CN106685435A - Method for improving effective-signal variation rapidly in low-SNR (Signal to Noise Ratio) signals - Google Patents
Method for improving effective-signal variation rapidly in low-SNR (Signal to Noise Ratio) signals Download PDFInfo
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- CN106685435A CN106685435A CN201611153045.8A CN201611153045A CN106685435A CN 106685435 A CN106685435 A CN 106685435A CN 201611153045 A CN201611153045 A CN 201611153045A CN 106685435 A CN106685435 A CN 106685435A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
- H04B1/0003—Software-defined radio [SDR] systems, i.e. systems wherein components typically implemented in hardware, e.g. filters or modulators/demodulators, are implented using software, e.g. by involving an AD or DA conversion stage such that at least part of the signal processing is performed in the digital domain
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Abstract
The invention discloses a method for improving effective-signal variation rapidly in low-SNR signals. The method is characterized in that an AD module is in a high conversion rate; a sample window N1 is set, and sequence data is obtained to form D1; data in D1 is pre-processed by filtering to obtain filtering data D2; accumulation and summation are carried out on data in D2 to obtain single-frame data A1; A1 data in different moments is processed by filtering; and a low noise signal is obtained. According to the invention, variable of the effective signal is improved without change of a hardware circuit, retardation of processing signals can be reduced by setting software parameters reasonably, and the method is suitable for an application system which is high in original signal noise, includes effective signals and is used for existence of excitation only.
Description
Technical field
The invention belongs to signal processing technology field, more particularly to a kind of processing method of Low SNR signal.
Background technology
Life be unable to do without the process of signal, and in the epoch that digital computer is developed rapidly, almost all of things can
Used as signal, signal only can just become useful information and be used through process, and then the process of signal is particularly important.Signal
The main purpose of process is exactly to weaken the superfluous content in signal, filters the noise and interference for mixing, or is translated the signals into into
It is easily processed, transmits, analyzing and the form for recognizing, so as to other follow-up process.For signal processing, occur in face of us
Do not processed by the pure mathematics of physical constraint, i.e. algorithm, and establish the field of signal processing.
And in actual application, useful signal is often limited to the shadow that applied environment, hardware circuit wait factors
Ring, can not react well in the middle of the signal data gathered in application system, the signal data for now gathering generally is carried
Larger noise jamming, it is difficult to tell the situation of change of wherein useful signal.
This signal with larger noise jamming is referred to as Low SNR signal.
For Low SNR signal, common process way is：
1st, hardware is changed, by adding filter circuit, from ways such as high-performance A/D chips；
2nd, software algorithm, adds data filtering, data smoothing scheduling algorithm in routine processes；
For hardware handles, system cost is undesirably increased, be not suitable for some inexpensive schemes
Using；And for software processes, by substantial amounts of software processes, although can to a certain extent improve signal noise
Than, but have the impact that can introduce useful signal data variation sluggishness, require higher application scenario for system is responded at some
And do not apply to.
Join as patent application 201510049964.X discloses a kind of signal based on empirical mode decomposition and wavelet analysises
Denoising method is closed, the method seeks signal auto-correlation according to the autocorrelation of signal, and the auto-correlation function of the signal is in zero point
Place obtains maximum, amplitude change poor over time and change, the value for decaying to very little that can't be quickly.To being mixed with Gauss
The signal of white noise carries out EMD decomposition, and due to the property that EMD decomposes, white Gaussian noise has no longer been real white noise, but in vain
The statistical property of noise is approximately present, i.e., the auto-correlation function of the described signal for being mixed with white Gaussian noise obtains maximum in zero point
Value, amplitude change poor over time and change, but its decay over time is quickly.Can be selected using this species diversity and be made an uproar
The active IMF components of sound effectively reduce impact of the noise to signal.Although the method can to a certain extent improve letter
Number signal to noise ratio, reduces noise, but has the sluggish impact of signal data change.
The content of the invention
Based on this, thus the primary mesh of the present invention be to provide in a kind of Low SNR signal the quick useful signal that improves and become
The method of change amount, the method can improve the variable quantity of useful signal, while reaching reduction letter by reasonable set software parameter
Number process sluggish effect.
