CN110207689A - A kind of pulsar signal denoising and discrimination method based on Wavelet Entropy - Google Patents
A kind of pulsar signal denoising and discrimination method based on Wavelet Entropy Download PDFInfo
- Publication number
- CN110207689A CN110207689A CN201910464593.XA CN201910464593A CN110207689A CN 110207689 A CN110207689 A CN 110207689A CN 201910464593 A CN201910464593 A CN 201910464593A CN 110207689 A CN110207689 A CN 110207689A
- Authority
- CN
- China
- Prior art keywords
- wavelet
- signal
- pulsar
- entropy
- coefficient
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/02—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by astronomical means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/211—Selection of the most significant subset of features
Abstract
The invention belongs to pulsar signal processing technology field, a kind of pulsar signal denoising based on Wavelet Entropy and discrimination method are disclosed.Pulsar signal is decomposed first with wavelet transformation, calculates the Wavelet Entropy in each subinterval after wavelet decomposition.Wavelet Entropy and wavelet threshold are combined so that it is determined that each layer high-frequency wavelet coefficient threshold value thresholding.Pulsar signal using threshold value threshold processing with noise.Further according to different pulsar signals each section energy value present differentiation the characteristics of, calculate sample in all pulsar signals the Wavelet Entropy in each section numerical value.Being loaded into database as a result, carrying out construction feature parameter in conjunction with corresponding title for the Wavelet Entropy numerical value of each pulsar signal is recorded, then is compared with unknown pulsar signal feature, and completes the identification to pulsar signal.The present invention only uses Wavelet Entropy algorithm, while signal denoising, can extract the small echo entropy amount of being characterized of signal to complete the task of identification.
Description
Technical field
The invention belongs to pulse signal processing technology field more particularly to a kind of pulsar signal denoisings based on Wavelet Entropy
And discrimination method.
Background technique
Currently, the immediate prior art: the ultrastability in pulsar signal period, it is provided for spacecraft navigation
New research direction, but the performance of the high interference and receiving device of long propagation distance and interstellar space material, are received
It include very noisy in pulsar signal.Under normal circumstances, signals with noise, which will become workable useful signal, to be gone
It makes an uproar processing.During pulsar identification, and need first to carry out denoising.Existing algorithm is mostly by pulsar signal
Denoising, identification is divided into two parts to study, and proposes different algorithms for the two parts.These algorithms are all used for
Single-issue is solved, and solves the denoising of pulsar navigation system using two kinds of algorithms and recognizes the two compatible signal processings
Problem reduces the efficiency of system.
In conclusion problem of the existing technology is: existing pulse signal Processing Algorithm is all used to solve single ask
Topic solves the denoising of pulsar navigation system using two kinds of algorithms and recognizes the two compatible signal processing problems, reduces
The efficiency of system.
Solve the difficulty of above-mentioned technical problem:
It solves to denoise and recognize both of these problems simultaneously using a kind of method, should meet and filter out noise and reach extraction again
The purpose of noise.Existing technology is intended to inhibit and eliminate noise signal, and other way is recycled to extract signal characteristic.It is existing
Technology does not provide a possibility that extracting both features, and Wavelet Entropy method is effectively extracted noise signal after signal decomposition
Feature and pulsar signal feature.
Solve the meaning of above-mentioned technical problem:
X-ray pulsar signal processing is a key component of airmanship, while restrict navigation accuracy.Therefore it grinds
The denoising and identification for studying carefully X-ray pulsar signal have the precision and efficiency that improve X-ray pulsar navigation system important
Meaning.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of, and the pulsar signal based on Wavelet Entropy is denoised and is distinguished
Knowledge method.
The invention is realized in this way a kind of pulsar signal denoising and discrimination method based on Wavelet Entropy, described to be based on
The pulsar signal of Wavelet Entropy denoises and discrimination method includes:
The first step decomposes pulsar signal using wavelet transformation, calculates the Wavelet Entropy in each subinterval after wavelet decomposition；
Wavelet Entropy and wavelet threshold are combined and determine each layer high-frequency wavelet coefficient threshold value thresholding；Using threshold value threshold processing with noise
Pulsar signal；
The characteristics of second step, differentiation is presented in the energy value according to different pulsar signals in each section；Calculate sample
In all pulsar signals the Wavelet Entropy in each section numerical value；
Third step, by the Wavelet Entropy numerical value of each pulsar as a result, in conjunction with corresponding title, construction feature parameter is loaded into
It is compared into database, then with unknown pulsar signal, and completes the identification to pulsar signal.
