CN102427344A - Method and apparatus for noise elimination - Google Patents
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Abstract
The invention provides a method and an apparatus for noise elimination. According to the method, an original input signal and a reference input signal are obtained; an adaptive filter is used to adjust the reference input signal; signal processing is carried out to cancel noises in the original input signal and only a useful signal is preserved, so that an output signal is obtained. According to the invention, in order to solve a problem of impulsive noise elimination or decreasing in a signal, noises that are collected and have certain correlation are utilized and adaptive cancellation is carried out on the impulsive noise based on a robustness optimization criterion and an adaptive filtering technology, so that an expected useful signal is output to be obtained.
Description
Technical field
The present invention relates to the signal processing technology field, particularly a kind of removing method of noise and device.
Background technology
Noise is ubiquitous; Adaptive noise cancellation is a kind of noise cancellation method, normally based on the Adaptive Signal Processing technology, the noise removing problem is converted into the parameter Estimation of sef-adapting filter; Under certain criterion, adjust filter parameter automatically and realize optimum.Noise characteristic is to carry out one of subject matter that noise removing need consider.When noise met Gaussian distribution, traditional adaptive noise elimination algorithm based on least mean-square error (LMS) criterion had good estimated performance, and is convenient to theory analysis.But regular meeting runs into one type of noise with remarkable spike characteristic in the practical application; For example; Medical signals noise, atmospheric noise, underwater sound signal noise etc.; Its probability density function (p.d.f.) departs from Gaussian distribution, and comparing with Gaussian distribution has thicker hangover, does not have second order and above statistic.At this moment, the self adaptation TDE algorithm performance that designs under least mean-square error (LMS) criterion is degenerated, even can not use.A kind of solution of this situation is to adopt more to meet actual noise model, selects more sane criterion function.The stable sub-category that distributes of α---symmetrical α is stable, and distribute (S α S) can better describe this type pulsive noise.The minimum coefficient of dispersion (LMP) criterion is a kind of optiaml ciriterion that α stablizes the distribution linear theory, and MD is equivalent to the fractional lower-order square (FLOM) that minimizes evaluated error, and (0＜p＜α) norm is directly proportional the FLOM rank square of S α S with its p.Consider the convexity and the differentiability of cost function, in the stable actual TDE algorithm that distributes, adopt minimum average B configuration p (the norm criterion of 1＜p＜α) usually based on α.
Elimination or weakening problem to impulsive noise in the signal; How to utilize and collect noise with certain correlation; And carry out adaptive cancellation based on the optimization criterion and the auto-adaptive filtering technique paired pulses noise of robustness; Design a kind of removing method and device of noise, so output to obtain desired useful signal be one of signal processing technology field problem at present anxious to be solved.
Summary of the invention
In view of this, the embodiment of the invention has proposed a kind of removing method and device of noise, through obtaining original input signal and reference-input signal; And through sef-adapting filter adjusting reference-input signal; To the noise in the original input signal be offset through signal processing then, only keep useful signal, and then obtain the output signal; Elimination or weakening problem to impulsive noise in the signal; Utilization collects the noise with certain correlation, carries out adaptive cancellation based on the optimization criterion and the auto-adaptive filtering technique paired pulses noise of robustness, and then output obtains desired useful signal.
For solving the problems of the technologies described above, the purpose of the embodiment of the invention realizes through following technical scheme:
A kind of removing method of noise comprises:
Step 1, obtain original input signal;
Step 2, obtain reference-input signal;
Step 3, the sef-adapting filter adjusting reference-input signal of passing through;
Step 4, the noise in the original input signal is offset, only keep useful signal, and then obtain the output signal through signal processing.
Preferably, in the above-mentioned steps one, original input signal is useful signal and noise sum.
Preferably, in the above-mentioned steps two, reference-input signal be with original input signal in the relevant signal of noise.
