A method and system for controlling power amplifier of vehicle-mounted equipment

Abstract

The present invention discloses a vehicle-mounted device power amplifier control method and system, which relates to the field of signal processing technology. It includes automatically detecting vehicle type and configuration information, receiving user mode instructions through a user interface, and the user setting the audio mode according to personal preferences; obtaining the working state parameters of the vehicle in real time, and then using a digital signal processor or a microcontroller to dynamically adjust the power amplifier parameters according to the working state parameters of the vehicle type or user instructions; obtaining the audio signal data of the music device and preprocessing the audio signal data; the present invention uses DSP technology to perform deep preprocessing on the audio signal; and then uses the GS-EKF method and LMS algorithm to achieve dynamic adjustment of the power amplifier parameters, solving the problem that the fixed gain power amplifier design in traditional vehicle-mounted multimedia devices cannot adapt to the changes in sound quality requirements in different vehicle models and usage environments.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and in particular to a method and system for controlling a power amplifier of an on-vehicle device. Background Art

[0002] Traditional in-vehicle multimedia devices usually use a fixed-gain amplifier design, that is, the gain parameters of the amplifier are fixed during design and will not be adjusted with changes in vehicle models or usage environments. This approach has limitations when used in different vehicle models and cannot meet the audio performance requirements of different vehicles. Especially for luxury models or users with high requirements for sound quality, fixed-gain design is difficult to provide an optimized listening experience. If the amplifier hardware is designed separately for each model, not only will the R&D and production costs be high, but it will also greatly increase production complexity and time costs. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for controlling a vehicle-mounted device power amplifier, which can intelligently control the device power amplifier according to different vehicle models and the working status of the vehicle, thereby achieving low cost and providing high-quality power amplifier performance.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] The present application provides a method for controlling a power amplifier of an in-vehicle device, comprising the following steps:

[0006] S1, using a vehicle communication interface or a vehicle identification system to automatically detect the vehicle type and configuration information, receiving a user's mode command through a user interface of the vehicle infotainment system, and the user setting the audio mode according to personal preference;

[0007] S2, obtaining the working state parameters of the vehicle in real time through the vehicle-mounted sensors and the vehicle bus system, and then using a digital signal processor or a microcontroller to dynamically adjust the power amplifier parameters according to the working state parameters of the vehicle type or user instructions;

[0008] Further, the operating state parameters of the vehicle are obtained in real time through the vehicle-mounted sensors and the vehicle bus system, and further includes: estimating the vehicle state parameters using a GS-EKF-based method;

[0009] The GS-EKF-based method for estimating vehicle state parameters specifically includes:

[0010] S21. Based on the vehicle's dynamic model and sensor characteristics, a state space model is established, including the vehicle's lateral and longitudinal positions, driving trajectory, yaw angle, vehicle speed, acceleration, and yaw rate as state variables;

[0011] S22, using the established state space model, the extended Kalman filter algorithm is used to estimate the state of the vehicle, and the EKF recursively estimates the state through prediction and update. In the prediction process, the vehicle's motion model is used to predict the next state estimate value based on the current state estimate value and the system's dynamic equation; in the update process, the vehicle's measurement model is used to correct the state based on the predicted state estimate value and the measurement value;

[0012] S23, adjusting the parameters in the EKF, including the covariance matrix between states, the process noise covariance matrix and the measurement noise covariance matrix;

[0013] S24. Based on the hierarchical series connection concept, the result of the primary extended Kalman filter is used as the measurement control input signal of the secondary state estimation. In the primary EKF, the vehicle state is estimated once; in the secondary EKF, the primary result is used for secondary state estimation.

[0014] S3, obtaining audio signal data of the music device, preprocessing the audio signal data, performing real-time analysis on the input audio signal through a dynamic power driving algorithm, and dynamically adjusting the power output of the amplifier;

[0015] Furthermore, the vehicle communication interface or vehicle identification system is used to automatically detect the vehicle type and configuration information, specifically including:

[0016] S11, using the vehicle communication interface to exchange data with the vehicle's ECU, parsing the vehicle's diagnostic information through the controller area network protocol, and obtaining the vehicle type and configuration information;

[0017] S12, scanning the VIN code of the vehicle by using a vehicle identification code reader, segmenting each character in the VIN code separately by using an image processing algorithm, recognizing the VIN code in the image by using an optical character recognition technology, converting the segmented character image into text, and parsing the vehicle information according to the encoding rules of the VIN code;

[0018] Furthermore, a digital signal processor or a microcontroller is used to dynamically adjust the power amplifier parameters according to the working state parameters of the vehicle type or the user instructions, specifically including:

[0019] A real-time loop power monitoring system is constructed, including a power amplifier, a ring coupler, an AD8362 power true value detection module and a microcontroller. Through the ring coupler and the attenuator, a part of the energy output by the RF power amplifier is coupled to the RF input port of the AD8362 power true value detection module. The AD8362 power true value detection module outputs the detected power in the form of voltage to the analog-to-digital conversion module connected to the microcontroller to perform real-time monitoring of the power amplifier output power.

