An automotive active noise reduction control system based on FXLMS

Abstract

The present invention relates to the technical field of noise elimination, and more specifically, it relates to an automobile active noise reduction control system based on FXLMS. The present invention includes a data acquisition module, a primary channel module, an estimated secondary channel module, an adaptive filter, a momentum FxLMS algorithm module, a secondary channel module, an error signal collection module, a signal delay module, an estimated delay module and a signal detection module. The present invention converts noise into a primary noise signal, and through the action of the signal delay module, the primary noise signal is delayed and propagated and output, so that the primary noise signal and the anti-noise signal are combined at the same time, so that the frequency of the noise is synchronized, and the noise reduction effect is optimized.

Description

Technical field:

[0001] The present invention relates to the technical field of noise elimination, and more particularly to an automobile active noise reduction control system based on FXLMS. Background technology:

[0002] Currently, noise reduction methods can be mainly divided into active noise reduction and passive noise reduction. Active noise reduction is achieved by combining an anti-noise signal with the same amplitude and opposite phase as the external noise to offset the external noise, thereby achieving the purpose of noise reduction.

[0003] Application number CN201610214373.8 discloses an optimized active noise reduction method based on FXLMS, which is implemented as follows: in the first stage, white noise is generated, output through a speaker and collected through a microphone, and then the secondary channel coefficient is obtained by multiple iterations using the least mean square criterion; in the second stage, the external noise signal is collected through the microphone, and the secondary channel coefficient obtained in the first stage is used for compensation. The anti-noise is calculated according to the optimized FXLMS algorithm and sent out through the speaker to offset the external noise, and multiple iterations are performed to basically eliminate the noise to achieve the noise reduction effect. Although the above invention can quickly converge, has strong robustness, and can achieve a good noise reduction effect, and the noise reduction operation speed meets the real-time requirements, the above invention does not take into account the time delay error between the external noise signal and the anti-noise signal, so that the noise reduction effect is not optimized when the noise signal is denoised. Summary of the invention:

[0004] In order to overcome the problem that the prior art does not take into account the time delay error between the external noise signal and the anti-noise signal, which may easily lead to the noise reduction effect not being optimized, the present invention provides an automobile active noise reduction control system based on FXLMS that can delay the output of the primary noise signal so that the primary noise signal and the anti-noise signal are combined at the same time.

[0005] An automotive active noise reduction control system based on FXLMS includes a data acquisition module, a primary channel module, an estimated secondary channel module, an adaptive filter, a momentum FxLMS algorithm module, a secondary channel module and an error signal collection module, wherein

[0006] A data acquisition module, used for collecting the noise generated when the automobile engine is in operation, and for converting the noise into a corresponding noise signal x(n);

[0007] A primary channel module, used for calculating a noise signal to obtain a primary noise signal d(n), and outputting the calculated primary noise signal d(n);

[0008] An estimated secondary channel module, in which an additional filter is embedded, for estimating the estimated secondary channel of the noise signal x(n) to obtain a pre-estimated calculated filtered signal x′(n), and having a response, and inputting the pre-estimated calculated filtered signal x′(n) into the momentum FxLMS algorithm module as a reference coefficient;

[0009] An adaptive filter is used to filter the collected external noise signal x(n), and use the estimated filtered signal x′(n) as a reference signal to generate a filtered signal y(n), which then becomes an input signal for generating an anti-noise signal;

[0010] Momentum FxLMS algorithm module: used to apply the momentum FxLMS algorithm to the external noise signal x(n) filtered by the adaptive filter, update the weight coefficient W(n) in the adaptive filter, and adjust the filtered signal y(n) produced after the original noise signal passes through the adaptive filter;

[0011] The secondary channel module is used to process the filtered signal y(n) output by the momentum FxLMS algorithm module to generate an anti-noise signal y′(n). The anti-noise signal y′(n) is combined with the primary noise signal d(n) to reduce noise;

[0012] The error signal collection module is used to collect the error signal e(n) that still exists after the anti-noise signal y′(n) is combined with the primary noise signal d(n). The error signal collection module is embedded with a sorting module, which can sort the size of the error signal e(n). The error signal collection module can pass the error signal e(n) to the momentum FxLMS algorithm module for adjusting the weight coefficient W(n) in the adaptive filter.

