CN104048680A - Independent underwater robot external interference suppression method based on DONOHO threshold value - Google Patents
Independent underwater robot external interference suppression method based on DONOHO threshold value Download PDFInfo
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Abstract
The invention aims to provide an independent underwater robot external interference suppression method based on the DONOHO threshold value. An independent underwater robot sensor signals are processed through the method of the DONOHO threshold value, DONOHO threshold value estimation is conducted on all-layer detail coefficients after multi layers of wavelet are decomposed, soft threshold value processing is conducted on the detail coefficients through the estimated DONOHO threshold value, and noise and random external interference are removed. The independent underwater robot external interference suppression method solves the problem that the independent underwater robot sensor signals are influenced through external interference, and the external interference floods the detail characteristics of useful signals, and solves the problem that the control effect is poor influenced by sensor data accuracy as well. The problems of over-suppression and under-suppression of an existing method are solved through the highly relevant feature between sensor data and control volume data, the state sensor signal accuracy of the independent underwater robot is greatly improved, and the control accuracy of the robot is finally improved.
Description
Technical field
What the present invention relates to is a kind of underwater robot control method.
Background technology
Along with land resources day by day reduces, the paces of human development ocean are more and more faster.Autonomous type underwater robot is the current unique carrier that can survey, develop at deep-sea in unmanned situation, is subject to domestic and international researchist's great attention always.But be operated in complicated marine environment without cable because autonomous type underwater robot is unmanned, external disturbance produces very large negative effect to its control accuracy and homework precision.The suffered external disturbance of autonomous type underwater robot generally can be divided into lasting ocean current to be disturbed and random external disturbance, and wherein random external disturbance can suppress by disturbing inhibition method outward.Therefore external disturbance inhibition method has become the emphasis of autonomous type underwater robot sensing data processing.
Mean filter is the simplyst to disturb inhibition method outward, and its algorithm is simple, and in a lot of fields, denoising effect is good, but general only for static or low current intelligence; Finite Impulse Response filter has been inherited the advantage of analog filter, and can realize with Fast Fourier Transform (FFT), has greatly improved arithmetic speed.FIR wave filter carries out filtering and noise reduction to signal to be completed in frequency domain, relies on the different spectral feature of signal and noise to realize noise filtering, be adapted to quiet, Dynamic Signal denoising, but denoising effect is general, is not so good as mean filter good.Above two kinds of methods are all not suitable for marine environment random external complicated and changeable and disturb inhibition
Wavelet transformation is the time frequency analyzing tool developing rapidly in recent years, overcome Fourier transform and can only represent the frequecy characteristic of signal but the defect of local message on can not reflecting time territory, wavelet transformation has partial analysis feature and the multiresolution analysis characteristic of time and frequency simultaneously, and has obtained a wide range of applications at aspects such as image processing, signal filtering and feature extractions.DONOHO threshold method, taking wavelet transformation as basis, according to signal and the noise different qualities that corresponding wavelet coefficient has after wavelet decomposition, by wavelet coefficient is carried out to threshold process, can be realized well external disturbance and suppress.DONOHO threshold value is disturbed inhibition outward and is greatly better than mean filter and FIR wave filter, all use for essence, Dynamic Signal, therefore using it for autonomous type underwater robot external disturbance suppresses, not only can be used for the inhibition that random external is disturbed, and can effectively remove measurement noise, for follow-up control improves status information more accurately.
The sensor signal that autonomous type underwater robot collection is returned is that noise is mixed in together, owing to cannot obtaining the signal of real not Noise and random external interference, therefore directly adopt DONOHO threshold method to carry out external disturbance inhibition and easily caused inhibition or suppressed not enough.
Summary of the invention
The object of the present invention is to provide and can effectively solve the autonomous type underwater robot external disturbance inhibition method based on DONOHO threshold value that autonomous type underwater robot sensor signal is affected by external disturbance and measurement noise.