[0020] Through the calibration program embedded in the MCU, the digital attenuator at the front end of the power amplifier is adjusted to change the size of the source signal to adjust the power amplifier output or change the gate voltage of the power amplifier to change its working state, so as to achieve fine-tuning of the maximum output power;

[0021] According to the working state parameters of the vehicle or user instructions, the power amplifier parameters are dynamically adjusted, and the adaptive vehicle state prediction based on the model and dynamic adjustment of parameters is carried out. The vehicle model parameters are dynamically updated through the recursive least squares method to correct the vehicle dynamics model.

[0022] Furthermore, the vehicle model parameters are dynamically updated by the recursive least squares method to correct the vehicle dynamics model, which is specifically expressed as:

[0023] ,in

[0024] is at the time step The updated parameter vector, is at the time step The parameter vector estimate of is at the time step The gain vector, is the time step The observed output is is the time step The transpose of the regression vector containing the input variables;

[0025] Also includes covariance matrix updates:

[0026] ,in is the time step The covariance matrix of is the time step The covariance matrix of is the forgetting factor, which is used to balance the weight of historical data and new data, and its value is between 0 and 1. represents the regression vector at time step k.

[0027] Furthermore, the preprocessing of the audio signal data specifically includes: using DSP technology to filter, reduce noise and enhance the audio signal,

[0028] Filtering an audio signal through a FIR filter is expressed as:

[0029] ,in It's in time The filter output is is the filter input signal In time The value delay at m time unit, are the coefficients of the filter, is the order of the filter, which indicates the number of filter coefficients;

[0030] The noise reduction of the audio signal is expressed as: ,in is the frequency response of the filter, is the cross-correlation function between the input signal and the desired signal, is the autocorrelation function of the input signal, is the variance of the noise.

[0031] Furthermore, the input audio signal is analyzed in real time through a dynamic power driving algorithm to dynamically adjust the power output of the amplifier, specifically including:

[0032] By continuously comparing the actual signal output by the power amplifier with the expected signal, the error between the two is calculated, and then the filter coefficients are updated using the LMS algorithm based on the error and the input signal to construct a pre-distortion filter.

[0033] Furthermore, the method further includes: S4, after pre-processing the audio signal, using a digital signal processor to amplify the audio signal according to the adjusted power amplifier parameters.

[0034] Further, according to the adjusted power amplifier parameters, a digital signal processor is used to amplify the audio signal, specifically including: using an LMS algorithm to dynamically adjust the gain coefficient so that the amplified signal is close to the expected sound quality. The LMS algorithm is expressed as:

[0035] ,in It's time The filter coefficients of , which correspond to the gain coefficients in the amplification process, is the learning rate, is the error signal, which is the difference between the expected signal and the actual output signal, Indicates time The input signal.

[0036] The present invention provides a vehicle-mounted device power amplifier control system, which is used to implement a vehicle-mounted device power amplifier control method, specifically comprising:

[0037] The vehicle information recognition module is responsible for automatically detecting the type and configuration information of the vehicle, exchanging data with the vehicle's ECU through the vehicle communication interface, parsing the vehicle's diagnostic information using the CAN protocol, and scanning, identifying and parsing the VIN code through the vehicle identification code reader combined with image processing and optical character recognition technology;

[0038] The vehicle power amplifier parameter adjustment module obtains the vehicle's working state parameters in real time through the vehicle-mounted sensors and the vehicle bus system, and adopts the GS-EKF-based method to estimate the vehicle state parameters. By establishing a state space model, the extended Kalman filter algorithm is used to estimate the vehicle state, and the power amplifier parameters are dynamically adjusted to adapt to the vehicle's working state and user instructions;

[0039] The audio signal preprocessing module is responsible for acquiring the audio signal data of the music device and performing preprocessing, including filtering, noise reduction and enhancement operations. It uses FIR filters for frequency selective filtering, Wiener filters for noise reduction, and dynamic power drive algorithms to perform real-time analysis of input audio signals and dynamically adjust the power output of the amplifier.