Further, the first step specifically includes:
(1) wavelet transformation for carrying out m scale to the pulsar signal x (n) that band is made an uproar first, obtains the discrete of each scale
Wavelet coefficient, the coefficient of the high fdrequency component at k moment is expressed as d under jth decomposition scaleJ, k, the coefficient of low frequency component is expressed as aJ, k,
The sample frequency of signal is expressed as fs, wherein j=1,2 ..., 5；K=1,2 ..., 10；Preferably, using db6 as wavelet basis
Function carries out the wavelet transform of 5 scales to pulsar sequence；
(2) according to each layer scattering wavelet coefficient, the wavelet energy E of the high frequency coefficient in each section is calculated separatelyJ, k；Letter
Number jth layer coefficients dJ, kIt is divided into n section, then the energy in this layer of each section are as follows:
Wherein m is sampling number；
(3) wavelet energy distribution probability is k-th of subinterval wavelet coefficient ENERGY EJ, kWith this layer of wavelet coefficient gross energy Ej
Ratio；
PJ, k=EJ, k/Ej；
The Wavelet Entropy W in the section KkFor；
Wk=-∑jPJ, kln(PJ, k)；
The maximum section of small echo entropy is found, and calculates the noise estimate variance σ in this section:
WhereinReach the wavelet coefficient intermediate value in maximum section for small echo entropy in wavelet coefficient section.System
The number specific calculation method of intermediate value is as follows:
Wherein, γ1For the adjustment factor of threshold value thresholding, regulatory factorβ；According to the feature of signal, regulation coefficient and because
The value of son, adaptively determines threshold value thresholding.λjFor the threshold value of high frequency coefficient, then the threshold value of high frequency coefficient is；
Wherein, γ2For the adjustment factor of threshold value thresholding, the threshold value of each layer coefficients is adjusted according to the actual situation, and N is sample letter
Number length；
Threshold process is carried out to high frequency coefficient, the pulsar signal after being denoised is reconstructed；By calculating mean square error
Difference obtains the performance indicator of denoising effect；
Wherein, x ' (n) is denoised signal.
Further, the second step specifically includes:
(1) pulsar signal database is established, the Wavelet Entropy in each section is successively calculated, as characteristic parameter, with pulsar
Signal name is stored together in the database；
(2) the small echo entropy for calculating unknown pulse star signal, is compared with the data in database；Utilize two groups of signals
Variance between characteristic parameter is compared, and determines whether feature has uniqueness；The formula of variance of characteristic parameter is；
Wherein, s1j(n), s2j(n) the small echo entropy of two kinds of signal jth layers；
(3) it draws a conclusion, pulsar PSR 0531+21 signal whether there is in existing database.If it does, side
Difference is 0；Otherwise the pulsar is considered as not in given data storehouse.
Another object of the present invention is to provide the pulsar signal denoisings and identification described in a kind of application based on Wavelet Entropy
The spacecraft of method.
Another object of the present invention is to provide the pulsar signal denoisings and identification described in a kind of application based on Wavelet Entropy
The pulsar signal processing system of method.
In conclusion advantages of the present invention and good effect are as follows: the present invention provides one in view of the deficiency of existing algorithm
Pulsar signal denoising and discrimination method of the kind based on Wavelet Entropy；Wavelet Entropy algorithm is only used, it, can be with while signal denoising
The small echo entropy amount of being characterized of signal is extracted to complete the task of identification.
Detailed description of the invention
Fig. 1 is the pulsar signal denoising provided in an embodiment of the present invention based on Wavelet Entropy and discrimination method flow chart.
Fig. 2 is the original signal waveform figure of not Noise provided in an embodiment of the present invention.
Fig. 3 is the signals and associated noises waveform diagram that signal-to-noise ratio provided in an embodiment of the present invention is 20db.
Fig. 4 is each scale discrete wavelet coefficient waveform diagram provided in an embodiment of the present invention.
Fig. 5 is each layer coefficients small echo entropy provided in an embodiment of the present invention.
Fig. 6 is the waveform diagram of signal after denoising provided in an embodiment of the present invention.
Fig. 7 is original signal provided in an embodiment of the present invention, signals and associated noises, denoised signal comparison diagram.
