A reference signal selection method for a vehicle road noise active control system

Abstract

This invention belongs to the field of automotive technology, specifically a method for selecting reference signals in an active control system for vehicle road noise. It includes: 1. Data acquisition; 2. Determining the frequency band to be reduced and the evaluation indicators; 3. Selecting an initial reference signal with the highest coherence to the target noise; 4. Selecting reference signals with the largest information content and including them in the initial reference signal set; 5. Updating the initial reference signal set until all reference signals are included in the initial reference signal set in sequence; 6. Determining the required number of input reference signals, thereby identifying the necessary reference signals from the initial sensor set; 7. Outputting the final reference signal selection result and inputting the result into the active noise control system for effect verification. This invention is based on the Fisher information matrix method, which not only selects the optimal combination of reference signals more efficiently but also provides a basis for determining the number of reference signals.

Description

Technical Field

[0001] This invention belongs to the field of automotive technology, specifically a method for selecting reference signals in an active control system for vehicle road noise. Background Technology

[0002] With the continuous increase in vehicle sales and consumers' growing emphasis on vehicle NVH performance in recent years, noise issues in the driver's cabin have gradually attracted attention. For passenger car drivers, prolonged driving in high-noise environments can easily lead to fatigue, increasing the risk of traffic accidents. Currently, noise control in automobiles mainly relies on traditional methods, such as using sound-absorbing materials to absorb noise, sound-insulating materials to isolate noise, and mufflers to reduce noise. These traditional passive methods are effective at suppressing high-frequency noise, but because low-frequency noise has a longer wavelength and stronger diffraction ability in the propagation medium, passive methods are not very effective at suppressing low-frequency noise, especially below 400Hz. Compared to passive noise control technologies, active noise control technologies have the significant advantages of being proactive and having excellent low-frequency noise control effects.

[0003] Active road noise control technology refers to the proactive control of road noise generated by vehicles during operation. With the increasing proportion of electric vehicles, road noise will gradually become a major focus in optimizing vehicle NVH performance. Active road noise control typically employs control algorithms combining feedforward or feedback, and the effectiveness of the control largely depends on the appropriateness of the selected reference signal.

[0004] In the past, the selection of reference signals often involved determining a reasonable number of reference signals, then enumerating all possible combinations of reference signals, calculating the multiple coherence coefficients one by one, and finally selecting the largest set of reference signal combinations as the input reference signals for the active control system. However, this method is often extremely time-consuming, and once the number of reference signals changes, the previous calculations become completely useless. Summary of the Invention

[0005] This invention provides a reference signal selection method for an active control system for vehicle road noise. Based on the Fisher information matrix method, it can not only select the optimal combination of reference signals more efficiently, but also provide a basis for determining the number of reference signals, thus solving the above-mentioned problems in the selection of existing reference signals.

[0006] The technical solution of this invention is described below in conjunction with the accompanying drawings:

[0007] A method for selecting a reference signal in an active control system for vehicle road noise includes the following steps:

[0008] Step 1: Collect data;

[0009] Step 2: Determine the frequency band to be denoised and the evaluation metrics;

[0010] Step 3: Select the initial reference signal with the highest coherence to the target noise according to the evaluation index, and include the initial reference signal with the highest coherence into the initial reference signal set;

[0011] Step 4: Calculate the multicoherence information of the signals other than the initial reference signal set relative to the initial reference signal set, and select the reference signal with the maximum information content to include in the initial reference signal set.

[0012] Step 5: Repeat step 4 to continuously update the initial reference signal set until all reference signals are included in the initial reference signal set in sequence;

[0013] Step 6: Determine the required number of input reference signals by plotting the relationship between the number of reference signals and the multicoherence coefficient using the initial reference signal set and other constraints, thereby determining the required reference signals in the initial sensor set;

[0014] Step 7: Output the final reference signal selection result and input the result into the active noise control system for effect verification.

[0015] Furthermore, the specific method for step one is as follows:

[0016] A real-vehicle data acquisition test was conducted to obtain N sets of vibration acceleration reference signals collected by several sensors and M sets of in-vehicle noise signals picked up by the microphone.

[0017] Furthermore, the specific method for step two is as follows:

[0018] 21) The frequency band to be noise-reduced is selected based on the spectral characteristics of the actual vehicle noise collected inside the vehicle, the hearing range, and the frequency characteristics of the active noise reduction control instrument used.

