CN103227623A - Step value-variable LMS (Least Mean Square) self-adaptation filtering algorithm and filter - Google Patents
Step value-variable LMS (Least Mean Square) self-adaptation filtering algorithm and filter Download PDFInfo
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Abstract
The invention relates to the technical field of digital signal treatment, in particular to a step value-variable LMS (Least Mean Square) self-adaptation filtering algorithm and a step value-variable LMS (Least Mean Square) self-adaptation filter. According to the invention, since a step value capable of changing according to filtering stages can be supplied, a higher convergence rate and a smaller system stability error can be synchronously obtained, which shows that in the initial stage of self-adaptation filtering, a larger step value is provided, thereby obtaining a higher convergence rate, and a smaller step value is provided when the self-adaptation filtering approaches a stable state, so that a smaller stable state error value can be obtained.
Description
Technical field
The present invention relates to digital signal processing technique field, be specifically related to a kind of LMS(Least Mean Square of variable step size, lowest mean square) adaptive filter algorithm and filter.
Background technology
Sef-adapting filter is one of the research focus in signal processing field always, and through years of development, it has been widely used in fields such as digital communication, radar, sonar, seismology, navigation system, biomedicine and Industry Control.
Most widely used in the adaptive algorithm is lowest mean square (LMS, Least Mean Square) algorithm, and the LMS algorithm is a kind of searching algorithm, and it has simplified the calculating to gradient vector by target function is carried out suitable adjustment.Because the simplicity of its calculating, LMS algorithm and other associated algorithms have been widely used in the various application of adaptive-filtering.The LMS basic idea is to adjust the weight coefficient of filter, makes the output signal of filter and the mean square error minimum between the desired signal.In system identification (promptly using an Adaptable System to realize a unknown system convergence), channel equalization fields such as (promptly using a transfer function compensating signal to transmit the distortion that causes in channel), the LMS algorithm has obtained widely applying.
The LMS sef-adapting filter can not change the concrete structure of filter in actual moving process, but the filter weight coefficient can carry out iteration according to the difference of parameter to be upgraded, be and obtain the desired signal response, the filter weight coefficient can adapt to the situation of change of input signal automatically.
The structure of LMS sef-adapting filter of the prior art is as shown in fig. 1:
Wherein, X (k) is a primary signal, and W (k) is the filter weight coefficient matrix, u is the step-length of sef-adapting filter, and d (k) is a desired signal, and y (k) is an output signal, e (k) is the error amount of desired signal d (k) and output signal y (k), i.e. e (k)=d (k)-y (k).
Its concrete course of work is: k at a time, primary signal X (k) through a series of delay lines transmission after, form corresponding different input signal x (0), the x (1) that postpone ... x (k); Multiply each other through input signal after different delay the and corresponding filter weight coefficient, and then, obtain the output signal y (k) of k constantly the product addition of gained, promptly y (k)=x (0) w (0)+... + x (k) w (k)=X (k) W (k); Output signal error amount e (k) is obtained by e (k)=d (k)-y (k), after error amount e (k) multiplies each other with step value u, and transient change amount ue (k) X (k) when multiplying each other the renewal of acquisition filter weight coefficient matrix with X (k).When clock signal arrival next time, the filter weight coefficient matrix value after obtaining to upgrade is W (k+1)=W (k)+ue (k) X (k), thereby finishes the adaptive updates process of filter weight coefficient.
Yet there is following defective in LMS adaptive filter algorithm of the prior art:
In the LMS filtering algorithm in the prior art, its algorithm step-length is a fixed value, can not obtain filter convergence time and steady state error value preferably simultaneously.Be in particular in that when step-length u is provided with when big, filter can reach convergence state fast, if but in filtering during near stable state, the steady state error value can be bigger, influences the error performance of system; When step-length u is provided with hour, filter can obtain less steady state error value, if in the starting stage of adaptive-filtering, convergence time is longer, needs to increase the training sequence length of algorithm.
In sum, a kind of adaptive filter algorithm of less steady-state error value and filter can realized when obtaining very fast convergence rate demanded urgently providing.