Fig. 8 is each layer Wavelet Entropy line chart of different pulsar signals provided in an embodiment of the present invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
The high frequency coefficient of present invention combination wavelet transformation, determines suitable threshold value, achievees the purpose that denoising.Gained will be calculated
Characteristic parameter of the Wavelet Entropy as identification signal, achieve the purpose that identify pulsar signal.
Application principle of the invention is explained in detail with reference to the accompanying drawing.
As shown in Figure 1, the pulsar signal denoising and discrimination method provided in an embodiment of the present invention based on Wavelet Entropy includes
Following steps:
S101: pulsar signal is decomposed using wavelet transformation, calculates the Wavelet Entropy in each subinterval after wavelet decomposition；It will
Wavelet Entropy and wavelet threshold, which combine, determines each layer high-frequency wavelet coefficient threshold value thresholding；Arteries and veins using threshold value threshold processing with noise
Rush star signal；
S102: the characteristics of differentiation is presented in the energy value according to different pulsar signals in each section；It calculates in sample
Numerical value of all pulsar signals in the Wavelet Entropy in each section；
S103: by the Wavelet Entropy numerical value of each pulsar as a result, in conjunction with corresponding title, construction feature parameter is loaded into
It in database, then is compared with unknown pulsar signal, and completes the identification to pulsar signal.
Pulsar signal denoising and discrimination method provided in an embodiment of the present invention based on Wavelet Entropy specifically includes following step
It is rapid:
Step 1, denoising；
(1) wavelet transformation for carrying out m scale to the pulsar signal x (n) that band is made an uproar first, obtains the discrete of each scale
Wavelet coefficient, the coefficient of the high fdrequency component at k moment is expressed as d under jth decomposition scaleJ, k, the coefficient of low frequency component is expressed as aJ, k,
The sample frequency of signal is expressed as fs, wherein j=1,2 ..., 5；K=1,2 ..., 10；Preferably, using db6 as wavelet basis
Function carries out the wavelet transform of 5 scales to pulsar sequence；
(2) according to each layer scattering wavelet coefficient, the wavelet energy E of the high frequency coefficient in each section is calculated separatelyJ, k.Letter
Number jth layer coefficients dJ, kIt is divided into n section, then the energy in this layer of each section are as follows:
Wherein m is sampling number.
(3) wavelet energy distribution probability is k-th of subinterval wavelet coefficient ENERGY EJ, kWith this layer of wavelet coefficient gross energy Ej
Ratio；
PJ, k=EJ, k/Ej (3)
The Wavelet Entropy W in the section KkFor；
Wk=-∑jPJ, kln(PJ, k) (4)
The maximum section of small echo entropy is found, and calculates the noise estimate variance σ in this section:
WhereinReach the wavelet coefficient intermediate value in maximum section for small echo entropy in wavelet coefficient section.System
The number specific calculation method of intermediate value is as follows:
Wherein, γ1For the adjustment factor of threshold value thresholding, regulatory factorβ.According to the feature of signal, regulation coefficient and because
The value of son, to adaptively determine threshold value thresholding.λjFor the threshold value of high frequency coefficient, then the threshold value of high frequency coefficient is；
Wherein, γ2For the adjustment factor of threshold value thresholding, the threshold value of each layer coefficients is adjusted according to the actual situation, and N is sample letter
Number length.
Threshold process is carried out to high frequency coefficient by above formula (6), the pulsar signal after being denoised is reconstructed.Formula
(8) it is the expression formula of mean square error, by calculating mean square error, obtains the performance indicator of denoising effect.
Wherein, x ' (n) is denoised signal.
Step 2: classification
(1) pulsar signal database is established, the Wavelet Entropy in each section is successively calculated according to (7) formula, as characteristic parameter,
It is stored together in the database with pulsar signal title.
(2) the small echo entropy for calculating unknown pulse star signal, is compared with the data in database.Utilize two groups of signals
Variance between characteristic parameter is compared, and determines whether feature has uniqueness.The formula of variance of characteristic parameter is；
VARj=[∑n(s1j(n)-s2j(n))2]1/2 (9)
Wherein, s1j(n), s2j(n) the small echo entropy of two kinds of signal jth layers.
(3) it draws a conclusion, pulsar PSR 0531+21 signal whether there is in existing database.If it does, side
Difference is 0；Otherwise the pulsar is considered as not in given data storehouse.
