CN104935359A - Low voltage power line carrier communication signal modulation mode identification device and system - Google Patents
Low voltage power line carrier communication signal modulation mode identification device and system Download PDFInfo
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Abstract
The invention discloses a low voltage power line carrier communication signal modulation mode identification device comprising a sample unit, a signal pre-processing unit, an input vector forming unit and a determination unit; the signal pre-processing unit pre-processes a modulation signal so as to obtain source information; the input vector forming unit extracts feature parameters contained in the source information, and a feature parameter matrix formed by the feature parameters serves as an input vector of a nerve network; a nerve network algorithm identifies a modulation mode corresponding to the modulation signal. In addition, the invention also provides a system comprising the low voltage power line carrier communication signal modulation mode identification device.
Description
Technical field
The present invention relates to carrier communication field, particularly relate to a kind of low-voltage powerline carrier communication signal madulation mode recognition device and system.
Background technology
Power-line carrier communication refers to a kind of communication mode utilizing the information such as power line transmission data, voice, image.Because communication line is without the need to other laying, and widely distributed, therefore power line carrier communication has broad application prospects.
Electric power line carrier chip is mainly used in power line carrier communication programmable device or concentrator, is the vitals realizing power-line carrier communication.At present, different chip producers adopts different physical layer standards (modulation system, baud rate, carrier frequency, signal bandwidth etc.), therefore, brings certain difficulty for formulation electric power line carrier chip physical layer testing standard.Wherein, identify that the modulation system of electric power line carrier chip is one of important step setting up testing standard, therefore identify that the modulation system of electric power line carrier chip has great importance.
Summary of the invention
The object of this invention is to provide a kind of low-voltage powerline carrier communication signal madulation mode recognition device and system, for identifying the modulation system of electric power line carrier chip.
For solving the problems of the technologies described above, the invention provides a kind of low-voltage powerline carrier communication signal madulation mode recognition device, comprising:
To what get, sampling unit, for treating that the modulation signal that power line carrier module sends is sampled and record;
Signal Pretreatment unit, carries out analysis preliminary treatment for the signal exported described sampling unit, and according to the source information of baud rate picks symbols sampled value as Modulation Mode Recognition;
Input vector structural unit, for the characteristic parameter matrix that constructed by the characteristic parameter extracted from the described source information input vector as neural net;
Determining unit, for utilizing neural network algorithm, determines the modulation system that signal that described source information characterizes is corresponding.
Preferably, described characteristic parameter specifically comprises:
The maximum γ of normalize and center instantaneous amplitude spectrum density
max;
The absolute moment of the orign f of single order of normalize and center non-weak signal section instantaneous frequency
absorg;
The First Order Absolute Central Moment P of zero center non-weak signal section instantaneous phase nonlinear component
abscen;
The standard deviation of zero center non-weak signal section instantaneous phase nonlinear component absolute value
ap.
Preferably, described determining unit specifically comprises:
Neural network computing subelement, for utilizing described neural network algorithm, adjusting first threshold, Second Threshold, the 3rd threshold value, the 4th threshold value and being connected weights;
Signal determination subelement, for determining that according to the described output matrix of neural network computing subelement and the mean square error of expected matrix the signal that described source information characterizes is Binary Frequency Shift Keying BFSK signal, phase shift keying bpsk signal or Quadrature Phase Shift Keying QPSK signal;
Modulation system determination subelement, for determining the modulation system of described source information according to the result of described signal determination subelement;
Wherein, described first threshold, Second Threshold, the 3rd threshold value and the 4th threshold value are the boundary value of described characteristic parameter.
Preferably, described neural network computing subelement utilizes neural network algorithm, adjusts first threshold, Second Threshold, the 3rd threshold value, the 4th threshold value and be connected weights specifically to comprise:
By described first threshold, Second Threshold, the 3rd threshold value, the 4th threshold value be connected weights and be initialized as non-zero random number;
By described characteristic parameter Input matrix neural net;
By ranking operation process, if described output matrix and described expected matrix exceed preset range, just to described first threshold, Second Threshold, the 3rd threshold value, the 4th threshold value be connected weights and automatically adjust according to minimum mean square error criterion;
If described output matrix and described expected matrix do not exceed described preset range, then stop described automatic adjustment.