[0013] Furthermore, the noise signal x(n) is a signal that is internally converted based on the engine speed. The engine speed can be estimated based on the tachometer pulse sequence signal, that is, the noise signal x(n) is expressed as,

[0014]

[0015] f i =iv / 60

[0016] Where n is the time index, i is the engine order index, and f i is the frequency of the i-th order, v is the engine speed in rpm (revolutions per minute), F s is the sampling rate.

[0017] Furthermore, the estimated filtered signal x′(n) is expressed as,

[0018]

[0019] Where s^(n) is the estimated impulse response of the secondary and * denotes linear convolution.

[0020] Furthermore, it also includes a signal delay module, an estimated delay module and a signal detection module, wherein

[0021] A signal delay module is used to delay the output of the primary noise signal d(n) so that the primary noise signal d(n+t) and the anti-noise signal y′(n) are combined at the same time;

[0022] The estimated delay module is used to record the delay time t in the signal delay module and import the delay time t into the signal delay module, so as to directly call the delay time t when the primary noise signal d(n+t) and the anti-noise signal y′(n) are actually output;

[0023] The signal detection module is used to detect the degree of noise cancellation after the primary noise signal d(n+t) is combined with the anti-noise signal y′(n). When the error signal e(n) is detected to be the minimum value, a signal is sent to control the estimated delay module to record the delay time t.

[0024] Furthermore, the error signal e(n) is expressed as,

[0025] e(n)=d(n+t)+y'(n)

[0026] Furthermore, the formula for updating the weight coefficients in the adaptive filter is expressed as:

[0027]

[0028] μ(n)=Ψ{e(n)}

[0029] Among them, μ(n) is the step size factor, and Ψ{e(n)} represents a nonlinear function with e(n) as the independent variable.

[0030] Furthermore, the signal delay module includes multiple delay paths, the time of each delay path increases by 0.01 seconds from top to bottom, and the number of delay paths is greater than 20.

[0031] Furthermore, the data acquisition module includes but is not limited to a microphone.

[0032] The present invention has the following advantages:

[0033] 1. The present invention converts noise into a noise signal and delays the propagation and output of the primary noise signal, so that the primary noise signal and the anti-noise signal are combined at the same time, so that the frequency of canceling the noise is synchronized, and the noise reduction effect is optimized;

[0034] 2. The present invention realizes the conversion of noise to be reduced and produces the estimated anti-noise signal by inputting the noise signal into the primary channel and estimating the secondary channel, thereby ensuring the stability of the momentum FxLMS algorithm module and performing real-time modeling of the secondary channel in the secondary channel module to avoid the instability or even divergence of the momentum FxLMS algorithm module; Description of the drawings:

[0035] Figure 1 This is a schematic diagram of the structure of an automobile active noise reduction control system based on FXLMS applied in an embodiment of the present invention.

[0036] Figure 2 Flow chart of the active noise reduction process of the present invention. Specific implementation method:

[0037] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0038] Example 1

[0039] like Figure 1 As shown, an automotive active noise reduction control system based on FXLMS includes: a data acquisition module, a primary channel module, an estimated secondary channel module, an adaptive filter, a momentum FxLMS algorithm module, a secondary channel module, an error signal collection module, a signal delay module, an estimated delay module and a signal detection module, wherein

[0040] A data acquisition module, used to collect the noise generated when the automobile engine is in operation, and used to convert the noise into a corresponding noise signal x(n), and the noise signal x(n) is temporarily stored in a memory center. The data acquisition module includes but is not limited to a pickup;

[0041] The primary channel module is used to calculate the noise signal to obtain the primary noise signal d(n), and output the calculated primary noise signal d(n), where the primary noise signal d(n) is the signal that needs to be denoised in the end;

[0042] The estimated secondary channel module has an additional filter embedded in it. The additional filter can filter the noise signal x(n), and then estimate the estimated secondary channel to obtain the estimated calculated filtered signal x′(n), and can respond to the filtered signal x′(n) and then output it. The estimated secondary channel module inputs the estimated calculated filtered signal x′(n) into the momentum FxLMS algorithm module as a reference coefficient, that is, the estimated secondary channel module is used to ensure the stability of the momentum FxLMS algorithm module.