Another mesh ground of the present invention is to provide quick raising useful signal variable quantity in a kind of Low SNR signal
Method, the method does not need the change on hardware, can reach less signal hysteresis and can recognize the change of useful signal
Situation, it is adaptable to which primary signal noise is larger and useful signal is only used for determining whether the application system of excitation.
For achieving the above object, the technical scheme is that：
A kind of quick method for improving useful signal variable quantity in Low SNR signal, it is characterised in that the method includes：
Step 1：The configuration of AD sampling modules is set, makes A/D module be in high conversion rate；
Step 2：In setting sampling window N1, continuous acquisition N1 frame sampling data and the memorizer of preservation, the sequence data
Become D1；
Step 3：Pretreatment is filtered to the data in D1, pretreated filtering data D2 is obtained and preserve；
Step 4：Data in D2 are carried out with cumulative summation, frame data A1 is obtained；
Step 5：To in the same time A1 data do not carry out data filtering process；Obtain low noise signal.
Wherein, AD conversion rate method is improved in step 1 is, is matched somebody with somebody by arranging the clock system and corresponding registers of A/D module
Put so that A/D module is in high-speed transitions speed.
Further, AD conversion speed is set in normal use as normal conversion speed, then current AD conversion rates>It is normal to turn
During throw-over rate, the AD conversion speed becomes high conversion rate.
Wherein, it is to the method that D1 data are filtered pretreatment in step 3:Given threshold TH1, asks for D1 sequence datas
In meansigma methodss, ask for the absolute value of the difference of each data and meansigma methodss in D1 sequence datas, if the absolute value for detecting its
In have more than threshold value TH1, then corresponding D1 data are then modified to identical with its previous secondary data.
Further, the filter preprocessing, process flow is：
(1) calculate D1 data in all data summation and obtain meansigma methodss；
(2) successively D1 data and meansigma methodss are subtracted each other and are obtained the absolute value of result；
(3) when the absolute value is more than threshold value TH1 for arranging, corresponding D1 data corrections are the previous number of times in its sequence
According to if a data headed by the D1 data of the needs amendment, are modified to meansigma methodss；Complete to obtain correcting sequence after filter preprocessing
Column data D2.
Wherein, for the method that in the same time A1 data do not carry out data filtering is in step 5, it is assumed that A1 [0], A1 [1], A1
[2] be newest acquisition three A1 data, its obtain moment be respectively T0, T1, T2, and T0>T1>T2, is currently needed at filtering
The data of reason are A1 [1]；
(1) judge that A1 [1] is simultaneously greater than A1 [0] and A1 [2], A1 [0] and A1 is modified to if A1 [1] data if setting up
[2] meansigma methodss；
(2) A1 [1] is judged while less than A1 [0] and A1 [2], A1 [0] and A1 is modified to if A1 [1] data if setting up
[2] meansigma methodss；
(3) in the case of other, A1 [1] data are varied without.
The quick method for improving useful signal variable quantity, can not change in the Low SNR signal that the present invention is realized
In the case of hardware circuit, the variable quantity of useful signal is improved by way of software processes, while passing through reasonable set software
Parameter can reach the signal hysteresis for reducing software processes, it is adaptable to which primary signal noise is larger and useful signal is only used for sentencing
The disconnected application system for whetheing there is excitation.
Description of the drawings
Fig. 1 is the control flow chart that the present invention is implemented.
Fig. 2 is the flow chart that the present invention implements filter preprocessing.
Fig. 3 is the control flow chart that the present invention implements Filtering Processing.
Specific embodiment
In order that the objects, technical solutions and advantages of the present invention become more apparent, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only to explain the present invention, and
It is not used in the restriction present invention.
As shown in figure 1, for the flow chart of institute's implementation method of the present invention, shown in figure, control flow is as follows：
101st, A/D module is set for high-speed transitions speed (as being 500Hz during normal sample, being now set to 4KHz).
102nd, sampling window N1=16, i.e. 16 initial datas of continuous sampling are set and obtain sequence data D1, and preserve
In the middle of memorizer.