Preferably, described signal determination subelement determines that the signal that described source information characterizes is that Binary Frequency Shift Keying BFSK signal, phase shift keying bpsk signal or Quadrature Phase Shift Keying QPSK signal specifically comprise;
Calculate the first mean square error of described output matrix and BFSK signal target matrix;
Calculate the second mean square error of described output matrix and bpsk signal objective matrix;
Calculate the 3rd mean square error of described output matrix and QPSK signal target matrix;
More described first mean square error, described second mean square error and described 3rd mean square error extent;
If described first mean square error is minimum, then determine that the signal that described source information characterizes is described Binary Frequency Shift Keying BFSK signal, if described second mean square error is minimum, then determine that the signal that described source information characterizes is described phase shift keying bpsk signal, if described 3rd mean square error is minimum, then determine that the signal that described source information characterizes is described Quadrature Phase Shift Keying QPSK signal.
A kind of low-voltage powerline carrier communication signal madulation mode recognition system, comprising: carrier module to be tested, carrier communication testing apparatus, signal pickup assembly and above-mentioned low-voltage powerline carrier communication signal madulation mode recognition device.
Preferably, also comprise: artificial mains network, for powering for described low-voltage powerline carrier communication signal madulation mode recognition device.
Low-voltage powerline carrier communication signal madulation mode recognition device provided by the present invention carries out preliminary treatment by Signal Pretreatment unit to modulation signal and obtains source information, the characteristic parameter matrix formed by the characteristic parameter that comprises in input vector structural unit extraction source information and using characteristic parameter, as the input vector of neural net, adopts the modulation system corresponding to neural network algorithm identification modulation signal.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention, simple introduction is done below by the accompanying drawing used required in embodiment, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 is the structure chart of a kind of low-voltage powerline carrier communication signal madulation mode recognition device provided by the invention;
Fig. 2 is the structure chart of a kind of determining unit provided by the invention;
Fig. 3 is the structure chart of a kind of low-voltage powerline carrier communication signal madulation mode recognition system provided by the invention;
Fig. 4 is the structure chart of another kind of low-voltage powerline carrier communication signal madulation mode recognition system provided by the invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiment.Based on the embodiment in the present invention, those of ordinary skill in the art are not making under creative work prerequisite, and the every other embodiment obtained, all belongs to scope.
Core of the present invention is to provide a kind of low-voltage powerline carrier communication signal madulation mode recognition device, and comprises the system of this device.
In order to make those skilled in the art person understand the present invention program better, below in conjunction with the drawings and specific embodiments, the present invention is described in further detail.
Embodiment one
Fig. 1 is the structure chart of a kind of low-voltage powerline carrier communication signal madulation mode recognition device provided by the invention.Low-voltage powerline carrier communication signal madulation mode recognition device, comprising:
Sampling unit 10, samples and record for the modulation signal sent the power line carrier module got.
In concrete enforcement, when carrier communication tester sends meter reading instruction, power line carrier module just sends modulation signal, after sampling unit 10 gets modulation signal, samples and record to this modulation signal.
Signal Pretreatment unit 11, carries out analysis preliminary treatment for the signal exported described sampling unit 10, and according to the source information of baud rate picks symbols sampled value as Modulation Mode Recognition.
Signal Pretreatment unit 11 is connected with sampling unit 10, just transfers in Signal Pretreatment unit 11 after sampling unit 10 is to modulation signal sampling and record.The signal that Signal Pretreatment unit 11 pairs of sampling units 10 export carries out analysis preliminary treatment, and according to the source information of baud rate picks symbols sampled value as Modulation Mode Recognition.
Input vector structural unit 12, for the characteristic parameter matrix that constructed by the characteristic parameter extracted from the described source information input vector as neural net.