Preferably, above-mentioned useful signal and noise and input signal are the zero-mean stationary random process, and it is uncorrelated mutually to satisfy useful signal and noise and reference-input signal.
Preferably, in the above-mentioned steps three, comprise further through sef-adapting filter and regulate reference-input signal that the mean square error that satisfies output signal and useful signal is minimum.
Preferably, in the above-mentioned steps four, after reference-input signal regulated, subtract each other, the noise of output signal is cancelled fully, and only keeps useful signal with noise in the original input signal.
Preferably, the necessary condition that further comprises sef-adapting filter is: reference-input signal must be relevant with the noise that is cancelled.
Preferably, in the above-mentioned steps four, further comprise: must must satisfy 1≤p＜α≤2 in the processing procedure, will guarantee convergence simultaneously, must choose the suitable substance P norm according to the α value of signal noise.
A kind of cancellation element of noise; Comprise first acquiring unit, second acquisition unit, regulon and processing unit; Through obtaining original input signal and reference-input signal, and regulate reference-input signal, will the noise in the original input signal be offset through signal processing then through sef-adapting filter; Only keep useful signal, and then obtain the output signal.
Preferably, above-mentioned first acquiring unit is used to obtain original input signal.
Preferably, above-mentioned second acquisition unit is used to obtain reference-input signal.
Preferably, above-mentioned regulon is used for regulating reference-input signal through sef-adapting filter.
Preferably, above-mentioned processing unit is used for through signal processing the noise of original input signal being offset, and only keeps useful signal, and then obtains the output signal.
In sum, the invention provides a kind of removing method and device of noise, through obtaining original input signal and reference-input signal; And through sef-adapting filter adjusting reference-input signal; To the noise in the original input signal be offset through signal processing then, only keep useful signal, and then obtain the output signal; Elimination or weakening problem to impulsive noise in the signal; Utilization collects the noise with certain correlation, carries out adaptive cancellation based on the optimization criterion and the auto-adaptive filtering technique paired pulses noise of robustness, and then output obtains desired useful signal.
Description of drawings
Fig. 1 is the method flow diagram of the embodiment of the invention;
Fig. 2 for the present invention based on first convergence curve under the LMP algorithm;
Fig. 3 for the present invention based on second convergence curve under the LMP algorithm;
Fig. 4 is the apparatus structure sketch map of the embodiment of the invention.
Embodiment
The removing method of a kind of noise that the embodiment of the invention provides and device; Through obtaining original input signal and reference-input signal, and regulate reference-input signal, will the noise in the original input signal be offset through signal processing then through sef-adapting filter; Only keep useful signal; And then obtain the output signal, to the elimination or the weakening problem of impulsive noise in the signal, utilize to collect noise with certain correlation; Optimization criterion and auto-adaptive filtering technique paired pulses noise based on robustness carry out adaptive cancellation, and then output obtains desired useful signal.
For making the object of the invention, technical scheme and advantage clearer, the embodiment that develops simultaneously with reference to the accompanying drawings is to further explain of the present invention.
The embodiment of the invention provides a kind of removing method of noise, and is as shown in Figure 1, and concrete steps comprise:
Step 1, obtain original input signal;
Particularly, in embodiments of the present invention, original input signal d (n) is useful signal s (n) and noise v (n) sum.