Application principle of the invention is further described combined with specific embodiments below.
For carrying out denoising using PSR 0531+21 signal below and establish database identification, said in conjunction with attached drawing
Bright a specific embodiment of the invention.
In order to which more efficiently the denoising of processing pulsar signal and identification problem, the present invention are handled simultaneously using Wavelet Entropy
Both of these problems.For signal after wavelet transformation, the size of entropy reflects the position of noise power and the feature of Energy distribution.In conjunction with
The above two o'clock, the present invention is by threshold denoising and aspect ratio to solving both of these problems.At the same time, the present invention is real in emulation
29 group pulse star signal characteristic databases are established as an example, compared the residual values between 21 group pulse star signals in example,
Comparison process can be rapidly completed.For not unknown pulse star signal in the database, can be included in after determining pulsar title
Database.The characteristic information constantly updated in property data base can also be used as the reference frame of other research contents.Specifically
Implementation process includes the following steps:
Step S1: from European pulsar database (European Pulsar Network Data Archive, EPNDA)
The observation data for obtaining pulsar, obtain pulsar PSR 0531+21 signal sequence by processing.Small echo is write in MATLAB
Threshold denoising program.Pulsar signal sequence is read from file, as shown in Fig. 2, drawing the contour curve of pulsar.With noise
Carrying out wavelet threshold denoising than the signals and associated noises for 20db is example.
The step of this example uses PSR 0531+21 signal, carries out wavelet threshold denoising is as follows:
S1-1: as shown in figure 3, drawing the PSR 0531+21 signal profile for having white Gaussian noise.Respectively to noisy pulse
Star signal carries out wavelet transformation, obtains the discrete wavelet coefficient of each scale.Different values is set, preferably, find suitable
Decomposition order.The wavelet transform of 5 scales is finally carried out to pulsar sequence as wavelet basis function using db6.Arteries and veins
It is as shown in Figure 4 to rush star signal transformation results.
S1-2: every layer coefficients are divided into 10 sections.As shown in table 1, the high frequency in each section is calculated separately according to formula (2)
The wavelet energy value of coefficient.As shown in figure 5, calculating the small echo entropy in each section according to formula (4).
The wavelet energy value of the high frequency coefficient in 1 10 sections of table
The maximum section of small echo entropy is the 6th section, and according to formula (5) calculate the noise estimate variance σ in this section=
0.0142.According to the feature of signal, the value of regulation coefficient and the factor, to adaptively determine threshold value thresholding.Threshold value thresholding
Adjustment factor γ1=1.5, γ2=0.8, regulatory factorβ=1.As shown in table 2, high frequency coefficient is obtained according to formula (7)
Threshold value.
The threshold value of 2 each layers of high frequency coefficient of table
| Decomposition order | 1 | 2 | 3 | 4 | 5 |
| Threshold value | 0.0796 | 0.0632 | 0.0300 | 0.0070 | 0.0066 |
Threshold process is carried out to high frequency coefficient by above formula (6), the pulsar signal after being denoised is reconstructed.Such as figure
Pulsar signal waveform diagram shown in 4, after being denoised.Mean square error is calculated using formula (8), the performance for obtaining denoising effect refers to
Mark.As shown in table 3, as signal-to-noise ratio becomes larger, denoising effect is become better and better.The processor of computer used in emulation experiment is Intel
(R) Core (TM) i5-3470CPU@3.20Ghz, system type are 64 bit manipulation systems, the processor based on X64.Program operation
On matlabR2017 platform, simulation time 0.349s.The calculation method of this example is relatively simple, is not related to complicated fortune
It calculates, the speed of service is very fast.
3 PSR 0531+21 Signal-to-Noise of table and mean variance
| SNR/db | 5 | 10 | 20 | 25 | 30 | 35 |
| RMSE | 0.5098 | 0.2961 | 0.1048 | 0.0585 | 0.0348 | 0.0231 |
Step S2: using the signal sequence of 25 pulsars as sample, whether verifying Wavelet Entropy can become characteristic parameter
The problem of.
S2-1: whether according to the matching degree of characteristic signal, determining Wavelet Entropy can be used as characteristic parameter steps are as follows:
S2-1-1: from European pulsar database (European Pulsar Network Data Archive, EPNDA)
The observation data for obtaining pulsar, obtain the signal sequence of 25 pulsars.