Input vector structural unit 12 is connected with Signal Pretreatment unit 11, after getting source information, extracts characteristic parameter to source information, and the characteristic parameter matrix constructed by characteristic parameter is as the input vector of neural net.
Determining unit 13, for utilizing neural network algorithm, determines the modulation system that signal that described source information characterizes is corresponding.
Determining unit 13 is connected with input vector structural unit 12, using the characteristic parameter matrix of input vector structural unit 12 structure as input vector, utilize neural network algorithm, determine the adjustment mode that signal that source information characterizes is corresponding, thus identify the modulation system corresponding to modulation signal.
The low-voltage powerline carrier communication signal madulation mode recognition device that the present embodiment provides carries out preliminary treatment by Signal Pretreatment unit to modulation signal and obtains source information, the characteristic parameter matrix formed by the characteristic parameter that comprises in input vector structural unit extraction source information and using characteristic parameter, as the input vector of neural net, adopts the modulation system corresponding to neural network algorithm identification modulation signal.
As one preferred embodiment, described characteristic parameter specifically comprises:
The maximum γ of normalize and center instantaneous amplitude spectrum density
max;
The absolute moment of the orign f of single order of normalize and center non-weak signal section instantaneous frequency
absorg;
The First Order Absolute Central Moment P of zero center non-weak signal section instantaneous phase nonlinear component
abscen;
The standard deviation of zero center non-weak signal section instantaneous phase nonlinear component absolute value
ap.
In the present invention, when will carry out characteristic parameter extraction to source information, the concrete feature extracted comprises: γ
max, f
absorg, P
abscenand σ
apthese four kinds of characteristic parameters.
(1) γ
maxaccount form be:
γ
max＝max{FFT([a
cn(i)]
2)/N
S}
In formula, N
sfor number of sampling, a
cni () is normalize and center range value, calculated: a by following formula
cn(i)=a
n(i)-1, in formula,
and
for the mean value of instantaneous amplitude a (i).Impact in order to eliminate channel gain with mean value on the object that instantaneous amplitude is normalized.
F
absorgaccount form be:
In formula,
wherein R
sfor the character rate of digital signal, the instantaneous frequency that f (i) is signal, a
tfor judging the normalization amplitude threshold of weak signal, a
n(i) > a
tensure that summation scope is non-weak signal section, c is that weak signal is at gross sample data N
sin belong to the number of non-weak signal value.
(3) p
abscenaccount form be:
In formula,
and
for instantaneous phase.
(4) σ
apaccount form be:
Fig. 2 is the structure chart of a kind of determining unit provided by the invention.As one preferred embodiment, described determining unit specifically comprises:
Neural network computing subelement 130, for utilizing described neural network algorithm, adjusting first threshold, Second Threshold, the 3rd threshold value, the 4th threshold value and being connected weights;
Signal determination subelement 131, for determining that according to the described output matrix of neural network computing subelement and the mean square error of expected matrix the signal that described source information characterizes is Binary Frequency Shift Keying BFSK signal, phase shift keying bpsk signal or Quadrature Phase Shift Keying QPSK signal;
Modulation system determination subelement 132, for determining the modulation system of described source information according to the result of described signal determination subelement;
Wherein, described first threshold, Second Threshold, the 3rd threshold value and the 4th threshold value are the boundary value of described characteristic parameter.
In order to improve the accuracy of identification, in the training process of neural network algorithm, the present invention proposes the division that Twin-Characteristic-Parameters Method threshold value distinguished number tentatively determines the classification of modulation signal local.In concrete enforcement, the signal that electric power line carrier chip sends usually comprises: BFSK signal, bpsk signal and QPSK signal, concrete, utilizes γ
maxand f
absorgdivide the BFSK signal in above-mentioned three kinds of signals, utilize P
abscenand σ
apdivide bpsk signal and QPSK signal.When utilizing neural network algorithm, first to the fixed initial value of first threshold, Second Threshold, the 3rd threshold value and the 4th threshold value, for convenience of description, in specification, the form of symbolization is illustrated, wherein, th1 is first threshold, th2 is Second Threshold, th3 is the 3rd threshold value, th4 is the 4th threshold value.The foundation of adjustment is:
Judge γ
maxwhether be less than th1 and judge f
absorgwhether be greater than th2, work as γ
maxbe less than th1 and f
absorgwhen being greater than th2, then can mark off BFSK signal; Work as γ
maxbe not less than th1 or f
absorgwhen being not more than th2, be defined as interference signal.