[0043] The adaptive filter can automatically adjust the performance according to the external noise signal x(n) to perform digital signal processing and filtering, and use the estimated calculated filtered signal x′(n) as the reference signal, and the adaptive filter can adjust the filter coefficient and frequency response according to the feedback of the estimated calculated filtered signal x′(n), generate a filtered signal y(n) and then become the input signal for generating the anti-noise signal;

[0044] Momentum FxLMS algorithm module: used to apply the momentum FxLMS algorithm to the external noise signal x(n) filtered by the adaptive filter, update the weight coefficient W(n) in the adaptive filter, and adjust the filtered signal y(n) produced after the original noise signal passes through the adaptive filter;

[0045] The secondary channel module is used to process the filtered signal y(n) output by the momentum FxLMS algorithm module to generate an anti-noise signal y′(n). The anti-noise signal y′(n) is combined with the primary noise signal d(n) to reduce noise. Since the secondary channel has real-time characteristics, in order to avoid instability or even divergence in the momentum FxLMS algorithm module, it is necessary to model the secondary channel in the secondary channel module in real time.

[0046] An error signal collection module is used to collect the error signal e(n) that still exists after the anti-noise signal y′(n) is combined with the primary noise signal d(n+t). The error signal collection module is embedded with a sorting module, which can sort the size of the error signal e(n). The error signal collection module can pass the error signal e(n) to the momentum FxLMS algorithm module for adjusting the weight coefficient W(n) in the adaptive filter;

[0047] The signal delay module is used to delay the output of the primary noise signal d(n), so that the primary noise signal d(n+t) and the anti-noise signal y′(n) are combined at the same time, and the peak value of the primary noise signal d(n+t) and the minimum value of the anti-noise signal y′(n) are at the same time;

[0048] The estimated delay module is used to record the delay time t in the signal delay module and import the delay time t into the signal delay module, so as to directly call the delay time t when the primary noise signal d(n+t) and the anti-noise signal y′(n) are actually output, and quickly output the anti-noise signal y′(n);

[0049] The signal detection module is used to detect the degree of noise cancellation after the primary noise signal d(n) is combined with the anti-noise signal y′(n). When the error signal e(n) is detected to be the minimum value, a signal is sent to control the estimated delay module to record the delay time t.

[0050] The noise signal x(n) is a signal that is internally converted based on the engine speed. The engine speed can be estimated based on the tachometer pulse sequence signal, that is, the noise signal x(n) is expressed as,

[0051]

[0052] f i =iv / 60

[0053] Where n is the time index, i is the engine order index, and f i is the frequency of the i-th order, v is the engine speed in rpm (revolutions per minute), F s is the sampling rate.

[0054] The estimated filtered signal x′(n) is expressed as,

[0055]

[0056] Where s^(n) is the estimated impulse response of the secondary and * denotes linear convolution.

[0057] The error signal e(n) is expressed as,

[0058] e(n)=d(n+t)+y'(n)

[0059] The formula for updating the weight coefficients in the adaptive filter is expressed as:

[0060]

[0061] μ(n)=Ψ{e(n)}

[0062] Among them, μ(n) is the step size factor, and Ψ{e(n)} represents a nonlinear function with e(n) as the independent variable.

[0063] The signal delay module contains multiple delay paths. The time of each delay path increases by 0.01 seconds from top to bottom. The number of delay paths is greater than 20. The primary noise signal d(n) needs to pass through each different delay path to generate a different delayed primary noise signal d(n+t).

[0064] Based on the above settings, when it is necessary to reduce the noise of the engine in the car, such as Figure 2 As shown, the specific working process of an active noise reduction control system for automobiles based on FXLMS is as follows: S1: After the user starts the car, the car engine generates vibration and noise, and the noise generated by the operation of the car engine is collected through the data acquisition module. The data acquisition module includes but is not limited to a pickup, and the collected external noise is converted into a corresponding noise signal x(n) through calculation. The calculation formula of the noise signal x(n) is,

[0065]

[0066] f i =iv / 60

[0067] Where n is the time index, i is the engine order index, and f i is the frequency of the i-th order, v is the engine speed in rpm (revolutions per minute), F s is the sampling rate;

[0068] S2: The calculated noise signal x(n) is used as the data input source of the primary channel module, the noise signal x(n) is calculated and transformed to obtain the primary noise signal d(n), and the calculated primary noise signal d(n) is output. The primary noise signal d(n) is the output signal that needs to be denoised in the end;

[0069] S3: The primary noise signal d(n) is used as the input signal of the signal delay module. The signal delay module delays the primary noise signal d(n) and outputs it. The primary noise signal d(n) passes through each path in the signal delay module. The time of each delay path increases by 0.01 seconds from top to bottom. That is, the signal output by the primary noise signal d(n) after passing through the signal delay module is the primary noise signal d(n+t), where t is the delay time.

