CN116312448A - Reference sensing optimization method of road noise active control system - Google Patents
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Abstract
The invention discloses a reference sensing optimization method of a road noise active control system. The method comprises the following steps: (1) The method comprises the steps of configuring hardware of a vehicle, wherein the hardware comprises a layout sensor, a road noise detection microphone and a cancellation loudspeaker, and is connected with a multichannel signal collector and a real-time road noise controller; (2) Determining the running working condition and the preferred road noise reference quantity, and establishing a reference preferred database and a test set; (3) Based on a strong causal iterative reference sensing optimization strategy, screening out vibration reference combinations of the optimal road noise control performance under the A weight, and calculating a fixed control filter coefficient by combining road noise signals acquired by a road noise detection microphone; (4) And leading the filter coefficients into a road noise controller to perform real-time noise reduction at the position to be noise reduced of the vehicle cabin. The method can be used in an automobile road noise active control system based on a feedforward strategy, and a reference sensor combination with optimal road noise control performance can be screened out on the premise of ensuring the causality of the system.
Description
Technical Field
The invention belongs to the technical field of active noise control, and particularly relates to a reference sensing optimization method of a road noise active control system.
Background
With the rapid development of the automotive industry, automotive NVH (Noise Vibration Harshness, NVH) performance has become an important indicator for evaluating automotive comfort. Road noise is one of the main sources of automobile noise, and has broadband random noise characteristics influenced by road conditions, so that the automobile road noise active control (Active Road Noise Control, ARNC) technology is an important direction in the automobile NVH industry at present. In an actual ARNC system, vibration information is picked up as a reference signal by a chassis layout accelerometer serving as a reference sensor, and noise reduction at the ears of a vehicle cabin is verified to be reliable by adopting a feedforward multichannel FxLMS algorithm (Sutton T J, elliott S J, mcDonald AM, et al, active control of road noise inside vehicles [ J ] Noise Control Engineering Journal,1994,42 (4): 137-147). More reference signals can keep better coherence with the noise signals of the human ear so as to obtain higher noise reduction, but the noise reduction can bring great burden to the real-time performance and the calculated amount of the system, so that the key problems of the ARNC system adopting a feedforward strategy are that the reasonable number of the reference signals is used and the optimal reference signal combination is screened on the premise of ensuring the noise reduction performance.
At present, a multiple coherence (Multiple Coherence, MC) method is widely used for calculating multiple coherence coefficients of a reference signal and a path noise signal at each frequency point to estimate the maximum noise reduction amount, and the reference signal is optimized by utilizing the coherence quality of the reference signal and the path noise signal (Oh S H, kim H, park Y. Active control of road booming noise in automotive interiors [ J ]. The Journal of the Acoustical Society of America,2002,111 (1): 180-188.). However, in actual road noise control, the causality of the system has a great influence on the noise reduction amount. Because of the limitation of physical position, the high-coherence vehicle body vibration signal is possibly generated by a passive excitation source, and is delayed from the road noise signal to show non-causal characteristics, the road noise signal cannot be tracked and predicted, namely, the causality cannot be ensured due to stronger coherence, and the reference combination with the best coherence cannot obtain the maximum noise reduction amount which can be realized physically. Therefore, how to screen out the reference signal combination with optimal control performance on the premise of ensuring the causality of the system becomes the first problem of reference preference and noise control in the ARNC system.
Disclosure of Invention
In order to solve the technical problems, the invention provides a reference sensing optimization method of a road noise active control system under a strong causal iteration strategy. The strong causal strategy is based on a wiener filtering principle, and the maximum noise reduction amount is estimated on the basis of considering the causality of an actual physical system; and the iteration strategy acquires a local optimal solution by expanding the autocorrelation matrix of the filtered reference signal and the cross-correlation matrix of the filtered reference signal and the expected signal step by step, and the overall optimal solution is obtained when the iteration is carried out to the optimal reference combination.
The invention adopts the technical scheme that:
a reference sensing optimization method of a road noise active control system comprises the following steps:
and 4, leading the filter coefficient into the real-time road noise controller to perform real-time noise reduction at the region to be noise reduced of the vehicle cabin.
Further, the specific implementation manner of the step 2 is as follows: determining the running condition of the vehicle and the optimized road noise reference quantity, and acquiring P-path reference signals x by a multi-channel signal acquisition device in the running process of the vehicle i Road noise detection transmissionDesired signal d at the acoustic device j Establishing a reference preference database and a reference combination generalization test set; optimizing Q paths of reference signal combinations from the P paths of reference signals by utilizing a reference optimization database; performing generalization test on the preferred combination by using the test set; where i=1, 2,..p, j=1, 2.