Judge P
abscenwhether be greater than th3 and judge σ
apwhether be greater than th4, work as P
abscenbe greater than th3 and σ
apwhen being greater than th4, then can mark off QPSK signal; Work as P
abscenbe not more than th3 and σ
apwhen being not more than th4, then bpsk signal can be marked off; Work as P
abscenbe not more than the 3rd threshold value th3 or σ
apwhen being not more than th4, be defined as interference signal.
This is due to γ
maxbe used for distinguishing BFSK signal and bpsk signal, QPSK signal, because BFSK signal has invariable instantaneous amplitude, its normalize and center instantaneous amplitude is zero, and the power spectral density of signal is also zero, and therefore BFSK signal does not comprise amplitude information (γ in essence
max< th1).But bpsk signal and QPSK signal comprise amplitude information (γ
max> th1) because amplitude information is superimposed upon adjacent-symbol migration place by frequency band limits.
For BFSK signal because its instantaneous frequency f (i) comprises two centrifugal pumps, so, its f after zero center, normalization, the process that takes absolute value
absorgnon-vanishing (f
absorg> th2), be a constant for bpsk signal and its instantaneous frequency of QPSK signal, therefore its f
absorgbe zero (f
absorg< th2).Therefore f is utilized
absorgbFSK signal and bpsk signal, QPSK signal can be distinguished.
Instantaneous phase for QPSK signal comprises 4 centrifugal pumps, φ after zero center, the process that takes absolute value
nLcomprise 2 centrifugal pumps, its p
abscennon-vanishing (p
abscen> th3).Therefore, this characteristic parameter is utilized can to distinguish bpsk signal and QPSK signal.
σ
apbe used for distinguishing bpsk signal and QPSK signal.The Direct Phase value of bpsk signal is 0 and π, and after center is aimed at, its absolute value is constant pi/2, therefore it does not comprise absolute phase information (σ
ap< th4), and QPSK signal is in essence containing absolute Direct Phase information (σ
ap> th4).
As one preferred embodiment, described neural network computing subelement utilizes neural network algorithm, adjusts first threshold, Second Threshold, the 3rd threshold value, the 4th threshold value and be connected weights specifically to comprise:
By described first threshold, Second Threshold, the 3rd threshold value, the 4th threshold value be connected weights and be initialized as non-zero random number;
By described characteristic parameter Input matrix neural net;
By ranking operation process, if output matrix and described expected matrix exceed preset range, just to described first threshold, Second Threshold, the 3rd threshold value, the 4th threshold value be connected weights and automatically adjust according to minimum mean square error criterion;
If described output matrix and described expected matrix do not exceed described preset range, then stop described automatic adjustment.
In concrete enforcement, first by first threshold, Second Threshold, the 3rd threshold value, the 4th threshold value be connected weights and be initialized as non-zero random number, and by characteristic parameter Input matrix neural net, characteristic parameter matrix is computed weighted process, if output matrix and expected matrix exceed preset range, then illustrate and need first threshold, Second Threshold, the 3rd threshold value, the 4th threshold value and be connected weighed value adjusting.Concrete way automatically adjusts according to minimum mean square error criterion.Repeat this step, until output matrix and expected matrix do not exceed preset range.The first threshold obtained for the last time, Second Threshold, the 3rd threshold value, the 4th threshold value with the output matrix be connected corresponding to weights as final output matrix.