[0019] 22) The evaluation indicators are determined as follows:

[0020]

[0021] In the formula, n f The discrete number of frequencies when solving for the coherence coefficient; SPL is the coherence coefficient of the i-th group of reference signals relative to the M-th group of noise signals; M (f) represents the sound pressure level of the Mth noise signal at frequency f;

[0022] The calculation formula is as follows:

[0023]

[0024] In the formula, Let be the cross power spectral density between the i-th group of reference signals and the M-th group of in-vehicle noise signals; are the self-power spectral densities of the i-th group of reference signals and the M-th group of in-vehicle noise signals, respectively.

[0025] Furthermore, the specific method for step four is as follows:

[0026] 41) Calculate the average multicoherence coefficient of the reference signal in the initial reference signal set relative to the in-vehicle noise signal. The calculation formula is as follows:

[0027]

[0028] In the formula, For in-vehicle noise signal y j The multicoherence coefficients of the initial reference signal set x are calculated using the following formula:

[0029]

[0030] In the formula, The reference signal in the initial reference signal set X and the in-vehicle noise signal y j The cross-power spectral density matrix; P XX (f) represents the self-power spectral density functions of the in-vehicle noise signal and the initial reference signal set, respectively; the symbol (·) H The symbol (·) represents the collinear transpose of a matrix. -1 This represents finding the generalized inverse of a matrix.

[0031] 42) Calculate the covariance matrix

[0032]

[0033] 43) Calculate the multicoherence information of the reference signals other than the initial reference signal set relative to the initial reference signal set:

[0034]

[0035] In the formula, Let be the average coherence coefficient between the i-th group of reference signals and the in-vehicle noise signal; det(·) is the determinant of the matrix;

[0036] 44) Compare the calculated multicoherence information of each group and select the reference signal with the largest information content to include in the initial reference signal set.

[0037] The beneficial effects of this invention are as follows:

[0038] 1) The reference signal selection method for the vehicle road noise active control system based on Fisher information matrix provided by this invention greatly reduces time consumption and improves efficiency compared with the traditional enumeration method.

[0039] 2) The graph showing the relationship between the number of reference signals and the multicoherence coefficients, generated using the initial reference signal set obtained by this invention, can intuitively display the multicoherence of different numbers of reference signals and can provide a basis for determining the number of reference signals to be selected.

[0040] 3) This invention facilitates the variation of the number of input reference signals in subsequent active noise control systems;

[0041] 4) The evaluation criteria incorporate the sound pressure level factor of in-vehicle noise signals, which enhances the correlation information at the main frequency bands of the noise signals. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a schematic diagram of the process of the present invention;

[0044] Figure 2 The frequency domain diagram of the noise signal collected by the microphone in the driver's seat of the car;

[0045] Figure 3 The frequency domain diagram of the noise signal collected by the microphone in the passenger seat of the car;

[0046] Figure 4 A comparison chart of the multicoherence of the 13 reference signals selected for the output results with all 24 reference signals relative to the noise signal.

[0047] Figure 5 A graph showing the relationship between the number of reference signals in the initial reference signal set and the multicoherence coefficient;

[0048] Figure 6 A graph showing the average noise reduction at the first pickup microphone position of the active control system;

[0049] Figure 7 A graph showing the average noise reduction at the position of the second pickup microphone in the active control system; Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Example 1

[0052] See Figure 1 A method for selecting a reference signal for an active vehicle road noise control system includes the following steps:

[0053] Step 1: Collect data;

[0054] A real-vehicle data acquisition test was conducted to obtain N sets of vibration acceleration reference signals collected by several sensors and M sets of in-vehicle noise signals picked up by the microphone.

[0055] Step 2: Determine the frequency band to be denoised and the evaluation metrics;

[0056] Step 3: Select the initial reference signal with the highest coherence to the target noise according to the evaluation index, and include the initial reference signal with the highest coherence into the initial reference signal set;

[0057] Step 4: Calculate the multicoherence information of the signals other than the initial reference signal set relative to the initial reference signal set, and select the reference signal with the maximum information content to include in the initial reference signal set.