Summary of the invention
(1) technical problem that will solve
The object of the present invention is to provide and a kind ofly can when obtaining very fast convergence rate, can realize the LMS adaptive filter algorithm and the filter of less steady-state error value.
(2) technical scheme
Technical solution of the present invention is as follows:
A kind of LMS adaptive filter algorithm of variable step size comprises step:
S1. obtain the corresponding different input signal that postpones after the delayed processing of primary signal;
The product addition of S2. that each input signal is corresponding with it filter weight coefficient obtains the output signal in this moment;
S3. desired signal and described output signal are done the poor error amount that obtains;
S4. the product with described error amount and step value and input signal upgrades described filter weight coefficient as the transient change amount;
Described step value is variable.
Preferably, described step-length u (k)=u0+ α | e (k)-e (k-1) |; Wherein, u0 is an initial step length, and α is a regulatory factor, and e (k) is this computing gained error amount, and e (k-1) is a computing last time gained error amount.
Preferably, described desired signal is a training sequence, and described training sequence was selected the training sequence value of regular length for use before each transmission burst.
The present invention also provides a kind of LMS sef-adapting filter of realizing the variable step size of said method:
A kind of LMS sef-adapting filter of variable step size comprises the delay memory module, adaptive-filtering module and the error generation module that set gradually; Described delay memory module, adaptive-filtering module and error generation module all are connected with the variable step size generation module;
Described delay memory module comprises some delayers, is used for primary signal is postponed to handle, and obtains the corresponding different input signal that postpones;
Described adaptive-filtering module is used to upgrade the filter weight coefficient and calculates output signal;
Described error generation module is used for obtaining error amount in conjunction with desired signal and output signal;
Described variable step size generation module is according to step-length u (k)=u0+ α | e (k)-e (k-1) | variable step value is provided; Wherein, u0 is an initial step length, and α is a regulatory factor, and e (k) is this computing gained error amount, and e (k-1) is a computing last time gained error amount.
Preferably, described adaptive-filtering module comprises filter weight coefficient updating block and output signal arithmetic element; Described filter weight coefficient updating block and output signal arithmetic element include multiplier and the adder that sets gradually.
(3) beneficial effect
LMS adaptive filter algorithm provided by the present invention and filter because step-length is variable, therefore can obtain convergence rate and less system stability error faster simultaneously; Show as, provide bigger step value, thereby can obtain convergence rate faster, provide the smaller step size value at adaptive-filtering during near stable state, thereby can obtain less steady-state error value in the starting stage of adaptive-filtering.
Description of drawings
Fig. 1 is the structural representation of LMS sef-adapting filter in the prior art;
Fig. 2 is the module connection diagram of LMS sef-adapting filter in the embodiment of the invention;
Fig. 3 is the structural representation of LMS sef-adapting filter in the embodiment of the invention.
Embodiment
Below in conjunction with drawings and Examples, the specific embodiment of the present invention is described further.Following examples only are used to illustrate the present invention, but are not used for limiting the scope of the invention.
A kind of LMS adaptive filter algorithm of variable step size at first is provided in the present embodiment, and it mainly comprises step:
S1. obtain the corresponding different input signal that postpones after the delayed processing of primary signal;
The product addition of S2. that each input signal is corresponding with it filter weight coefficient obtains the output signal in this moment;
S3. desired signal and output signal are done the poor error amount that obtains;
S4. the product with error amount and step value and input signal upgrades the filter weight coefficient as the transient change amount;
One of greatest improvement point of the present invention is that wherein step value is variable, therefore can obtain convergence rate and less system stability error faster simultaneously; Show as, provide bigger step value, thereby can obtain convergence rate faster, provide the smaller step size value at adaptive-filtering during near stable state, thereby can obtain less steady-state error value in the starting stage of adaptive-filtering.Particularly, the step-length u in the present embodiment (k)=u
0+ α | e (k)-e (k-1) |; Wherein, u
0Be initial step length, α is a regulatory factor, and e (k) is this computing gained error amount, and e (k-1) is a computing last time gained error amount.Because step-length can be adjusted automatically according to the error amount of twice output signal and desired signal, adjustment process can be so that provide bigger step value u in the starting stage of adaptive-filtering, thereby obtain convergence rate faster, provide smaller step size value u at adaptive-filtering during near stable state, thereby obtain less steady-state error value.