S2-1-2: Wavelet Entropy identification program is write in MATLAB.Pulsar signal sequence is read from file, establishes number
According to library Pulsar.mat.The Wavelet Entropy that each section is successively calculated according to (7) formula, as characteristic parameter, with pulsar signal title
It is stored in the database pul_feature.mat of characteristic variable together.
S2-1-3: according to the information of database, different each layer Wavelet Entropy broken lines of pulsar signal are drawn in the same coordinate system
Figure.As shown in fig. 7, wherein preceding 25 pulsar signals are the small echo entropy without the original pulse star of noise.From figure, we
Available, each layer small echo entropy for being stored in the different pulsar signals of database is not completely overlapped.For different observation frequencies
Same pulsar under rate, part level will appear overlapping phenomenon, this just illustrates that same pulsar has similar profile.
Under different observing frequencies, profile has different performance characteristics.Same pulsar signal adds different white Gaussian noises,
The difference of the Wavelet Entropy of different levels is less, and figure middle conductor tends towards stability.The small echo entropy of different levels is in certain region
It floats.The case where straight line occur is less.The experimental result of amplitude from the sample of broken line fluctuation is available, different pulsar letters
Number there is different small echo entropy, Wavelet Entropy can be used as the parameter of pulsar signal feature identification.
S2-2: by 25 group pulse sing datas in database, by one group of PSR 0531+21 signal and other 24 groups of signals
Wavelet Entropy data do variance, the variance of the Wavelet Entropy of available different levels.
The step of S2-2-1: this example uses PSR 0531+21 signal, carries out Wavelet Entropy identification is as follows:
S2-2-1: the small echo entropy for calculating PSR 0531+21 signal obtains comparison result using formula of variance (9).Such as
Shown in table 4.
The variance of pulsar signal in each layer small echo entropy and database of 4 PSR 0531+21 signal of table
S2-3: drawing a conclusion, PSR 0531+21 signal and identical PSR 0531+21 signal present in database
Residual error be 0, the residual error of remaining signal is all larger than 0.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.
Claims (5)
1. a kind of pulsar signal denoising and discrimination method based on Wavelet Entropy, which is characterized in that the arteries and veins based on Wavelet Entropy
It rushes star signal denoising and discrimination method includes:
The first step decomposes pulsar signal using wavelet transformation, calculates the Wavelet Entropy in each subinterval after wavelet decomposition；It will be small
Wave entropy and wavelet threshold, which combine, determines each layer high-frequency wavelet coefficient threshold value thresholding；Pulse using threshold value threshold processing with noise
Star signal；
The characteristics of second step, differentiation is presented in the energy value according to different pulsar signals in each section；Calculate institute in sample
There is pulsar signal in the numerical value of the Wavelet Entropy in each section；
Third step, by the Wavelet Entropy numerical value of each pulsar as a result, in conjunction with corresponding title, construction feature parameter is loaded into number
It is compared according in library, then with unknown pulsar signal, and completes the identification to pulsar signal.
2. the pulsar signal denoising based on Wavelet Entropy and discrimination method as described in claim 1, which is characterized in that described the
One step specifically includes:
(1) wavelet transformation for carrying out m scale to the pulsar signal x (n) that band is made an uproar first, obtains the discrete wavelet of each scale
Coefficient, the coefficient of the high fdrequency component at k moment is expressed as d under jth decomposition scaleJ, k, the coefficient of low frequency component is expressed as aJ, k, signal
Sample frequency be expressed as fs, wherein j=1,2 ..., 5；K=1,2 ..., 10；Preferably, using db6 as wavelet basis function,
The wavelet transform of 5 scales is carried out to pulsar sequence；
(2) according to each layer scattering wavelet coefficient, the wavelet energy E of the high frequency coefficient in each section is calculated separatelyJ, k；Signal jth
Layer coefficients dJ, kIt is divided into n section, then the energy in this layer of each section are as follows:
Wherein m is sampling number；
(3) wavelet energy distribution probability is k-th of subinterval wavelet coefficient ENERGY EJ, kWith this layer of wavelet coefficient gross energy EjRatio
Value；
PJ, k=EJ, k/Ej；
The Wavelet Entropy W in the section KkAre as follows:
Wk=-∑jPJ, kln(PJ, k)；
The maximum section of small echo entropy is found, and calculates the noise estimate variance σ in this section:
WhereinReach the wavelet coefficient intermediate value in maximum section for small echo entropy in wavelet coefficient section.Median coefficient
Specific calculation method is as follows:
Wherein, γ1For the adjustment factor of threshold value thresholding, regulatory factorβ；According to the feature of signal, regulation coefficient and the factor
Value, adaptively determines threshold value thresholding；λjFor the threshold value of high frequency coefficient, then the threshold value of high frequency coefficient are as follows:
Wherein, γ2For the adjustment factor of threshold value thresholding, the threshold value of each layer coefficients is adjusted according to the actual situation, and N is sample signal
Length.