[0058] 41) Calculate the average multicoherence coefficient of the reference signal in the initial reference signal set relative to the in-vehicle noise signal. The calculation formula is as follows:

[0059]

[0060] In the formula, For in-vehicle noise signal y j The multicoherence coefficients of the initial reference signal set x are calculated using the following formula:

[0061]

[0062] In the formula, The reference signal in the initial reference signal set X and the in-vehicle noise signal y j The cross-power spectral density matrix; P XX (f) represents the self-power spectral density functions of the in-vehicle noise signal and the initial reference signal set, respectively; the symbol (·) H The symbol (·) represents the collinear transpose of a matrix. -1 This represents finding the generalized inverse of a matrix.

[0063] 42) Calculate the covariance matrix

[0064]

[0065] 43) Calculate the multicoherence information of the reference signals other than the initial reference signal set relative to the initial reference signal set:

[0066]

[0067] In the formula, Let be the average coherence coefficient between the i-th group of reference signals and the in-vehicle noise signal; det(·) is the determinant of the matrix;

[0068] 44) Compare the calculated multicoherence information of each group and select the reference signal with the largest information content to include in the initial reference signal set.

[0069] Step 5: Repeat step 4 to continuously update the initial reference signal set until all reference signals are included in the initial reference signal set in sequence;

[0070] Step 6: Plot the relationship between the number of reference signals and the multicoherence coefficient using the initial reference signal set. The horizontal axis of the graph represents the number of required input reference signals in the initial reference signal set, determined according to the order of inclusion. The minimum number is 1, and the maximum is the number of all reference signals included. The vertical axis represents the calculated multicoherence coefficient of the corresponding combination. The relationship between the number of reference signals and the multicoherence coefficient can be used to more intuitively weigh the relationship between the number of reference signals and coherence. At the same time, various constraints, including the computing power of the active noise control system processing unit and the real-time performance of the control algorithm, must also be considered. Here, a multicoherence coefficient of 0.55 is used as the selection criterion to determine the required reference signals in the initial sensor set.

[0071] Step 7: Output the final reference signal selection result and input the result into the active noise control system for effect verification.

[0072] Example 2

[0073] This embodiment uses a certain vehicle model as an example to introduce the reference signal selection method of the vehicle road noise active control system based on the Fisher information matrix, as follows:

[0074] Step 1: Conduct a real vehicle data acquisition test. Use 8 acceleration sensors to collect 24 sets of reference signals and use microphones to pick up 2 sets of in-vehicle noise signals. The microphones are placed at the headrests of the driver's seat and the passenger seat, respectively.

[0075] Step 2, according to Figure 2 and Figure 3In addition to the range of human hearing, 25-500Hz was selected as the noise reduction frequency band.

[0076] Step 3: Select the initial reference signal with the highest coherence to the target noise according to the evaluation index and include it in the initial reference signal set;

[0077] The evaluation criteria for selecting the initial reference signal are:

[0078]

[0079] In the formula, n f The discrete number of frequencies when solving for the coherence coefficient; SPL is the coherence coefficient of the i-th group of reference signals relative to the M-th group of noise signals; M (f) represents the sound pressure level of the Mth noise signal at frequency f.

[0080] Evaluation indicators The calculation formula is as follows:

[0081]

[0082] in Let be the cross-power spectral density between the i-th group of reference signals and the M-th group of in-vehicle noise signals. are the self-power spectral densities of the i-th group of reference signals and the M-th group of in-vehicle noise signals, respectively.

[0083] Step 4: Calculate the multicoherence information of the remaining signals (excluding the initial signal set) relative to the initial reference signal set, and select the reference signal with the maximum information content to include it in the initial reference signal set.

[0084] The formula for the average multicoherence coefficient of the reference signal in the initial reference signal set relative to the in-vehicle noise signal is:

[0085]

[0086] In the formula, For in-vehicle noise signal y j The multicoherence coefficients of the initial reference signal set X are calculated using the following formula:

[0087]

[0088] In the formula, The reference signal in the initial reference signal set X and the in-vehicle noise signal y j The cross power spectral density matrix, P XX (f) are the self-power spectral density functions of the in-vehicle noise signal and the initial reference signal set, respectively.

[0089] The covariance matrix is:

[0090]

[0091] The formula for calculating the multicoherence information of reference signals other than the initial reference signal set relative to the initial reference signal set is as follows:

[0092]

[0093] in Let be the average coherence coefficient between the i-th group of reference signals and the in-vehicle noise signal.

