CN110719121A - PLC channel impulse noise detection method and system using square exponential kernel - Google Patents

PLC channel impulse noise detection method and system using square exponential kernel Download PDF

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CN110719121A
CN110719121A CN201911062086.XA CN201911062086A CN110719121A CN 110719121 A CN110719121 A CN 110719121A CN 201911062086 A CN201911062086 A CN 201911062086A CN 110719121 A CN110719121 A CN 110719121A
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impulse noise
sequence
nth
signal
square
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翟明岳
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Guangdong University of Petrochemical Technology
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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
    • H04B3/46—Monitoring; Testing
    • 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 channel impulse noise detection method and a system by utilizing a square exponential kernel, wherein the method comprises the following steps: step 1, inputting an actually measured signal sequence S; and 2, detecting the PLC channel impulse noise according to the square exponential kernel property. The method specifically comprises the following steps: if the square index kernel of the Kth window is HKSatisfies the judgment condition | HK|≥e0Detecting impulse noise at the Kth point of the signal sequence S; otherwise, impulse noise is not detected. Wherein e is0A threshold is determined for the impulse noise.

Description

PLC channel impulse noise detection method and system using square exponential kernel
Technical Field
The invention relates to the field of communication, in particular to a method and a system for detecting pulse noise of a PLC channel.
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 to 3k500 kHz; the power line communication technology includes a prescribed bandwidth (3148.5kHz) of european CENELEC, a prescribed bandwidth (9490kHz) of the Federal Communications Commission (FCC) in the united states, a prescribed bandwidth (9450kHz) of the Association of Radio Industries and Businesses (ARIB) in japan, and a prescribed bandwidth (3500kHz) 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 1.630MHz 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 Clipping, Blanking and Clipping/Blanking techniques, but these research methods must work well under a certain signal-to-noise ratio, and only the elimination of impulse noise is considered, in the 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, the signal will be 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, pulse noise becomes more common and more serious, and to filter the pulse noise, the pulse noise is detected first, and then corresponding measures can be further taken, but the existing method and system lack sufficient attention on the detection of the pulse noise.
The invention aims to provide a PLC channel impulse noise detection method and system by using a square index kernel. The method has better robustness and simpler calculation.
In order to achieve the purpose, the invention provides the following scheme:
a PLC channel impulse noise detection method using square exponential kernel includes:
step 001 inputting an actually measured signal sequence S;
step 002 detects the PLC channel impulse noise according to the square exponential kernel property. The method specifically comprises the following steps: if the square index kernel of the Kth window is HKSatisfies the judgment condition | HK|≥e0Detecting impulse noise at the Kth point of the signal sequence S; otherwise, impulse noise is not detected. Wherein e is0A threshold is determined for the impulse noise.
A PLC channel impulse noise detection system using square exponential kernels, comprising:
an acquisition module inputs an actually measured signal sequence S;
and the judging module detects the PLC channel impulse noise according to the square exponential kernel property. The method specifically comprises the following steps: if the square index kernel of the Kth window is HKSatisfies the judgment condition | HK|≥e0Detecting impulse noise at the Kth point of the signal sequence S; otherwise, impulse noise is not detected. Wherein e is0A threshold is determined for the impulse noise.
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, pulse noise becomes more common and more serious, and to filter the pulse noise, the pulse noise is detected first, and then corresponding measures can be further taken, but the existing method and system lack sufficient attention on the detection of the pulse noise.