Compared with the prior art, the invention has the beneficial effects that: in the reference optimization of the automobile ARNC, the capability of screening the reference signal combination with the maximum noise reduction amount on the premise of ensuring the causality of the system is provided, so that a better road noise control effect is achieved.
Drawings
Fig. 1 is a block diagram of the overall structure of the method of the present invention.
Fig. 2 is a hardware configuration diagram of the method of the invention, (a) is a schematic layout diagram of an acceleration vibration sensor of a chassis, and (b) is a schematic installation diagram of an active noise reduction headrest of a vehicle cabin.
FIG. 3 is a process flow diagram of a reference preference strategy using strong causal iterations.
FIG. 4 is a plot of the secondary path time domain impulse response and amplitude-frequency response of an active noise reduction headrest in an embodiment, where (a) and (b) are plots of the secondary path time domain impulse response and amplitude-frequency response of a left speaker to left and right ear canal noise detection microphones; (c) And (d) is a secondary path time domain impulse response and amplitude-frequency response curve of the right speaker to the left and right ear canal noise detection microphones.
Fig. 5 is a fixed control filter weight coefficient map calculated in the embodiment.
Fig. 6 shows time-frequency domain noise reduction curves of the cabin road noise detection microphone in the embodiment, wherein (a) and (b) are time-frequency domain noise reduction curves of the left ear and (c) and (d) are time-frequency domain noise reduction curves of the right ear.
Detailed Description
The invention relates to a reference sensing optimization method of a road noise active control system under a strong causal iteration strategy, which mainly comprises the following parts:
1. hardware configuration
1) Automobile chassis acceleration sensor installation
The feedforward ARNC system needs to obtain road noise vibration source information as a reference signal, and therefore, an acceleration sensor is disposed (single-axis, multi-axis) on the chassis of the vehicle to pick up vehicle body vibration heave information. The reference signal used in the ARNC should have a strong coherence with the road noise signal at the cabin road noise detection microphone, thereby obtaining a large noise reduction. The paper (Park Y S, cho M H, oh C S, et al Coherence-based sensor set expansion for optimal sensor placement in active road noise control [ J ]. Mechanical Systems and Signal Processing,2022, 169:108788.) places acceleration sensors on the subframe (zone 1), steering shaft (zone 2), shock absorber (zone 3), trailing arm (zone 4) and the like to obtain reference signals with stronger coherence, as shown in FIG. 2 (a).
2) Active noise reduction headrest for vehicle cabin
As shown in fig. 2 (b), the active noise reduction headrest includes two road noise detection microphones (M1 and M2) and cancellation speakers (S1 and S2) that simulate the quiet zone of the human ear; two road noise detection microphones are respectively arranged at the ears of a person, and reliable audio hardware support is provided for an ARNC system. On one hand, the low-frequency response of the loudspeaker is required to be relatively flat, so that the secondary path between the loudspeaker and the microphone is ensured to have relatively good low-frequency response; on the other hand, the comfortable and beautiful headrest is ensured as much as possible, and better riding experience is provided for passengers. The specific functions are as follows:
firstly, white noise generated by a controller in the secondary path modeling process is used as a reference signal to drive a secondary source (cancellation loudspeaker) to sound, collected by a microphone at the human ear and then used as a desired signal to be input into the controller, and the secondary path from the secondary source to the microphone is calculated and matched by the controller. Deriving the secondary path after the secondary path modeling is completed, and calculating the reference combined optimization and fixed control filter coefficients;
secondly, picking up a double-ear road noise signal by using a road noise detection microphone in the optimization process of the reference combination, and carrying out optimization operation by combining the road noise signal acquired by the multichannel acquisition device;
thirdly, in the road noise real-time control process, noise at the side of the ear is counteracted by utilizing sound production of a cancellation loudspeaker, so that the real-time noise reduction function of the area to be reduced in the vehicle cabin is realized.
2. Reference optimization and fixed control filter coefficient calculation
1) Noise reduction amount statistical strategy and principle
The principle of noise reduction amount estimation by the multi-phase dry method which is widely used at present is as follows.
By calculating multiple coherence coefficients of reference signal and path noise signal at each frequency point
The incoherent output can be expressed as:
in the formula (1), S dd (f) For the self-power spectrum of the path noise signal, S nn (f) The residual noise spectrum at the microphone uncorrelated with the reference signal is detected for the road noise. Thus, the incoherent output can be used to estimate the maximum noise reduction of the reference signal on the road noise signal, namely:
the maximum noise reduction amount is estimated by calculating multiple coherence coefficients of the reference signal and the road noise signal at each frequency point through the process, and the reference preference is carried out by utilizing the coherence quality of the reference signal and the road noise signal, so that the causality problem of an actual physical system is not considered.
The noise reduction amount statistics principle of the strong correlation iterative reference optimization strategy provided by the invention is as follows.
Assuming that the numbers of the acceleration sensor, the cancellation speaker, and the road noise detection microphone for picking up the reference signal in the ARNC system are K, M and L, respectively, the control filter weight coefficients share the mxk groups, and the secondary paths share the lxm groups. s is(s) lm ＝[s lm (0)s lm (1)…s lm (J-1)] T Representing a secondary path from the mth speaker to the first road noise detection microphone, the path being modeled with a J-order FIR filter; w (w) mk ＝[w mk (0)w mk (1)…w mk (I-1)] T The control filter weight vector from the kth reference sensor to the mth speaker is represented, with a total order of I.
x k (n) represents the kth reference signal at time n, then the output signal of the mth speaker is:
at the road noise detection microphone, the first error signal e l (n) is:
in the formula (4), d l (n) represents the road noise signal at the first microphone. Reference signal x k (n) the filtered reference signal after passing through the ml-th secondary path can be expressed as:
substituting the formula (3) and the formula (5) into the formula (4) to obtain
Thus, the error signal vector e (n) can be expressed as:
in equation (7), the path noise signal vector d (n) is:
d(n)＝[d 1 (n) d 2 (n) … d L (n)] T (8)
the filter weight vector W (i) for a single tap is written as:
W(i)＝[w 11 (i) w 12 (i) … w 1K (i) w 21 (i) … w MK (i)] T (9)
the filtered reference signal matrix at time n is:
the weight vectors W (i) of the single tap combinations are further combined into the form of a length MKI, namely:
W＝[W T (0) W T (1) … W T (I-1)] T (11)
the corresponding filtered reference signal composite matrix can be written as:
in this case, the expression (7) can be rewritten as:
let the objective function be the mean square error, there are:
in formula (14), E (. Cndot.) represents time-averaging the independent variables.
The road noise signal is generally considered stationary and J is a quadratic function of the filter weight vector W. Let the gradient of the mean square error be 0, the weight wiener solution can be obtained as:
W opt ＝-R -1 p (15)
in the formula (15), the amino acid sequence of the compound,
will W opt Substituting the error signal into the filter reference signal (13) according to the path noise signal and the filter reference signal to obtain an error signal under wiener solution:
the average noise reduction amount at this time can be expressed as:
2) Reference combination preference and generalization
The optimized road noise reference combination is regarded as an overall optimal solution, the process of solving the noise reduction amount corresponding to different reference combinations is divided into a plurality of sub-problems, each sub-problem is solved to obtain a local optimal solution (namely, the reference combination corresponding to the maximum noise reduction amount), and the local optimal solutions of the sub-problems are combined to obtain the overall optimal solution. According to the thought, firstly, screening out a reference signal with the maximum noise reduction under causal conditions; and then, gradually expanding an autocorrelation matrix of the filtered reference signal and a cross correlation matrix of the filtered reference signal and the road noise signal until the reference signal combination with the largest noise reduction amount under the expected channel is screened out. The experimental procedure is shown in figure 3.
In order to ensure that the optimal road noise reference combination has stronger universality, the reference signal combinations screened by different data sets in the database are substituted into a test set to carry out the Venus noise reduction test, and the combination with the largest noise reduction amount is used as the optimal reference combination.
3. And (3) leading the fixed control filter coefficient obtained by wiener filtering by using the optimized reference combination into a controller, collecting the vibration signal of the chassis of the automobile by using the acceleration sensor as a reference signal, sending the reference signal into the controller, outputting the reference signal by using the control filter, and driving a cancellation loudspeaker of the active noise reduction headrest to sound, thereby realizing the function of controlling the noise of the area to be reduced in the automobile cabin in real time.
Examples
The present invention will be further illustrated with reference to the drawings and the specific embodiments, it being understood that these examples are intended to illustrate the invention and not to limit the scope of the invention, and that modifications of the invention in its various equivalents will fall within the scope of the claims appended hereto, after reading the invention.
1. Arrangement of vehicle chassis accelerometer
The vehicle type used in the experimental example is Changan UNI-K, 20 3-axis accelerometers are distributed to pick up P=60 paths of reference signals, and the aim is to screen out Q=16 paths of reference optimal combinations for noise control of a region to be noise reduced in a vehicle cabin.
2. Reference optimization and fixed control filter coefficient calculation
1) Secondary path acquisition
The active noise reduction headrest designed by the present invention is composed of left and right ear speakers (Speaker 1, S1, 2, S2), left and right ear microphones (Microphone 1, M1, 2) simulating noise reduction points of human ears, as shown in fig. 2 (b). The sound of S1 is collected through M1 and M2 to obtain secondary paths S11 and S12, and the sound of S2 is collected through M1 and M2 to obtain secondary paths S21 and S22. In order to make the secondary path modeling as accurate as possible, selecting a quiet road section without noise interference during measurement, and closing an automobile engine; in order to simulate the real situation during driving, a tester should be arranged at the secondary driving place. The 512-order time domain impulse response and amplitude frequency response of the secondary paths S11, S12 are shown in fig. 4 (a) and (b); the 512-order time domain impulse responses and amplitude frequency responses of the secondary paths S21 and S22 are shown in fig. 4 (c) and (d).
2) Strong causal iterative reference combination preference
First, a reference preferred database and test set are established. In the embodiment, the running of the vehicle keeps a steady-state working condition of 60km/h, 6 groups of 30s reference signal library and road noise signal library are recorded, 1 group of 120s reference signal set and road noise signal set are recorded, and each group of data comprises 60 paths of reference signals x 1 (n)～x 60 (n) and 2-way noise signal d 1 (n)～d 2 (n) preferably reference signal combinations for wiener filtering to control the error signal at the left and right ears after control to e 1 (n)～e 2 (n)。
And secondly, taking the filter order I in the wiener filtering as 512 orders, preferably obtaining 6 groups of reference combinations by 8 groups of data sets, substituting the 6 groups of reference combinations into a test set for wiener filtering and counting the average noise reduction amount at the left ear and the right ear respectively, wherein the counting mode is as shown in a formula (18), and taking a group with the largest noise reduction amount as an optimal reference combination for real-time noise reduction at a vehicle cabin road noise detection microphone.
In order to embody the performance improvement of the invention relative to the existing method, the comparison and verification of the reference preferred noise reduction amount by using the wiener filtering method and the multi-phase dry method are carried out in the embodiment, and the experimental results are shown in tables 1 and 2. The noise reduction of the reference signal combination screened by the wiener filtering method on the test set is superior to that of the reference signal combination screened by the multiple coherence method. In table 1, group 1 is selected as the final preferred reference combination of road noise, and the selected reference numbers are 50, 40, 22, 59, 5, 20, 39, 24, 55, 37, 51, 4, 1, 45, 36, 44, respectively.
TABLE 1 wiener filtering reference preferred results
TABLE 2 multiple phase Dry reference preferred results
3. Real vehicle noise reduction verification
1) Fixed control filter coefficient acquisition
And carrying out wiener filtering calculation by using the reference signal, the path noise signal and the secondary path which are derived by the controller, wherein the data recording length is 30s. For a 16 x 2 ANC system, when the control filter length is 512, a total of 32 x 512 filter coefficients are obtained, as shown in fig. 5. In order to avoid the influence of the low frequency component (not more than 50Hz, which may be regarded as a dc component in the signal) in the signal on the control filter, all the signals are passed through a 50Hz high pass filter during calculation.
2) Road noise control performance test based on A weight
Under the same road condition and the same running speed, respectively recording 30s data of closing ARNC and opening ARNC at the position of an error microphone as an expected signal and a processed error signal, and comparing the difference of sound pressure levels of the two under the A weight counting to obtain e 1 The average noise reduction in (left ear) 30s was 6.06dBA, e 2 The average noise reduction amount in (right ear) 30s was 5.01dBA, and the time-frequency domain noise reduction result is shown in fig. 6.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (7)
1. A reference sensing optimization method of a road noise active control system, which is characterized by comprising the following steps:
step 1, configuring hardware of a vehicle, wherein the hardware comprises a layout sensor, a road noise detection microphone and a cancellation loudspeaker, and is connected with a multichannel signal collector and a real-time road noise controller;
step 2, determining the running working condition of the vehicle and the preferred road noise reference quantity, and establishing a reference preferred database and a test set;
step 3, based on a strong causal iterative reference optimization strategy, screening out vibration reference combinations of the optimal road noise control performance under the A weight, and calculating a fixed control filter coefficient by combining road noise signals acquired by the road noise detection microphone;
and 4, leading the filter coefficient into the real-time road noise controller to perform real-time noise reduction at the region to be noise reduced of the vehicle cabin.
2. The method according to claim 1, wherein in step 1, an acceleration sensor for picking up vibration information of the road surface is disposed on the chassis of the vehicle, and the acceleration sensor is used for acquiring multiple reference signals.
3. The method according to claim 1, wherein in the step 1, an active noise reduction headrest is installed in the cabin, and the active noise reduction headrest includes two road noise detection microphones and a cancellation speaker, which simulate the human ear quiet zone; the two road noise detection microphones are respectively arranged at the ears and the ears of a person.
4. The reference sensing optimization method of the road noise active control system according to claim 1, wherein the specific implementation manner of the step 2 is as follows: determining the running condition of the vehicle and the optimized road noise reference quantity, and acquiring P-path reference signals x by a multi-channel signal acquisition device in the running process of the vehicle i Desired signal d at road noise detection microphone j Establishing a reference preference database and a reference combination generalization test set; optimizing Q paths of reference signal combinations from the P paths of reference signals by utilizing a reference optimization database; performing generalization test on the preferred combination by using the test set; where i=1, 2,..p, j=1, 2.
5. The method of claim 1, wherein in step 3, the wiener solution expression obtained by using the strong causal iterative reference optimization strategy is:
W opt ＝-R -1 p
wherein ,
is->
N is the time index, E (·) represents the time-averaged argument, ++>
A composite matrix for the filtered reference signal; />
Is->
A cross correlation matrix with d (n), d (n) being a path noise signal vector; the error signal vector can be expressed as:
6. the method for optimizing reference sensing of active road noise control system according to claim 5, wherein in step 3, first, a fixed control filter coefficient under a single reference is calculated according to a wiener solution expression, so as to obtain a weight-counting noise reduction amount at the moment, and a reference signal with the maximum noise reduction amount under a causal condition is screened out; secondly, expanding an autocorrelation matrix of the filtering reference signal and a cross correlation matrix of the filtering reference signal and the road noise signal step by step until a reference signal combination with the largest noise reduction amount under a desired channel is screened out; in order to ensure that the screened reference signal combination has stronger universality, substituting the reference signal combination screened by different data sets in the reference preferred database into a test set to carry out the Venus noise reduction test, and using the combination with the largest noise reduction amount as the optimal reference signal combination.
7. The method according to claim 1, wherein in the step 4, the filter coefficient is led into the real-time road noise controller, the sensor collects the vibration signal of the vehicle chassis as the reference signal and sends the reference signal to the real-time road noise controller, and the cancellation speaker is driven to sound after the output of the control filter, so as to realize the function of real-time noise control at the area where the vehicle cabin is to be denoised.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117353703A (en) * | 2023-10-12 | 2024-01-05 | 哈尔滨工业大学 | Magneto-electric speed sensor low-frequency band expansion circuit for ultra-low frequency vibration isolator |
| CN117608208A (en) * | 2024-01-23 | 2024-02-27 | 中汽研（天津）汽车工程研究院有限公司 | Road noise active control hardware-in-loop simulation verification method, system and medium |
| CN117831499A (en) * | 2024-03-06 | 2024-04-05 | 科大讯飞(苏州)科技有限公司 | Vehicle noise reduction method, device, equipment, vehicle and storage medium |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117353703A (en) * | 2023-10-12 | 2024-01-05 | 哈尔滨工业大学 | Magneto-electric speed sensor low-frequency band expansion circuit for ultra-low frequency vibration isolator |
| CN117608208A (en) * | 2024-01-23 | 2024-02-27 | 中汽研（天津）汽车工程研究院有限公司 | Road noise active control hardware-in-loop simulation verification method, system and medium |
| CN117608208B (en) * | 2024-01-23 | 2024-04-16 | 中汽研（天津）汽车工程研究院有限公司 | Road noise active control hardware-in-loop simulation verification method, system and medium |
| CN117831499A (en) * | 2024-03-06 | 2024-04-05 | 科大讯飞(苏州)科技有限公司 | Vehicle noise reduction method, device, equipment, vehicle and storage medium |
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