CN109448686A - Intersected based on secondary channel on-line identification new algorithm and updates active noise control system - Google Patents
Intersected based on secondary channel on-line identification new algorithm and updates active noise control system Download PDFInfo
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- G—PHYSICS
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- G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
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Abstract
The present invention is claimed a kind of based on secondary channel on-line identification new algorithm intersection update active noise control system.Including 6 modules: noise signal filtering, momentum FxLMS algorithm, white noise generator, secondary channel modeling, main channel path and third sef-adapting filter update module.Present invention aims at solve the problems, such as that convergence rate is slow in the application of noise elimination indoors of active noise elimination (ANC) system, noise reduction is small.The unevenness for being directed to noise power spectrum density that innovative point is causes the convergence rate of control filter and modeling filter in the application of noise elimination indoors of traditional LMS algorithm to will receive strong influence, it proposes the weight for updating control filter using momentum FxLMS algorithm, the weight of modeling filter is updated using New variable step-size LMS.It is used to eliminate error signal signal relevant to reference-input signal using the third sef-adapting filter of the newton LMS algorithm of proposition, improves the modeling accuracy for modeling filter and entire ANC system convergence rate.
Description
Technical field
The invention belongs to noise cancellation technique fields, more particularly to a kind of secondary channel on-line identification new algorithm that is based on to hand over
Fork updates the research of active noise control system.
Background technique
Traditional noise control mainly based on the acoustic control of noise, main technological means include sound absorption processing, every
Sonication uses silencer, the isolation of vibration and reduction etc..The mechanism of these noise control methods is to make noise sound wave and sound
Learn material or structural interaction and sound energy consumption to achieve the purpose that noise reduction belongs to the method for Passive Shape Control, referred to as " nothing
Source " noise control.Generally speaking, the method for Passive Shape Control is more effective to high-frequency noises are reduced, and to reduction low-frequency noise
Effect it is little.And active noise controlling (ANC) has good noise reduction effect to low-frequency noise, therefore receives very big
Concern.
Up to the present, active noise reduction technology there has been biggish development, and application scenarios are more and more.Active noise reduction technology
Just increasingly go deep into people's lives, commercialized product is also more and more.The development prospect of active noise reduction technology is also increasingly
Wide, the product of current active noise reduction technological development is mainly or for high-end product, due to technology, product
The cost is relatively high, and some field technologies are not mature enough, and need more research and developments.ANC is eliminated as noise
In important component, the significant challenge that is faced is related with the convergence time of ANC system and anti-acoustic capability in design.And
Traditional FxLMS algorithm uses LMS algorithm to update the weight of control filter, due to the autocorrelation matrix of input signal
Characteristic value dispersion the problem of will lead to the slow convergent pathway of LMS algorithm, for this to control filter using momentum LMS algorithm pair
The weight of control filter is updated.Secondary channel (is output to error pick-up measurement remnants from noise control filtering device
The path of noise) presence will lead to the unstability of standard lowest mean square (LMS) algorithm, and secondary channel path is at any time
Between change or it is nonlinear, this will lead to the decline or diverging of ANC system anti-acoustic capability again.Therefore in order to ensure ANC system
Convergence, needs to model secondary channel path, to track the variation of secondary path, to improve ANC system anti-acoustic capability
Stability.