The invention aims to provide a PLC channel impulse noise detection method and system by using a square index kernel. The method has better robustness and simpler 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 channel impulse noise detection method using square exponential kernels
Fig. 1 is a schematic flow chart of a PLC channel impulse noise detection method using a square exponential kernel according to the present invention. As shown in fig. 1, the PLC channel impulse noise detection method using square exponential kernel specifically includes the following steps:
step 001 inputting an actually measured signal sequence S;
step 002 detects the PLC channel impulse noise according to the square exponential kernel property. The method specifically comprises the following steps: if the square index kernel of the Kth window is HKSatisfies the judgment condition | HK|≥e0Detecting impulse noise at the Kth point of the signal sequence S; otherwise, impulse noise is not detected. Wherein e is0A threshold is determined for the impulse noise.
Prior to the step 002, the method further comprises:
step 003 of solving the square index kernel HKAnd the impulse noise judgment threshold e0。
The step 003 further includes:
step 301 generates an nth signal first-order difference sequence, specifically:
Figure BDA0002258203310000031
wherein:
Figure BDA0002258203310000032
the nth signal first order difference sequence
sn: the nth element, N, of the signal sequence S is 1,2, N
N: length of the signal sequence S
n: subscripts of the elements, if n>N, element s corresponding ton＝0
Step 302 generates an nth signal second order difference sequence, specifically:
Figure BDA0002258203310000033
wherein:
Figure BDA0002258203310000034
the nth signal second order difference sequence
n: subscripts of the elements, if n>N, element s corresponding ton＝0
Step 303 finds the nth expected difference sequence
Figure BDA0002258203310000035
The method specifically comprises the following steps:
wherein:
Wn: nth desired weight matrix
Figure BDA0002258203310000037
λ: maximum eigenvalue of the correlation matrix a
Figure BDA0002258203310000038
Figure BDA0002258203310000039
J-th feature vector of the correlation matrix A
j: subscripts, j ═ 1,2, ·, N
ρ: traces of the correlation matrix A
Step 304, calculating the K-th window square exponent kernel, specifically:
Figure BDA0002258203310000041
wherein:
the nth signal first order difference sequence
Figure BDA0002258203310000043
Mean square error of
Figure BDA0002258203310000044
The nth signal second order difference sequence
Figure BDA0002258203310000045
Mean square error of
σn: sequence BnMean square error of
Step 305 of obtaining the state determination threshold e0The method specifically comprises the following steps:
Figure BDA0002258203310000047
wherein:
κj: correlation difference matrix CNJ characteristic value
Figure BDA0002258203310000048
j: subscripts, j ═ 1,2, ·, N
Figure BDA0002258203310000049
First order difference sequence of Nth signal
Figure BDA00022582033100000410
Mean square error of
Figure BDA00022582033100000411
Second order difference sequence of Nth signal
Figure BDA00022582033100000412
Mean square error of
FIG. 2 structural intent of a PLC channel impulse noise detection system using square-exponential kernels
Fig. 2 is a schematic structural diagram of a PLC channel impulse noise detection system using a square exponential kernel according to the present invention. As shown in fig. 2, the PLC channel impulse noise detection system using square exponential kernel includes the following structure:
the acquisition module 401 inputs an actually measured signal sequence S;
the decision block 402 detects PLC channel impulse noise based on square exponential kernel properties. The method specifically comprises the following steps: if the square index kernel of the Kth window is HKSatisfies the judgment condition | HK|≥e0Detecting impulse noise at the Kth point of the signal sequence S; otherwise, impulse noise is not detected. Wherein e is0A threshold is determined for the impulse noise.
The system further comprises:
calculation module 403 finds the square exponent kernel HKAnd the impulse noise judgment threshold e0。
The calculation module 403 further includes the following units, which specifically include:
