CN101577536A - Method for realizing improved LMS algorithm - Google Patents
Method for realizing improved LMS algorithm Download PDFInfo
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- CN101577536A CN101577536A CNA2009100868649A CN200910086864A CN101577536A CN 101577536 A CN101577536 A CN 101577536A CN A2009100868649 A CNA2009100868649 A CN A2009100868649A CN 200910086864 A CN200910086864 A CN 200910086864A CN 101577536 A CN101577536 A CN 101577536A
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Abstract
A method for realizing an improved LMS algorithm is the improvement realized to the LMS algorithm in a self-adapting filter. Only the phase of an input signal x(n) is stored in an input register, and phase rotation operation is carried out to the result which is obtained by the multiplication of an error signal e(n) and 2Mu, wherein the phase rotation amount is the phase value of the input signal x(n), and Mu is a step parameter. The method for realizing the improved LMS algorithm has a detail structure shown in the picture. The realization of the method is formed by a phase register, a multiplier, a phase rotator, an adder and a weight coefficient register, wherein the phase register is used for registering the phase value of the input signal x(n); the multiplier leads the error signal e(n) to be multiplied with the 2Mu; the phase rotator conducts phase rotation operation to the data transmitted by the multiplier; the adder is used for generating a new round of weight coefficients; and the weight coefficient register is used for registering new weight coefficients and then transmitting the new weight coefficients to the adder.
Description
Technical field
The present invention relates to digital processing field, particularly, relate to adaptive-filtering field in the carrier wave communication system.
Background technology
Sef-adapting filter is one of the research focus in signal processing field always, and through 30 years of development, it has been widely used in digital communication, radar, sonar, seismology, navigation system, biomedicine and industrial control field.
Sef-adapting filter can use adaptive algorithm to change the parameter and the structure of filter according to the change of environment.Generally speaking, do not change the structure of sef-adapting filter, and the coefficient of sef-adapting filter is the time-varying coefficient that is upgraded by adaptive algorithm.Be that its coefficient is adapted to given signal automatically and continuously, to obtain Expected Response.The most important characteristic of sef-adapting filter just is that it can effectively work in circumstances not known, and can follow the tracks of the time varying characteristic of input signal, and its principle as shown in Figure 1.X (n) is an input signal, and y (n) is the output of filter, and d (n) is a desired signal, and promptly with reference to input, e (n) is an error signal, and w (n) is variable filter weight coefficient.
Input signal x (n) obtains output signal y (n) through behind the filtering operation, output signal y (n) and desired signal d (n) carry out obtaining error signal e (n) behind the subtraction, error signal e (n) and input signal x (n) influence digital filter weight coefficient w (n) through computing that must adaptive algorithm, reach adaptive effect.
Adaptive algorithm mainly contains two kinds of least mean-square error (LMS) algorithm and recursive least-squares (RLS) algorithms.The LMS algorithm that is proposed by Widow and Hoff is to utilize the gradient estimated value to replace a kind of fast search algorithm of gradient vector, and amount of calculation is little, the advantage of easy realization is widely adopted because of it has.Its basic thought is a weighting parameter of adjusting filter, makes the output signal of filter and the mean square error minimum between the desired signal.LMS algorithm, i.e. least-mean-square error algorithm, the structure chart of the sef-adapting filter of realization as shown in Figure 2, μ is a step parameter among the figure.
Input signal x (n) through Fourier transform carries out filtering operation in frequency domain, obtain output signal y (n), i.e. the output y (n) of digital filter.Y (n) is not only as the output of whole sef-adapting filter, and it promptly carries out the subtraction operation with desired signal d (n) again through carrying out the calculation process in the time domain behind the inverse fourier transform, obtains error signal e (n).Error signal e (n) is through a Fourier transform, carry out the calculation process of frequency domain again, promptly with step-length 2 μ and input register in x (n) computing of multiplying each other, the result who obtains is used to influence the weight coefficient of digital filter, be w (n+1)=w (n)+2 μ e (n) x (n), finish adaptive process like this.