Summary of the invention
Present invention seek to address that the above problem of the prior art.Propose a kind of stabilization for improving ANC system anti-acoustic capability
Property based on secondary channel on-line identification new algorithm intersect update active noise control system.Technical scheme is as follows:
One kind is intersected based on secondary channel on-line identification new algorithm updates active noise control system comprising: noise letter
Number filter module, momentum LMS algorithm module, white noise generator, secondary channel modeling module main channel path and third from
Adaptive filter module；Wherein
Noise signal filter module, the reference signal x (n) for generating to noise source model filter S ' by secondary
(z) it is filtered to obtain x ' (n), and is transferred to momentum FxLMS algoritic module；
Momentum LMS algorithm module, including control filter W (z) and momentum LMS algorithm, for believing filtered x ' (n)
Number be input to momentum LMS least mean square algorithm to update the weight coefficient of control filter W (z), control filter W (z) respectively with
Noise source, secondary modeling filter S ' (z), white noise generator module are connected with momentum LMS algorithm, and reference signal x (n) is logical
It crosses control filter w (z) and generates output signal y (n), y (n) generates anti-noise signal y ' (n) by secondary path S (z)；
White noise generator module injects white Gaussian to secondary path when carrying out Real-time modeling set to secondary path
Noise；White noise generator generates one group of random signal v (n), and v (n) generates modeling signal v ' (n) by secondary path, in addition
One end v (n) passes through modeling filterGenerate modeling signalError signal e (n) is participated in make the difference with modeling signal；
Secondary channel modeling module obtains estimating for secondary path transmission function for modeling to secondary path S (z)
Evaluation, including S (z), modeling filterAnd selection Variable Step Algorithm VSS LMS module, S (z) are initial for providing one
Secondary path acoustic response function, model filterFor providing the secondary path acoustic response function of an estimation,
Selection Variable Step Algorithm VSS LMS module is used to update the weight of modeling filter faster, S (z), modeling filterAnd selection superimposed signal u ' (n) of Variable Step Algorithm VSS LMS module is connected with third sef-adapting filter module；
Main channel path module, for providing an initial main path acoustic response function, the reference that noise source generates
Signal x (n) generates interference signal d (n) by main channel, and its anti-acoustic capability is monitored at error microphone e (n)；
Third sef-adapting filter module, including LMSN newton least mean square algorithm, H (z), LMSN newton lowest mean square are calculated
Weight of the method for third sef-adapting filter is updated, and H (z) is for generating signal u (n), thus in modeling filter
Middle elimination component.With the output of third sef-adapting filter H (z) come relevant to reference input point in analog error signal
Amount participates in error signal e (n) and modeling to obtain a desired signal relevant to white noise in error path identification link
Signal, which makes the difference, generates error signal f (n), error signal of the f (n) as momentum LMS module and VSS LMS module.
Further, error microphone e (n) place monitors its anti-acoustic capability, including following formula:
The wherein quality of the anti-acoustic capability of R:ANC system；E (n): the error letter of ANC system main control sef-adapting filter
Number；D (n): the desired signal of ANC system main control sef-adapting filter；The accuracy of secondary channel modeling in △ S:ANC system
Size；Si(n): the path function of practical secondary channel in ANC system；The path of secondary channel is simulated in ANC system
Function
Further, the main channel path module is for providing an initial main path acoustic response function P (z).
Further, the momentum LMS algorithm only increases the momentum introduced by weight coefficient correlation than LMS algorithm
, the weight coefficient of control filter W (z) is updated for filtered x ' (n) signal to be input to LMS algorithm, momentum LMS is calculated
Method specifically includes:
In equation: α is factor of momentum, is taken | α | < 1, uwIndicate the step parameter of control filter.
Further, the LMSN updates the weight of third sef-adapting filter, and steps are as follows:
Reference noise signal x (n) is converted into posteriori prediction errors sample vector b (n) using lattice filter:
f0(n)=b0(n)=x (n)
fm(n)=fm-1(n)-km(n)bm-1(n-1)
bm(n)=bm-1(n)-km(n)fm-1(n-1)
β indicates the forgetting factor for estimation, fm(n) positive error of m rank fallout predictor, b are indicatedmIndicate that m rank is predicted
The forward error of device, km(n) partial correlation coefficient of m rank, u are indicatedp,0Indicate fallout predictor step-length, Pm(n) b is indicatedm(n) and fm
(n) short-term energy estimation, γ indicate amplitude peak；
if|km(n)|>γ,km(n)=km(n-1)
The signal u (n) of reconstruct is updated:
fm' (n)=fm-1′(n)-km(n)bm-1′(n-1)
bm' (n)=bm-1′(n)-km(n)fm-1′(n-1)
ua(n)=(PM(n)+ε)-1(f′M-1(n)-kMb′M-1(n-1))
The tap weight coefficient of filter is updated:
Y (n)=wTx(n-M)
E (n)=d (n-M)-y (n)
W (n+1)=w (n)+2ua(n)e(n)。
Further, the selection Variable Step Algorithm VSS LMS module estimates the power of e (n) and f (n)
Pe(n)=λ Pe(n)+(1-λ)e2(n)
Pf(n)=λ Pf(n)+(1-λ)f2(n)
Here Pe(n) and PfIt (n) is respectively residual error signal e (n) and modeling error signal f (n)；
Calculate the ratio of two power: ρ (n)=Pf(n)/Pe(n)
Material calculation parameter μs(n) specific value:
μs(n)=ρ (n) μmin+(1-ρ(n))μmax
μminAnd μmaxFor the minimum step and maximum step-length of the modeling filter measured in specific experiment.