the calculating unit 4031 generates an nth signal first-order difference sequence, specifically:
Figure BDA00022582033100000413
wherein:
Figure BDA0002258203310000051
the nth signal first order difference sequence
sn: the nth element, N, of the signal sequence S is 1,2, N
N: length of the signal sequence S
n: subscripts of the elements, if n>N, element s corresponding ton＝0
The calculating unit 4032 generates an nth signal second-order difference sequence, specifically:
wherein:
Figure BDA0002258203310000053
the nth signalSecond order difference sequence
n: subscripts of the elements, if n>N, element s corresponding ton＝0
Calculation unit 4033 finds the nth expected difference sequence
Figure BDA0002258203310000054
The method specifically comprises the following steps:
wherein:
Wn: nth desired weight matrix
Figure BDA0002258203310000056
λ: maximum eigenvalue of the correlation matrix a
Figure BDA0002258203310000057
Figure BDA0002258203310000058
J-th feature vector of the correlation matrix A
j: subscripts, j ═ 1,2, ·, N
ρ: traces of the correlation matrix A
The calculating unit 4034 calculates the K-th window square index kernel, specifically:
wherein:
Figure BDA00022582033100000510
the nth signal first order difference sequence
Figure BDA00022582033100000511
Mean square ofDifference (D)
Figure BDA00022582033100000512
The nth signal second order difference sequenceMean square error of
σn: sequence BnMean square error of
Figure BDA00022582033100000514
Calculation unit 4035 obtains state determination threshold e0The method specifically comprises the following steps:
Figure BDA00022582033100000515
wherein:
κj: correlation difference matrix CNJ characteristic value
j: subscripts, j ═ 1,2, ·, N
Figure BDA0002258203310000062
First order difference sequence of Nth signal
Figure BDA0002258203310000063
Mean square error of
Second order difference sequence of Nth signal
Figure BDA0002258203310000065
Mean square error of
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:
0 start: inputting measured signal data sequence
S＝[s1,s2,···,sN-1,sN]
Wherein:
s: measured signal sequence of length N
sn: the nth element in the signal sequence S
n: subscript, N ═ 1,2,. cndot., N
1, generating an nth signal first-order difference sequence, specifically:
Figure BDA0002258203310000066
wherein:
Figure BDA0002258203310000067
the nth signal first order difference sequence
sn: the nth element, N, of the signal sequence S is 1,2, N
N: length of the signal sequence S
n: subscripts of the elements, if n>N, element s corresponding ton＝0
2, generating an nth signal second-order difference sequence, specifically:
wherein:
Figure BDA0002258203310000069
the nth signal second order difference sequence
n: subscripts of the elements, if n>N, element s corresponding ton＝0
3 calculating the nth expected difference sequenceColumn(s) ofThe method specifically comprises the following steps:
Figure BDA00022582033100000611
wherein:
Wn: nth desired weight matrix
λ: maximum eigenvalue of the correlation matrix a
Figure BDA0002258203310000072
Figure BDA0002258203310000073
J-th feature vector of the correlation matrix A
j: subscripts, j ═ 1,2, ·, N
ρ: traces of the correlation matrix A
4, solving the kth window square index kernel, specifically:
Figure BDA0002258203310000074
wherein:
Figure BDA0002258203310000075
the nth signal first order difference sequenceMean square error of
Figure BDA0002258203310000077
The nth signal second order difference sequence
Figure BDA0002258203310000078
Mean square error of
σn: sequence BnMean square error of
Figure BDA0002258203310000079
5 obtaining the state judgment threshold e0The method specifically comprises the following steps:
Figure BDA00022582033100000710
wherein:
κj: correlation difference matrix CNJ characteristic value
j: subscripts, j ═ 1,2, ·, N
Figure BDA00022582033100000712
First order difference sequence of Nth signal
Figure BDA00022582033100000713
Mean square error of
Second order difference sequence of Nth signal
Figure BDA00022582033100000715
Mean square error of
And 6, finishing: determining an event
And detecting the PLC channel impulse noise according to the square exponential kernel property. The method specifically comprises the following steps: if the square index kernel of the Kth window is HKSatisfies the judgment condition | HK|≥e0Detecting impulse noise at the Kth point of the signal sequence S; if not, then,no impulse noise was detected. Wherein e is0A threshold is determined for the impulse noise.
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 (5)