[0070] S4: The calculated noise signal x(n) is used as the data input source of the estimated secondary channel module. Since the estimated secondary channel module is embedded with an additional filter, the noise signal x(n) is estimated to estimate the secondary channel, and the estimated calculated filtered signal x′(n) is obtained, and has a response. The estimated calculated filtered signal x′(n) is expressed as,

[0071]

[0072] Where s^(n) is the estimated impulse response of the secondary, and * represents linear convolution;

[0073] S5: The estimated calculated filtered signal x′(n) is input into the momentum FxLMS algorithm module as a reference coefficient, and the adaptive filter filters the collected external noise signal x(n), and uses the estimated calculated filtered signal x′(n) as a reference signal to generate a filtered signal y(n);

[0074] S6: using the filtered signal y(n) output by the momentum FxLMS algorithm module as the input signal of the secondary channel module, generating an anti-noise signal y′(n) through calculation by the secondary channel module, and outputting the anti-noise signal y′(n) through the secondary channel module;

[0075] S6: The anti-noise signal y′(n) is pre-simulated and combined with the primary noise signal d(n+t) in a computer. The error signal collection module collects the error signal e(n) that still exists after the anti-noise signal y′(n) is simulated and combined with the primary noise signal d(n+t). The signal detection module detects and records the magnitude of the error signal e(n). The error signal e(n) is expressed as:

[0076] e(n)=d(n+t)+y'(n)

[0077] The error signals e(n) obtained by simulating the combination of all anti-noise signals y′(n) and the primary noise signal d(n+t) are sorted from small to large. When the signal detection module detects that the error signal e(n) is the minimum value, the signal control estimation delay module records the delay time t.

[0078] S7: the estimated delay module records the delay time t of the minimum value of the error signal e(n) after delay in the signal delay module, and imports the delay time t into the signal delay module, so as to directly call the delay time t;

[0079] S8: The signal detection module detects and determines the minimum value of the error signal e(n). When the minimum value of the error signal e(n) is greater than 50HZ, the process jumps to S9. When the minimum value of the error signal e(n) is less than or equal to 50HZ, the process jumps to S11.

[0080] S9: The error signal collection module transmits the minimum value of the delayed error signal e(n) to the momentum FxLMS algorithm module. The momentum FxLMS algorithm module adjusts the weight coefficient W(n) in the adaptive filter according to the error signal e(n), thereby adjusting the filter signal y(n) produced after the original noise signal passes through the adaptive filter. The formula for updating and iterating the weight coefficient in the adaptive filter is expressed as follows:

[0081]

[0082] μ(n)=Ψ{e(n)}

[0083] Among them, μ(n) is the step size factor, Ψ{e(n)} represents a nonlinear function with e(n) as the independent variable;

[0084] S10: After updating the weight coefficient W(n), the output filter signal y(n) after the operation of the momentum FxLMS algorithm module is used as the input signal of the secondary channel module, and the anti-noise signal y′(n) is generated through the calculation of the secondary channel module, and the anti-noise signal y′(n) is output through the secondary channel module;

[0085] S11: The anti-noise signal y′(n) is outputted through a loudspeaker to offset the external noise, so that the external noise is basically eliminated to achieve a noise reduction effect.

[0086] Through the above steps, the noise is converted into a noise signal, and through the primary channel and the estimated secondary channel, the noise to be reduced is converted and the estimated anti-noise signal is produced, ensuring the stability of the momentum FxLMS algorithm module, and the secondary channel in the secondary channel module is modeled in real time to avoid instability or even divergence in the momentum FxLMS algorithm module; the noise is delayed and propagated to achieve delayed output of the primary noise signal, so that the primary noise signal and the anti-noise signal are combined at the same time, optimizing the noise reduction effect.