It advantages of the present invention and has the beneficial effect that:
It is a kind of new based on secondary channel on-line identification that the present invention combines the problems in above-mentioned ANC system to propose that the present invention proposes
Algorithm, which intersects, updates active noise control system, and secondary channel on-line identification new algorithm is updated based on momentum FxLMS algorithm
Control filter weights: the modeling filter using variable step- size LMS (VSS LMS) algorithm is for modeling secondary path；
It is used to eliminate error signal and reference-input signal using the third sef-adapting filter of newton LMS (LMSN) algorithm of modification
Relevant signal improves the modeling accuracy and convergence rate of modeling filter.
Active noise controlling (ANC) is mainly based upon sound principle of stacking, inhibits acoustic noise signal using electromechanics combination
Method.Compared with traditional passive noise control (PNC) method, it is relatively narrow that traditional noise control method may only reduce frequency range
Low frequency signal and the device or bulky and heavy that needs, the scene of application it is limited.And ANC system is made an uproar in low frequency
The noise reduction of sound, the convenience of installation, stabilization of working performance etc. have good effect and can also pass through control parameter
To offset the noise of different characteristics.
And the ANC system of the secondary channel modeling based on momentum FxLMS algorithm, it not only solves for the fast of secondary path
The tracking of speed to guarantee to control the stability of algorithm filter, and solves the reference signal generated due to noise source
The characteristic value the dispersion of autocorrelation matrix causes the convergence rate for controlling filter to cause entire ANC system runing time slow slowly
The problem of.
Secondary path convergence rate is not only accelerated in modeling based on VSS LMS algorithm to secondary path, but also improves secondary
The modeling accuracy in path also has better tracking performance to secondary path.
Based on the third sef-adapting filter module of newton LMS (LMSN) algorithm, come in analog error signal e (n) with
The relevant component of reference input x (n) is eliminated to obtain a desired signal relevant to v (n) in error path identification link
To the adverse effect of error path identification link.Auto-correlation due to reference noise in practical applications is made using LMSN algorithm
The problem of characteristic value dispersion of matrix, carrys out the convergence of accelerating algorithm, improve the convergence rate of entire ANC system.
This technology priority and difficulty is to solve traditional FxLMS algorithm for the spy of the autocorrelation matrix of input signal
The sensibility of value indicative is higher, using momentum FxLMS algorithm.In addition to balance secondary path convergence rate and tracking it is sensitive
Property between contradiction, use VSSLMS algorithm.And the influence that reference signal in actual operation models secondary path,
And how to improve the precision of secondary channel modeling.
The reference signal x (n) that noise source generates generates interference signal d (n) by main channel, in order to generate anti-noise signal
Y ' (n), reference signal are generated output signal y (n) by control filter w (z), and y (n) generates antinoise by secondary path
Signal y ' (n), in order to enable the stability that momentum LMS algorithm updates control filter weights, it is necessary to allow reference signal x (n)
Filter is modeled by secondaryChanged over time to solve secondary path, it is necessary to it is online to secondary modeling filter into
Row estimation, need thus using with the incoherent pseudo-random noise injection of reference signal to secondary path the inside, white noise generator
Such one group of random signal v (n) is generated, v (n) generates modeling signal v ' (n) by secondary path, and other end v (n) is by building
Mode filterModeling signal v ' (n) is generated, error signal e (n) is participated in and modeling signal makes the difference and generates error signal f (n).f
(n) error signal as momentum LMS algorithm and VSS LMS algorithm.
The present invention improves the performance of ANC system whole system in the noise reduction to low-frequency noise to a certain extent,
Have the advantages that protrude as follows:
1. fast convergence rate, momentum LMS only increases the momentum introduced due to weight coefficient correlation than LMS algorithm
, in the case where weight coefficient changes greatly, then current weight coefficient just will increase, and can play accelerating gradient decline, make to weigh
Coefficient Mean is convergent faster more stably to be acted on.After momentum FxLMS algorithm, the value of convergence coefficient is calculated compared with FxLMS
Method increased, to reduce step-length to the sensibility of the characteristic value degree of scatter of reference signal autocorrelation matrix, to accelerate to control
The convergence rate of filter processed.