Further, suppose s (n), v (n), u (n) is the zero-mean stationary random process, and it is uncorrelated mutually with v (n) and u (n) to satisfy s (n).Wherein, reference-input signal x (n) is the noise u (n) relevant with v (n).Because the output y (n) of sef-adapting filter is the filtering signal of noise u (n), so the output y (n) of adaptive noise cancellation system is:
Y (n)=s (n)+v (n)-v ' is formula (1) (n)
Can obtain following formula through formula (1):
y
2(n)=s
2(n)+(v (n)-v ' (n))
2+ 2s (n) (v (n)-v ' (n)) formula (2)
Step 2, obtain reference-input signal;
Particularly, in embodiments of the present invention, reference-input signal x (n) is the noise u (n) relevant with v (n).Further, suppose s (n), v (n), u (n) is the zero-mean stationary random process, and it is uncorrelated mutually with v (n) and u (n) to satisfy s (n).Wherein, original input signal d (n) is useful signal s (n) and noise v (n) sum.Because the output y (n) of sef-adapting filter is the filtering signal of noise u (n), so the output y (n) of adaptive noise cancellation system is:
Y (n)=s (n)+v (n)-v ' is formula (1) (n)
Can obtain following formula through formula (1):
y
2(n)=s
2(n)+(v (n)-v ' (n))
2+ 2s (n) (v (n)-v ' (n)) formula (2)
Step 3, the sef-adapting filter adjusting reference-input signal of passing through;
Particularly, in embodiments of the present invention, expectation is got on formula (2) both sides, because useful signal s (n) is uncorrelated mutually with v (n) and noise u (n), so can obtain following formula:
E [y
2(n)]=E [s
2(n)]+E [(v (n)-v ' is (n))
2] formula (3)
Signal power E [s
2(n)] irrelevant with the adjusting of sef-adapting filter, traditional LMS algorithm is so the adjusting sef-adapting filter makes E [y
2(n)] minimum is equivalent to the E [(v (n)-v ' (n)) that makes in the formula (3)
2] minimum.Can obtain formula (4) by formula (1) like this:
V (n)-v ' (n)=y (n)-s (n) formula (4)
This shows, when E [(v (n)-v ' (n))
2] hour, E [(y (n)-s (n))
2] also reach minimum, promptly the output signal y (n) of adaptive noise cancellation system is minimum with the mean square error of useful signal s (n).
Step 4, the noise in the original input signal is offset, only keep useful signal, and then obtain the output signal through signal processing.
Particularly, in embodiments of the present invention, in the ideal case,, y (n)=s (n) is arranged as v (n)=v ' (n) time.At this moment, sef-adapting filter will automatically be regulated its weights, be v (n) with u (n) processed, subtract each other with noise v (n) among the original input signal d (n), the noise of output signal y (n) is cancelled fully, and only keep useful signal s (n).But sef-adapting filter can be accomplished the necessary condition of above-mentioned task: reference-input signal x (n)=u (n) must be relevant with the noise v (n) that is cancelled.
Further, in this programme, minimum average B configuration P norm be to the characteristic that α stablize partition noise improve by the LMS algorithm and, in the LMP algorithm, the cost function among the LMS is changed into the α norm J=||e (n) of error function by the mean square error function ||
αSquare is theoretical at a low price by mark, as long as satisfy 1≤p＜α, the α norm is directly proportional with its P valency square in this process.Like this, the cost function of Adaptable System can be write as following form:
J=E [| e (n) |
p] formula (5)
The formula that adopts steepest descent method can derive adaptive iteration is:
W (n+1)=w (n)+μ | e (n) |
P-1Sgn [e (n)] x (n) 1≤p＜α≤2 formula (6)
Aforementioned algorithm is the scope of application that regulation is arranged; Must must satisfy 1≤p＜α≤2, will guarantee convergence simultaneously, must choose the suitable substance P norm according to the α value of signal noise; Requirement according to above-mentioned is handled, the noise in can better offseting signal.
For the present invention's technical scheme better is described, enumerates a specific embodiment below and represent: the result of Computer Simulation proposition method, as shown in Figure 2.Selected parameter is step size mu=0.0001 under the LMP algorithm; The input signal that system adopts is similarly Alpha and stablizes partition noise (noise that arrives earlier); The value of α is 1.8; The value of P is 1.5, and reference signal is similarly the noise (10 sampled points of noise delay that arrive earlier) that useful signal (usefulness is that 10K is sinusoidal wave) adds delay, signal to noise ratio snr=0 here.Algorithm iteration 30000 times, the minimal error of output is bigger at first, is stabilized in a less value afterwards gradually, and this has just explained that this waveform has had good convergence effect.