The object of the present invention is achieved like this:
The present invention
Advantage of the present invention is: the present invention had both efficiently solved autonomous type underwater robot sensor signal and affected by external disturbance, external disturbance is flooded the problem of useful signal minutia, has solved again the problem that is subject to sensing data Accuracy to cause controlling poor effect.And utilize the height correlation characteristic between sensing data and controlled quentity controlled variable data, and overcome existing methodical mistake and suppressed and suppressed not enough problem, greatly improve autonomous type underwater robot state sensor signal accuracy, and finally improve the control accuracy of robot.
Brief description of the drawings
Fig. 1 is process flow diagram of the present invention;
Fig. 2 is DONOHO threshold value of the present invention and cross correlation process process flow diagram;
Fig. 3 is autonomous type underwater robot sensor signal detail coefficients after Traditional Wavelet method is processed;
Fig. 4 is autonomous type underwater robot sensor signal detail coefficients after processing by system of the present invention;
Fig. 5 is autonomous type underwater robot sensor signal time domain waveform figure after Traditional Wavelet method is processed;
Fig. 6 is autonomous type underwater robot sensor signal time domain waveform figure after processing by system of the present invention.
Embodiment
For example the present invention is described in more detail below in conjunction with accompanying drawing:
In conjunction with Fig. 1～6, the object of the invention is to be achieved through the following technical solutions: based on DONOHO threshold value and cross-correlation coefficient external disturbance inhibition method, performing step is as follows:
(1) first, the sensing data that autonomous type underwater robot is collected carries out sliding window processing, after the data that meet length requirement, start external disturbance Restrainable algorithms when collecting, when again collecting after new data, give up first data and be placed on the end of array by newly gathering the data of returning, remain that data length is preset value;
(2) data in the array in sliding window are carried out to multi-level Wavelet Transform decomposition.Decomposable process: selected a kind of suitable wavelet basis function " db4 ", determine that decomposing the number of plies is 3 layers, carry out multi-level Wavelet Transform decomposition to sensor raw data with the controlled quentity controlled variable of sensor signal height correlation, obtain corresponding low frequency wavelet coefficient and high frequency wavelet coefficient;
(3) ask for the median of this array according to the high frequency wavelet coefficient of sensing data, in the time that array length is even number 2n, median is taken as 1+n; In the time that array length is odd number 2n+1, median is taken as n;
(4) by
the DONOHO threshold value of estimating high frequency wavelet coefficient, wherein N is the number of respective layer high frequency wavelet coefficient, σ
2for the variance of noise, estimated by MAD/0.6475, MAD is median wavelet coefficient amplitude;
(5) sensing data high frequency wavelet coefficient and controlled quentity controlled variable high frequency wavelet coefficient are normalized respectively;
(6), according to the sensing data high frequency wavelet coefficient after normalization and controlled quentity controlled variable high frequency wavelet coefficient, calculate cross-correlation coefficient R.Computing method are:
r is for first closing mutually coefficient, and N is signal length, and x (t) is t moment sensor signal, and y is t moment controlled quentity controlled variable signal, and τ is time delay.
(7) the DONOHO threshold value that the cross-correlation coefficient R obtaining according to relevant treatment and estimation draw comprehensively judges, cross-correlation coefficient R is greater than 0.8 the corresponding wavelet coefficient in t place and retains, cross-correlation coefficient R is less than 0.8 but high frequency wavelet coefficient is greater than the wavelet coefficient of DONOHO threshold value also retains, and the wavelet coefficient that other cross-correlation coefficient is less than 0.8 the corresponding wavelet coefficient in t place and is less than DONOHO threshold value all does zero setting processing.
(8) carry out wavelet reconstruction to low frequency wavelet coefficient with through each floor height frequency wavelet coefficient of threshold process, after reconstruct, signal is the rear signal of external disturbance inhibition.
External disturbance inhibition method in the present invention is that the external disturbance intensity existing for autonomous type underwater robot sensor signal is large, and the problem that external disturbance is flooded useful signal minutia proposes.Wherein, DONOHO threshold denoising can be used for noise remove and the external disturbance inhibition of dynamic and static signal; Cross correlation process needs to have high correlation between two processed variablees.
