CN109150245B - Method and system for filtering nonstationary and non-Gaussian noise in PLC communication signal - Google Patents

Method and system for filtering nonstationary and non-Gaussian noise in PLC communication signal Download PDF

Info

Publication number
CN109150245B
CN109150245B CN201810970673.8A CN201810970673A CN109150245B CN 109150245 B CN109150245 B CN 109150245B CN 201810970673 A CN201810970673 A CN 201810970673A CN 109150245 B CN109150245 B CN 109150245B
Authority
CN
China
Prior art keywords
layer
plc communication
sequence
communication signal
signal sequence
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
CN201810970673.8A
Other languages
Chinese (zh)
Other versions
CN109150245A (en
Inventor
翟明岳
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong University of Petrochemical Technology
Original Assignee
Guangdong University of Petrochemical Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong University of Petrochemical Technology filed Critical Guangdong University of Petrochemical Technology
Priority to CN201810970673.8A priority Critical patent/CN109150245B/en
Publication of CN109150245A publication Critical patent/CN109150245A/en
Application granted granted Critical
Publication of CN109150245B publication Critical patent/CN109150245B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/54—Systems for transmission via power distribution lines
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H21/00—Adaptive networks
    • H03H21/0012—Digital adaptive filters
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H21/00—Adaptive networks
    • H03H21/0012—Digital adaptive filters
    • H03H21/0043—Adaptive algorithms
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H21/00—Adaptive networks
    • H03H21/0012—Digital adaptive filters
    • H03H2021/0085—Applications
    • H03H2021/0094—Interference Cancelling
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B2203/00—Indexing scheme relating to line transmission systems
    • H04B2203/54—Aspects of powerline communications not already covered by H04B3/54 and its subgroups
    • H04B2203/5404—Methods of transmitting or receiving signals via power distribution lines
    • H04B2203/5425—Methods of transmitting or receiving signals via power distribution lines improving S/N by matching impedance, noise reduction, gain control

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)

Abstract

The invention discloses a method and a system for filtering non-stationary non-Gaussian noise in a PLC communication signal. The filtering method comprises the following steps: acquiring an actually measured PLC communication signal sequence and the number of multi-scale decomposition layers in a PLC communication system; determining a PLC communication signal sequence after each layer of filtering according to the number of multi-scale decomposition layers and the cut-off frequency of a multi-layer low-pass filter; determining the length of a time window of a current layer according to the PLC communication signal sequence filtered by the current layer; constructing an analytic sequence; determining the pseudo Wigner-Ville distribution of the analytic sequence under the length of the current layer time window; restoring the filtered PLC communication signal sequence of each layer according to the pseudo Wigner-Ville distribution, and determining a restored PLC communication signal sequence; and filtering non-stationary non-Gaussian noise in the PLC communication system from the last layer of the multi-scale decomposition layer according to the recovery PLC communication signal sequence of the current layer and the interpolated PLC communication signal sequence of the previous layer. The invention can effectively filter the non-stationary non-Gaussian noise in the PLC communication system.