The implementation method of a kind of improved LMS algorithm that the present invention proposes is compared with the implementation method of general LMS algorithm, reduces and uses half memory space of input register, and convergence is preferably arranged.
Summary of the invention
The implementation method of a kind of improved LMS algorithm that the present invention proposes, in input register, only store the phase place of input signal x (n), in the calculating process of adaptive algorithm, error signal e (n) and the step-length 2 μ result who draws that multiplies each other is carried out the phase place rotary manipulation, and the amount of phase rotation is the phase value of input signal x (n).
The implementation method of a kind of improved LMS algorithm that the present invention proposes, the structure of its specific implementation as shown in Figure 3, it is made up of phase register, multiplier, adder and weight coefficient register.
Phase register is used to deposit the value of the phase place of input signal x (n), in general, the information of input signal comprises mould and phase place, only be used for the phase place of storage input x (n) herein,, saved the memory space of half so compare with the implementation method of general LMS algorithm.Phase register flows to phase rotation device to the value of the phase place of input signal x (n) as the amount of phase rotation.
Multiplier is that error signal e (n) is multiplied each other with adaptive step 2 μ, and the result flows to phase rotation device, is used to be rotated.
Phase rotation device is that the data that multiplier transports are carried out the phase place twiddle operation, and the phase mass of rotation is provided by phase register.The output of phase rotation device flows to adder.
Adder is used to produce the weight coefficient of a new round, and it carries out addition with weight coefficient original in the output of phase place rotary module and the weight coefficient register, and the result is new weight coefficient.
The weight coefficient register is used to deposit the new weight coefficient that adder draws, and this weight coefficient is offered adder again, is used to produce the new weight coefficient of next round.
Description of drawings
Fig. 1 is the schematic diagram of general sef-adapting filter;
Fig. 2 is the structure chart that general sef-adapting filter is realized;
The structure chart of the LMS algorithm that the method that Fig. 3 proposes for the present invention realizes;
The structure chart that Fig. 4 realizes for specific embodiment;
Fig. 5 is the structure chart of general filtering operation.
Embodiment
The implementation method of a kind of improved LMS algorithm that the present invention proposes is described below by a specific embodiment.The implementation structure of this specific embodiment is made up of error calculating module, filter module, input memory module, phase place rotary module and multiplication module, as shown in Figure 4.
Digital filter comprises weight coefficient generation module and filtering operation module.The weight coefficient generation module is made up of adder and weight coefficient register, function is said in the summary of the invention as mentioned, and adder is used to produce the weight coefficient of a new round, it carries out addition with weight coefficient original in the output of phase place rotary module and the weight coefficient register, and the result is new weight coefficient.The filtering operation module realizes filter function, is made up of some adders, shift register and multiplier, and the structure of general filtering operation as shown in Figure 5.Digital filter is whenever finished a filtering and is calculated, and weight coefficient is deposited module and just new weight coefficient flowed to the filtering computing module, changes the coefficient of multiplier in the filtering computing module, has realized self adaptation.
Phase register is used for the number of phases of storage input x (n), its function also as mentioned in the summary of the invention institute say that phase register flows to phase rotation device to the value of the phase place of input signal x (n) as the amount of phase rotation.
Multiplier is that error signal e (n) is multiplied each other with adaptive step 2 μ, and the result flows to the phase place rotary module.
Phase rotation device is that the data that multiplication module transports are carried out the phase place twiddle operation, and the phase amplitude of rotation is provided by the input memory module.The output of phase rotation device flows to the adder in the weight coefficient generation module in the digital filter.
Error calculating module, be to carry out the processing of time domain behind output signal y (n) the process inverse fourier transform, promptly to carry out the subtraction operation, to obtain error signal e (n) with desired signal d (n), through a Fourier transform, the result who obtains flows to multiplication module again.
Present embodiment can be realized the adaptive-filtering process through actual checking, and is functional.
The implementation method of a kind of improved LMS algorithm that the present invention proposes is compared with the implementation method of general LMS algorithm to reduce and is used half memory space of input register, and convergence is preferably arranged.