A kind of LMS sef-adapting filter of realizing the variable step size of said method also is provided in the present embodiment, and as shown in Figure 2, it mainly comprises delay memory module, adaptive-filtering module and the error generation module that sets gradually; Postponing memory module, adaptive-filtering module and error generation module all is connected with the variable step size generation module; Below each module is illustrated respectively.
Postpone memory module and mainly comprise a series of delayer, it is mainly used in primary signal is postponed to handle accordingly, obtains the corresponding different input signal that postpones, and is used for the subsequent filter weight coefficient and upgrades.
The adaptive-filtering module is used to upgrade the filter weight coefficient and calculates output signal; In the present embodiment, the adaptive-filtering module mainly comprises filter weight coefficient updating block and output signal arithmetic element two large divisions; Filter weight coefficient updating block is made up of adder that sets gradually and multiplier, can be according to the filter weight coefficient of input signal values x (k), variable step size value u (k) and the corresponding generation of output error value e (k) constantly new round, its filter weight coefficient more new formula is w (k+1)=w (k)+u (k) e (k) x (k); The output signal arithmetic element comprises adder and the multiplier that sets gradually equally, and it can calculate the corresponding output signal y (k) of k constantly according to filter weight coefficient matrix W (k) and input signal matrix X (k).
The error generation module is used for obtaining error amount in conjunction with desired signal and output signal, and its computing formula is e (k)=d (k)-y (k).
The variable step size generation module, be mainly used in according to the real-time output error e(k of filter) size and relevant parameter the step-length u(k that adjusts sef-adapting filter is set), show as according to step-length u (k)=u0+ α | e (k)-e (k-1) | variable step value is provided; Wherein, u0 is an initial step length, and α is a regulatory factor, and e (k) is this computing gained error amount, and e (k-1) is a computing last time gained error amount.Thereby make sef-adapting filter provide big step-length can produce very fast convergence rate, when sef-adapting filter is stable, provide less step-length that less steady-state error value can be provided at initial operating stage.
Below in conjunction with Fig. 3 the calculating process of the LMS sef-adapting filter of above-mentioned variable step size is illustrated:
K at any one time, input signal X (k) forms a series of different burst x (0), x (1) that postpone through postponing memory module ... x (k) is as the input of follow-up adaptive-filtering module; Delayer in this module and unit period signal clk are synchronous operation.
Output signal y (k) makes difference with corresponding expected signal value d (k) constantly can get output signal error amount e (k), i.e. e (k)=d (k)-y (k).In the present embodiment, expected signal value d (k) replaces with training sequence, and training sequence can send and select for use the known training sequence value of regular length before each transmission burst.In the variable step size generation module, under clock cycle signal clk effect, obtain corresponding variable step size value u (k)=u constantly
0+ α | e (k)-e (k-1) |.U wherein
0Be system's initial step length, generally select smaller value, α is a regulatory factor.In system's operation starting stage, system output signal y (k) is bigger with desired signal d (k) error e (k), and adjacent moment error signal relative difference is bigger, thus can produce bigger step value, thus quicken the convergence of sef-adapting filter.In the stable stage of system, systematic error signal e (k) is less, and the relative error of adjacent moment error value is less, thus can produce the smaller step size value, thus make system obtain less steady-state error value.
The adaptive-filtering module comprises filter weight coefficient updating block and output signal arithmetic element, and function is as indicated above.Utilize the input signal sequence and the variable step size value u (k) that produce that the filter weight coefficient is carried out the synchronous renewal with unit period signal clk.Simultaneously, filter weight coefficient after the utilization of output signal arithmetic element is upgraded and input signal sequence calculate the output signal y (k) of sef-adapting filter, and its computing formula is y (k)=X (k) W (k).
The LMS adaptive filter algorithm of variable step size provided by the present invention and filter are through experimental verification, after only increasing by two adders aspect the enforcement complexity, and can in the convergence rate that significantly increases adaptive-filtering, provide steady-state error value preferably, higher performance is provided.
Above execution mode only is used to illustrate the present invention; and be not limitation of the present invention; the those of ordinary skill in relevant technologies field; under the situation that does not break away from the spirit and scope of the present invention; can also make various variations and modification, so all technical schemes that are equal to also belong to protection category of the present invention.
Claims (5)
1. the LMS adaptive filter algorithm of a variable step size comprises step:
S1. obtain the corresponding different input signal that postpones after the delayed processing of primary signal;
The product addition of S2. that each input signal is corresponding with it filter weight coefficient obtains the output signal in this moment;
S3. desired signal and described output signal are done the poor error amount that obtains;
S4. the product with described error amount and step value and input signal upgrades described filter weight coefficient as the transient change amount;
It is characterized in that described step value is variable.
2. LMS adaptive filter algorithm according to claim 1 is characterized in that, described step-length u (k)=u
0+ α | e (k)-e (k-1) |; Wherein, u
0Be initial step length, α is a regulatory factor, and e (k) is this computing gained error amount, and e (k-1) is a computing last time gained error amount.
3. LMS adaptive filter algorithm according to claim 1 and 2 is characterized in that, described desired signal is a training sequence, and described training sequence was selected the training sequence value of regular length for use before each transmission burst.
4. the LMS sef-adapting filter of a variable step size is characterized in that, comprises the delay memory module, adaptive-filtering module and the error generation module that set gradually; Described delay memory module, adaptive-filtering module and error generation module all are connected with the variable step size generation module;
Described delay memory module comprises some delayers, is used for primary signal is postponed to handle, and obtains the corresponding different input signal that postpones;
Described adaptive-filtering module is used to upgrade the filter weight coefficient and calculates output signal;
Described error generation module is used for obtaining error amount in conjunction with desired signal and output signal;
Described variable step size generation module is according to step-length u (k)=u
0+ α | e (k)-e (k-1) | variable step value is provided; Wherein, u
0Be initial step length, α is a regulatory factor, and e (k) is this computing gained error amount, and e (k-1) is a computing last time gained error amount.
5. LMS sef-adapting filter according to claim 4 is characterized in that, described adaptive-filtering module comprises filter weight coefficient updating block and output signal arithmetic element; Described filter weight coefficient updating block and output signal arithmetic element include multiplier and the adder that sets gradually.
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| CN104283528A (en) * | 2014-09-18 | 2015-01-14 | 河海大学 | Variable-step LMS adaptive filtering method |
| WO2016131178A1 (en) * | 2015-02-16 | 2016-08-25 | 华为技术有限公司 | Method and device for processing signal |
| CN106919808A (en) * | 2017-02-28 | 2017-07-04 | 哈尔滨工业大学深圳研究生院 | Gene identification system based on change step length least mean square error sef-adapting filter |
| WO2017113570A1 (en) * | 2015-12-30 | 2017-07-06 | 南方科技大学 | Method and system for calibrating transmission power and radio frequency system |
| CN106998229A (en) * | 2016-12-14 | 2017-08-01 | 吉林大学 | It is a kind of based on variable step without constraint FD LMS mode division multiplexing system Deplexing method |
| CN107395158A (en) * | 2017-07-14 | 2017-11-24 | 歌尔科技有限公司 | Data calibration method and device |
| CN109217844A (en) * | 2018-10-30 | 2019-01-15 | 哈尔滨理工大学 | Hyperparameter optimization method based on the random Fourier's feature core LMS of pre-training |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1993016527A1 (en) * | 1992-02-17 | 1993-08-19 | Thomson Consumer Electronics S.A. | Method and apparatus for ghost cancelling and/or equalizing |
| CN1716926A (en) * | 2004-06-28 | 2006-01-04 | 三星电子株式会社 | Automatically control the method and apparatus of the step-length of lowest mean square type equalizer |
| CN101252559A (en) * | 2007-10-12 | 2008-08-27 | 电子科技大学中山学院 | Method of training sequence time change step length least mean square |
| 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 |
-
2013
- 2013-03-29 CN CN201310108307.9A patent/CN103227623B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1993016527A1 (en) * | 1992-02-17 | 1993-08-19 | Thomson Consumer Electronics S.A. | Method and apparatus for ghost cancelling and/or equalizing |
| CN1716926A (en) * | 2004-06-28 | 2006-01-04 | 三星电子株式会社 | Automatically control the method and apparatus of the step-length of lowest mean square type equalizer |
| CN101252559A (en) * | 2007-10-12 | 2008-08-27 | 电子科技大学中山学院 | Method of training sequence time change step length least mean square |
| 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 |
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| CN104283528A (en) * | 2014-09-18 | 2015-01-14 | 河海大学 | Variable-step LMS adaptive filtering method |
| WO2016131178A1 (en) * | 2015-02-16 | 2016-08-25 | 华为技术有限公司 | Method and device for processing signal |
| CN107210986A (en) * | 2015-02-16 | 2017-09-26 | 华为技术有限公司 | The method and apparatus of process signal |
| CN107210986B (en) * | 2015-02-16 | 2020-01-03 | 华为技术有限公司 | Method and apparatus for processing signals |
| WO2017113570A1 (en) * | 2015-12-30 | 2017-07-06 | 南方科技大学 | Method and system for calibrating transmission power and radio frequency system |
| CN106998229B (en) * | 2016-12-14 | 2019-02-15 | 吉林大学 | A kind of mode division multiplexing system Deplexing method based on variable step without constraint FD-LMS |
| CN106998229A (en) * | 2016-12-14 | 2017-08-01 | 吉林大学 | It is a kind of based on variable step without constraint FD LMS mode division multiplexing system Deplexing method |
| CN106919808B (en) * | 2017-02-28 | 2019-06-14 | 哈尔滨工业大学深圳研究生院 | Gene identification system based on change step length least mean square error sef-adapting filter |
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| CN107395158A (en) * | 2017-07-14 | 2017-11-24 | 歌尔科技有限公司 | Data calibration method and device |
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| CN109217844A (en) * | 2018-10-30 | 2019-01-15 | 哈尔滨理工大学 | Hyperparameter optimization method based on the random Fourier's feature core LMS of pre-training |
| CN109217844B (en) * | 2018-10-30 | 2022-02-25 | 哈尔滨理工大学 | Hyper-parameter optimization method based on pre-training random Fourier feature kernel LMS |
| CN109884596A (en) * | 2019-01-24 | 2019-06-14 | 北京海兰信数据科技股份有限公司 | The GPS filtering system and filtering method of marine navigation radar |
| CN109884596B (en) * | 2019-01-24 | 2020-11-03 | 北京海兰信数据科技股份有限公司 | GPS filtering system and filtering method of marine navigation radar |
| CN110429921A (en) * | 2019-07-30 | 2019-11-08 | 西安电子科技大学 | A kind of variable step- size LMS adaptive filter method and its storage medium |
| CN112054782A (en) * | 2020-08-20 | 2020-12-08 | 中国人民解放军陆军勤务学院 | Variable step size factor construction method for LMS adaptive filtering |
| CN112039498A (en) * | 2020-08-27 | 2020-12-04 | 重庆邮电大学 | Adaptive signal processing method and medium based on polymorphic variable step length least mean square |
| CN112039498B (en) * | 2020-08-27 | 2023-11-14 | 重庆邮电大学 | Self-adaptive signal processing method and medium based on polymorphic variable step-length least mean square |
| CN114063649A (en) * | 2021-11-17 | 2022-02-18 | 国网天津市电力公司电力科学研究院 | Novel variable-step-size transformer robot fish obstacle avoidance device and method |
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