Threshold process is carried out to high frequency coefficient, the pulsar signal after being denoised is reconstructed；By calculating mean square error, obtain
To the performance indicator of denoising effect；
Wherein, x ' (n) is denoised signal.
3. the pulsar signal denoising based on Wavelet Entropy and discrimination method as described in claim 1, which is characterized in that described the
Two steps specifically include:
(1) pulsar signal database is established, the Wavelet Entropy in each section is successively calculated, as characteristic parameter, with pulsar signal
Title is stored together in the database；
(2) the small echo entropy for calculating unknown pulse star signal, is compared with the data in database；Utilize two groups of signal characteristics
Variance between parameter is compared, and determines whether feature has uniqueness；The formula of variance of characteristic parameter is；
Wherein, s1j(n), s2j(n) the small echo entropy of two kinds of signal jth layers；
(3) it draws a conclusion, pulsar PSR 0531+21 signal whether there is in existing database.If it does, variance is
0；Otherwise the pulsar is considered as not in given data storehouse.
4. a kind of pulsar signal denoising and discrimination method using described in claims 1 to 3 any one based on Wavelet Entropy
Spacecraft.
5. a kind of pulsar signal denoising and discrimination method using described in claims 1 to 3 any one based on Wavelet Entropy
Pulsar signal processing system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910464593.XA CN110207689B (en) | 2019-05-30 | 2019-05-30 | Pulsar signal denoising and identifying method based on wavelet entropy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910464593.XA CN110207689B (en) | 2019-05-30 | 2019-05-30 | Pulsar signal denoising and identifying method based on wavelet entropy |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110207689A true CN110207689A (en) | 2019-09-06 |
| CN110207689B CN110207689B (en) | 2022-09-16 |
Family
ID=67789707
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910464593.XA Active CN110207689B (en) | 2019-05-30 | 2019-05-30 | Pulsar signal denoising and identifying method based on wavelet entropy |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110207689B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110943771A (en) * | 2019-11-22 | 2020-03-31 | 大连海事大学 | Wavelet resolution processing method based on angle entropy |
| CN112101141A (en) * | 2020-08-25 | 2020-12-18 | 中国人民解放军火箭军工程大学 | Frequency domain n-gamma identification method and device |
| CN112350787A (en) * | 2020-10-13 | 2021-02-09 | 电子科技大学 | Radio signal abnormity detection method |
| CN117520752A (en) * | 2024-01-05 | 2024-02-06 | 梁山公用水务有限公司 | Hydraulic engineering information management method based on big data |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102495343A (en) * | 2011-12-30 | 2012-06-13 | 重庆大学 | Partial discharge detection identification method based on ultrasound and ultraviolet information fusion and system thereof |
| CN105136138A (en) * | 2015-08-05 | 2015-12-09 | 西安电子科技大学 | X-ray pulsar photon signal identification method based on nuclear extreme learning machine |
| CN106033669A (en) * | 2015-03-18 | 2016-10-19 | 展讯通信（上海）有限公司 | Voice identification method and apparatus thereof |
| CN106405339A (en) * | 2016-11-11 | 2017-02-15 | 中国南方电网有限责任公司 | Power transmission line fault reason identification method based on high and low frequency wavelet feature association |
| CN107894231A (en) * | 2017-11-06 | 2018-04-10 | 哈尔滨工业大学 | A kind of X-ray pulsar discrimination method based on Hilbert transform |
-
2019
- 2019-05-30 CN CN201910464593.XA patent/CN110207689B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102495343A (en) * | 2011-12-30 | 2012-06-13 | 重庆大学 | Partial discharge detection identification method based on ultrasound and ultraviolet information fusion and system thereof |
| CN106033669A (en) * | 2015-03-18 | 2016-10-19 | 展讯通信（上海）有限公司 | Voice identification method and apparatus thereof |
| CN105136138A (en) * | 2015-08-05 | 2015-12-09 | 西安电子科技大学 | X-ray pulsar photon signal identification method based on nuclear extreme learning machine |
