈值。
在本申请的一个可能的例子中，所述第一调整模块还配置成在调整所述第一预设倍数之前，将所述第二预设倍数置零；
所述第二调整模块具体配置成逐渐增大所述第二预设倍数，使所述滤波后的信号的平滑度大于所述第二预设平滑度阈值。
在本申请的一个可能的例子中，不同阶数差分对应有不同的所述第二预设倍数；所述第二调整模块具体配置成从1阶差分起，依次增大各阶差分对应的第二预设倍数。
本申请还提供一种数据处理设备，包括机器可读存储介质及处理器，所述机器可读存储介质存储有机器可执行指令，所述机器可执行指令在被所述处理器执行时本申请提供的信号滤波方法。
本申请还提供一种机器可读存储介质，所述机器可读存储介质存储有机器可执行指令，所述机器可执行指令在被处理器执行时实现本申请提供的信号滤波方法。
相对于现有技术而言，本申请包括以下有益效果：
本申请提供的信号滤波方法、装置及数据处理设备，从研究真实光滑信号与含噪音的信号之间的差异角度出发，通过使用具有差分约束的预设滤波函数对所述待处理信号进行滤波，可以在预设滤波函数的滤波结果上进一步减小信号差分，从而可以在不影响相位偏差的情况下进一步提高滤波结果的平滑性。
附图说明
为了更清楚地说明本申请实施例的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，应当理解，以下附图仅示出了本申请的某些实施例，因此不应被看作是对范围的限定，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相关的附图。
图1a和图1b为滤波结果平滑性和相位偏差的示意图；
图2为真实信号与带有噪声的信号的差分示意图；
图3为本申请实施例提供的数据处理设备的示意图；
图4为本申请实施例提供的信号滤波方法的流程示意图；
图5为本申请实施例提供的信号滤波方法与其他滤波器的滤波结果波形对比示意图；
图6为本申请实施例提供的信号滤波方法与其他滤波器的滤波结果的相位偏差对比示意图；
图7为本申请实施例提供的信号滤波方法与其他滤波器的滤波结果的差分对比示意图；
图8为本申请实施例提供的信号滤波装置的功能模块示意图之一；
图9为本申请实施例提供的信号滤波装置的功能模块示意图之二。
图标：100-数据处理设备；110-信号滤波装置；111-信号获取模块；112-信号处理模块；113-第一调整模块；114-第二调整模块；120-机器可读存储介质；130-处理器。
具体实施方式
为使本申请实施例的目的、技术方案和优点更加清楚，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，所描述的实施例是本申请一部分实施例，而不是全部的实施例。通常在此处附图中描述和示出的本申请实施例的组件可以以各种不同的配置来布置和设计。
因此，以下对在附图中提供的本申请的实施例的详细描述并非旨在限制要求保护的本申请的范围，而是仅仅表示本申请的选定实施例。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。
应注意到：相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步定义和解释。
在本申请的描述中，术语“第一”、“第二”、“第三”等仅用于区分描述，而不能理解为指示或暗示相对重要性。
在一种滤波器设计思路中，考虑真实信号特性通常由产生该真实信号的系统的物理特性决定，虽然真实信号在传输过程中会受到噪声干扰，但如果对产生真实信号的系统足够了解，就可以根据该系统的物理特性从受噪声污染的信号中复现出真实信号。
基于这种思路，滤波器的设计主要考虑参与信号发生到收集过程的主要系统的特性，例如传感器、被测机器、传输介质等，并基于它们的物理特性，根据信号发生到收集的过程特性构建物理模型(如微分方程，状态空间)或获取物理特性信息(如带宽)。然后基于这些理模型进行参数辨识，并对辨识出来的模型加以适当的优化，进而完成滤波器的设计。其中，状态观测器(如状态扩充观测器)、带通滤波器(如低通滤波器，高通滤波器)等就是典型的例子。
在另一种滤波设计思路中，考虑受到干扰的信号是真实信号与噪声的叠加，如果对噪声足够的了解，就可以根据噪声的特性，从包含噪声的信号中抑制或剔除噪声。
基于这种思路，滤波器的设计主要考虑对噪声进行相关数学统计和分析挖掘，获取对应环境下的噪声的特性。然后基于这些噪声的特性，构造或者优化相关滤波器，进而完成滤波器的设计。其中，高斯滤波器、均值滤波器等就是典型的例子。
