CN110635824B - PLC channel impulse noise detection method and system using classification regression tree - Google Patents

PLC channel impulse noise detection method and system using classification regression tree Download PDF

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CN110635824B
CN110635824B CN201910996945.6A CN201910996945A CN110635824B CN 110635824 B CN110635824 B CN 110635824B CN 201910996945 A CN201910996945 A CN 201910996945A CN 110635824 B CN110635824 B CN 110635824B
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order difference
impulse noise
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CN110635824A (en
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翟明岳
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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 using a classification regression treeAn impulse noise detection method and system, the method comprising: step 1, inputting an actually measured signal sequence S; and 2, detecting the PLC channel impulse noise according to the classification regression tree property. The method specifically comprises the following steps: if the Kth window classifies the regression coefficient 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 classification regression tree
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 between 3k and 500 kHz; the power line communication technology includes a regulated bandwidth (3-148.5 kHz) of European CENELEC, a regulated bandwidth (9-490 kHz) of the U.S. Federal Communications Commission (FCC), a regulated bandwidth (9-450 kHz) of the Association of Radio Industries and Businesses (ARIB), and a regulated bandwidth (3-500 kHz) of 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 the bandwidth limited between 1.6-30 MHz and the communication speed 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.
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.
Disclosure of Invention
The invention aims to provide a PLC channel impulse noise detection method and system by utilizing a classification regression tree. The method has the advantages of good robustness and simple calculation.
In order to achieve the purpose, the invention provides the following scheme:
a PLC channel impulse noise detection method using a classification regression tree includes:
step 1, inputting an actually measured signal sequence S;
and 2, detecting the PLC channel impulse noise according to the classification regression tree property. The method specifically comprises the following steps: if the Kth window classifies the regression coefficient 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 a classification regression tree, comprising:
the acquisition module inputs an actually measured signal sequence S;
and the judging module is used for detecting the PLC channel impulse noise according to the classification regression tree property. The method specifically comprises the following steps: if the Kth window classification regressionCoefficient 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:
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; 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 utilizing a classification regression tree. The method has the advantages of good robustness and simple 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 present invention;
FIG. 2 is a schematic diagram 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 a classification regression tree
Fig. 1 is a schematic flow chart of a PLC channel impulse noise detection method using a classification regression tree according to the present invention. As shown in fig. 1, the PLC channel impulse noise detection method using a classification regression tree specifically includes the following steps:
step 1, inputting an actually measured signal sequence S;
and 2, detecting the PLC channel impulse noise according to the classification regression tree property. The method specifically comprises the following steps: if the Kth window classifies the regression coefficient 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.
Before the step 2, the method further comprises:
step 3, calculating the classification regression coefficient H of the Kth windowKAnd the impulse noise judgment threshold e0。
The step 3 comprises the following steps:
step 301, generating the nth signal first order difference sequence
Figure BDA0002240060390000041
The method specifically comprises the following steps:
Figure BDA0002240060390000042
wherein:
Figure BDA0002240060390000043
the nth signal first-order difference sequence [ N ═ 1,2, …, N]
Sn: the nth element in the signal sequence S