[0087] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, making several equivalent substitutions or obvious modifications without departing from the concept of the present invention, and having the same performance or use, should be regarded as belonging to the patent protection scope of the present invention determined by the submitted claims.

Claims

1. An automotive active noise reduction control system based on FXLMS, characterized in that: It includes a data acquisition module, a primary channel module, an estimated secondary channel module, an adaptive filter, a momentum FxLMS algorithm module, a secondary channel module and an error signal collection module, wherein A data acquisition module, used for collecting the noise generated when the automobile engine is in operation, and for converting the noise into a corresponding noise signal x(n); A primary channel module, used for calculating a noise signal to obtain a primary noise signal d(n), and outputting the calculated primary noise signal d(n); An estimated secondary channel module, in which an additional filter is embedded, for estimating the estimated secondary channel of the noise signal x(n) to obtain a pre-estimated calculated filtered signal x′(n), and having a response, and inputting the pre-estimated calculated filtered signal x′(n) into the momentum FxLMS algorithm module as a reference coefficient; An adaptive filter is used to filter the collected external noise signal x(n), and use the estimated filtered signal x′(n) as a reference signal to generate a filtered signal y(n), which then becomes an input signal for generating an anti-noise signal; Momentum FxLMS algorithm module: used to apply the momentum FxLMS algorithm to the external noise signal x(n) filtered by the adaptive filter, update the weight coefficient W(n) in the adaptive filter, and adjust the filtered signal y(n) produced after the original noise signal passes through the adaptive filter; The secondary channel module is used to process the filtered signal y(n) output by the momentum FxLMS algorithm module to generate an anti-noise signal y′(n). The anti-noise signal y′(n) is combined with the primary noise signal d(n) to reduce noise; An error signal collection module is used to collect the error signal e(n) that still exists after the anti-noise signal y′(n) is combined with the primary noise signal d(n). The error signal collection module is embedded with a sorting module, which can sort the size of the error signal e(n). The error signal collection module can pass the error signal e(n) to the momentum FxLMS algorithm module for adjusting the weight coefficient W(n) in the adaptive filter; It also includes a signal delay module, an estimated delay module and a signal detection module, wherein the signal delay module is used to delay the output of the primary noise signal d(n) so that the primary noise signal d(n+t) and the anti-noise signal y′(n) are combined at the same time; The estimated delay module is used to record the delay time t in the signal delay module and import the delay time t into the signal delay module, so as to directly call the delay time t when the primary noise signal d(n+t) and the anti-noise signal y′(n) are actually output; The signal detection module is used to detect the degree of noise cancellation after the primary noise signal d(n+t) is combined with the anti-noise signal y′(n). When the error signal e(n) is detected to be the minimum value, a signal is sent to control the estimated delay module to record the delay time t.

2. The FXLMS-based automobile active noise reduction control system according to claim 1, characterized in that: The noise signal x(n) is a signal that is internally converted based on the engine speed. The engine speed can be estimated based on the tachometer pulse sequence signal, that is, the noise signal x(n) is expressed as, f i =iv / 60 Where n is the time index, i is the engine order index, and f i is the frequency of the i-th order, v is the engine speed in rpm (revolutions per minute), F s is the sampling rate.

3. The FXLMS-based automobile active noise reduction control system according to claim 1, characterized in that: The estimated filtered signal x′(n) is expressed as, Where s^(n) is the estimated impulse response of the secondary and * denotes linear convolution.

4. The FXLMS-based automobile active noise reduction control system according to claim 1, characterized in that: The error signal e(n) is expressed as, e(n)=d(n+t)+y′(n).

5. The automobile active noise reduction control system based on FXLMS according to claim 1 is characterized in that: The formula for updating the weight coefficients in the adaptive filter is expressed as: μ(n)=Ψ{e(n)} Among them, μ(n) is the step size factor, and Ψ{e(n)} represents a nonlinear function with e(n) as the independent variable.

6. The FXLMS-based automobile active noise reduction control system according to claim 1, characterized in that: The signal delay module contains multiple delay paths, the time of each delay path increases by 0.01 seconds from top to bottom, and the number of delay paths is greater than 20.

7. The automobile active noise reduction control system based on FXLMS according to claim 1, characterized in that: The data acquisition module includes but is not limited to a microphone.

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