Detailed description of the invention
Fig. 1 is that the present invention provides preferred embodiment adaptive active feedforward system schematic diagram；
Fig. 2 is adaptive active feedforward control system block diagram；
Fig. 3 is that active noise proposed by the present invention eliminates system block diagram；
Fig. 4 is ANC system comprehensive simulating result figure.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, detailed
Carefully describe.Described embodiment is only a part of the embodiments of the present invention.
The technical solution that the present invention solves above-mentioned technical problem is:
It is a kind of new based on secondary channel on-line identification that the present invention combines the problems in above-mentioned ANC system to propose that the present invention proposes
Algorithm, which intersects, updates active noise control system, and secondary channel on-line identification new algorithm is updated based on momentum FxLMS algorithm
Control filter weights: the modeling filter using variable step- size LMS (VSS LMS) algorithm is for modeling secondary path；
It is used to eliminate error signal and reference-input signal using the third sef-adapting filter of newton LMS (LMSN) algorithm of modification
Relevant signal improves the modeling accuracy and convergence rate of modeling filter.
Active noise controlling (ANC) is mainly based upon sound principle of stacking, inhibits acoustic noise signal using electromechanics combination
Method.Compared with traditional passive noise control (PNC) method, it is relatively narrow that traditional noise control method may only reduce frequency range
Low frequency signal and the device or bulky and heavy that needs, the scene of application it is limited.And ANC system is made an uproar in low frequency
The noise reduction of sound, the convenience of installation, stabilization of working performance etc. have good effect and can also pass through control parameter
To offset the noise of different characteristics.
And the ANC system of the secondary channel modeling based on momentum FxLMS algorithm, it not only solves for the fast of secondary path
The tracking of speed to guarantee to control the stability of algorithm filter, and solves the reference signal generated due to noise source
The characteristic value the dispersion of autocorrelation matrix causes the convergence rate for controlling filter to cause entire ANC system runing time slow slowly
The problem of.
Secondary path convergence rate is not only accelerated in modeling based on VSS LMS algorithm to secondary path, but also improves secondary
The modeling accuracy in path also has better tracking performance to secondary path.
Based on the third sef-adapting filter module of newton LMS (LMSN) algorithm, come in analog error signal e (n) with
The relevant component of reference input x (n) is eliminated to obtain a desired signal relevant to v (n) in error path identification link
To the adverse effect of error path identification link.Auto-correlation due to reference noise in practical applications is made using LMSN algorithm
The problem of characteristic value dispersion of matrix, carrys out the convergence of accelerating algorithm, improve the convergence rate of entire ANC system.
This technology priority and difficulty is to solve traditional FxLMS algorithm for the spy of the autocorrelation matrix of input signal
The sensibility of value indicative is higher, using momentum FxLMS algorithm.In addition to balance secondary path convergence rate and tracking it is sensitive
Property between contradiction, use VSSLMS algorithm.And the influence that reference signal in actual operation models secondary path,
And how to improve the precision of secondary channel modeling.
The present invention improves the performance of ANC system whole system in the noise reduction to low-frequency noise to a certain extent,
Have the advantages that protrude as follows:
1. fast convergence rate, momentum LMS only increases the momentum introduced due to weight coefficient correlation than LMS algorithm
, in the case where weight coefficient changes greatly, then current weight coefficient just will increase, and can play accelerating gradient decline, make to weigh
Coefficient Mean is convergent faster more stably to be acted on.After momentum FxLMS algorithm, the value of convergence coefficient is calculated compared with FxLMS
Method increased, to reduce step-length to the sensibility of the characteristic value degree of scatter of reference signal autocorrelation matrix, to accelerate to control
The convergence rate of filter processed.
2. carrying out the weight of modeling filter more using New variable step-size LMS for secondary channel modeling filter
Newly, focus below to introduce VSSLMS and update modeling filterWeight coefficient, steps are as follows:
Estimate the power of e (n) and f (n)
Pe(n)=λ Pe(n)+(1-λ)e2(n)
Pf(n)=λ Pf(n)+(1-λ)f2(n)
Here Pe(n) and PfIt (n) is respectively residual error signal e (n) and modeling error signal f (n).