As shown in Figure 1, its concrete implementation step is as follows for this outside disturbance restraining method structured flowchart:
1, first gather the autonomous type underwater robot sensor raw data of a period of time,
The length of 2, getting sliding window is 500 data, according to predefined sliding window length, raw data is intercepted;
3, get db4 wavelet basis function, decomposing the number of plies is 3 layers.
4, with db4 wavelet basis function, data in sliding window are carried out to multi-level Wavelet Transform decomposition, obtain corresponding high frequency wavelet coefficient, three layers of wavelet coefficient result that obtain as shown in Figure 3.
5, the DONOHO threshold value of computed improved, Fig. 2 is calculation flow chart, specific implementation process is as follows:
5.1, the high frequency wavelet coefficient of sensing data is asked to median, in the time that array length is even number 2n, median is taken as 1+n; In the time that array length is odd number 2n+1, median is taken as n;
5.2, the high frequency wavelet coefficient of sensing data is done to normalized; To doing normalized with the high frequency wavelet coefficient of the controlled quentity controlled variable of sensing data height correlation;
5.3, according to the median of the high frequency wavelet coefficient of sensing data, estimation DONOHO threshold value, by
the DONOHO threshold value of estimating high frequency wavelet coefficient, wherein N is the number of respective layer high frequency wavelet coefficient, σ
2for the variance of noise, estimated by MAD/0.6475, MAD is median wavelet coefficient amplitude;
5.4, sensing data high frequency wavelet coefficient normalization result and controlled quentity controlled variable high frequency wavelet coefficient normalization result are done to cross correlation process, computing method are:
r is for first closing mutually coefficient, and N is signal length, and x (t) is t moment sensor signal, and y is t moment controlled quentity controlled variable signal, and τ is time delay;
5.5, utilize the improved DONOHO threshold value obtaining comprehensively to judge high frequency wavelet coefficient, cross-correlation coefficient R is greater than 0.8 the corresponding wavelet coefficient in t place and retains, cross-correlation coefficient R is less than 0.8 but high frequency wavelet coefficient is greater than the wavelet coefficient of DONOHO threshold value also retains, the wavelet coefficient that other cross-correlation coefficient is less than 0.8 the corresponding wavelet coefficient in t place and is less than DONOHO threshold value all does zero setting processing, and three layers of wavelet coefficient result that obtain as shown in Figure 4;
6, utilize the high frequency wavelet coefficient that improves after DONOHO threshold process and original low frequency wavelet coefficient successively to carry out wavelet reconstruction, obtain the sensing data of external disturbance after suppressed as shown in Figure 6;
Fig. 5 is autonomous type underwater robot sensor signal time domain waveform figure after Traditional Wavelet method is processed.As seen from the figure, due to factor impacts such as random external interference, sensor self errors, autonomous type underwater robot sensing data after conventional process has retained more burr, catastrophe point etc., based on these sensing datas, autonomous type underwater robot is controlled, control accuracy is difficult to ensure.
Fig. 6 is autonomous type underwater robot sensor signal time domain waveform figure after system of the present invention is processed.As seen from the figure, system of the present invention signal after treatment is comparatively level and smooth, and burr, catastrophe point are less, and useful detail coefficients in original signal is all subject to the protection of related coefficient and keeps down, and does not therefore damage the really degree of original signal.
In sum, first the present invention adopts sliding window method to intercept raw data, then raw data is carried out to multi-level Wavelet Transform decomposition; Estimate DONOHO threshold value to decomposing the high frequency wavelet coefficient that obtains, and and do cross correlation process to prevent suppressing or suppress not enough with the controlled quentity controlled variable high frequency wavelet coefficient of its height correlation; Finally utilize the improved DONOHO threshold value obtaining to do soft-threshold processing to each floor height frequency wavelet coefficient, and after utilizing processing, high frequency wavelet coefficient do wavelet reconstruction.Finally can improve accuracy and the authenticity of sensing data, and then improve the control accuracy of autonomous type underwater robot, be a kind of novel, effective autonomous type underwater robot external disturbance inhibition method.