103rd, software filtering pretreatment is carried out to D1 data, process flow is as shown in Figure 2：
(1) calculate D1 data in all data summation and obtain meansigma methodss.
(2) successively D1 data and meansigma methodss are subtracted each other and are obtained the absolute value of result.
(3) when the absolute value is more than threshold value TH1 for arranging, corresponding D1 data corrections are the previous number of times in its sequence
According to if a data headed by the D1 data of the needs amendment, are modified to meansigma methodss；Complete to obtain correcting sequence after filter preprocessing
Column data D2.
104th, data in D2 are added up, is obtained and value A1.
105th, A1 needs signal data to be processed as present frame, arranges 3 grades of back up memory space for A1 data, respectively
Storage A1 [0], A1 [1], A1 [2], A1 [0]~A1 [2] is respectively the A1 data for obtaining in chronological order, and wherein A1 [0] is for most
The new data for obtaining.When there are new A1 data to produce, A1 [0]~A1 [2] data are moved right successively, A1 [2] data are picked
Remove, and new A1 data are stored in A1 [0].
106th, final data filtering is carried out after new A1 data are obtained to process, filtering method flow process is as shown in Figure 3：
(1) judge that A1 [1] is simultaneously greater than A1 [0] and A1 [2], A1 [0] and A1 is modified to if A1 [1] data if setting up
[2] meansigma methodss.
(2) A1 [1] is judged while less than A1 [0] and A1 [2], A1 [0] and A1 is modified to if A1 [1] data if setting up
[2] meansigma methodss.
(3) in the case of other, A1 [1] data are varied without.
In said method, the situation of change of useful signal is difficult to identify that in Low SNR signal, and by multiple product
Cumulative mode is divided to be amplified the change of the useful signal of single, so as to lift the resolution of useful signal.And wherein,
Need carrying out necessary software noise reduction process to signal before and after cumulative to signal, made an uproar with avoiding amplifying after signal is cumulative simultaneously
Sound.During multiple cumulative signal, signal sampling period is undesirably increased, it is slow that signal intensity process is produced
It is stagnant, thus, the switching rate of AD is improved in the 1st step, sampling number is improved by losing precision, shorten signal sampling week
Phase, and passing through the later stage is carrying out software noise reduction process.This method for only need recognize signal have in the middle of unconverted system
More it is suitable for.
In the method, AD conversion speed, window N1 values and threshold value TH1 need to be set according to actually used situation, such as work as window
When mouth N1 arranges larger, although the signal quantitative change for obtaining is big, but simultaneously because multiple repairing weld may take the longer sampling time
So that signal becomes sluggish, and N1 is not enough to distinguish the situation of change of useful signal when arranging less.
Therefore, the quick method for improving useful signal variable quantity, Ke Yi in the Low SNR signal that the present invention is realized
In the case of not changing hardware circuit, the variable quantity of useful signal is improved by way of software processes, while by rationally setting
Determining software parameter can reach the signal hysteresis for reducing software processes, it is adaptable to primary signal noise is larger and useful signal only
For determining whether the application system of excitation.
Presently preferred embodiments of the present invention is the foregoing is only, not to limit the present invention, all essences in the present invention
Any modification, equivalent and improvement made within god and principle etc., should be included within the scope of the present invention.
Claims (6)
1. a kind of method that useful signal variable quantity is quickly improved in Low SNR signal, it is characterised in that the method includes：
Step 1：The configuration of AD sampling modules is set, makes A/D module be in high conversion rate；
Step 2：In setting sampling window N1, continuous acquisition N1 frame sampling data and the memorizer of preservation, the sequence data becomes
D1；
Step 3：Pretreatment is filtered to the data in D1, pretreated filtering data D2 is obtained and preserve；
Step 4：Data in D2 are carried out with cumulative summation, frame data A1 is obtained；
Step 5：To in the same time A1 data do not carry out data filtering process；Obtain low noise signal.
2. the method that useful signal variable quantity is quickly improved in Low SNR signal as claimed in claim 1, it is characterised in that
In step 1, improving AD conversion rate method is, by the clock system and corresponding registers configuration for arranging A/D module so that AD moulds
Block is in high-speed transitions speed.