现有的滤波器大多存在平滑性和相位偏差之间难以平衡的问题。例如，请参照图1a，当滤波后获得的信号的平滑度较高时，滤波结果的相位与真实信号之间的偏差就会越来越大。请参照图1b，当滤波结果与真实信号之间的相位偏差减小时，滤波后获得的信号平滑度会降低。也就是说，在同等相位偏差要求下，现有的滤波器难以进一步提高滤波结果的平滑性。
经研究发现，在受到噪声干扰后，信号的某些特性会发生较为明显的改变，例如，请参见图2，图2为含高频噪声的信号和不含噪声的真实信号的差分对比示意图。从图2可以看出真实信号通常是比较平滑的信号，其差分值通常较小，并且差分的阶数越高差分值越小。但对于噪声，无论差分阶数是多少，其差分值都无法减小，甚至随着差分阶数的增大，其差分值可能会更大。
据此，本实施例提供一种通过使用差分约束控制减少噪声的信号与真实信号之间的差异，从而进一步提高滤波结果平滑度的方案。下面对本实施例提供的方案进行详细解释。
本实施例提供的一种数据处理设备，该数据处理设备可以为任何具有数据处理能力的电子设备，该数据处理设备可接收信号并对信号进行滤波处理，或者该数据处理设备可以获取其他设备接收到的信号并对获取到的信号进行滤波处理。
例如，所述数据处理设备可以是，服务器、个人电脑(personal computer，PC)、平板电 脑、个人数字助理(personal digital assistant，PDA)、移动上网设备(mobile Internet device，MID)等。
请参照图3，图3是本申请实施例提供的一种数据处理设备100的方框示意图。所述数据处理设备100可以包括信号滤波装置110、机器可读存储介质120及处理器130。
所述机器可读存储介质120及处理器130直接或间接地电性连接，以实现数据的传输或交互。例如，这些元件相互之间可通过一条或多条通讯总线或信号线实现电性连接。所述信号滤波装置110包括至少一个可以软件或固件(firmware)的形式存储于所述机器可读存储介质120中或固化在所述数据处理设备100的操作系统(operating system，OS)中的软件功能模块。所述处理器130配置成执行所述机器可读存储介质120中存储的可执行模块，例如所述信号滤波装置110所包括的软件功能模块及计算机程序等。
其中，所述机器可读存储介质120可以是，但不限于，随机存取存储器(Random Access Memory，RAM)，只读存储器(Read Only Memory，ROM)，可编程只读存储器(Programmable Read-Only Memory，PROM)，可擦除只读存储器(Erasable Programmable Read-Only Memory，EPROM)，电可擦除只读存储器(Electric Erasable Programmable Read-Only Memory，EEPROM)等。其中，机器可读存储介质120配置成存储程序，所述处理器130在接收到执行指令后，执行所述程序。
请参照图4，本实施例提供一种应用于上述数据处理设备的信号滤波方法，下面对该方法的各个步骤进行详细阐述。
步骤S110，获取待处理信号。
步骤S120，使用具有差分约束的预设滤波函数对所述待处理信号进行滤波，获得滤波后的信号，其中，所述差分约束为在所述预设滤波函数的滤波结果上叠加该预设滤波函数的差分值。
在本实施例中，所述预设滤波函数可以为任意一种已知的数字滤波函数，例如，低通滤波函数。
通过在预设滤波函数的滤波结果上叠加该滤波函数的差分，可以减少采样点间的信号值突变差异，从而可以在不影响滤波结果相位偏差的情况下使滤波结果更加平滑。
可选地，在本实施例中，可以针对待处理信号的每个采样点，使用第一预设倍数的预设滤波函数对该采样点的信号值进行处理，并在滤波结果上叠加所述预设滤波函数在该采样点的第二预设倍数的i阶差分的和，获得该采样点滤波后的信号值。在本实施例中，k的值可以大于等于1，即至少在预设滤波函数的滤波结果上叠加该预设滤波函数的一阶差分值。
下面以式(1)所示广义数字滤波器的数学模型为例解释本实施例采用的滤波方法。
Figure PCTCN2019121693-appb-000001
Figure PCTCN2019121693-appb-000002
其中，x(n)为第n个采样点的信号值，X(n)为由第n个采样点以及第n个采样点之前若干个采样点的采样信号值构成的相关矩阵或向量；Y(n)是由第n个采样点之前若干采样点的滤波结果信号值构成相关矩阵或向量；f(●)为滤波函数，ω(n)为滤波函数f(●)的参数，g(●)和h(●)均为和实际应用有关的限制性约束。
在满足式(1)约束条件的情况下对滤波函数的参数求解可以获得式(2)，其中，y(n)为对x(n)的进行滤波后的滤波结果信号值，
Figure PCTCN2019121693-appb-000003
为在式(1)的约束条件下对ω(n)求解的结果。
结合本实施例提供的滤波方法，对于式(1)可以采用以下方法进行改良：
Figure PCTCN2019121693-appb-000004
其中，Δ if[X(n),Y(n),ω(n)]为f(●)的第i阶差分，c 0即为所述第一预设倍数，用以控制滤波结果相位差的逼近性能以及滤波结果的平滑性；c i即为所述第二预设倍数，c i≥0，用以控制滤波结果的平滑性。
在式(3)基础上，选用不同滤波函数f(●)及不同的约束条件g(●)和h(●)，可以获得各种不同的具有差分约束的滤波器。例如，若f(●)，g(●)和h(●)均为凸函数，式(3)可以表示一个凸优化模型，因此可以通过梯度下降对参数ω(n)进行实时迭代优化，并获得最优解，实现自适应滤波。
可选地，在本实施例中，可以通过调整所述第一预设倍数和第二预设倍数使滤波结果的相位差尽量小且平滑度尽量高。
由于第一预设倍数c 0的改变会对滤波结果的相位差及平滑度均产生影响，因此可以先调整所述第一预设倍数，使所述滤波后的信号的平滑度大于第一预设平滑度阈值且所述滤波后的信号与所述待处理信号的相位差小于预设相位差阈值。
然后调整所述第二预设倍数c i，使所述滤波后的信号的平滑度进一步增加至大于第二预设平滑度阈值，其中，所述第二预设平滑度阈值大于第一预设平滑度阈值。
可选地，在调整所述第一预设倍数之前可以先将所述第二预设倍数置零，即先对所述 预设滤波函数本身滤波结果的相位差和平滑度进行调整，然后再逐渐增大所述第二预设倍数，以进一步调整滤波结果的平滑度。
可选地，请再次参照式(3)，对不同阶数的差分，第二预设倍数c i可以是不同的，在本实施例中，可以从1阶差分起，依次增大各阶差分对应的第二预设倍数。例如，可以先调整一阶差分对应的第二预设倍数c 1，至平滑度不在提高后，再调整二阶差分对应的第二预设倍数c 2，以此类推，依次调整各阶差分对应的第二预设倍数。
下面以两种具体的滤波模型为例解释本实施提供的方案。
以常见的低通滤波模型y(n)＝ω 1(n)y(n-1)+ω 2(n)y(n-2)+ω 3(n)x(n-2)为例，其中，x(n)为第n个采样点的采样信号值，y(n)为滤波后的滤波结果信号值。
选取合适约束条件确定参数后，可以获得式(4)所示的低通滤波器，
Figure PCTCN2019121693-appb-000005
通过以下参数自适应过程可以获得相应的性能更高的自适应滤波器：
Figure PCTCN2019121693-appb-000006
其中：
Figure PCTCN2019121693-appb-000007
第n个采样点对应的滤波后的滤波结果信号值为：
Figure PCTCN2019121693-appb-000008
结合本实施例提供的滤波方法，采用一阶差分约束对上述低通滤波函数进行改进可以获得式(8)所示的滤波模型：
Figure PCTCN2019121693-appb-000009
由于该模型具有凸性质，其参数自适应方法为：
Figure PCTCN2019121693-appb-000010
其中，与实际应用有关的限制性约束包括：
Figure PCTCN2019121693-appb-000011
第n个采样点对应的滤波后的滤波结果信号值为：
Figure PCTCN2019121693-appb-000012
其中，参数η该满足0<η<T，T为信号的采样周期。
在式(11)的基础上，可以先将c 1设置为0，调节参数c 0至使滤波结果的相位差和平滑度满足需求。然后再调节c 1，以进一步提高滤波结果的平滑度。
再以批量样本滤波为例，结合本实施例提供的滤波方法对批量样本滤波模型进行改进后，可以获得式(12)所示的滤波模型：
Figure PCTCN2019121693-appb-000013
其中：ω＝[ω 1 ω 2 … ω m] T，X＝[x(1) x(2) … x(n)] T，
Figure PCTCN2019121693-appb-000014
ΔY为Y的一阶差分矩阵。
该差分滤波模型的参数可采用式(13)所示方式进行估计：
Figure PCTCN2019121693-appb-000015
其中，
Figure PCTCN2019121693-appb-000016
Z 0＝[x(1)-0 x(2)-y(1) … x(n)-y(n-1)] T，
Z 1＝[0 Δy(1) … Δy(n-1)] T，
Figure PCTCN2019121693-appb-000017
Figure PCTCN2019121693-appb-000018
批量滤波估计：
Figure PCTCN2019121693-appb-000019
其中：
Figure PCTCN2019121693-appb-000020
请参照图5，图5为使用低通滤波器(图5“Lowpass Filter”曲线所示)、自适应参数的低通滤波器(图5“Adaptive Filter”曲线所示)及本实施例提供滤波方法(图5“LDC”曲线所示)对具有噪声的信号(图5“Withnoise”曲线所示)进行滤波的滤波结果波形的对比示意图，其中，真实信号为“Real”曲线所示。可以看出，采用本实施例提供的滤波方法得到的滤波结果平滑度明显优于低通滤波器和自适应参数的低通滤波器。
再请参见图6，图6示出了使用低通滤波器(图6“Lowpass Filter”曲线所示)、自适应参数的低通滤波器(图6“Adaptive Filter”曲线所示)及本实施例提供滤波方法(图6“LDC”曲线所示)对具有噪声的信号进行滤波的结果的相位偏移对比示意图，其中，“Zero Line”为零偏移基准线。采用本实施例提供的滤波方法得到的滤波结果相位偏移明显小于普通低通滤波器，且与自适应阐述的低通滤波器相差无几。
再请参见图7，图7示出了使用低通滤波器(图7“Lowpass Filter”曲线所示)、自适应参数的低通滤波器(图7“Adaptive Filter”曲线所示)及本实施例提供滤波方法(图7“LDC”曲线所示)对具有噪声的信号进行滤波的结果的信号差分比较示意图。可以看出，采用本实施例提供的滤波方法得到的滤波结果信号差分明显小于低通滤波器和自适应参数的低通滤波器。
结合图5、图6及图7所示的结果可以看出，采用本实施例提供的滤波方法，在同等相位偏移程度的情况下可以更进一步的提高滤波结果的平滑度。
请参照图8，基于相同的构思，本实施例还提供一种信号滤波装置110，从功能上划分，该信号滤波装置110可以包括信号获取模块111及信号处理模块112。
所述信号获取模块111配置成获取待处理信号。
本实施例中，所述信号获取模块111可配置成执行图4所示的步骤S110，关于所述信号获取模块111的具体描述可参对所述步骤S110的描述。
所述信号处理模块112，配置成使用具有差分约束的预设滤波函数对所述待处理信号进行滤波，获得滤波后的信号，其中，所述差分约束为在所述预设滤波函数的滤波结果上叠 加该预设滤波函数的预设倍数的差分值。
本实施例中，所述信号处理模块112可配置成执行图4所示的步骤S120，关于所述信号处理模块112的具体描述可参对所述步骤S120的描述。
可选地，在本实施例中，所述信号处理模块112具体配置成针对待处理信号的每个采样点，使用第一预设倍数的预设滤波函数对该采样点的信号值进行处理，并在滤波结果上叠加所述预设滤波函数在该采样点的第二预设倍数的i阶差分的和，获得该采样点滤波后的滤波结果信号值；其中，i大于等于1。
可选地，请参见图9，在本实施例中，所述滤波装置还包括第一调整模块113及第二调整模块114。
所述第一调整模块113配置成调整所述第一预设倍数，使所述滤波后的信号的平滑度大于第一预设平滑度阈值，且所述滤波后的信号与所述待处理信号的相位差小于预设相位差阈值。
所述第二调整模块114配置成调整所述第二预设倍数，使所述滤波后的信号的平滑度大于第二预设平滑度阈值，其中，所述第二预设平滑度阈值大于第一预设平滑度阈值。
可选地，在本实施例中，所述第一调整模块113还配置成在调整所述第一预设倍数之前，将所述第二预设倍数置零；
所述第二调整模块114具体配置成逐渐增大所述第二预设倍数，使所述滤波后的信号的平滑度大于所述第二预设平滑度阈值。
可选地，在本实施例中，不同阶数差分对应有不同的所述第二预设倍数；所述第二调整模块114具体配置成从1阶差分起，依次增大各阶差分对应的第二预设倍数。
在本申请所提供的实施例中，应该理解到，所揭露的装置和方法，也可以通过其它的方式实现。以上所描述的装置实施例仅仅是示意性的，例如，附图中的流程图和框图显示了根据本申请的多个实施例的装置、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或代码的一部分，所述模块、程序段或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现方式中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
另外，在本申请各个实施例中的各功能模块可以集成在一起形成一个独立的部分，也可以是各个模块单独存在，也可以两个或两个以上模块集成形成一个独立的部分。
所述功能如果以软件功能模块的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
以上所述，仅为本申请的各种实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应所述以权利要求的保护范围为准。
工业实用性
本申请提供的信号滤波方法、装置及数据处理设备，从研究真实光滑信号与含噪音的信号之间的差异角度出发，通过使用具有差分约束的预设滤波函数对所述待处理信号进行滤波，可以在预设滤波函数的滤波结果上进一步减小信号差分，从而可以在不影响相位偏差的情况下进一步提高滤波结果的平滑性。

Claims (12)

  1. 一种信号滤波方法，其特征在于，所述方法包括：
    获取待处理信号；
    使用具有差分约束的预设滤波函数对所述待处理信号进行滤波，获得滤波后的信号，其中，所述差分约束为在所述预设滤波函数的滤波结果上叠加该预设滤波函数的预设倍数的差分值。
  2. 根据权利要求1所述的方法，其特征在于，使用具有差分约束的预设滤波函数对所述待处理信号进行滤波的步骤，包括：
    针对待处理信号的每个采样点，使用第一预设倍数的预设滤波函数对该采样点的信号值进行处理，并在滤波结果上叠加所述预设滤波函数在该采样点的第二预设倍数的i阶差分的和，获得该采样点滤波后的滤波结果信号值；其中，i大于等于1。
  3. 根据权利要求2所述的方法，其特征在于，所述方法还包括：
    调整所述第一预设倍数，使所述滤波后的信号的平滑度大于第一预设平滑度阈值，且所述滤波后的信号与所述待处理信号的相位差小于预设相位差阈值；
    调整所述第二预设倍数，使所述滤波后的信号的平滑度大于第二预设平滑度阈值，其中，所述第二预设平滑度阈值大于第一预设平滑度阈值。
  4. 根据权利要求3所述的方法，其特征在于，
    在调整所述第一预设倍数之前，所述方法还包括：
    将所述第二预设倍数置零；
    调整所述第二预设倍数的步骤，包括：
    逐渐增大所述第二预设倍数，使所述滤波后的信号的平滑度大于所述第二预设平滑度阈值。
  5. 根据权利要求4所述的方法，其特征在于，不同阶数差分对应有不同的所述第二预设倍数；所述逐渐增大所述第二预设倍数的步骤，包括：
    从1阶差分起，依次增大各阶差分对应的第二预设倍数。
  6. 一种信号滤波装置，其特征在于，所述装置包括：
    信号获取模块，配置成获取待处理信号；
    信号处理模块，配置成使用具有差分约束的预设滤波函数对所述待处理信号进行滤波，获得滤波后的信号，其中，所述差分约束为在所述预设滤波函数的滤波结果上叠加该预设滤波函数的预设倍数的差分值。
  7. 根据权利要求6所述的滤波装置，其特征在于，所述信号处理模块具体配置成针对待处理信号的每个采样点，使用第一预设倍数的预设滤波函数对该采样点的信号值进行处理，并在滤波结果上叠加所述预设滤波函数在该采样点的第二预设倍数的i阶差分的和，获得该采样点滤波后的滤波结果信号值；其中，i大于等于1。
  8. 根据权利要求7所述的滤波装置，其特征在于，所述滤波装置还包括：
    第一调整模块，配置成调整所述第一预设倍数，使所述滤波后的信号的平滑度大于第一预设平滑度阈值，且所述滤波后的信号与所述待处理信号的相位差小于预设相位差阈值；
    第二调整模块，配置成调整所述第二预设倍数，使所述滤波后的信号的平滑度大于第二预设平滑度阈值，其中，所述第二预设平滑度阈值大于第一预设平滑度阈值。
  9. 根据权利要求8所述的滤波装置，其特征在于，所述第一调整模块还配置成在调整所述第一预设倍数之前，将所述第二预设倍数置零；
    所述第二调整模块具体配置成逐渐增大所述第二预设倍数，使所述滤波后的信号的平滑度大于所述第二预设平滑度阈值。
  10. 根据权利要求9所述的滤波装置，其特征在于，不同阶数差分对应有不同的所述第二预设倍数；所述第二调整模块具体配置成从1阶差分起，依次增大各阶差分对应的第二预设倍数。
  11. 一种数据处理设备，其特征在于，包括机器可读存储介质及处理器，所述机器可读存储介质存储有机器可执行指令，所述机器可执行指令在被所述处理器执行时实现权利要求1-5任意一项所述的方法。
  12. 一种机器可读存储介质，其特征在于，所述机器可读存储介质存储有机器可执行指令，所述机器可执行指令在被处理器执行时实现权利要求1-5任意一项所述的方法。
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