Claims (7)
1, a kind of implementation method of improved LMS algorithm, its characteristics are only to store the phase value of input signal x (n) in input register, in adaptive algorithm, error signal e (n) and the step-length 2 μ result who draws that multiplies each other is carried out the phase place rotary manipulation, and the amount of phase rotation is the phase value of input signal x (n).
2, a kind of implementation method of improved LMS algorithm, its characteristics are that the structure that realizes comprises phase register, multiplier, phase rotation device, adder and weight coefficient register.
3, phase register as claimed in claim 2 is used to deposit the value of the phase place of input signal x (n), and phase register flows to phase rotation device to the value of the phase place of input signal x (n) as the amount of phase rotation.
4, multiplier as claimed in claim 2 is that error signal e (n) is multiplied each other with adaptive step 2 μ, and the result flows to phase rotation device, is used to be rotated.
5, phase place rotary module as claimed in claim 2 is that the data that multiplier transports are carried out the phase place twiddle operation, and the phase mass of rotation is provided by phase register, and the output of phase rotation device flows to adder.
6, adder as claimed in claim 2 is used to produce the weight coefficient of a new round, and adder is carried out addition with weight coefficient original in the output of phase place rotary module and the weight coefficient register, and the result is new weight coefficient.
7, weight coefficient register as claimed in claim 2 is used to deposit the weight coefficient that newly obtains, and this weight coefficient is offered adder, is used to produce the new weight coefficient of next round.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102394593A (en) * | 2011-09-19 | 2012-03-28 | 北京东方联星科技有限公司 | Integrated least-mean-square (LMS) adaptive filter and method |
| CN102545835A (en) * | 2012-01-19 | 2012-07-04 | 北京大学 | Rapid convergence self-adaptive filter based on both interpolation and quick clock |
| CN107147374A (en) * | 2017-04-26 | 2017-09-08 | 鲁东大学 | Change exponent number LMS wave filters based on auto-adaptive parameter |
| CN109104200A (en) * | 2017-06-20 | 2018-12-28 | 希捷科技有限公司 | Approximation parameters are adaptive |
| CN110133425A (en) * | 2019-06-10 | 2019-08-16 | 集美大学 | A kind of submarine cable fault-signal filtering method, terminal device and storage medium |
| CN116016787A (en) * | 2022-12-30 | 2023-04-25 | 南方医科大学南方医院 | Nonlinear echo cancellation based on Sigmoid transformation and RLS algorithm |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3544643B2 (en) * | 2000-07-14 | 2004-07-21 | 松下電器産業株式会社 | Channel estimation device and channel estimation method |
| JP4108029B2 (en) * | 2003-09-29 | 2008-06-25 | 三洋電機株式会社 | Calibration method and wireless device using the same |
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2009
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102394593A (en) * | 2011-09-19 | 2012-03-28 | 北京东方联星科技有限公司 | Integrated least-mean-square (LMS) adaptive filter and method |
| CN102394593B (en) * | 2011-09-19 | 2014-06-25 | 北京东方联星科技有限公司 | Integrated least-mean-square (LMS) adaptive filter and method |
| CN102545835A (en) * | 2012-01-19 | 2012-07-04 | 北京大学 | Rapid convergence self-adaptive filter based on both interpolation and quick clock |
| CN107147374A (en) * | 2017-04-26 | 2017-09-08 | 鲁东大学 | Change exponent number LMS wave filters based on auto-adaptive parameter |
| CN109104200A (en) * | 2017-06-20 | 2018-12-28 | 希捷科技有限公司 | Approximation parameters are adaptive |
| CN109104200B (en) * | 2017-06-20 | 2022-07-01 | 希捷科技有限公司 | Approximate parameter adaptation |
| CN110133425A (en) * | 2019-06-10 | 2019-08-16 | 集美大学 | A kind of submarine cable fault-signal filtering method, terminal device and storage medium |
| CN116016787A (en) * | 2022-12-30 | 2023-04-25 | 南方医科大学南方医院 | Nonlinear echo cancellation based on Sigmoid transformation and RLS algorithm |
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