[0094] Step 5: Repeat step 4 to continuously update the initial reference signal set until all 24 sets of reference signals are included in the initial reference signal set in sequence.

[0095] Step Six, according to Figure 4 The final number of input reference signals is determined to be 13, and the first 13 groups of reference signals are selected from the initial reference signal set.

[0096] Step 7: Output the final reference signal selection result and input the result into the active noise control system for effect verification.

[0097] In summary, the average multicoherence coefficients of the final 13 reference signals were calculated, resulting in... Figure 5 As can be seen from the figure, when the number of reference signal paths is reduced by nearly half, the multivariate correlation at each frequency point does not decrease significantly. The results are input into an active noise control system, which uses a dual-channel feedforward NFxLMS algorithm with two secondary speakers and two error microphones to obtain the average noise reduction of the first and second paths. The results are as follows... Figure 6 , 7 It was found that the average noise reduction of the first channel can reach nearly 5dB, and the average noise reduction of the second channel can reach 4dB, which can achieve a good noise reduction effect.

[0098] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the scope of protection of the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, any person skilled in the art can make equivalent substitutions or changes based on the technical solution and inventive concept of the present invention within the scope of the technology disclosed in the present invention. These simple modifications are all within the scope of protection of the present invention.

[0099] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

[0100] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

Claims

1. A method for selecting a reference signal in an active control system for vehicle road noise, characterized in that, Includes the following steps: Step 1: Collect data; Step 2: Determine the frequency band to be denoised and the evaluation metrics; Step 3: Select the initial reference signal with the highest coherence to the target noise according to the evaluation index, and include the initial reference signal with the highest coherence into the initial reference signal set; Step 4: Calculate the multicoherence information of the signals other than the initial reference signal set relative to the initial reference signal set, and select the reference signal with the maximum information content to include in the initial reference signal set. Step 5: Repeat step 4 to continuously update the initial reference signal set until all reference signals are included in the initial reference signal set in sequence; Step 6: Determine the required number of input reference signals by plotting the relationship between the number of reference signals and the multicoherence coefficient using the initial reference signal set and other constraints, thereby determining the required reference signals in the initial sensor set; Step 7: Output the final reference signal selection result and input the result into the active noise control system for effect verification; The specific method of step one is as follows: conduct a real vehicle data acquisition test to obtain N sets of vibration acceleration reference signals collected by several sensors and M sets of in-vehicle noise signals picked up by the microphone; The specific method for step two is as follows: 21) The frequency band to be noise-reduced is selected based on the spectral characteristics of the actual vehicle noise collected inside the vehicle, the hearing range, and the frequency characteristics of the active noise reduction control instrument used. 22) The evaluation indicators are determined as follows: In the formula, n f The discrete number of frequencies when solving for the coherence coefficient; SPL is the coherence coefficient of the i-th group of reference signals relative to the M-th group of noise signals; M (f) represents the sound pressure level of the Mth noise signal at frequency f; The calculation formula is as follows: In the formula, Let be the cross power spectral density between the i-th group of reference signals and the M-th group of in-vehicle noise signals; are the self-power spectral densities of the i-th group of reference signals and the M-th group of in-vehicle noise signals, respectively.

2. The reference signal selection method for a vehicle road noise active control system according to claim 1, characterized in that, The specific method for step four is as follows: 41) Calculate the average multicoherence coefficient of the reference signal in the initial reference signal set relative to the in-vehicle noise signal. The calculation formula is as follows: In the formula, For in-vehicle noise signal y j The multicoherence coefficients of the initial reference signal set x are calculated using the following formula: In the formula, The reference signal in the initial reference signal set X and the in-vehicle noise signal y j The cross-power spectral density matrix; P XX (f) represents the self-power spectral density functions of the in-vehicle noise signal and the initial reference signal set, respectively; the symbol (·) H The symbol (·) represents the collinear transpose of a matrix. -1 This represents finding the generalized inverse of a matrix. 42) Calculate the covariance matrix 43) Calculate the multicoherence information of the reference signals other than the initial reference signal set relative to the initial reference signal set: In the formula, Let be the average coherence coefficient between the i-th group of reference signals and the in-vehicle noise signal; det(·) is the determinant of the matrix; 44) Compare the calculated multicoherence information of each group and select the reference signal with the largest information content to include in the initial reference signal set.

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