As one preferred embodiment, described signal determination subelement determines that the signal that described source information characterizes is that Binary Frequency Shift Keying BFSK signal, phase shift keying bpsk signal or Quadrature Phase Shift Keying QPSK signal specifically comprise;
Calculate the first mean square error of described output matrix and BFSK signal target matrix;
Calculate the second mean square error of described output matrix and bpsk signal objective matrix;
Calculate the 3rd mean square error of described output matrix and QPSK signal target matrix;
More described first mean square error, described second mean square error and described 3rd mean square error extent;
If described first mean square error is minimum, then determine that the signal that described source information characterizes is Binary Frequency Shift Keying BFSK signal, if described second mean square error is minimum, then determine that the signal that described source information characterizes is phase shift keying bpsk signal, if described 3rd mean square error is minimum, then determine that the signal that described source information characterizes is Quadrature Phase Shift Keying QPSK signal.
In concrete enforcement, the first threshold obtained for the last time, Second Threshold, the 3rd threshold value, the 4th threshold value and the output matrix be connected corresponding to weights respectively with BFSK signal target matrix, bpsk signal objective matrix and QPSK signal target matrix computations, obtain the first mean square error, the second mean square error and the 3rd mean square error respectively.By comparing the first mean square error, the second mean square error and the 3rd mean square error extent, if the first mean square error is minimum, then show that the signal that source information characterizes is BFSK signal; If the second mean square error is minimum, then show that the signal that source information characterizes is bpsk signal; If the 3rd mean square error is minimum, then show that the signal that source information characterizes is QPSK signal.
It should be noted that, BFSK signal target matrix, bpsk signal objective matrix and QPSK signal target matrix are in advance according to BFSK signal, bpsk signal and QPSK signal sets.
Embodiment two
Fig. 3 is the structure chart of a kind of low-voltage powerline carrier communication signal madulation mode recognition system provided by the invention.Low-voltage powerline carrier communication signal madulation mode recognition system, comprising: carrier module 30 to be tested, carrier communication testing apparatus 31, signal pickup assembly 32 and the low-voltage powerline carrier communication signal madulation mode recognition device 33 described in embodiment one.
In concrete enforcement, carrier communication testing apparatus 31 sends instruction, and by low-voltage power spider lines 34 by this command to carrier module 30 to be tested, carrier module 30 to be tested sends the modulation signal carrying power information according to this instruction.Signal pickup assembly 32 obtains the modulation signal of carrier module 30 to be tested transmission by low-voltage power spider lines 34, and is sent to by this modulation signal in low-voltage powerline carrier communication signal madulation mode recognition device 33.Determine the modulation system that this modulation signal is corresponding by the identification of low-voltage powerline carrier communication signal madulation mode recognition device 33 pairs of modulation signals.
Low-voltage powerline carrier communication signal madulation mode recognition system provided by the invention realizes the identification to the modulation signal that carrier module to be tested sends by carrier module to be tested, carrier communication testing apparatus, signal pickup assembly and low-voltage powerline carrier communication signal madulation mode recognition device.
Fig. 4 is the structure chart of another kind of low-voltage powerline carrier communication signal madulation mode recognition system provided by the invention.As one preferred embodiment, also comprise: artificial mains network, for powering for described low-voltage powerline carrier communication signal madulation mode recognition device.
In concrete enforcement, artificial mains network 40 powers for low-voltage powerline carrier communication signal madulation mode recognition device.
Above low-voltage powerline carrier communication signal madulation mode recognition device provided by the present invention and system are described in detail.Apply specific case herein to set forth principle of the present invention and execution mode, the explanation of above embodiment just understands method of the present invention and core concept thereof for helping.It should be pointed out that for those skilled in the art, under the premise without departing from the principles of the invention, can also carry out some improvement and modification to the present invention, these improve and modify and also fall in the protection range of the claims in the present invention.
Claims (7)
1. a low-voltage powerline carrier communication signal madulation mode recognition device, is characterized in that, comprising:
To what get, sampling unit, for treating that the modulation signal that power line carrier module sends is sampled and record;
Signal Pretreatment unit, carries out analysis preliminary treatment for the signal exported described sampling unit, and according to the source information of baud rate picks symbols sampled value as Modulation Mode Recognition;
Input vector structural unit, for the characteristic parameter matrix that constructed by the characteristic parameter extracted from the described source information input vector as neural net;
Determining unit, for utilizing neural network algorithm, determines the modulation system that signal that described source information characterizes is corresponding.
2. low-voltage powerline carrier communication signal madulation mode recognition device according to claim 1, it is characterized in that, described characteristic parameter specifically comprises:
The maximum γ of normalize and center instantaneous amplitude spectrum density
max;
The absolute moment of the orign f of single order of normalize and center non-weak signal section instantaneous frequency
absorg;
The First Order Absolute Central Moment P of zero center non-weak signal section instantaneous phase nonlinear component
abscen;
The standard deviation of zero center non-weak signal section instantaneous phase nonlinear component absolute value
ap.
3. low-voltage powerline carrier communication signal madulation mode recognition device according to claim 2, it is characterized in that, described determining unit specifically comprises:
Neural network computing subelement, for utilizing described neural network algorithm, adjusting first threshold, Second Threshold, the 3rd threshold value, the 4th threshold value and being connected weights;
Signal determination subelement, for determining that according to the described output matrix of neural network computing subelement and the mean square error of expected matrix the signal that described source information characterizes is Binary Frequency Shift Keying BFSK signal, phase shift keying bpsk signal or Quadrature Phase Shift Keying QPSK signal;
Modulation system determination subelement, for determining the modulation system of described source information according to the result of described signal determination subelement;
Wherein, described first threshold, Second Threshold, the 3rd threshold value and the 4th threshold value are the boundary value of described characteristic parameter.
4. low-voltage powerline carrier communication signal madulation mode recognition device according to claim 3, it is characterized in that, described neural network computing subelement utilizes neural network algorithm, adjusts first threshold, Second Threshold, the 3rd threshold value, the 4th threshold value and be connected weights specifically to comprise:
By described first threshold, Second Threshold, the 3rd threshold value, the 4th threshold value be connected weights and be initialized as non-zero random number;
By described characteristic parameter Input matrix neural net;
By ranking operation process, if described output matrix and described expected matrix exceed preset range, just to described first threshold, Second Threshold, the 3rd threshold value, the 4th threshold value be connected weights and automatically adjust according to minimum mean square error criterion;
If described output matrix and described expected matrix do not exceed described preset range, then stop described automatic adjustment.
5. low-voltage powerline carrier communication signal madulation mode recognition device according to claim 4, it is characterized in that, described signal determination subelement determines that the signal that described source information characterizes is that Binary Frequency Shift Keying BFSK signal, phase shift keying bpsk signal or Quadrature Phase Shift Keying QPSK signal specifically comprise;
Calculate the first mean square error of described output matrix and BFSK signal target matrix;
Calculate the second mean square error of described output matrix and bpsk signal objective matrix;
Calculate the 3rd mean square error of described output matrix and QPSK signal target matrix;
More described first mean square error, described second mean square error and described 3rd mean square error extent;
If described first mean square error is minimum, then determine that the signal that described source information characterizes is described Binary Frequency Shift Keying BFSK signal, if described second mean square error is minimum, then determine that the signal that described source information characterizes is described phase shift keying bpsk signal, if described 3rd mean square error is minimum, then determine that the signal that described source information characterizes is described Quadrature Phase Shift Keying QPSK signal.
6. a low-voltage powerline carrier communication signal madulation mode recognition system, it is characterized in that, comprising: carrier module to be tested, carrier communication testing apparatus, signal pickup assembly and the low-voltage powerline carrier communication signal madulation mode recognition device as described in claim 1-5 any one.
7. low-voltage powerline carrier communication signal madulation mode recognition system according to claim 6, is characterized in that, also comprise: artificial mains network, for powering for described low-voltage powerline carrier communication signal madulation mode recognition device.
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| CN112488092B (en) * | 2021-02-05 | 2021-08-24 | 中国人民解放军国防科技大学 | Navigation frequency band signal type identification method and system based on deep neural network |
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