CN111641436A - PLC signal filtering method and system using LP optimization - Google Patents

PLC signal filtering method and system using LP optimization Download PDF

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Publication number
CN111641436A
CN111641436A CN202010482666.0A CN202010482666A CN111641436A CN 111641436 A CN111641436 A CN 111641436A CN 202010482666 A CN202010482666 A CN 202010482666A CN 111641436 A CN111641436 A CN 111641436A
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signal sequence
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signal
matrix
gaussian
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不公告发明人
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Guangdong University of Petrochemical Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/02—Details
    • H04B3/04—Control of transmission; Equalising
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/54—Systems for transmission via power distribution lines

Abstract

The embodiment of the invention discloses a PLC signal filtering method and a system using LP optimization, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; step 102, obtaining a normalized average matrix B; step 103, obtaining LP separation degree P; 104, solving a Gaussian projection matrix A; step 105 finds a noise-filtered signal sequence Snew。

Description

PLC signal filtering method and system using LP optimization
Technical Field
The invention relates to the field of communication, in particular to a PLC signal filtering method and system.
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 divided 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 the bandwidth limited between 3k and 500 kHz; the power line communication technology includes a prescribed bandwidth (3148.5kHz) of european CENELEC, a prescribed bandwidth (9 to 490kHz) of the Federal Communications Commission (FCC) in the united states, a prescribed bandwidth (9 to 450kHz) of the Association of Radio Industries and Businesses (ARIB) in japan, and a prescribed bandwidth (3 to 500kHz) in china. The narrow-band power line communication technology mainly adopts a single carrier modulation technology, such as a PSK technology, a DSSS technology, a Chirp technology and the like, and the communication speed is less than 1 Mbits/s; the broadband power line communication technology refers to a power line carrier communication technology with a bandwidth limited between 1.6 and 30MHz and a communication rate generally above 1Mbps, and adopts various spread spectrum communication technologies with OFDM as a core.
Although power line communication systems are widely used and the technology is relatively mature, a large number of branches and electrical devices in the power line communication system generate a large amount of noise in the power line channel; random impulse noise has high randomness and high noise intensity, and seriously damages a power line communication system, so that the technology for inhibiting the random impulse noise is always the key point for the research of scholars at home and abroad; and the noise model does not fit into a gaussian distribution. Therefore, the traditional communication system designed aiming at the gaussian noise is not suitable for a power line carrier communication system any more, and a corresponding noise suppression technology must be researched to improve the signal-to-noise ratio of the power line communication system, reduce the bit error rate and ensure the quality of the power line communication system.
In practical applications, some simple non-linear techniques are often applied to eliminate power line channel noise, such as Clip-ping, Blanking and Clipping/Blanking techniques, but these research methods all have to work well under a certain signal-to-noise ratio condition, and only consider the elimination of impulse noise, in a power line communication system, some commercial power line transmitters are characterized by low transmission power, and in some special cases, the transmission power may be even lower than 18w, so that in some special cases, signals are submerged in a large amount of noise, resulting in a low signal-to-noise ratio condition of the power line communication system.
Disclosure of Invention
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, a common low-pass filter is difficult to achieve an ideal filtering effect in a non-stationarity and non-Gaussian noise environment, the non-stationarity and non-Gaussian noise is difficult to filter, and the performance of a PLC communication system is seriously influenced. .
The invention aims to provide a PLC signal filtering method and a system by utilizing LP optimization, the proposed method utilizes the difference of a PLC modulation signal, impulse noise and background noise in the field of signal mixing representation, and the PLC modulation signal, the impulse noise and the background noise are distinguished by the LP optimization property. The method has good noise filtering performance and is simple in calculation.
In order to achieve the purpose, the invention provides the following scheme:
a PLC signal filtering method using LP optimization, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102 finds a normalized average matrix B, specifically
Figure BDA0002517725080000021
Wherein m is0Is the mean of the signal sequence S;
Figure BDA0002517725080000022
is the variance of the signal sequence S; n is the length of the signal sequence S;
step 103 of determining the LP separation P, specifically
Figure BDA0002517725080000023
Wherein, SNR is the signal-to-noise ratio of the signal sequence S; lambda [ alpha ]minIs a non-zero minimum eigenvalue of the normalized average matrix B; lambda [ alpha ]maxThe maximum eigenvalue of the normalized average matrix B;
step 104, solving a Gaussian projection matrix A, wherein the solving method of the Gaussian projection matrix A comprises the following steps: the ith row and the jth column element a of the Gaussian projection matrix AijIs calculated by the formula
Figure BDA0002517725080000024
Wherein i is a row serial number, and the value range of the row serial number i is i ═ 1,2, ·, N; j is a column serial number, and the value range of the column serial number j is 1,2, ·, N; gijIs a mean value of m0Variance is
Figure BDA0002517725080000025
(ii) a gaussian random variable;
step 105 finds a noise-filtered signal sequence SnewThe method specifically comprises the following steps:
Figure BDA0002517725080000026
wherein x is an intermediate parameter vector.
A PLC signal filtering system optimized with LP, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 finds the normalized average matrix B, in particular
Figure BDA0002517725080000027
Wherein m is0Is the mean of the signal sequence S;
Figure BDA0002517725080000028
is the variance of the signal sequence S; n is the length of the signal sequence S;
the module 203 finds the LP separation P, specifically
Figure BDA0002517725080000029
Wherein, SNR is the signal-to-noise ratio of the signal sequence S; lambda [ alpha ]minIs a non-zero minimum eigenvalue of the normalized average matrix B; lambda [ alpha ]maxThe maximum eigenvalue of the normalized average matrix B;
the module 204 calculates a gaussian projection matrix a, and the calculation method of the gaussian projection matrix a is as follows: the ith row and the jth column element a of the Gaussian projection matrix AijIs calculated by the formula
Figure BDA00025177250800000210
Wherein i is a row serial number, and the value range of the row serial number i is i ═ 1,2, ·, N; j is a column serial number, and the value range of the column serial number j is 1,2, ·, N; gijIs a mean value of m0Variance is
Figure BDA00025177250800000211
(ii) a gaussian random variable;
module 205 finds a noise-filtered signal sequence SnewThe method specifically comprises the following steps:
Figure BDA00025177250800000212
wherein x is an intermediate parameter vector.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
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, a common low-pass filter is difficult to achieve an ideal filtering effect in a non-stationarity and non-Gaussian noise environment, the non-stationarity and non-Gaussian noise is difficult to filter, and the performance of a PLC communication system is seriously influenced. .
The invention aims to provide a PLC signal filtering method and a system by utilizing LP optimization, the proposed method utilizes the difference of a PLC modulation signal, impulse noise and background noise in the field of signal mixing representation, and the PLC modulation signal, the impulse noise and the background noise are distinguished by the LP optimization property. The method has good noise filtering performance and is simple in calculation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic flow chart of the system of the present invention;
FIG. 3 is a flow chart illustrating an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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.
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 flow chart illustrating a PLC signal filtering method using LP optimization
Fig. 1 is a flow chart illustrating a PLC signal filtering method using LP optimization according to the present invention. As shown in fig. 1, the PLC signal filtering method using LP optimization specifically includes the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102 finds a normalized average matrix B, specifically
Figure BDA0002517725080000031
Wherein m is0Is the mean of the signal sequence S;
Figure BDA0002517725080000032
is the variance of the signal sequence S; n is the length of the signal sequence S;
step 103 of determining the LP separation P, specifically
Figure BDA0002517725080000033
Wherein, SNR is the signal-to-noise ratio of the signal sequence S; lambda [ alpha ]minIs a non-zero minimum eigenvalue of the normalized average matrix B; lambda [ alpha ]maxThe maximum eigenvalue of the normalized average matrix B;
step 104, solving a Gaussian projection matrix A, wherein the solving method of the Gaussian projection matrix A comprises the following steps: the ith row and the jth column element a of the Gaussian projection matrix AijIs calculated by the formula
Figure BDA0002517725080000034
Wherein i is a row serial number, and the value range of the row serial number i is i ═ 1,2, ·, N; j is a column serial number, and the value range of the column serial number j is 1,2, ·, N; gijIs a mean value of m0Variance is
Figure BDA0002517725080000035
(ii) a gaussian random variable;
step 105 finds a noise-filtered signal sequence SnewThe method specifically comprises the following steps:
Figure BDA0002517725080000036
wherein x is an intermediate parameter vector.
FIG. 2 structural intention of PLC signal filtering system using LP optimization
Fig. 2 is a schematic structural diagram of a PLC signal filtering system optimized by LP according to the present invention. As shown in fig. 2, the PLC signal filtering system optimized by LP includes the following structures:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 finds the normalized average matrix B, in particular
Figure BDA0002517725080000041
Wherein m is0Is the mean of the signal sequence S;
Figure BDA0002517725080000042
is the variance of the signal sequence S; n is the length of the signal sequence S;
the module 203 finds the LP separation P, specifically
Figure BDA0002517725080000043
Wherein, SNR is the signal-to-noise ratio of the signal sequence S; lambda [ alpha ]minIs a non-zero minimum eigenvalue of the normalized average matrix B; lambda [ alpha ]maxThe maximum eigenvalue of the normalized average matrix B;
the module 204 calculates a gaussian projection matrix a, and the calculation method of the gaussian projection matrix a is as follows: the ith row and the jth column element a of the Gaussian projection matrix AijIs calculated by the formula
Figure BDA0002517725080000044
Wherein i is a row serial number, and the value range of the row serial number i is i ═ 1,2, ·, N; j is a column serial number, and the value range of the column serial number j is 1,2, ·, N; gijIs a mean value of m0Variance is
Figure BDA0002517725080000045
(ii) a gaussian random variable;
module 205 finds a noise-filtered signal sequence SnewThe method specifically comprises the following steps:
Figure BDA0002517725080000046
wherein x is an intermediate parameter vector.
The following provides an embodiment for further illustrating the invention
FIG. 3 is a flow chart illustrating an embodiment of the present invention. As shown in fig. 3, the method specifically includes the following steps:
step 301, acquiring a signal sequence S acquired according to a time sequence;
step 302 finds a normalized average matrix B, specifically
Figure BDA0002517725080000047
Wherein m is0Is the mean of the signal sequence S;
Figure BDA0002517725080000048
is the variance of the signal sequence S; n is the length of the signal sequence S;
step 303 finds the LP separation P, specifically
Figure BDA0002517725080000049
Wherein, SNR is the signal-to-noise ratio of the signal sequence S; lambda [ alpha ]minIs a non-zero minimum eigenvalue of the normalized average matrix B; lambda [ alpha ]maxThe maximum eigenvalue of the normalized average matrix B;
step 304, a gaussian projection matrix a is obtained, and the obtaining method of the gaussian projection matrix a is as follows: the ith row and the jth column element a of the Gaussian projection matrix AijIs calculated by the formula
Figure BDA00025177250800000410
Wherein i is a row serial number, and the value range of the row serial number i is i ═ 1,2, ·, N; j is a column serial number, and the value range of the column serial number j is 1,2, ·, N; gijIs a mean value of m0Variance is
Figure BDA00025177250800000411
(ii) a gaussian random variable;
step 305 finds a noise-filtered signal sequence SnewThe method specifically comprises the following steps:
Figure BDA00025177250800000412
wherein x is an intermediate parameter vector.
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 simple because the system corresponds to the method disclosed by the embodiment, and the relevant part 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 (2)

1. The PLC signal filtering method using LP optimization is characterized by comprising the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102 finds a normalized average matrix B, specifically
Figure FDA0002517725070000011
Wherein m is0Is the mean of the signal sequence S;
Figure FDA0002517725070000012
is the variance of the signal sequence S; n is the signal-to-noise ratio of the signal sequence S;
step 103 of determining the LP separation P, specifically
Figure FDA0002517725070000013
Wherein, SNR is the signal-to-noise ratio of the signal sequence S; lambda [ alpha ]minIs a non-zero minimum eigenvalue of the normalized average matrix B; lambda [ alpha ]maxThe maximum eigenvalue of the normalized average matrix B;
step 104, solving a Gaussian projection matrix A, wherein the solving method of the Gaussian projection matrix A comprises the following steps: the ith row and the jth column element a of the Gaussian projection matrix AijIs calculated by the formula
Figure FDA0002517725070000014
Wherein i is a row serial number, and the value range of the row serial number i is i ═ 1,2, ·, N; j is a column serial number, and the value range of the column serial number j is 1,2, ·, N; gijIs a mean value of m0Variance is
Figure FDA0002517725070000015
(ii) a gaussian random variable;
step 105 finds a noise-filtered signal sequence SnewThe method specifically comprises the following steps:
Figure FDA0002517725070000016
Figure FDA0002517725070000017
wherein x is an intermediate parameter vector.
2. The PLC signal filtering system optimized by means of LP is characterized by comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 finds the normalized average matrix B, in particular
Figure FDA0002517725070000018
Wherein m is0Is the signalThe mean value of the sequence S;
Figure FDA0002517725070000019
is the variance of the signal sequence S; n is the signal-to-noise ratio of the signal sequence S;
the module 203 finds the LP separation P, specifically
Figure FDA00025177250700000110
Wherein, SNR is the signal-to-noise ratio of the signal sequence S; lambda [ alpha ]minIs a non-zero minimum eigenvalue of the normalized average matrix B; lambda [ alpha ]maxThe maximum eigenvalue of the normalized average matrix B;
the module 204 calculates a gaussian projection matrix a, and the calculation method of the gaussian projection matrix a is as follows: the ith row and the jth column element a of the Gaussian projection matrix AijIs calculated by the formula
Figure FDA00025177250700000111
Wherein i is a row serial number, and the value range of the row serial number i is i ═ 1,2, ·, N; j is a column serial number, and the value range of the column serial number j is 1,2, ·, N; gijIs a mean value of m0Variance is
Figure FDA00025177250700000112
(ii) a gaussian random variable;
module 205 finds a noise-filtered signal sequence SnewThe method specifically comprises the following steps:
Figure FDA00025177250700000113
Figure FDA00025177250700000114
wherein x is an intermediate parameter vector.
CN202010482666.0A 2020-06-01 2020-06-01 PLC signal filtering method and system using LP optimization Withdrawn CN111641436A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112383326A (en) * 2020-11-03 2021-02-19 华北电力大学 PLC signal filtering method and system using spectral mode threshold

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112383326A (en) * 2020-11-03 2021-02-19 华北电力大学 PLC signal filtering method and system using spectral mode threshold
CN112383326B (en) * 2020-11-03 2021-12-31 华北电力大学 PLC signal filtering method and system using spectral mode threshold

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