Calculate the ratio of two power: ρ (n)=Pf(n)/Pe(n)
Material calculation parameter μs(n) specific value:
μs(n)=ρ (n) μmin+(1-ρ(n))μmax
μminAnd μmaxFor the minimum step and maximum step-length of the modeling filter measured in specific experiment.
3. carrying out the power to third sef-adapting filter using LMSN algorithm for third sef-adapting filter
Value is updated, and highlights the weight that LMSN updates third sef-adapting filter below, steps are as follows:
Reference noise signal x (n) is converted into posteriori prediction errors sample vector b (n) using lattice filter:
f0(n)=b0(n)=x (n)
fm(n)=fm-1(n)-km(n)bm-1(n-1)
bm(n)=bm-1(n)-km(n)fm-1(n-1)
The effect of constant ε is to work as Pm(n) guarantee the stability of algorithm when dropping to very small numerical value.
if|km(n)|>γ,km(n)=km(n-1)
The signal u (n) of reconstruct is updated:
fm' (n)=fm-1′(n)-km(n)bm-1′(n-1)
bm' (n)=bm-1′(n)-km(n)fm-1′(n-1)
ua(n)=(PM(n)+ε)-1(f′M-1(n)-kMb′M-1(n-1))
The tap weight coefficient of filter is updated:
Y (n)=wTx(n-M)
E (n)=d (n-M)-y (n)
W (n+1)=w (n)+2ua(n)e(n)
4. a kind of active noise control system based on momentum FxLMS algorithm, which is characterized in that control is filtered
Device is updated weight using following algorithm.
In equation: α is factor of momentum, is taken | α | < 1, uwIndicate the step parameter of control filter.
Primary noise source as shown in Fig. 1 issues sound wave, and reference sensor picks up reference signal x (t) as controller
Input.Controller calculates secondary signal y (t) according to algorithmic rule, drives secondary sound source through overpower amplifier after output.Just
The sound wave that grade sound source and secondary sound source generate is respectively formed primary sound field and secondary sound field, and error microphone receives primary simultaneously
The acoustic pressure (or other acoustical parameters) of sound field and secondary sound field forms error signal e (t) after the two superposition.Error signal input
Into controller, adaptive algorithm changes secondary signal according to preset control target adjustment controller weight coefficient
Intensity (including amplitude and phase).Such process constantly continues, until meeting control target, system reaches stable.
As shown in Fig. 2, the transmission function of controller is denoted as W (ω), and sets Mr、LsAnd MeThe sensitivity of equal electro-acoustic elements
Respectively Mr(ω)、Ls(ω) and Me(ω).In space, sound wave is from primary sound source P to reference sensor Mr, P to error sensing
Device MeAnd secondary sound source LsTo MeThe transmission function of acoustic propagation access be denoted as H respectivelypr(ω)、Hpe(ω) and Hse(ω), control
The A/D converter of device periphery processed, preamplifier, antialiasing filter transmission function be N1(ω), it is D/A converter, smooth
Filter, power amplifier transmission function be N2One adaptive active feedforward control system of figure is converted to Fig. 2's by (ω)
Form.The access that dotted line in Fig. 2 indicates is that secondary acoustic feedback access has a great impact to the stability of system, needs to use
Special filter construction or algorithm are handled.
As shown in Fig. 3, a kind of that update active noise control system is intersected based on secondary channel on-line identification new algorithm, it is secondary
Grade channel on-line identification new algorithm is that control filter weights are updated based on momentum FxLMS algorithm: using variable step- size LMS (VSS
LMS) the modeling filter of algorithm is for modeling secondary path；Use the third of newton LMS (LMSN) algorithm of modification
A sef-adapting filter is used to eliminate error signal signal relevant to reference-input signal, improves the modeling essence of modeling filter
Degree and convergence rate.It is characterised by comprising:
6 modules: (1) noise signal filtering, (2) momentum FxLMS algorithm, (3) white noise generator, (4) secondary channel
Modeling, (5) main channel path and (6) third sef-adapting filter module.
Momentum FxLMS algoritic module includes (1) and (2), it is therefore intended that S ' (z) of the secondary channel modeling filter of addition
It is the stability in order to guarantee momentum LMS algorithm, the signal that noise source generates passes through S ' (z) and is input to LMS algorithm to update control
The weight coefficient of filter processed.Original noise signal generates anti-noise by control filter output y (n), using secondary path
Signal y ' (n), y ' (n) are combined with the noise d (n) of main path to reduce the acoustic pressure around error loudspeaker.
White noise generator module (3) needs to infuse in secondary path when carrying out Real-time modeling set to secondary path
The input signal entered is uncorrelated to the signal that noise source generates, in order to solve this problem to use white noise generator to secondary
White Gaussian noise is injected in path.
Secondary channel modeling module (4), since secondary path is time-varying, is led in this case in actual engineering
Momentum LMS algorithm in control filter will appear unstable or even diverging, and the effect of noise reduction can also be made to deteriorate.In order to solve
This problem needs to model secondary channel in real time.
Variable Step Algorithm is used in secondary channel modeling, when updating the weight of modeling filter, it is noted that no
Only to pay close attention to convergence speed of the algorithm also needs algorithm to possess more sensitive tracking performance, in order to obtain between this restrictive condition
Better effect selects Variable Step Algorithm (VSS LMS).
Main channel path module (5), it is therefore intended that an initial main path acoustic response function is provided to ANC system,
And then the noise reduction to initial reference noise x (n) is constituted together in conjunction with other several modules, and monitor at error microphone e (n)
Its anti-acoustic capability.
Third sef-adapting filter module (6), it is therefore intended that mistake is simulated with the output of third sef-adapting filter
Component relevant to reference input in difference signal, to obtain an expectation letter relevant to white noise in error path identification link
Number, eliminate the adverse effect to error path identification link.
Preferably, momentum LMS algorithm only increases the momentum term introduced by weight coefficient correlation than LMS algorithm,
That is can play and add if the correction amount of current weight coefficient is increased by when pervious weight coefficient variable quantity is larger
Fast gradient decline, so that weight coefficient convergence in the mean faster more stably acts on.
Preferably, in order to solve the problems, such as that secondary path changes over time, secondary channel modeling is being carried out to ANC system
At the beginning, using line modeling, the variation of secondary channel can be accurately tracked, guarantees the stability of control filter.
Preferably, the dispersion of the characteristic value of the autocorrelation matrix of reference signal is extremely tight in true noise circumstance
Weight, it using momentum LMS algorithm is increased in traditional LMS algorithm that the right value update for directly resulting in control filter is slack-off
Momentum term can make iteration step length μS(n) value range increased around FXLMS algorithm, reduce iteration step length pair
The sensibility of characteristic value dispersion degree reduces.
Preferably, New variable step-size LMS (VSS LMS) has been used to remove adaptive modeling modeling filterIt can be more
The good variation for following secondary path, and improve the convergence rate of modeling filter.
Preferably, introduce third sef-adapting filter, with its output come in analog error signal e (n) with input
The relevant component of the relevant x (n) of signal, to obtain error identification link only desired signal relevant to v (n).
Preferably, in order to which the dispersion for solving the characteristic value of the autocorrelation matrix of input signal in actual noise circumstance is asked
Topic, using the weight coefficient of improved newton LMS algorithm (LMSN) Lai Gengxin third sef-adapting filter, to improve convergence speed
Degree.
Active noise elimination is according to principle of stacking, by generating the signal of same amplitude and opposite in phase, noise source
The reference signal x (n) of generation generates interference signal d (n) by main channel, in order to generate anti-noise signal y ' (n), reference signal
By controlling filter w (z), generation output signal y (n), y (n) generates anti-noise signal y ' (n) by secondary path, in order to
So that the stability that momentum LMS algorithm updates control filter weights, it is necessary to reference signal x (n) be allowed to pass through secondary modeling filter
Wave deviceIt is changed over time to solve secondary path, it is necessary to which online estimates secondary modeling filter, needs thus
Use with the incoherent pseudo-random noise injection of reference signal to inside secondary path, white noise generator generate such one group with
Machine signal v (n), v (n) generate modeling signal v ' (n) by secondary path, and other end v (n) passes through modeling filterIt produces
Raw modeling signal v ' (n), participates in error signal e (n) and modeling signal makes the difference and generates error signal f (n).F (n) is used as momentum
The error signal of LMS algorithm and VSS LMS algorithm.