Claims (5)
1. the autonomous type underwater robot external disturbance inhibition method based on DONOHO threshold value, is characterized in that:
(1) data that the sensor of autonomous type underwater robot collected are carried out sliding window processing, and the preset value of sliding window length is A, by preset value, the data of sensor collection is intercepted;
(2) data that intercept are carried out to multi-level Wavelet Transform decomposition, its decomposable process is: selected wavelet basis function db4, decomposing the number of plies is 3 layers, carry out multi-level Wavelet Transform decomposition to sensor image data and with the controlled quentity controlled variable of sensor signal height correlation respectively, obtain respectively the low frequency wavelet coefficient corresponding with sensing data and high frequency wavelet coefficient, and the low frequency wavelet coefficient corresponding with controlled quentity controlled variable and high frequency wavelet coefficient;
(3) ask for the median of this array according to the high frequency wavelet coefficient of sensing data;
(4) by
the DONOHO threshold value δ that estimates high frequency wavelet coefficient, wherein N is the number of respective layer high frequency wavelet coefficient, σ
2for the variance of noise, estimated by MAD/0.6475, MAD is median high frequency wavelet coefficient amplitude;
(5) sensing data high frequency wavelet coefficient and controlled quentity controlled variable high frequency wavelet coefficient are normalized respectively;
(6), according to the sensing data high frequency wavelet coefficient after normalization and controlled quentity controlled variable high frequency wavelet coefficient, calculate cross-correlation coefficient R;
(7) according to cross-correlation coefficient R with estimate that the DONOHO threshold value that draws comprehensively judges: cross-correlation coefficient R is more than or equal to 0.8 the corresponding wavelet coefficient in t place and retains, cross-correlation coefficient R is less than 0.8 but high frequency wavelet coefficient is greater than the wavelet coefficient of DONOHO threshold value also retains, and the wavelet coefficient that other cross-correlation coefficient is less than 0.8 the corresponding wavelet coefficient in t place and is less than DONOHO threshold value all does zero setting processing;
(8) the low frequency wavelet coefficient to sensing data and the each floor height frequency wavelet coefficient through threshold process carry out wavelet reconstruction, and after reconstruct, signal is the rear signal of external disturbance inhibition.
2. the autonomous type underwater robot external disturbance inhibition method based on DONOHO threshold value according to claim 1, it is characterized in that: in the time that the data of sensor collection are intercepted, when again collecting after new data, give up first data and be placed on the end of data intercept by newly gathering the data of returning, remain that data intercept length is preset value.
3. the autonomous type underwater robot external disturbance inhibition method based on DONOHO threshold value according to claim 1 and 2, is characterized in that: while asking for median, in the time that array length is even number 2n, median is taken as 1+n; In the time that array length is odd number 2n+1, median is taken as n.
4. the autonomous type underwater robot external disturbance inhibition method based on DONOHO threshold value according to claim 1 and 2, is characterized in that: the cross-correlation coefficient R between sensor signal and controlled quentity controlled variable signal
xy(τ) computing method are:
r in formula
xy(τ) be cross-correlation coefficient, N is signal length, and x (t) is t moment sensor signal data, and y (t+ τ) is (t+ τ) moment controlled quentity controlled variable signal, and τ is time delay.
5. the autonomous type underwater robot external disturbance inhibition method based on DONOHO threshold value according to claim 3, is characterized in that: the cross-correlation coefficient R between sensor signal and controlled quentity controlled variable signal
xy(τ) computing method are:
r in formula
xy(τ) be cross-correlation coefficient, N is signal length, and x (t) is t moment sensor signal data, and y (t+ τ) is (t+ τ) moment controlled quentity controlled variable signal, and τ is time delay.
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| CN110634109A (en) * | 2019-08-22 | 2019-12-31 | 东北大学 | Method for removing medical speckle noise based on stationary wavelet transform and Canny operator |
| CN112800835A (en) * | 2020-12-25 | 2021-05-14 | 中国工程物理研究院机械制造工艺研究所 | External interference suppression method for planetary reducer based on wavelet threshold |
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| CN112800835A (en) * | 2020-12-25 | 2021-05-14 | 中国工程物理研究院机械制造工艺研究所 | External interference suppression method for planetary reducer based on wavelet threshold |
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