3. the method that useful signal variable quantity is quickly improved in Low SNR signal as claimed in claim 2, it is characterised in that
AD conversion speed is set in normal use as normal conversion speed, then current AD conversion rates>During normal conversion speed, the AD turns
Throw-over rate becomes high conversion rate.
4. the method that useful signal variable quantity is quickly improved in Low SNR signal as claimed in claim 1, it is characterised in that
It is to the method that D1 data are filtered pretreatment in step 3:Given threshold TH1, asks for the meansigma methodss in D1 sequence datas, asks
The absolute value of the difference of each data and meansigma methodss in D1 sequence datas is taken, if the absolute value for detecting wherein has more than threshold value
TH1, then corresponding D1 data be then modified to identical with its previous secondary data.
5. the method that useful signal variable quantity is quickly improved in Low SNR signal as claimed in claim 4, it is characterised in that
The filter preprocessing, process flow is：
(1) calculate D1 data in all data summation and obtain meansigma methodss；
(2) successively D1 data and meansigma methodss are subtracted each other and are obtained the absolute value of result；
(3) when the absolute value is more than threshold value TH1 for arranging, corresponding D1 data corrections are the previous secondary data in its sequence,
If a data, is modified to meansigma methodss headed by the D1 data of the needs amendment；Complete to obtain Orders Corrected after filter preprocessing
Data D2.
6. the method that useful signal variable quantity is quickly improved in Low SNR signal as claimed in claim 1, it is characterised in that
In step 5, for the method that in the same time A1 data do not carry out data filtering is, A1 [0], A1 [1], A1 [2] are newest acquisition
Three A1 data, its acquisition moment is respectively T0, T1, T2, and T0>T1>T2, the data for being currently needed for Filtering Processing are A1 [1]；
(1) judge that A1 [1] is simultaneously greater than A1 [0] and A1 [2], be modified to A1 [0] and A1 [2] if A1 [1] data if setting up
Meansigma methodss；
(2) A1 [1] is judged while less than A1 [0] and A1 [2], being modified to A1 [0] and A1 [2] if A1 [1] data if setting up
Meansigma methodss；
(3) in the case of other, A1 [1] data are varied without.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109900952A (en) * | 2019-03-13 | 2019-06-18 | 清华四川能源互联网研究院 | A kind of transient signal rapidly extracting processing method |
| CN113872709A (en) * | 2021-10-14 | 2021-12-31 | 上海橙科微电子科技有限公司 | System for continuously monitoring existence of high-speed signals |
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| US6862558B2 (en) * | 2001-02-14 | 2005-03-01 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Empirical mode decomposition for analyzing acoustical signals |
| CN104270239A (en) * | 2014-10-23 | 2015-01-07 | 天津市德力电子仪器有限公司 | Timing error recovery method suitable for WCDMA |
| CN104811991A (en) * | 2015-04-17 | 2015-07-29 | 合肥工业大学 | Wireless link quality predicting method based on dynamic time warping algorithm |
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2016
- 2016-12-14 CN CN201611153045.8A patent/CN106685435B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6862558B2 (en) * | 2001-02-14 | 2005-03-01 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Empirical mode decomposition for analyzing acoustical signals |
| CN104270239A (en) * | 2014-10-23 | 2015-01-07 | 天津市德力电子仪器有限公司 | Timing error recovery method suitable for WCDMA |
| CN104811991A (en) * | 2015-04-17 | 2015-07-29 | 合肥工业大学 | Wireless link quality predicting method based on dynamic time warping algorithm |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109900952A (en) * | 2019-03-13 | 2019-06-18 | 清华四川能源互联网研究院 | A kind of transient signal rapidly extracting processing method |
| CN113872709A (en) * | 2021-10-14 | 2021-12-31 | 上海橙科微电子科技有限公司 | System for continuously monitoring existence of high-speed signals |
| CN113872709B (en) * | 2021-10-14 | 2023-10-24 | 上海橙科微电子科技有限公司 | System for continuously monitoring presence or absence of high-speed signal |
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