The data finally obtained can reflect the size and secondary channel modeling of its anti-acoustic capability according to the following formula
Accuracy:
The wherein quality of the anti-acoustic capability of R:ANC system；
E (n): the error function of ANC system main control sef-adapting filter；
D (n): the desired signal of ANC system main control sef-adapting filter；
The accuracy size of secondary channel modeling in △ S:ANC system；
Si(n): the path function of practical secondary channel in ANC system；
The path function of secondary channel is simulated in ANC system
As shown in attached drawing 4 (a), indoors under noise circumstance, the innovatory algorithm and Eriksson of proposition and the algorithm of Zhang
Comparison on anti-acoustic capability R (n).It can be seen that logical LMSN algorithm eliminates error signal, innovatory algorithm shows better drop
It makes an uproar effect.
As shown in attached drawing 4 (b), indoors under noise circumstance, Δ S (n) is stable quickly to drop to -40dB, this illustrates to improve
Algorithm can be accelerated to model the convergence rate of filter, improve the modeling accuracy to secondary path, and the drop of ANC system is optimized
It makes an uproar performance, guarantees the stability of entire ANC system.
Since the algorithm proposed accelerates the convergence of control filter shown in Fig. 4 (c), so that secondary channel variable step is joined
Number μs(n) reach maximum step-length faster than traditional method, to accelerate the noise reduction speed of ANC system, improve entire
The convergence rate of ANC system.
The above embodiment is interpreted as being merely to illustrate the present invention rather than limit the scope of the invention.?
After the content for having read record of the invention, technical staff can be made various changes or modifications the present invention, these equivalent changes
Change and modification equally falls into the scope of the claims in the present invention.
Claims (6)
1. one kind is intersected based on secondary channel on-line identification new algorithm updates active noise control system characterized by comprising
Noise signal filter module (1), momentum LMS algorithm module (2), white noise generator (3), secondary channel modeling module (4), master
Channel path (5) and third sef-adapting filter module (6)；Wherein
Noise signal filter module (1), the reference signal x (n) for generating to noise source model filter S ' (z) by secondary
It is filtered to obtain x ' (n), and is transferred to momentum FxLMS algoritic module (2)；
Momentum LMS algorithm module (2), including control filter W (z) and momentum LMS algorithm, for believing filtered x ' (n)
Number be input to momentum LMS least mean square algorithm to update the weight coefficient of control filter W (z), control filter W (z) respectively with
Noise source, secondary modeling filter S ' (z), white noise generator module (3) are connected with momentum LMS algorithm, reference signal x
(n) output signal y (n) is generated by control filter w (z), y (n) generates anti-noise signal y ' by secondary path S (z)
(n)；
White noise generator module (3) injects Gauss white noise to secondary path when carrying out Real-time modeling set to secondary path
Sound；White noise generator generates one group of random signal v (n), and v (n) is generated by secondary path and modeled signal v ' (n), and in addition one
V (n) is held to pass through modeling filterGenerate modeling signalError signal e (n) is participated in make the difference with modeling signal；
Secondary channel modeling module (4) obtains the estimation of secondary path transmission function for modeling to secondary path S (z)
Value, including S (z), modeling filterAnd selection Variable Step Algorithm VSS LMS module, S (z) be used for provides one initially
Secondary path acoustic response function models filterFor providing the secondary path acoustic response function of an estimation, choosing
It selects weight of the Variable Step Algorithm VSS LMS module for modeling filter to be updated faster, S (z), modeling filterAnd selection superimposed signal u ' (n) of Variable Step Algorithm VSS LMS module is connected with third sef-adapting filter module (6)
It connects；
Main channel path module (5), for providing an initial main path acoustic response function, the reference that noise source generates is believed
Number x (n) generates interference signal d (n) by main channel, and its anti-acoustic capability is monitored at error microphone e (n)；
Third sef-adapting filter module (6), including LMSN newton least mean square algorithm, H (z), LMSN newton lowest mean square are calculated
Weight of the method for third sef-adapting filter is updated, and H (z) is for generating signal u (n), thus in modeling filter
Middle elimination component.With the output of third sef-adapting filter H (z) come relevant to reference input point in analog error signal
Amount participates in error signal e (n) and modeling to obtain a desired signal relevant to white noise in error path identification link
Signal, which makes the difference, generates error signal f (n), error signal of the f (n) as momentum LMS module and VSS LMS module.