Description

Method and system for filtering nonstationary and non-Gaussian noise in PLC communication signal
Technical Field
The invention relates to the field of communication, in particular to a method and a system for filtering non-stationary non-Gaussian noise in a PLC communication signal.
Background
Compared with various wired communication technologies, the power line communication has the advantages of no need of rewiring, easiness in networking and the like, and has wide application prospect. The power line communication technology is classified into narrowband over power line (nPL) and Broadband over power line (BPL). The narrow-band power line communication refers to a power line carrier communication technology with a bandwidth limited to 3 k-500 kHz, and specifically includes a specified bandwidth (3-148.5 kHz) of European CEnELEC, a specified bandwidth (9-490 kHz) of the U.S. Federal communications Commission (fCC), a specified bandwidth (9-450 kHz) of the Japanese Association of Radio Industries and Businesses (ARIB), and a specified bandwidth (3-500 kHz) of China. The narrow-band power line communication technology mostly adopts a single carrier modulation technology, such as a PSK technology, a DSSS technology, a Chirp technology and the like, and the communication rate is less than 1 mbits/s; the broadband power line communication technology refers to a power line carrier communication technology with the bandwidth limited between 1.6-30 mHz and the communication rate generally higher than 1mbps, and adopts various spread spectrum communication technologies with OfDm as a core.
Although the power line communication system has wide application and relatively mature technology, a large amount of branches and electrical equipment in the power line communication system can generate a large amount of noise in a power line channel, wherein random impulse noise has high randomness and high noise intensity, and seriously damages the power line communication system, so that the technology for suppressing the random impulse noise is always the key point of research of domestic and foreign scholars.
In practical applications, some simple nonlinear technologies are often applied to eliminate power line channel noise, such as Clipping, Blanking and Clipping/Blanking technologies, but these research methods all have to work well under a certain signal-to-noise ratio, and only consider the elimination of impulse noise; with the application and popularization of nonlinear electrical appliances, background noise in a medium and low voltage Power transmission and distribution network presents obvious non-stationarity and non-Gaussian characteristics, impact noise and non-stationary non-Gaussian noise exist, a common low-pass filter cannot achieve an ideal filtering effect in a non-stationary non-Gaussian noise environment, and the performance of a Power Line communication system (PLC) is seriously influenced.
Disclosure of Invention
The invention aims to provide a method and a system for filtering non-stationary non-Gaussian noise in a PLC communication signal, so as to solve the problem of low efficiency of filtering the non-stationary non-Gaussian noise in a PLC communication system.
In order to achieve the purpose, the invention provides the following scheme:
a method for filtering non-stationary non-Gaussian noise in a PLC communication signal comprises the following steps:
acquiring an actually measured PLC communication signal sequence and the number of multi-scale decomposition layers in a PLC communication system; the multi-scale decomposition layer number is used for decomposing the actually measured PLC communication signal sequence;
determining the cut-off frequency of a multilayer low-pass filter according to the number of the multi-scale decomposition layers;
performing low-pass filtering processing on the actually measured PLC communication signal sequence layer by layer according to the number of multi-scale decomposition layers and the cut-off frequency of the multi-layer low-pass filter, and determining each layer of the filtered PLC communication signal sequence;
acquiring a PLC communication signal sequence after current layer filtering;
determining the length of a time window of the current layer according to the PLC communication signal sequence filtered by the current layer;
performing point-by-point processing on the power signals in the PLC communication signal sequence after the current layer is filtered, and constructing an analytic sequence;
determining pseudo Wigner-Ville distribution of the analytic sequence under the length of the current layer time window;
according to the sequence of the number of the multi-scale decomposition layers from large to small, restoring the filtered PLC communication signal sequence of each layer according to the pseudo Wigner-Ville distribution, and determining a restored PLC communication signal sequence;
performing linear interpolation processing on the recovered PLC communication signal sequence according to the sequence of the number of the multi-scale decomposition layers from large to small, and determining the PLC communication signal sequence after interpolation;
and filtering non-stationary non-Gaussian noise in the PLC communication system from the last layer of the multi-scale decomposition layer according to the recovery PLC communication signal sequence of the current layer and the interpolated PLC communication signal sequence of the previous layer.
Optionally, the determining the cut-off frequency of the multilayer low-pass filter according to the number of the multi-scale decomposition layers specifically includes:
according to the formula
Figure BDA0001776095960000031
Determining a cut-off frequency of a multi-layer low-pass filter; wherein the content of the first and second substances,
Figure BDA0001776095960000032
cut-off frequency of the multi-layer low-pass filter; c. CC is the total number of multi-scale decomposition layers, and C is 1,2, …, C.
Optionally, the determining the length of the time window of the current layer according to the filtered PLC communication signal sequence of the current layer specifically includes:
acquiring the sampling frequency and the main frequency of the PLC communication signal sequence after the current layer is filtered;
according to the formula
Figure BDA0001776095960000033
Determining the length of a current layer time window; wherein the content of the first and second substances,
Figure BDA0001776095960000034
the length of a current layer time window;
Figure BDA0001776095960000035
filtering the PLC communication signal sequence P of the current layercThe sampling frequency of (d);
Figure BDA0001776095960000036
filtering the PLC communication signal sequence P of the current layercThe dominant frequency of (c); t iscAnd the sampling interval of the PLC communication signal sequence after the current layer is filtered.
Optionally, the performing point-to-point processing on the power signal in the PLC communication signal sequence after the current layer filtering to construct an analytic sequence specifically includes:
according to the formula
Figure BDA0001776095960000037
Performing point-by-point processing on the power signals in the PLC communication signal sequence after the current layer is filtered, and constructing an analytic sequence; wherein z (n) is an analytic value for constructing the nth point; mu is a modulation coefficient, mu is more than 1 and less than or equal to 2; pc(j) For the current layer signal sequence PcThe jth element in (a).
Optionally, the determining the pseudo-Wigner-Ville distribution of the parsing sequence in the length of the current layer time window specifically includes:
according to the formula
Figure BDA0001776095960000038
Determining pseudo Wigner-Ville distribution of the analytic sequence under the length of the current layer time window; wherein, PWz c(n, f) is an analytic sequence zc(n) time window length in current layer
Figure BDA00017760959600000311
A pseudo Wigner-Ville distribution below; h isc(m) is a function of a time window;
Figure BDA0001776095960000039
is zcConjugation of (n-m), zc(n + m) is a real number,
Figure BDA00017760959600000310
zc(n + m) is the constructed analytical sequence zcAnalytic value of (n) < th > + m points, e-j4πfmCos (4 pi fm) -isin (4 pi fm), n is the number, m is the number, and f is the frequency.
A non-stationary non-gaussian noise filtering system comprising:
the signal sequence and decomposition layer number acquisition module is used for acquiring an actually measured PLC communication signal sequence and a multi-scale decomposition layer number in the PLC communication system; the multi-scale decomposition layer number is used for decomposing the actually measured PLC communication signal sequence;
the multilayer low-pass filter cut-off frequency determining module is used for determining the cut-off frequency of the multilayer low-pass filter according to the number of the multi-scale decomposition layers;
the low-pass filtering processing module is used for performing low-pass filtering processing on the actually measured PLC communication signal sequence layer by layer according to the number of multi-scale decomposition layers and the cut-off frequency of the multi-layer low-pass filter to determine the PLC communication signal sequence after each layer of filtering;
the PLC communication signal sequence obtaining module is used for obtaining the PLC communication signal sequence after the current layer is filtered;
a current layer time window length determining module, configured to determine a current layer time window length according to the PLC communication signal sequence filtered by the current layer;
the analysis sequence construction module is used for performing point-by-point processing on the power signals in the PLC communication signal sequence after the current layer of filtering to construct an analysis sequence;
the pseudo Wigner-Ville distribution determining module is used for determining the pseudo Wigner-Ville distribution of the analytic sequence under the current layer time window length;
the recovery module is used for recovering each layer of the filtered PLC communication signal sequence according to the sequence of the number of the multi-scale decomposition layers from large to small and the pseudo Wigner-Ville distribution, and determining a recovered PLC communication signal sequence;
the linear interpolation processing module is used for performing linear interpolation processing on the recovered PLC communication signal sequence according to the sequence of the number of the multi-scale decomposition layers from large to small to determine the PLC communication signal sequence after interpolation;
and the non-stationary non-Gaussian noise filtering module is used for filtering the non-stationary non-Gaussian noise in the PLC communication system from the last layer of the multi-scale decomposition layer according to the recovery PLC communication signal sequence of the current layer and the interpolated PLC communication signal sequence of the previous layer.
Optionally, the multi-layer low-pass filter cut-off frequency determining module specifically includes:
a multi-layer low-pass filter cut-off frequency determining unit for determining the cut-off frequency according to the formula
Figure BDA0001776095960000051
Determining a cut-off frequency of a multi-layer low-pass filter; wherein the content of the first and second substances,
Figure BDA0001776095960000052
cut-off frequency of the multi-layer low-pass filter; c is the number of multi-scale decomposition layers, C is the total number of multi-scale decomposition layers, and C is 1,2, …, C.
Optionally, the module for determining the length of the current-layer time window specifically includes:
the sampling frequency and main frequency acquisition unit is used for acquiring the sampling frequency and main frequency of the PLC communication signal sequence after the current layer is filtered;
a current layer time window length determining unit for determining the length of the current layer time window according to a formula
Figure BDA0001776095960000053
Determining the length of a current layer time window; wherein the content of the first and second substances,
Figure BDA0001776095960000054
the length of a current layer time window;
Figure BDA0001776095960000055
filtering the PLC communication signal sequence P of the current layercThe sampling frequency of (d);
Figure BDA0001776095960000056
filtering the PLC communication signal sequence P of the current layercThe dominant frequency of (c); t iscAnd the sampling interval of the PLC communication signal sequence after the current layer is filtered.
Optionally, the parsing sequence building module specifically includes:
a parsing sequence construction unit for constructing a parsing sequence according to a formula
Figure BDA0001776095960000057
Performing point-by-point processing on the power signals in the PLC communication signal sequence after the current layer is filtered, and constructing an analytic sequence; wherein z (n) is an analytic value for constructing the nth point; mu is a modulation coefficient, mu is more than 1 and less than or equal to 2; pc(j) For the current layer signal sequence PcThe jth element in (a).
Optionally, the pseudo-Wigner-Ville distribution determining module specifically includes:
a pseudo Wigner-Ville distribution determining unit for determining the distribution of the Wigner-Ville according to a formula
Figure BDA0001776095960000058
Determining pseudo Wigner-Ville distribution of the analytic sequence under the length of the current layer time window; wherein, PWz c(n, f) is an analytic sequence zc(n) time window length in current layer
Figure BDA0001776095960000059
A pseudo Wigner-Ville distribution below; h isc(m) is a function of a time window;
Figure BDA00017760959600000510
is zcConjugation of (n-m), zc(n + m) is a real number,
Figure BDA00017760959600000511
zc(n + m) is the constructed analytical sequence zcAnalytic value of (n) < th > + m points, e-j4πfmCos (4 pi fm) -isin (4 pi fm), n is the number, m is the number, and f is the frequency.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the invention provides a method and a system for filtering non-stationary non-Gaussian noise in a PLC communication signal, which are used for filtering an actually measured PLC communication signal sequence and utilizing pseudo Wigner-Ville distribution, thereby overcoming the non-stationary characteristic of a power signal, having a stronger filtering effect on the non-stationary non-Gaussian noise in the PLC communication signal and further improving the filtering efficiency of the non-stationary non-Gaussian noise in a PLC communication system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a method for filtering non-stationary non-gaussian noise in a PLC communication signal according to the present invention;
FIG. 2 is a flow chart of the adaptive time-frequency domain filtering algorithm based on multi-scale decomposition according to the present invention;
fig. 3 is a structural diagram of a system for filtering non-stationary non-gaussian noise according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for filtering non-stationary non-Gaussian noise in a PLC communication signal, which can improve the filtering efficiency of the non-stationary non-Gaussian noise in a PLC communication system.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a flowchart of a method for filtering non-stationary non-gaussian noise in a PLC communication signal, as shown in fig. 1, the method for filtering non-stationary non-gaussian noise in a PLC communication signal includes:
step 101: acquiring an actually measured PLC communication signal sequence and the number of multi-scale decomposition layers in a PLC communication system; and the multi-scale decomposition layer number is used for decomposing the actually measured PLC communication signal sequence.
Fig. 2 is a flowchart of the adaptive time-frequency domain filtering algorithm based on multi-scale decomposition according to the present invention, as shown in fig. 2, inputting data: inputting measured PLC communication signal sequence P0＝[P(1),P(2),…,P(N)]N is the length of the power signal sequence and the sampling period is T0。
Step 102: and determining the cut-off frequency of the multilayer low-pass filter according to the number of the multi-scale decomposition layers.
The number of layers C for multi-scale decomposition is input, and the number of layers for multi-scale decomposition of the power signal is generally selected to be 3-6.
Determining the cut-off frequency of the multi-layer low-pass filter according to the number of the multi-scale decomposition layers
Figure BDA0001776095960000071
Figure BDA0001776095960000072
Step 103: and performing low-pass filtering processing on the actually measured PLC communication signal sequence layer by layer according to the number of multi-scale decomposition layers and the cut-off frequency of the multi-layer low-pass filter, and determining the filtered PLC communication signal sequence of each layer.
Assuming that the layer number currently undergoing decomposition is C, C is 1,2, …, C; the signal sequence obtained after the last layer of processing is Pc-1Wherein, the original signal without hierarchical processing is P0。
Data filtering of layer c: for the signal sequence Pc-1With a Gaussian low-pass filter cut-off frequency of
Figure BDA0001776095960000073
Data downsampling at layer c-downsampling (sampling interval T) of filtered signalc，
Figure BDA0001776095960000074
)。
The signal after filtering and down-sampling of this layer is denoted as Pc。
Step 104: and acquiring the PLC communication signal sequence after the current layer is filtered.
Step 105: and determining the length of the time window of the current layer according to the PLC communication signal sequence filtered by the current layer.
Calculating the time window length of the current layer
Figure BDA0001776095960000075
Figure BDA0001776095960000076
Wherein
Figure BDA0001776095960000077
Is the current layer signal PcThe sampling frequency of (d);
Figure BDA0001776095960000078
is the current layer signal PcThe dominant frequency of (c).
Step 106: and performing point-by-point processing on the power signals in the PLC communication signal sequence after the current layer is filtered, and constructing an analytic sequence.
For the current layer signal sequence PcThe data in (1) is processed point by point.
Assuming that the data point number currently processed is n, an analytic value z (n) of the nth point is constructed:
Figure BDA0001776095960000081
mu is a modulation coefficient, mu is more than 1 and less than or equal to 2, can be arbitrarily taken according to actual conditions, and has no essential influence;P c(j) For the current layer signal sequence PcThe jth element in (a).
Step 107: and determining the pseudo Wigner-Ville distribution of the analysis sequence under the current layer time window length.
Calculating the analytic sequence zc(n) time window length in current layer
Figure BDA00017760959600000814
Pseudo Wigner-Ville distribution PWz c(n,f)：
Figure BDA0001776095960000082
Wherein h isc(m) is a function of a time window;
Figure BDA0001776095960000083
is zcConjugation of (n-m), zc(n + m) is a real number,
Figure BDA0001776095960000084
zc(n + m) is the constructed analytical sequence zcAnalytic value of (n) < th > + m points, e-j4πfmCos (4 pi fm) -isin (4 pi fm), n is the number, m is the number, f is the numberFrequency.
Step 108: and restoring the filtered PLC communication signal sequence of each layer according to the sequence of the number of the multi-scale decomposition layers from large to small and the pseudo Wigner-Ville distribution, and determining the restored PLC communication signal sequence.
Restoring the signal of the current layer
Figure BDA0001776095960000085
Figure BDA0001776095960000086
Until all layers are processed, a series of signal recovery data are obtained:
Figure BDA0001776095960000087
since each layer recovers data
Figure BDA0001776095960000088
With different frequency spectrums and different sampling rates, the power data of each layer is processed by executing steps 109 to 110.
Step 109: and performing linear interpolation processing on the recovered PLC communication signal sequence according to the sequence of the number of the multi-scale decomposition layers from large to small, and determining the PLC communication signal sequence after interpolation.
Step 110: and filtering non-stationary non-Gaussian noise in the PLC communication system from the last layer of the multi-scale decomposition layer according to the recovery PLC communication signal sequence of the current layer and the interpolated PLC communication signal sequence of the previous layer.
Restoring data to the last layer (i.e., layer C)
Figure BDA0001776095960000089
Linear interpolation processing is carried out to obtain the interpolated data sequence
Figure BDA00017760959600000810
Require that
Figure BDA00017760959600000811
At a sampling rate of
Figure BDA00017760959600000812
At this time, twice as much
Figure BDA00017760959600000813
Is the same as the data sampling rate of the previous layer (i.e., layer C-1).
The following processing is performed in descending order of layer numbers: assume that the sequence number of the current layer is c.
The current layer has two data sequences: original recovery data
Figure BDA0001776095960000091
And the upper layer (c +1 th layer) recovers data
Figure BDA0001776095960000092
Obtained after interpolation
Figure BDA0001776095960000093
A sequence of data. Obtaining the data recovery sequence of the current layer according to the two sequences
Figure BDA0001776095960000094
Figure BDA0001776095960000095
Final recovery sequence for current layer
Figure BDA0001776095960000096
Linear interpolation is carried out to obtain the interpolated data sequence
Figure BDA0001776095960000097
Require that
Figure BDA0001776095960000098
At a sampling rate of
Figure BDA0001776095960000099
Twice as much. At this time
Figure BDA00017760959600000910
Is the same as the data sampling rate of the previous layer (i.e., layer c-2).
Repeating the steps until c is 1, and finishing to obtain the data sequence after the original signal sequence is filtered
Figure BDA00017760959600000911
The sequence is the sequence to be solved, the processing is finished, and the unstable non-Gaussian noise in the original signal sequence (namely the actually measured PLC communication signal sequence) is filtered.
When the pseudo Wigner-Ville time frequency distribution is obtained, the optimal window length is determined according to the main frequency hierarchy of the data, and time frequency distribution calculation and data recovery are carried out hierarchically, so that non-stable non-Gaussian noise is filtered more effectively.
Fig. 3 is a structural diagram of a non-stationary non-gaussian noise filtering system provided in the present invention, and as shown in fig. 3, a non-stationary non-gaussian noise filtering system includes:
a signal sequence and decomposition layer number acquisition module 301, configured to acquire an actually measured PLC communication signal sequence and a multi-scale decomposition layer number in a PLC communication system; and the multi-scale decomposition layer number is used for decomposing the actually measured PLC communication signal sequence.
A multi-layer low-pass filter cut-off frequency determining module 302, configured to determine a multi-layer low-pass filter cut-off frequency according to the number of multi-scale decomposition layers.
The multi-layer low-pass filter cut-off frequency determination module 302 specifically includes: a multi-layer low-pass filter cut-off frequency determining unit for determining the cut-off frequency according to the formula
Figure BDA00017760959600000912
Determining a cut-off frequency of a multi-layer low-pass filter; wherein the content of the first and second substances,
Figure BDA00017760959600000913
cut-off frequency of the multi-layer low-pass filter; c is the number of multi-scale decomposition layers, C is moreThe total number of scale-decomposition layers, C, is 1,2, …, C.
And a low-pass filtering processing module 303, configured to perform low-pass filtering processing on the actually measured PLC communication signal sequence layer by layer according to the number of multi-scale decomposition layers and the cut-off frequency of the multilayer low-pass filter, and determine a filtered PLC communication signal sequence of each layer.
And the current layer filtered PLC communication signal sequence obtaining module 304 is configured to obtain a current layer filtered PLC communication signal sequence.
A current layer time window length determining module 305, configured to determine a current layer time window length according to the PLC communication signal sequence after the current layer filtering.
The current-layer time window length determining module 305 specifically includes: the sampling frequency and main frequency acquisition unit is used for acquiring the sampling frequency and main frequency of the PLC communication signal sequence after the current layer is filtered; a current layer time window length determining unit for determining the length of the current layer time window according to a formula
Figure BDA0001776095960000101
Determining the length of a current layer time window; wherein the content of the first and second substances,
Figure BDA0001776095960000102
the length of a current layer time window;
Figure BDA0001776095960000103
filtering the PLC communication signal sequence P of the current layercThe sampling frequency of (d);
Figure BDA0001776095960000104
filtering the PLC communication signal sequence P of the current layercThe dominant frequency of (c); t iscAnd the sampling interval of the PLC communication signal sequence after the current layer is filtered.
And an analytic sequence constructing module 306, configured to perform point-to-point processing on the power signal in the PLC communication signal sequence after the current layer is filtered, and construct an analytic sequence.
The parsing sequence construction module 306 specifically includes: solution (II)A sequence-analyzing construction unit for constructing a sequence according to a formula
Figure BDA0001776095960000105
Performing point-by-point processing on the power signals in the PLC communication signal sequence after the current layer is filtered, and constructing an analytic sequence; wherein z (n) is an analytic value for constructing the nth point; mu is a modulation coefficient, mu is more than 1 and less than or equal to 2; pc(j) For the current layer signal sequence PcThe jth element in (a).
And a pseudo-Wigner-Ville distribution determining module 307, configured to determine a pseudo-Wigner-Ville distribution of the parsing sequence in the length of the current layer time window.
The pseudo Wigner-Ville distribution determining module 307 specifically includes: a pseudo Wigner-Ville distribution determining unit for determining the distribution of the Wigner-Ville according to a formula
Figure BDA0001776095960000106
Determining pseudo Wigner-Ville distribution of the analytic sequence under the length of the current layer time window; wherein, PWz c(n, f) is an analytic sequence zc(n) time window length in current layer
Figure BDA0001776095960000107
A pseudo Wigner-Ville distribution below; h isc(m) is a function of a time window;
Figure BDA0001776095960000108
is zcConjugation of (n-m), zc(n + m) is a real number,
Figure BDA0001776095960000109
zc(n + m) is the constructed analytical sequence zcAnalytic value of (n) < th > + m points, e-j4πfmCos (4 pi fm) -isin (4 pi fm), n is the number, m is the number, and f is the frequency.
And a restoring module 308, configured to restore the filtered PLC communication signal sequence of each layer according to the pseudo-Wigner-Ville distribution in order of the number of multi-scale decomposition layers from large to small, and determine to restore the PLC communication signal sequence.
And the linear interpolation processing module 309 is configured to perform linear interpolation processing on the recovered PLC communication signal sequence according to the sequence from the large number of the multi-scale decomposition layers to the small number of the multi-scale decomposition layers, and determine the interpolated PLC communication signal sequence.
And a non-stationary non-gaussian noise filtering module 310, configured to filter, starting from the last layer of the multi-scale decomposition layer, non-stationary non-gaussian noise in the PLC communication system according to the recovered PLC communication signal sequence of the current layer and the interpolated PLC communication signal sequence of the previous layer.
The filtering method and the system provided by the invention are applied to a signal filter of a PLC communication system, can effectively filter background noise in a PLC communication signal, particularly non-stationary non-Gaussian noise, and have higher calculation speed and simpler structure.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (10)

1. A method for filtering non-stationary non-Gaussian noise in a PLC communication signal is characterized by comprising the following steps:
acquiring an actually measured PLC communication signal sequence and the number of multi-scale decomposition layers in a PLC communication system; the multi-scale decomposition layer number is used for decomposing the actually measured PLC communication signal sequence;
determining the cut-off frequency of a multilayer low-pass filter according to the number of the multi-scale decomposition layers;
performing low-pass filtering processing on the actually measured PLC communication signal sequence layer by layer according to the number of multi-scale decomposition layers and the cut-off frequency of the multi-layer low-pass filter, and determining each layer of the filtered PLC communication signal sequence;
acquiring a PLC communication signal sequence after current layer filtering;
determining the length of a time window of the current layer according to the PLC communication signal sequence filtered by the current layer;
performing point-by-point processing on the power signals in the PLC communication signal sequence after the current layer is filtered, and constructing an analytic sequence;
determining pseudo Wigner-Ville distribution of the analytic sequence under the length of the current layer time window;
according to the sequence of the number of the multi-scale decomposition layers from large to small, restoring the filtered PLC communication signal sequence of each layer according to the pseudo Wigner-Ville distribution, and determining a restored PLC communication signal sequence;
performing linear interpolation processing on the recovered PLC communication signal sequence according to the sequence of the number of the multi-scale decomposition layers from large to small, and determining the PLC communication signal sequence after interpolation;
filtering non-stationary non-Gaussian noise in the PLC communication system according to the recovery PLC communication signal sequence of the current layer and the interpolated PLC communication signal sequence of the previous layer from the last layer of the multi-scale decomposition layer;
restoring data to the last layer
Figure FDA0002891068510000011
Linear interpolation processing is carried out to obtain the interpolated data sequence
Figure FDA0002891068510000012
Require that
Figure FDA0002891068510000013
At a sampling rate of
Figure FDA0002891068510000014
At this time, twice as much
Figure FDA0002891068510000015
The data sampling rate of (a) is the same as the data sampling rate of the previous layer;
the following processing is performed in descending order of layer numbers: assuming that the sequence number of the current layer is c;
the current layer has two data sequences: original recovery data
Figure FDA0002891068510000021
And the upper layer recovers the data
Figure FDA0002891068510000022
Obtained after interpolation
Figure FDA0002891068510000023
A data sequence; obtaining the data recovery sequence of the current layer according to the two sequences
Figure FDA0002891068510000024
Figure FDA0002891068510000025
Final recovery sequence for current layer
Figure FDA0002891068510000026
Linear interpolation is carried out to obtain the interpolated data sequence
Figure FDA0002891068510000027
Require that
Figure FDA0002891068510000028
At a sampling rate of
Figure FDA0002891068510000029
Twice of; at this time
Figure FDA00028910685100000210
The data sampling rate of (a) is the same as the data sampling rate of the previous layer;
repeating the steps until c is 1, and finishing to obtain the data sequence after the original signal sequence is filtered
Figure FDA00028910685100000211
The sequence is the sequence to be solved, the processing is finished, and the unstable non-Gaussian noise in the actually measured PLC communication signal sequence is filtered.
2. A filtering method according to claim 1, wherein the determining a cut-off frequency of a multi-layer low-pass filter according to the number of multi-scale decomposition layers specifically includes:
according to the formula
Figure FDA00028910685100000212
Determining a cut-off frequency of a multi-layer low-pass filter; wherein the content of the first and second substances,
Figure FDA00028910685100000213
cut-off frequency of the multi-layer low-pass filter; c is the number of multi-scale decomposition layers, C is the total number of multi-scale decomposition layers, and C is 1,2, …, C.
3. A filtering method according to claim 2, wherein the determining a length of a time window of a current layer according to the filtered PLC communication signal sequence of the current layer specifically includes:
acquiring the sampling frequency and the main frequency of the PLC communication signal sequence after the current layer is filtered;
according to the formula
Figure FDA00028910685100000214
Determining the length of a current layer time window; wherein the content of the first and second substances,
Figure FDA00028910685100000215
the length of a current layer time window;
Figure FDA00028910685100000216
filtering the PLC communication signal sequence P of the current layercThe sampling frequency of (d);
Figure FDA00028910685100000217
filtering the PLC communication signal sequence P of the current layercThe dominant frequency of (c); t iscAnd the sampling interval of the PLC communication signal sequence after the current layer is filtered.
4. A filtering method according to claim 3, wherein the performing point-by-point processing on the power signal in the PLC communication signal sequence after the current layer filtering to construct an analytic sequence specifically includes:
according to the formula
Figure FDA0002891068510000031
Performing point-by-point processing on the power signals in the PLC communication signal sequence after the current layer is filtered, and constructing an analytic sequence; wherein z (n) is an analytic value for constructing the nth point; mu is a modulation coefficient, mu is more than 1 and less than or equal to 2; pc(j) For the current layer signal sequence PcThe jth element in (a).
5. A filtering method according to claim 4, wherein the determining a pseudo-Wigner-Ville distribution of the analytic sequence over the length of the current layer time window specifically includes:
according to the formula
Figure FDA0002891068510000032
Determining pseudo Wigner-Ville distribution of the analytic sequence under the length of the current layer time window; wherein, PWz c(n, f) is an analytic sequence zc(n) Time window length at current layer
Figure FDA0002891068510000033
A pseudo Wigner-Ville distribution below; h isc(m) is a function of a time window; z is a radical ofc*(n-m) is zcConjugation of (n-m), zc(n + m) is a real number, zc*(n-m)＝zc(n-m)；zc(n + m) is the constructed analytical sequence zcAnalytic value of (n) < th > + m points, e-j4πfmCos (4 pi fm) -isin (4 pi fm), n is the number, m is the number, and f is the frequency.
6. A system for filtering non-stationary non-gaussian noise, comprising:
the signal sequence and decomposition layer number acquisition module is used for acquiring an actually measured PLC communication signal sequence and a multi-scale decomposition layer number in the PLC communication system; the multi-scale decomposition layer number is used for decomposing the actually measured PLC communication signal sequence;
the multilayer low-pass filter cut-off frequency determining module is used for determining the cut-off frequency of the multilayer low-pass filter according to the number of the multi-scale decomposition layers;
the low-pass filtering processing module is used for performing low-pass filtering processing on the actually measured PLC communication signal sequence layer by layer according to the number of multi-scale decomposition layers and the cut-off frequency of the multi-layer low-pass filter to determine the PLC communication signal sequence after each layer of filtering;
the PLC communication signal sequence obtaining module is used for obtaining the PLC communication signal sequence after the current layer is filtered;
a current layer time window length determining module, configured to determine a current layer time window length according to the PLC communication signal sequence filtered by the current layer;
the analysis sequence construction module is used for performing point-by-point processing on the power signals in the PLC communication signal sequence after the current layer of filtering to construct an analysis sequence;
the pseudo Wigner-Ville distribution determining module is used for determining the pseudo Wigner-Ville distribution of the analytic sequence under the current layer time window length;
the recovery module is used for recovering each layer of the filtered PLC communication signal sequence according to the sequence of the number of the multi-scale decomposition layers from large to small and the pseudo Wigner-Ville distribution, and determining a recovered PLC communication signal sequence;
the linear interpolation processing module is used for performing linear interpolation processing on the recovered PLC communication signal sequence according to the sequence of the number of the multi-scale decomposition layers from large to small to determine the PLC communication signal sequence after interpolation;
the non-stationary non-Gaussian noise filtering module is used for filtering non-stationary non-Gaussian noise in the PLC communication system from the last layer of the multi-scale decomposition layer according to the recovery PLC communication signal sequence of the current layer and the interpolated PLC communication signal sequence of the previous layer;
restoring data to the last layer
Figure FDA0002891068510000041
Linear interpolation processing is carried out to obtain the interpolated data sequence
Figure FDA0002891068510000042
Require that
Figure FDA0002891068510000043
At a sampling rate of
Figure FDA0002891068510000044
At this time, twice as much
Figure FDA0002891068510000045
The data sampling rate of (a) is the same as the data sampling rate of the previous layer;
the following processing is performed in descending order of layer numbers: assuming that the sequence number of the current layer is c;
the current layer has two data sequences: original recovery data
Figure FDA0002891068510000046
And the upper layer recovers the data
Figure FDA0002891068510000047
Obtained after interpolation
Figure FDA0002891068510000048
A data sequence; obtaining the data recovery sequence of the current layer according to the two sequences
Figure FDA0002891068510000049
Figure FDA00028910685100000410
Final recovery sequence for current layer
Figure FDA00028910685100000411
Linear interpolation is carried out to obtain the interpolated data sequence
Figure FDA00028910685100000412
Require that
Figure FDA00028910685100000413
At a sampling rate of
Figure FDA00028910685100000414
Twice of; at this time
Figure FDA00028910685100000415
The data sampling rate of (a) is the same as the data sampling rate of the previous layer;
repeating the steps until c is 1, and finishing to obtain the data sequence after the original signal sequence is filtered
Figure FDA0002891068510000051
The sequence is the sequence to be solved, the processing is finished, and the unstable non-Gaussian in the actually measured PLC communication signal sequence is filteredNoise.
7. The filtering system according to claim 6, wherein the multi-layer low-pass filter cut-off frequency determination module specifically comprises:
a multi-layer low-pass filter cut-off frequency determining unit for determining the cut-off frequency according to the formula
Figure FDA0002891068510000052
Determining a cut-off frequency of a multi-layer low-pass filter; wherein the content of the first and second substances,
Figure FDA0002891068510000053
cut-off frequency of the multi-layer low-pass filter; c is the number of multi-scale decomposition layers, C is the total number of multi-scale decomposition layers, and C is 1,2, …, C.
8. The filtering system according to claim 7, wherein the module for determining the length of the time window of the current layer specifically comprises:
the sampling frequency and main frequency acquisition unit is used for acquiring the sampling frequency and main frequency of the PLC communication signal sequence after the current layer is filtered;
a current layer time window length determining unit for determining the length of the current layer time window according to a formula
Figure FDA0002891068510000054
Determining the length of a current layer time window; wherein the content of the first and second substances,
Figure FDA0002891068510000058
the length of a current layer time window;
Figure FDA0002891068510000055
filtering the PLC communication signal sequence P of the current layercThe sampling frequency of (d);
Figure FDA0002891068510000056
filtering the PLC communication signal sequence P of the current layercThe dominant frequency of (c); t iscAnd the sampling interval of the PLC communication signal sequence after the current layer is filtered.
9. A filtering system according to claim 8, wherein the parsing sequence construction module specifically comprises:
a parsing sequence construction unit for constructing a parsing sequence according to a formula
Figure FDA0002891068510000057
Performing point-by-point processing on the power signals in the PLC communication signal sequence after the current layer is filtered, and constructing an analytic sequence; wherein z (n) is an analytic value for constructing the nth point; mu is a modulation coefficient, mu is more than 1 and less than or equal to 2; pc(j) For the current layer signal sequence PcThe jth element in (a).
10. A filtering system according to claim 9, wherein said pseudo-Wigner-Ville distribution determining module comprises in particular:
a pseudo Wigner-Ville distribution determining unit for determining the distribution of the Wigner-Ville according to a formula
Figure FDA0002891068510000061
Determining pseudo Wigner-Ville distribution of the analytic sequence under the length of the current layer time window; wherein, PWz c(n, f) is an analytic sequence zc(n) time window length in current layer
Figure FDA0002891068510000062
A pseudo Wigner-Ville distribution below; h isc(m) is a function of a time window; z is a radical ofc*(n-m) is zcConjugation of (n-m), zc(n + m) is a real number, zc*(n-m)＝zc(n-m)；zc(n + m) is the constructed analytical sequence zcAnalytic value of (n) < th > + m points, e-j4πfmCos (4 pi fm) -isin (4 pi fm), n is the number, m is the number, and f is the frequency.
CN201810970673.8A 2018-08-24 2018-08-24 Method and system for filtering nonstationary and non-Gaussian noise in PLC communication signal Expired - Fee Related CN109150245B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810970673.8A CN109150245B (en) 2018-08-24 2018-08-24 Method and system for filtering nonstationary and non-Gaussian noise in PLC communication signal

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810970673.8A CN109150245B (en) 2018-08-24 2018-08-24 Method and system for filtering nonstationary and non-Gaussian noise in PLC communication signal

Publications (2)

Publication Number Publication Date
CN109150245A CN109150245A (en) 2019-01-04
CN109150245B true CN109150245B (en) 2021-03-19

Family

ID=64827678

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810970673.8A Expired - Fee Related CN109150245B (en) 2018-08-24 2018-08-24 Method and system for filtering nonstationary and non-Gaussian noise in PLC communication signal

Country Status (1)

Country Link
CN (1) CN109150245B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112165342B (en) * 2020-11-09 2021-09-21 华北电力大学 Noise detection method and system by using mode feature vector
CN115859007B (en) * 2023-02-17 2023-05-12 广东石油化工学院 Petrochemical instrument sampling data sliding window constraint fault-tolerant filtering noise reduction method and device

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102195679A (en) * 2010-03-10 2011-09-21 青岛东软载波科技股份有限公司 Power line carrier modulation method, demodulation method, circuit and chip
CN102714521A (en) * 2009-10-26 2012-10-03 索尼公司 Device for use in a power line communication system, power line communication systems and power line communication method
CN106797234A (en) * 2016-09-09 2017-05-31 香港应用科技研究院有限公司 Interference Detection and elimination for power line communication

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7797062B2 (en) * 2001-08-10 2010-09-14 Rockwell Automation Technologies, Inc. System and method for dynamic multi-objective optimization of machine selection, integration and utilization
US8121222B2 (en) * 2008-06-23 2012-02-21 L-3 Communications Integrated Systems L.P. Systems and methods for construction of time-frequency surfaces and detection of signals
JP5515032B2 (en) * 2009-06-24 2014-06-11 国立大学法人京都大学 Noise detection method, noise detection device, simulation method, simulation device, and communication system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102714521A (en) * 2009-10-26 2012-10-03 索尼公司 Device for use in a power line communication system, power line communication systems and power line communication method
CN102195679A (en) * 2010-03-10 2011-09-21 青岛东软载波科技股份有限公司 Power line carrier modulation method, demodulation method, circuit and chip
CN106797234A (en) * 2016-09-09 2017-05-31 香港应用科技研究院有限公司 Interference Detection and elimination for power line communication

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
《基于EMD-TFPF算法的电力线通信噪声消除技术研究》;翟明岳;苏岭东;《电力系统保护与控制》;20151231;第43卷(第7期);第51-56页 *
《基于时频峰值滤波的电力线通信噪声消除方法》;苏岭东;翟明岳;何欣;《电力系统保护与控制 》;20151231(第1期);第15-20页 *

Also Published As

Publication number Publication date
CN109150245A (en) 2019-01-04

Similar Documents

Publication Publication Date Title
CN108880604B (en) Multi-component frequency modulation interference suppression method and device in spread spectrum communication system
CN109150245B (en) Method and system for filtering nonstationary and non-Gaussian noise in PLC communication signal
CN108880621B (en) Adaptive filtering method for power line communication signals
CN112861328B (en) Generator damping evaluation device and method based on random response signals
CN110336591B (en) PLC signal filtering method and system by utilizing signal separation
CN111641435A (en) PLC signal filtering method and system utilizing Fenchel conjugation
CN109039379B (en) Power line communication signal filtering method
CN110719123B (en) PLC signal reconstruction method and system using subspace optimization theory
CN111628804A (en) PLC signal filtering method and system based on Gilbert optimization
CN109117807B (en) Self-adaptive time-frequency peak value filtering method and system for P L C communication signals
CN109586763B (en) Denoising method and denoising system for spread spectrum signal in electronic communication system
CN110739986B (en) PLC channel impulse noise detection method and system using projection cumulant
CN110635824B (en) PLC channel impulse noise detection method and system using classification regression tree
CN110719121A (en) PLC channel impulse noise detection method and system using square exponential kernel
CN111541635A (en) PLC signal filtering method and system using t distribution
CN111800165A (en) PLC signal filtering method and system using singular value matrix
CN109309513B (en) Adaptive reconstruction method for power line communication signals
CN111641436A (en) PLC signal filtering method and system using LP optimization
CN112165342B (en) Noise detection method and system by using mode feature vector
CN110572189A (en) PLC signal filtering method and system utilizing Fermal theory
CN111641434A (en) PLC signal filtering method and system using complete vector
CN110784246A (en) PLC signal filtering method and system by utilizing Lagrande factor
CN111786700A (en) PLC signal filtering method and system utilizing Ke&#39;s energy scale
CN112383326B (en) PLC signal filtering method and system using spectral mode threshold
CN110661549B (en) PLC signal reconstruction method and system by utilizing dictionary atoms

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20210319

Termination date: 20210824