Error mean curve under the LMP algorithm from Fig. 2; Can find out that signal is through be tending towards convergence through one section training time error mean curve behind the sef-adapting filter basically; And there is not very big repetitive process; Be that outer signals has been accomplished adaptive process, filter is adjusted to the best with weights, can export and obtain desired useful signal.The LMP algorithm not only can be offset non-Gaussian noise can also offset Gaussian noise; Here as long as change the value of α into 2; Other parameter all keeps with original the same basically; According to Alpha stablize partition noise definition and characteristic when α=2 be Gaussian noise, can see when having Gaussian noise in the signal that by Fig. 3 the LMP algorithm still can be crossed and play good negative function.
Can see that from top figure the LMP algorithm equally also has good effect when offsetting Gaussian noise, be tending towards convergence basically through one section training time error mean curve, and if bigger number of iterations from what become for original 30000 times; Can also obtain better result; See as long as regulate some parameters down that from the effect here add a little bigger number of iterations, the LMP algorithm has good negative function equally when handling Gaussian noise; Not at all than handling the non-Gaussian noise time difference; Explain that the LMP algorithm has good toughness, the non-Gaussian noise in not only can offseting signal, and the Gaussian noise in can also offseting signal.
In addition, the embodiment of the invention also provides a kind of cancellation element of noise.As shown in Figure 4, the cancellation element sketch map of a kind of noise that provides for the embodiment of the invention.
A kind of cancellation element of noise comprises first acquiring unit 11, second acquisition unit 22, regulon 33 and processing unit 44.
First acquiring unit 11 is used to obtain original input signal;
Particularly, in embodiments of the present invention, original input signal d (n) is useful signal s (n) and noise v (n) sum.
Further, suppose s (n), v (n), u (n) is the zero-mean stationary random process, and it is uncorrelated mutually with v (n) and u (n) to satisfy s (n).Wherein, reference-input signal x (n) is the noise u (n) relevant with v (n).Because the output y (n) of sef-adapting filter is the filtering signal of noise u (n), so the output y (n) of adaptive noise cancellation system is:
Y (n)=s (n)+v (n)-v ' is formula (1) (n)
Can obtain following formula through formula (1):
y
2(n)=s
2(n)+(v (n)-v ' (n))
2+ 2s (n) (v (n)-v ' (n)) formula (2)
Particularly, in embodiments of the present invention, reference-input signal x (n) is the noise u (n) relevant with v (n).Further, suppose s (n), v (n), u (n) is the zero-mean stationary random process, and it is uncorrelated mutually with v (n) and u (n) to satisfy s (n).Wherein, original input signal d (n) is useful signal s (n) and noise v (n) sum.Because the output y (n) of sef-adapting filter is the filtering signal of noise u (n), so the output y (n) of adaptive noise cancellation system is:
Y (n)=s (n)+v (n)-v ' is formula (1) (n)
Can obtain following formula through formula (1):
y
2(n)=s
2(n)+(v (n)-v ' (n))
2+ 2s (n) (v (n)-v ' (n)) formula (2)
Particularly, in embodiments of the present invention, expectation is got on formula (2) both sides, because useful signal s (n) is uncorrelated mutually with v (n) and noise u (n), so can obtain following formula:
E [y
2(n)]=E [s
2(n)]+E [(v (n)-v ' is (n))
2] formula (3)
Signal power E [s
2(n)] irrelevant with the adjusting of sef-adapting filter, traditional LMS algorithm is so the adjusting sef-adapting filter makes E [y
2(n)] minimum is equivalent to the E [(v (n)-v ' (n)) that makes in the formula (3)
2] minimum.Can obtain formula (4) by formula (1) like this:
V (n)-v ' (n)=y (n)-s (n) formula (4)
This shows, when E [(v (n)-v ' (n))
2] hour, E [(y (n)-s (n))
2] also reach minimum, promptly the output signal y (n) of adaptive noise cancellation system is minimum with the mean square error of useful signal s (n).
Processing unit 44 is used for through signal processing the noise of original input signal being offset, and only keeps useful signal, and then obtains the output signal.
Particularly, in embodiments of the present invention, in the ideal case,, y (n)=s (n) is arranged as v (n)=v ' (n) time.At this moment, sef-adapting filter will automatically be regulated its weights, be v (n) with u (n) processed, subtract each other with noise v (n) among the original input signal d (n), the noise of output signal y (n) is cancelled fully, and only keep useful signal s (n).But sef-adapting filter can be accomplished the necessary condition of above-mentioned task: reference-input signal x (n)=u (n) must be relevant with the noise v (n) that is cancelled.
Further, in this programme, minimum average B configuration P norm be to the characteristic that α stablize partition noise improve by the LMS algorithm and, in the LMP algorithm, the cost function among the LMS is changed into the α norm J=||e (n) of error function by the mean square error function ||
αSquare is theoretical at a low price by mark, as long as satisfy 1≤p＜α, the α norm is directly proportional with its P valency square in this process.Like this, the cost function of Adaptable System can be write as following form:
J=E [| e (n) |
p] formula (5)
The formula that adopts steepest descent method can derive adaptive iteration is:
W (n+1)=w (n)+μ | e (n) |
P-1Sgn [e (n)] x (n) 1≤p＜α≤2 formula (6)
Aforementioned algorithm is the scope of application that regulation is arranged; Must must satisfy 1≤p＜α≤2, will guarantee convergence simultaneously, must choose the suitable substance P norm according to the α value of signal noise; Requirement according to above-mentioned is handled, the noise in can better offseting signal.
For the present invention's technical scheme better is described, enumerates a specific embodiment below and represent: the result of Computer Simulation proposition method, as shown in Figure 2.Selected parameter is step size mu=0.0001 under the LMP algorithm; The input signal that system adopts is similarly Alpha and stablizes partition noise (noise that arrives earlier); The value of α is 1.8; The value of P is 1.5, and reference signal is similarly the noise (10 sampled points of noise delay that arrive earlier) that useful signal (usefulness is that 10K is sinusoidal wave) adds delay, signal to noise ratio snr=0 here.Algorithm iteration 30000 times, the minimal error of output is bigger at first, is stabilized in a less value afterwards gradually, and this has just explained that this waveform has had good convergence effect.
Error mean curve under the LMP algorithm from Fig. 2; Can find out that signal is through be tending towards convergence through one section training time error mean curve behind the sef-adapting filter basically; And there is not very big repetitive process; Be that outer signals has been accomplished adaptive process, filter is adjusted to the best with weights, can export and obtain desired useful signal.The LMP algorithm not only can be offset non-Gaussian noise can also offset Gaussian noise; Here as long as change the value of α into 2; Other parameter all keeps with original the same basically; According to Alpha stablize partition noise definition and characteristic when α=2 be Gaussian noise, can see when having Gaussian noise in the signal that by Fig. 3 the LMP algorithm still can be crossed and play good negative function.
Can see that from top figure the LMP algorithm equally also has good effect when offsetting Gaussian noise, be tending towards convergence basically through one section training time error mean curve, and if bigger number of iterations from what become for original 30000 times; Can also obtain better result; See as long as regulate some parameters down that from the effect here add a little bigger number of iterations, the LMP algorithm has good negative function equally when handling Gaussian noise; Not at all than handling the non-Gaussian noise time difference; Explain that the LMP algorithm has good toughness, the non-Gaussian noise in not only can offseting signal, and the Gaussian noise in can also offseting signal.
One of ordinary skill in the art will appreciate that and realize that all or part of step that the foregoing description method is carried is to instruct relevant hardware to accomplish through program; Described program can be stored in a kind of computer-readable recording medium; This program comprises one of step or its combination of method embodiment when carrying out.
In addition, each functional unit in each embodiment of the present invention can be integrated in the processing module, also can be that the independent physics in each unit exists, and also can be integrated in the module two or more unit.Above-mentioned integrated module both can adopt the form of hardware to realize, also can adopt the form of software function module to realize.If said integrated module realizes with the form of software function module and during as independently production marketing or use, also can be stored in the computer read/write memory medium.
In sum, this paper provides a kind of removing method and device of noise, through obtaining original input signal and reference-input signal; And through sef-adapting filter adjusting reference-input signal; To the noise in the original input signal be offset through signal processing then, only keep useful signal, and then obtain the output signal; Elimination or weakening problem to impulsive noise in the signal; Utilization collects the noise with certain correlation, carries out adaptive cancellation based on the optimization criterion and the auto-adaptive filtering technique paired pulses noise of robustness, and then output obtains desired useful signal.
More than the removing method and the device of a kind of noise provided by the present invention carried out detailed introduction; Used concrete example among this paper principle of the present invention and execution mode are set forth, the explanation of above embodiment just is used for helping to understand scheme of the present invention; Simultaneously, for one of ordinary skill in the art, according to thought of the present invention, the part that on embodiment and range of application, all can change, in sum, this description should not be construed as limitation of the present invention.
Claims (13)
1. the removing method of a noise is characterized in that, said method comprises:
Step 1, obtain original input signal;
Step 2, obtain reference-input signal;
Step 3, the sef-adapting filter adjusting reference-input signal of passing through;
Step 4, the noise in the original input signal is offset, only keep useful signal, and then obtain the output signal through signal processing.
2. removing method according to claim 1 is characterized in that, in the said step 1, original input signal is useful signal and noise sum.
3. removing method according to claim 1 and 2 is characterized in that, in the said step 2, reference-input signal be with original input signal in the relevant signal of noise.
4. removing method according to claim 1 and 2 is characterized in that said useful signal and noise and input signal are the zero-mean stationary random process, and it is uncorrelated mutually to satisfy useful signal and noise and reference-input signal.
5. removing method according to claim 1 is characterized in that, in the said step 3, comprises further through sef-adapting filter and regulate reference-input signal that the mean square error that satisfies output signal and useful signal is minimum.
6. removing method according to claim 1 is characterized in that, in the said step 4, after reference-input signal regulated, subtracts each other with noise in the original input signal, the noise of output signal is cancelled fully, and only keeps useful signal.
7. removing method according to claim 6 is characterized in that, comprise that further the necessary condition of sef-adapting filter is: reference-input signal must be relevant with the noise that is cancelled.
8. removing method according to claim 1; It is characterized in that, in the said step 4, further comprise: must must satisfy 1≤p＜α≤2 in the processing procedure; To guarantee convergence simultaneously, must choose the suitable substance P norm according to the α value of signal noise.
9. the cancellation element of a noise; It is characterized in that said cancellation element comprises first acquiring unit, second acquisition unit, regulon and processing unit, through obtaining original input signal and reference-input signal; And through sef-adapting filter adjusting reference-input signal; To the noise in the original input signal be offset through signal processing then, only keep useful signal, and then obtain the output signal.
10. cancellation element according to claim 9 is characterized in that, said first acquiring unit is used to obtain original input signal.
11. cancellation element according to claim 9 is characterized in that, said second acquisition unit is used to obtain reference-input signal.
12. cancellation element according to claim 9 is characterized in that, said regulon is used for regulating reference-input signal through sef-adapting filter.
13. cancellation element according to claim 9 is characterized in that, said processing unit is used for through signal processing the noise of original input signal being offset, and only keeps useful signal, and then obtains the output signal.
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