S＝[S1，S2，…，SN]The length of the signal sequence is N
If the element SjSubscript j of>N, then Sj＝0。
Step 302, generating the nth signal second order difference sequence
Figure BDA0002240060390000044
The method specifically comprises the following steps:
Figure BDA0002240060390000045
wherein:
Figure BDA0002240060390000046
the nth signal second order difference sequence [ N ═ 1,2, …, N]
If the element SjSubscript j of>N, then Sj＝0。
Step 303, obtaining the classification regression coefficient H of the Kth windowKThe method specifically comprises the following steps:
Figure BDA0002240060390000051
wherein:
Figure BDA0002240060390000052
ith data purity
Figure BDA0002240060390000053
First order difference sequence of Kth signal
Figure BDA0002240060390000054
The ith element in
Figure BDA0002240060390000055
Second order difference sequence of Kth signal
Figure BDA0002240060390000056
The ith element in
Figure BDA0002240060390000057
First order difference sequence of Kth signal
Figure BDA0002240060390000058
Middle minimum element
Figure BDA0002240060390000059
Second order difference sequence of Kth signal
Figure BDA00022400603900000510
Middle minimum element
Figure BDA00022400603900000511
First order difference sequence of Kth signal
Figure BDA00022400603900000512
Middle and largest element
Figure BDA00022400603900000513
Second order difference sequence of Kth signal
Figure BDA00022400603900000514
Middle and largest element
Step 304, obtaining the impulse noise judgment threshold e0The method specifically comprises the following steps:
Figure BDA00022400603900000515
wherein:
Figure BDA00022400603900000516
the nth signal first order difference sequence
Figure BDA00022400603900000517
Mean value of
Figure BDA00022400603900000518
The nth signal second order difference sequence
Figure BDA00022400603900000519
Mean value of
Figure BDA00022400603900000520
Sequence of mean values
Figure BDA00022400603900000521
N is the mean of 1,2, …, N
Figure BDA00022400603900000522
Sequence of mean values
Figure BDA00022400603900000523
N is the mean of 1,2, …, N
Figure BDA00022400603900000524
Sequence of mean values
Figure BDA00022400603900000525
N1, 2, …, mean square error of N
Figure BDA00022400603900000526
Sequence of mean values
Figure BDA00022400603900000527
N1, 2, …, mean square error of N
Figure BDA00022400603900000528
Sequence of mean values
Figure BDA00022400603900000529
N is 1,2, …, maximum value of N
Figure BDA00022400603900000530
Sequence of mean values
Figure BDA00022400603900000531
N is the maximum of 1,2, …, N.
FIG. 2 structural intent of a PLC channel impulse noise detection system using a classification regression tree
Fig. 2 is a schematic structural diagram of a PLC channel impulse noise detection system using a classification regression tree according to the present invention. As shown in fig. 2, the PLC channel impulse noise detection system using the classification regression tree includes the following structures:
an obtaining module 401, which inputs an actually measured signal sequence S;
and a judging module 402, which detects the PLC channel impulse noise according to the property of the classification regression tree. The method specifically comprises the following steps: if the Kth window classifies the regression coefficient 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:
the calculation module 403 is used to calculate the data,calculating the classification regression coefficient H of the Kth windowKAnd the impulse noise judgment threshold e0。
The calculation module 403 further includes the following units, which specifically include:
a first calculation unit 4031 for generating the nth signal first order difference sequence
Figure BDA0002240060390000061
The method specifically comprises the following steps:
Figure BDA0002240060390000062
wherein:
Figure BDA0002240060390000063
the nth signal first-order difference sequence [ N ═ 1,2, …, N]
Sn: the nth element in the signal sequence S
S＝[S1，S2，…，SN]The length of the signal sequence is N
If the element SjSubscript j of>N, then Sj＝0。
A second calculation unit 4032 for generating the nth signal second order difference sequence
Figure BDA0002240060390000064
The method specifically comprises the following steps:
Figure BDA0002240060390000065
wherein:
Figure BDA0002240060390000066
the nth signal second order difference sequence [ N ═ 1,2, …, N]
If the element SjSubscript j of>N, then Sj＝0。
A third calculation unit 4033 for calculating the classification regression coefficient H of the Kth windowKThe method specifically comprises the following steps:
Figure BDA0002240060390000071
wherein:
Figure BDA0002240060390000072
ith data purity
Figure BDA0002240060390000073