1. A PLC channel impulse noise detection method using square exponential kernel includes:
step 001 inputting an actually measured signal sequence S;
step 002 detects the PLC channel impulse noise according to the square exponential kernel property. The method specifically comprises the following steps: if the square index kernel of the Kth window is HKSatisfies the judgment condition | HK|≥e0Detecting impulse noise at the Kth point of the signal sequence S; otherwise, impulse noise is not detected. Wherein e is0A threshold is determined for the impulse noise.
2. The method of claim 1, wherein prior to step 2, the method further comprises:
step 003 of solving the square index kernel HKAnd the impulse noise judgment threshold e0。
3. The method of claim 2, wherein step 3 comprises:
step 301 generates an nth signal first-order difference sequence, specifically:
Figure FDA0002258203300000011
wherein:
the nth signal first order difference sequence
sn: the nth element, N, of the signal sequence S is 1,2, N
N: length of the signal sequence S
n: subscripts of the elements, if n>N, element s corresponding ton＝0
Step 302 generates an nth signal second order difference sequence, specifically:
wherein:
Figure FDA0002258203300000014
the nth signal second order difference sequence
n: subscripts of the elements, if n>N, element s corresponding ton＝0
Step 303 finds the nth expected difference sequence
Figure FDA0002258203300000015
The method specifically comprises the following steps:
Figure FDA0002258203300000016
wherein:
Wn: nth desired weight matrix
Figure FDA0002258203300000017
λ: maximum eigenvalue of the correlation matrix a
Figure FDA0002258203300000018
Figure FDA0002258203300000019
J-th feature vector of the correlation matrix A
j: subscripts, j ═ 1,2, ·, N
ρ: traces of the correlation matrix A
Step 304, calculating the K-th window square exponent kernel, specifically:
Figure FDA0002258203300000021
wherein:
the nth signal first order difference sequence
Figure FDA0002258203300000023
Mean square error of
Figure FDA0002258203300000024
The nth signal second order difference sequenceMean square error of
σn: sequence BnMean square error of
Figure FDA0002258203300000026
Step 305 of obtaining the state determination threshold e0The method specifically comprises the following steps:
Figure FDA0002258203300000027
wherein:
κj: correlation difference matrix CNJ characteristic value
Figure FDA0002258203300000028
j: subscripts, j ═ 1,2, ·, N
Figure FDA0002258203300000029
First order difference sequence of Nth signal
Figure FDA00022582033000000210
Mean square error of
Second order difference sequence of Nth signal
Figure FDA00022582033000000212
The mean square error of (c).
4. A PLC channel impulse noise detection system using square exponential kernels, comprising:
an acquisition module inputs an actually measured signal sequence S;
and the judging module detects the PLC channel impulse noise according to the square exponential kernel property. The method specifically comprises the following steps: if the square index kernel of the Kth window is HKSatisfies the judgment condition | HK|≥e0Detecting impulse noise at the Kth point of the signal sequence S; otherwise, impulse noise is not detected. Wherein e is0A threshold is determined for the impulse noise.
5. The system of claim 4, further comprising:
the calculation module calculates the square exponent kernel HKAnd the impulse noise judgment threshold e0。
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Cited By (2)

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CN112104392A (en) * 2020-10-08 2020-12-18 广东石油化工学院 PLC channel impulse noise detection method and system using state matrix
CN112187317A (en) * 2020-10-08 2021-01-05 广东石油化工学院 PLC channel impulse noise detection method and system by utilizing curve stretching

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CN103258531B (en) * 2013-05-29 2015-11-11 安宁 A kind of harmonic characteristic extracting method of the speech emotion recognition had nothing to do for speaker
WO2016038585A1 (en) * 2014-09-12 2016-03-17 Blacktree Fitness Technologies Inc. Portable devices and methods for measuring nutritional intake

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112104392A (en) * 2020-10-08 2020-12-18 广东石油化工学院 PLC channel impulse noise detection method and system using state matrix
CN112187317A (en) * 2020-10-08 2021-01-05 广东石油化工学院 PLC channel impulse noise detection method and system by utilizing curve stretching
CN112104392B (en) * 2020-10-08 2021-06-11 广东石油化工学院 PLC channel impulse noise detection method and system using state matrix

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