2. according to claim 1 a kind of based on secondary channel on-line identification new algorithm intersection update Active noise control system
System, which is characterized in that error microphone e (n) place monitors its anti-acoustic capability, including following formula:
The wherein quality of the anti-acoustic capability of R:ANC system；E (n): the error function of ANC system main control sef-adapting filter；d
(n): the desired signal of ANC system main control sef-adapting filter；The accuracy of secondary channel modeling is big in △ S:ANC system
It is small；Si(n): the path function of practical secondary channel in ANC system；The path letter of secondary channel is simulated in ANC system
Number.
3. according to claim 1 a kind of based on secondary channel on-line identification new algorithm intersection update Active noise control system
System, which is characterized in that the main channel path module (5) is for providing an initial main path acoustic response function P (z).
4. according to claim 1 a kind of based on secondary channel on-line identification new algorithm intersection update Active noise control system
System, which is characterized in that the momentum LMS algorithm only increases the momentum term introduced by weight coefficient correlation than LMS algorithm,
The weight coefficient of control filter W (z), momentum LMS algorithm are updated for filtered x ' (n) signal to be input to LMS algorithm
It specifically includes:
In equation: α is factor of momentum, is taken | α | < 1, uwIndicate the step parameter of control filter.
5. according to claim 1 a kind of based on secondary channel on-line identification new algorithm intersection update Active noise control system
System, which is characterized in that the LMSN updates the weight of third sef-adapting filter, and steps are as follows:
Reference noise signal x (n) is converted into posteriori prediction errors sample vector b (n) using lattice filter:
f0(n)=b0(n)=x (n)
fm(n)=fm-1(n)-km(n)bm-1(n-1)
bm(n)=bm-1(n)-km(n)fm-1(n-1)
β indicates the forgetting factor for estimation, fm(n) positive error of m rank fallout predictor, b are indicatedmIndicate m rank fallout predictor
Forward error, km(n) partial correlation coefficient of m rank, u are indicatedp,0Indicate fallout predictor step-length, Pm(n) b is indicatedm(n) and fm(n)
Short-term energy estimation, γ indicate amplitude peak；
if|km(n)|>γ,km(n)=km(n-1)
The signal u (n) of reconstruct is updated:
fm' (n)=fm-1′(n)-km(n)bm-1′(n-1)
bm' (n)=bm-1′(n)-km(n)fm-1′(n-1)
ua(n)=(PM(n)+ε)-1(f′M-1(n)-kMb′M-1(n-1))
The tap weight coefficient of filter is updated:
Y (n)=wTx(n-M)
E (n)=d (n-M)-y (n)
W (n+1)=w (n)+2ua(n)e(n)。
6. according to claim 1 a kind of based on secondary channel on-line identification new algorithm intersection update Active noise control system
System, which is characterized in that the selection Variable Step Algorithm VSS LMS module estimates the power of e (n) and f (n)
Pe(n)=λ Pe(n)+(1-λ)e2(n)
Pf(n)=λ Pf(n)+(1-λ)f2(n)
Here Pe(n) and PfIt (n) is respectively residual error signal e (n) and modeling error signal f (n)；Calculate two power
Ratio: ρ (n)=Pf(n)/Pe(n)
Material calculation parameter μs(n) specific value:
μs(n)=ρ (n) μmin+(1-ρ(n))μmax
μminAnd μmaxFor the minimum step and maximum step-length of the modeling filter measured in specific experiment.
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| CN115248976A (en) * | 2021-12-31 | 2022-10-28 | 宿迁学院 | Secondary channel modeling method based on down-sampling sparse FIR filter |
| CN115248976B (en) * | 2021-12-31 | 2024-04-30 | 宿迁学院 | Secondary channel modeling method based on downsampling sparse FIR filter |
| CN117254782A (en) * | 2023-11-13 | 2023-12-19 | 电子科技大学 | Multi-channel active noise control method of unequal-order noise control filter |
| CN117254782B (en) * | 2023-11-13 | 2024-02-23 | 电子科技大学 | Multi-channel active noise control method of unequal-order noise control filter |
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