First order difference sequence of Kth signal
Figure BDA0002240060390000074
The ith element in
Figure BDA0002240060390000075
Second order difference sequence of Kth signal
Figure BDA0002240060390000076
The ith element in
Figure BDA0002240060390000077
First order difference sequence of Kth signal
Figure BDA0002240060390000078
Middle minimum element
Figure BDA0002240060390000079
Second order difference sequence of Kth signal
Figure BDA00022400603900000710
Middle minimum element
Figure BDA00022400603900000711
First order difference sequence of Kth signal
Figure BDA00022400603900000712
Middle and largest element
Figure BDA00022400603900000713
Second order difference sequence of Kth signal
Figure BDA00022400603900000714
Middle and largest element
Fourth calculation section 4034, which calculates impulse noise determination threshold e0The method specifically comprises the following steps:
Figure BDA00022400603900000715
wherein:
Figure BDA00022400603900000716
the nth signal first order difference sequence
Figure BDA00022400603900000717
Mean value of
Figure BDA00022400603900000718
The nth signal second order difference sequence
Figure BDA00022400603900000719
Mean value of
Figure BDA00022400603900000720
Sequence of mean values
Figure BDA00022400603900000721
N is 1,2, …, NValue of
Figure BDA00022400603900000722
Sequence of mean values
Figure BDA00022400603900000723
N is the mean of 1,2, …, N
Figure BDA00022400603900000724
Sequence of mean values
Figure BDA00022400603900000725
N1, 2, …, mean square error of N
Figure BDA00022400603900000726
Sequence of mean values
Figure BDA00022400603900000727
N1, 2, …, mean square error of N
Figure BDA00022400603900000728
Sequence of mean values
Figure BDA00022400603900000729
N is 1,2, …, maximum value of N
Figure BDA00022400603900000730
Sequence of mean values
Figure BDA00022400603900000731
N is the maximum of 1,2, …, N.
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:
1. inputting measured signal sequence
S＝[s1,s2,…,sN-1,sN]
Wherein:
s: measured signal data sequence of length N
siN is the measured signal with serial number i, i is 1,2, …
2. Generating a first order difference sequence of signals
Figure BDA0002240060390000081
Wherein:
Figure BDA0002240060390000082
the nth signal first-order difference sequence [ N ═ 1,2, …, N]
Sn: the nth element in the signal sequence S
S＝[S1，S2，…，SN]The length of the signal sequence is N
If the element SjSubscript j of>N, then Sj＝0。
3. Generating a second order difference sequence of signals
Figure BDA0002240060390000083
Wherein:
Figure BDA0002240060390000084
the nth signal second order difference sequence [ N ═ 1,2, …, N]
If the element SjSubscript j of>N, then Sj＝0。
4. Calculating classification regression coefficient of Kth window
Figure BDA0002240060390000091
Wherein:
Figure BDA0002240060390000092
ith data purity
Figure BDA0002240060390000093
First order difference sequence of Kth signal
Figure BDA0002240060390000094
The ith element in
Figure BDA0002240060390000095
Second order difference sequence of Kth signal
Figure BDA0002240060390000096
The ith element in
Figure BDA0002240060390000097
First order difference sequence of Kth signal
Figure BDA0002240060390000098
Middle minimum element
Figure BDA0002240060390000099
Second order difference sequence of Kth signal
Figure BDA00022400603900000910
Middle minimum element
Figure BDA00022400603900000911
First order difference sequence of Kth signal
Figure BDA00022400603900000912
Middle and largest element
Figure BDA00022400603900000913
Second order difference sequence of Kth signal
Figure BDA00022400603900000914
Middle and largest element
5. Calculating a pulse noise judgment threshold
Figure BDA00022400603900000915
Wherein:
Figure BDA00022400603900000916
the nth signal first order difference sequence
Figure BDA00022400603900000917
Mean value of
Figure BDA00022400603900000918
The nth signal second order difference sequence
Figure BDA00022400603900000919
Mean value of
Figure BDA00022400603900000920
Sequence of mean values
Figure BDA00022400603900000921
N is the mean of 1,2, …, N
Figure BDA00022400603900000922
Sequence of mean values
Figure BDA00022400603900000923
N is the mean of 1,2, …, N
Figure BDA00022400603900000924
Sequence of mean values
Figure BDA00022400603900000925
N1, 2, …, mean square error of N
Figure BDA00022400603900000926
Sequence of mean values
Figure BDA00022400603900000927
N1, 2, …, mean square error of N
Figure BDA00022400603900000928
Sequence of mean values
Figure BDA00022400603900000929
N is 1,2, …, maximum value of N
Figure BDA00022400603900000930
Sequence of mean values
Figure BDA00022400603900000931
N is the maximum of 1,2, …, N.
6. Determining switch events
And detecting the PLC channel impulse noise according to the classification regression tree property. The method specifically comprises the following steps: if the Kth window classifies the regression coefficient 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 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 (1)

1. A PLC channel impulse noise detection method using a classification regression tree is characterized by comprising the following steps:
step 1, inputting an actually measured signal sequence S;
step 2, generating the nth signal first-order difference sequence
Figure FDA0002957655410000011
The method specifically comprises the following steps:
Figure FDA0002957655410000012
wherein:
Figure FDA0002957655410000013
the nth signal first-order difference sequence [ N ═ 1,2, …, N]；
Sn: the nth element in the signal sequence S;
S＝[S1，S2，…，SN]: the signal sequence is N in length;
if the element SjSubscript j of>N, then Sj＝0；
Step 3, generating the nth signal second-order difference sequence
Figure FDA0002957655410000014
The method specifically comprises the following steps:
Figure FDA0002957655410000015
wherein:
Figure FDA0002957655410000016
the nth signal second order difference sequence [ N ═ 1,2, …, N]；
If the element SjSubscript j of>N, then Sj＝0；
Step 4, solving the classification regression coefficient H of the Kth windowKThe method specifically comprises the following steps:
Figure FDA0002957655410000017
wherein:
Figure FDA0002957655410000018
the ith data purity;
Figure FDA0002957655410000019
first order difference sequence of Kth signal
Figure FDA00029576554100000110
The ith element in (1);
Figure FDA00029576554100000111
second order difference sequence of Kth signal
Figure FDA00029576554100000112
The ith element in (1);
Figure FDA00029576554100000113
first order difference sequence of Kth signal
Figure FDA00029576554100000114
The smallest element of (1);
Figure FDA00029576554100000115
second order difference sequence of Kth signal
Figure FDA00029576554100000116
The smallest element of (1);
Figure FDA00029576554100000117
first order difference sequence of Kth signal
Figure FDA00029576554100000118
The medium-largest element;
Figure FDA00029576554100000119
second order difference sequence of Kth signal
Figure FDA00029576554100000120
The medium-largest element;
step 5, solving an impulse noise judgment threshold e0The method specifically comprises the following steps:
Figure FDA0002957655410000021
wherein:
Figure FDA0002957655410000022
the nth signal is first order differentialSequence of
Figure FDA0002957655410000023
The mean value of (a);
Figure FDA0002957655410000024
the nth signal second order difference sequence
Figure FDA0002957655410000025
The mean value of (a);
Figure FDA0002957655410000026
sequence of mean values
Figure FDA0002957655410000027
The mean value of (a);
Figure FDA0002957655410000028
sequence of mean values
Figure FDA0002957655410000029
The mean value of (a);
Figure FDA00029576554100000210
sequence of mean values
Figure FDA00029576554100000211
The mean square error of (d);
Figure FDA00029576554100000212
sequence of mean values
Figure FDA00029576554100000213
The mean square error of (d);
Figure FDA00029576554100000214
sequence of mean values
Figure FDA00029576554100000215
Maximum value of (d);
Figure FDA00029576554100000216
sequence of mean values
Figure FDA00029576554100000217
Maximum value of (d);
step 6, detecting the PLC channel impulse noise according to the classification regression tree property, specifically comprising the following steps: if the Kth window classifies the regression coefficient HKSatisfies the judgment condition | HK|≥e0Detecting impulse noise at the Kth point of the signal sequence S; otherwise, impulse noise is not detected.
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