| CN106405339A (en) * | 2016-11-11 | 2017-02-15 | 中国南方电网有限责任公司 | Power transmission line fault reason identification method based on high and low frequency wavelet feature association |
| CN107894231A (en) * | 2017-11-06 | 2018-04-10 | 哈尔滨工业大学 | A kind of X-ray pulsar discrimination method based on Hilbert transform |
Non-Patent Citations (2)
| Title |
|---|
| LIU XIUPING ET.AL: "X-ray pulsar signal de-noising for impulse noise using wavelet packet", 《AEROSPACE SCIENCE AND TECHNOLOGY 》 * |
| 闫丽等: "基于二次对数能量熵小波包的母猪哺乳声去噪方法研究", 《农业机械学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110943771A (en) * | 2019-11-22 | 2020-03-31 | 大连海事大学 | Wavelet resolution processing method based on angle entropy |
| CN112101141A (en) * | 2020-08-25 | 2020-12-18 | 中国人民解放军火箭军工程大学 | Frequency domain n-gamma identification method and device |
| CN112350787A (en) * | 2020-10-13 | 2021-02-09 | 电子科技大学 | Radio signal abnormity detection method |
| CN112350787B (en) * | 2020-10-13 | 2021-09-07 | 电子科技大学 | Radio signal abnormity detection method |
| CN117520752A (en) * | 2024-01-05 | 2024-02-06 | 梁山公用水务有限公司 | Hydraulic engineering information management method based on big data |
| CN117520752B (en) * | 2024-01-05 | 2024-04-12 | 梁山公用水务有限公司 | Hydraulic engineering information management method based on big data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110207689B (en) | 2022-09-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110207689A (en) | A kind of pulsar signal denoising and discrimination method based on Wavelet Entropy | |
| CN108845306B (en) | Laser radar echo signal denoising method based on variational modal decomposition | |
| Saucan et al. | A multisensor multi-Bernoulli filter | |
| Dobigeon et al. | Hierarchical Bayesian sparse image reconstruction with application to MRFM | |
| CN111860273A (en) | Magnetic resonance underground water detection noise suppression method based on convolutional neural network | |
| CN111947045A (en) | GVMD parameter optimization and singular value decomposition-based fluid pipeline leakage positioning method | |
| Fan et al. | Anti-interference technology of surface acoustic wave sensor based on k-means clustering algorithm | |
| Banbrook et al. | How to extract Lyapunov exponents from short and noisy time series | |
| Vargas et al. | Electrocardiogram signal denoising by clustering and soft thresholding | |
| CN109815849A (en) | Chaotic signal Denoising Algorithm based on singular value decomposition | |
| CN109410178A (en) | A kind of workpiece crack detection method and system | |
| Gao et al. | Research on deep convolutional neural network time-frequency domain seismic signal denoising combined with residual dense blocks | |
| CN107248869B (en) | A kind of multicomponent linear frequency-modulated signals denoising method based on Lyu's distribution | |
| Limin et al. | Low probability of intercept radar signal recognition based on the improved AlexNet model | |
| Zheng et al. | Time-frequency feature-based underwater target detection with deep neural network in shallow sea | |
| CN109752633B (en) | Method and system for positioning partial discharge signal of transformer substation | |
| CN116337449A (en) | Sparse self-coding fault diagnosis method and system based on information fusion | |
| CN113657593B (en) | Plasma parameter diagnosis method based on BP neural network | |
| Xie et al. | Data augmentation and deep neural network classification based on ship radiated noise | |
| CN115079052A (en) | Transformer fault diagnosis method and system | |
| Wu et al. | Classification of complex power quality disturbances based on modified empirical wavelet transform and light gradient boosting machine | |
| Chiou et al. | Ultrasonic flaw detection using neural network models and statistical analysis: Simulation studies | |
| CN113391172A (en) | Partial discharge diagnosis method and system based on time sequence integration and used for multi-source ultrasonic detection | |
| Ying et al. | On model order determination for complex exponential signals: Performance of an FFT-initialized ML algorithm | |
| Abdalla et al. | Brain tumor classification using principal component analysis and artificial neural network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |