CN103915091A - Active noise control method based on adaptive algorithm free of secondary channel modeling - Google Patents

Abstract

The invention provides an active noise control method based on an adaptive algorithm free of secondary channel modeling. Due to dependency on a secondary channel model, an existing self-adaption active noise control system is lack of adaptability on a wide system. Based on the active noise control algorithm free of secondary channel modeling, a self-adaption model-free control method based on a synchronous disturbance stochastic approximation algorithm is provided, and the problem of the uncertainty of a secondary channel in the active noise control process is solved.

Description

The active noise control method of the adaptive algorithm based on without secondary channel modeling

Technical field

The invention belongs to the field that shakes, rushes, makes an uproar, is a kind of active noise control method of the self-adaptation ANC algorithm based on without secondary channel modeling.

Background technology

Because do not relate to complicated space sound field environment, adopt LMS algorithm noise reduction in simulated environment remarkable.In actual sound field environment, due to the existence of secondary channel, the output signal of sef-adapting filter will just can be delivered to error microphone place through secondary channel, thereby traditional adaptive algorithm cannot directly apply in Active noise control using.

Summary of the invention

In order to overcome the deficiencies in the prior art, the object of this invention is to provide a kind of active noise control method of the adaptive algorithm based on without secondary channel modeling.

A kind of active noise control method of the adaptive algorithm based on without secondary channel modeling, gather by microphone the noise signal that engine produces, through the self-adaptation ANC algorithm without secondary channel modeling, in conjunction with error microphone, produce secondary sound source signal, play through indoor noise reduction loudspeaker, the stack by secondary noise and elementary noise in sound field space, in cabin, the finite space realizes Active noise control using.

Described self-adaptation ANC algorithm comprises the steps:

A, determine weight coefficient upgrade direction

1. first do not upgrade the weight coefficient of sef-adapting filter, choose N group sample data, error signal power maximum error signal amplitude e _max=max (| e (i) |), reference signal power wherein N is natural number, and e (i) is error signal, and x (i) is reference signal;

2. upgrade after the weight coefficient of sef-adapting filter, by producing simultaneous perturbation vector Δ _kchoose N group sample data, error signal power and reference signal power if

| e (i) | > (1+ δ ₂) e _maxstop upgrading;

If 3. or | e (i) | > (1+ δ ₂) e _max, by the sign modification before step size mu.

B, adaptive updates weight coefficient

4. upgrade the weight coefficient of sef-adapting filter;

C, performance monitoring

5. in the time of n=1, initialization χ (0)=χ ₁, ξ (0)=ξ ₁;

6. utilize ξ (n)=λ ξ (n-1)+e ²(n) error signal average power, utilizes χ (n)=λ χ (n-1)+x ²(n) computing reference average power signal, wherein λ ∈ [0.5,1), be forgetting factor, get

If 7. $\frac{\mathrm{\ξ}\left(n\right)}{\mathrm{\χ}\left(n\right)}>(1+{\mathrm{\δ}}_1)c\′\frac{\mathrm{\ξ}(n-N)}{\mathrm{\χ}(n-N)}$ Or $\frac{\mathrm{\ξ}\left(n\right)}{\mathrm{\χ}\left(n\right)}>(1+{\mathrm{\δ}}_1)c\′\frac{{\mathrm{\ξ}}_1}{{\mathrm{\χ}}_1},$ Get back to 1. right of search coefficient update direction again of step, 4. continue to upgrade weight coefficient otherwise return.

Beneficial effect of the present invention: by real time relatively more each reference signal energy value that gathers the system that enters, error energy value with it previous chronomere gather reference signal energy value, the error energy value of the system that enters, dynamically update the direction of step factor, thereby adjust in real time the weight coefficient of sef-adapting filter, and then generate corresponding secondary sound source controlled quentity controlled variable, reach stable noise reduction.This algorithm is particularly useful for space irregular, and is subject to the impact of structural design and space layout, also very complicated enclosure space of sound field environment.Accompanying drawing explanation

Below in conjunction with drawings and Examples, the present invention is further described.

Fig. 1 is the principle schematic of the active noise control system of the self-adaptation ANC algorithm based on without secondary channel modeling.

Embodiment

The active noise control system principle of the self-adaptation ANC algorithm based on without secondary channel modeling in the present invention as shown in Figure 1.When in control procedure, secondary channel immobilizes, the FX-LMS algorithm that utilizes the secondary channel model obtaining based on off-line modeling technology to control can be obtained comparatively satisfied noise reduction.But in practical application, secondary channel all can constantly change along with sound field environmental parameter and noisiness, if continue to use the secondary channel model obtaining based on off-line modeling technology to carry out system control, can cause the anti-acoustic capability of active noise control system to worsen, even occur the unsettled situation of system.A kind of effective method addressing this problem is exactly to use to carry out noise control without the control method of secondary channel model.Described in the present invention, do not need secondary channel modeling, not ignore the impact of secondary channel on real system anti-acoustic capability, but the dynamic change of secondary channel is embodied in by other means in the adaptive updates process of system, enter the reference signal energy value of system by relatively more each collection in real time, error energy value with it previous chronomere collection enters the reference signal energy value of system, error energy value, dynamically update the direction of step factor, thereby adjust in real time the weight coefficient of sef-adapting filter, and then generate corresponding secondary sound source controlled quentity controlled variable, reach stable noise reduction.

Utilize the right value update formula of FX-LMS algorithm

w(n)＝w(n-1)+μe(n)r _s(n) (1)

Suppose that input signal is that frequency is the pure sinusoid component r of ω _ω(n), utilize respectively and W _ω(n) transport function of primary channel, secondary channel, secondary channel estimation and adaptive controller when expression frequency is ω.

$\begin{array}{c}{W}_{\mathrm{\ω}}\left(n\right)=W_{\mathrm{\ω}}(n-1)+{\mathrm{\μW}}_{\mathrm{\ω}}\left(n\right){\hat{S}}_{\mathrm{\ω}}[r_{\mathrm{\ω}}\left(n\right)P_{\mathrm{\ω}}-r_{\mathrm{\ω}}\left(n\right)W_{\mathrm{\ω}}(n-1)S_{\mathrm{\ω}}]\\ =W_{\mathrm{\ω}}(n-1)+{\mathrm{\μP}}_r\left(\mathrm{\ω}\right){\hat{S}}_{\mathrm{\ω}}{S}_{\mathrm{\ω}}[\frac{P_{\mathrm{\ω}}}{S_{\mathrm{\ω}}}-W_{\mathrm{\ω}}(n-1)]\end{array}---\left(2\right)$

Wherein, P _r(ω) reference noise power when expression frequency is ω.When after algorithm convergence, there is W _ω(n)=W _ωand W (n-1) _ω(∞)=P _ω/ S _ω.In the time there is no error between secondary channel model and true secondary channel, formula (2) becomes

W _ω(n)＝W _ω(n-1)+μP _r(ω)|S _ω| ²[P _ω/S _ω-W _ω(n-1)] (3)

Obviously, W _ω(n) from W _ω(n-1) start along P _ω/ S _ωdirection upgrade the weight coefficient of sef-adapting filter, every step iteration W _ω(n) distance is _μp _r(ω) | S _ω| ²[P _ω/ S _ω-W _ω(n-1)].For assurance system can restrain, need _μp _r(ω) | S _ω| ²< 2,

$\mathrm{\&mu;}<\frac{2\cos (\&angle;S_{\mathrm{\&omega;}})}{P_r\left(\mathrm{\&omega;}\right)\left|S_{\mathrm{\&omega;}}\right|}---\left(4\right)$

Wherein, ∠ S _ωrepresent S _ωphase place, | S _ω| represent S _ωamplitude.Only has ∠ S _ω∈ [90 °, 90 _o° time, algorithm convergence.Otherwise can guarantee algorithm convergence by the symbol changing before step size mu,

w(n)＝w(n-1)+μe(n)r(n),∠S _ω∈[-90°,90°] (5)

w(n)＝w(n-1)-μe(n)r(n),otherwise (6)

A, determine weight coefficient upgrade direction

1. first do not upgrade the weight coefficient of sef-adapting filter, choose N group sample data, error signal power maximum error signal amplitude e _max=max (| e (i) |), reference signal power wherein N is natural number, and e (i) is error signal, and x (i) is reference signal;

2. upgrade after the weight coefficient of sef-adapting filter, by producing simultaneous perturbation vector Δ _kchoose N group sample data, error signal power and reference signal power if | e (i) | > (1+ δ ₂) e _maxstop upgrading;

If 3. or | e (i) | > (1+ δ ₂) e _max, by the sign modification before step size mu.

B, adaptive updates weight coefficient

4. upgrade the weight coefficient of sef-adapting filter;

C, performance monitoring

5. in the time of n=1, initialization χ (0)=χ ₁, ξ (0)=ξ ₁;

6. utilize ξ (n)=λ ξ (n-1)+e ²(n) error signal average power, utilizes χ (n)=λ χ (n-1)+x ²(n) computing reference average power signal, wherein λ ∈ [0.5,1), be forgetting factor, get

If 7. $\frac{\mathrm{\&xi;}\left(n\right)}{\mathrm{\&chi;}\left(n\right)}>(1+{\mathrm{\&delta;}}_1)c\&prime;\frac{\mathrm{\&xi;}(n-N)}{\mathrm{\&chi;}(n-N)}$ Or $\frac{\mathrm{\&xi;}\left(n\right)}{\mathrm{\&chi;}\left(n\right)}>(1+{\mathrm{\&delta;}}_1)c\&prime;\frac{{\mathrm{\&xi;}}_1}{{\mathrm{\&chi;}}_1},$ Get back to 1. right of search coefficient update direction again of step, 4. continue to upgrade weight coefficient otherwise return.

Be exactly more than the process of the self-adaptation ANC algorithm without secondary channel modeling of the present invention's employing, can save secondary channel and estimate link compared with FX-LMS algorithm.Self-adaptation ANC algorithm without secondary channel modeling can be summarized as three parts, the most important thing is that sef-adapting filter upgrades the selection of direction μ, and first initialization μ is the enough little value that meets the condition of convergence, then calculates residue noise power.If noise energy increases, change μ value symbol.

Claims (2)

1. the active noise control method of the adaptive algorithm based on without secondary channel modeling, it is characterized in that: gather by microphone the noise signal that engine produces, through the self-adaptation ANC algorithm without secondary channel modeling, in conjunction with error microphone, produce secondary sound source signal, play through indoor noise reduction loudspeaker, the stack by secondary noise and elementary noise in sound field space, in cabin, the finite space realizes Active noise control using.

2. method according to claim 1, is characterized in that: described self-adaptation ANC algorithm comprises the steps:

A, determine weight coefficient upgrade direction

1. first do not upgrade the weight coefficient of sef-adapting filter, choose N group sample data, error signal power maximum error signal amplitude e _max=max (| e (i) |), reference signal power wherein N is natural number, and e (i) is error signal, and x (i) is reference signal;

2. upgrade after the weight coefficient of sef-adapting filter, by producing simultaneous perturbation vector Δ _kchoose N group sample data, error signal power and reference signal power if | e (i) | > (1+ δ ₂) e _maxstop upgrading;

If 3. or | e (i) | > (1+ δ ₂) e _max, by the sign modification before step size mu.

B, adaptive updates weight coefficient

4. upgrade the weight coefficient of sef-adapting filter;

C, performance monitoring

5. in the time of n=1, initialization χ (0)=χ ₁, ξ (0)=ξ ₁;

6. utilize ξ (n)=λ ξ (n-1)+e ²(n) error signal average power, utilizes χ (n)=λ χ (n-1)+x ²(n) computing reference average power signal, wherein λ ∈ [0.5,1), be forgetting factor, get

If 7. $\frac{\mathrm{\&xi;}\left(n\right)}{\mathrm{\&chi;}\left(n\right)}>(1+{\mathrm{\&delta;}}_1)c\&prime;\frac{\mathrm{\&xi;}(n-N)}{\mathrm{\&chi;}(n-N)}$ Or $\frac{\mathrm{\&xi;}\left(n\right)}{\mathrm{\&chi;}\left(n\right)}>(1+{\mathrm{\&delta;}}_1)c\&prime;\frac{{\mathrm{\&xi;}}_1}{{\mathrm{\&chi;}}_1},$ Get back to 1. right of search coefficient update direction again of step, 4. continue to upgrade weight coefficient otherwise return.