[0040] The audio signal amplification processing module, after preprocessing the audio signal, uses a digital signal processor to amplify the audio signal according to the adjusted power amplifier parameters, wherein the gain coefficient is dynamically adjusted through the LMS algorithm so that the amplified signal is close to the expected sound quality.

[0041] The beneficial effects of the present invention are:

[0042] By adopting advanced digital signal processor (DSP) technology, the audio signal can be deeply pre-processed, including precise filtering, noise reduction and enhancement operations; the FIR filter is used to effectively remove unnecessary frequency components in the audio signal, while the Wiener filter retains key audio information while reducing background noise, solving the problem that the fixed gain amplifier design in traditional in-vehicle multimedia equipment cannot adapt to the changing sound quality requirements of different car models and usage environments. Ultimately, the quality of the audio signal is significantly improved, making the output sound clearer and purer, greatly enhancing the user's auditory experience and meeting the user's personalized needs for sound quality;

[0043] By introducing the GS-EKF-based method and LMS algorithm, dynamic adjustment of the power amplifier parameters is achieved, which not only reduces the need to design power amplifier hardware for each model separately, but also reduces the related costs. By building a real-time loop power monitoring system, the output power of the power amplifier can be monitored and fine-tuned in real time to ensure that the audio output is perfectly matched with the vehicle status and user needs, thereby improving the responsiveness and robustness of the system, enhancing the driving experience, and also enhancing the performance and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] For better understanding and implementation, the technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0045] Figure 1 A flowchart of a vehicle-mounted device power amplifier control method provided in Example 1 of the present application;

[0046] Figure 2 A schematic diagram of a process for automatically detecting vehicle type and configuration information in a vehicle-mounted device power amplifier control method provided in Example 1 of the present application;

[0047] Figure 3 A schematic flow chart of a vehicle-mounted device power amplifier control method provided in Example 1 of the present application using a GS-EKF-based method to estimate vehicle state parameters;

[0048] Figure 4 This is a structural schematic diagram of a vehicle-mounted device power amplifier control system provided in Example 2 of the present application. DETAILED DESCRIPTION

[0049] In order to further explain the technical means and effects taken by the present invention to achieve the predetermined invention purpose, exemplary embodiments will be described in detail here, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are only examples of methods and systems consistent with some aspects of the present application as detailed in the attached claims.

[0050] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0051] The specific implementation methods, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0052] Example 1

[0053] See also Figure 1-Figure 3 This embodiment provides a method for controlling a power amplifier of an in-vehicle device, comprising the following steps:

[0054] S1, using a vehicle communication interface or a vehicle identification system to automatically detect the vehicle type and configuration information, receiving a user's mode command through a user interface of the vehicle infotainment system, and the user setting the audio mode according to personal preference;

[0055] Furthermore, the vehicle communication interface or vehicle identification system is used to automatically detect the vehicle type and configuration information, specifically including:

[0056] S11. Use the vehicle communication interface (such as OBD-II) to exchange data with the vehicle's ECU (electronic control unit), parse the vehicle's diagnostic information through the CAN (controller area network) protocol, and obtain the vehicle type and configuration information;

[0057] S12. Scan the vehicle's VIN code (vehicle identification number) through a VIN code reader, separate each character in the VIN code using an image processing algorithm, recognize the VIN code in the image through OCR (optical character recognition) technology, convert the segmented character image into text, and parse out the relevant information of the vehicle according to the encoding rules of the VIN code.

[0058] Specifically, by utilizing the vehicle communication interface (such as OBD-II) to exchange data with the ECU, combining the CAN protocol to parse vehicle diagnostic information, and using VIN code reading equipment combined with image processing and OCR technology to scan, identify and parse the VIN code, it is possible to automatically and accurately obtain and parse the vehicle's type, configuration information, and key data such as manufacturer, model year, chassis type, etc., thereby achieving rapid acquisition and intelligent management of vehicle detailed information.

[0059] The encoding rules of the VIN code are as follows:

[0060] The first digit: Geographical region code, indicating the country or region where the vehicle is manufactured. For example, "L" represents a vehicle manufactured in China;

[0061] The second digit is the vehicle manufacturer code, which is used to identify a specific vehicle manufacturer.

[0062] The third digit is the vehicle type code, which indicates the type of vehicle. Different manufacturers have different interpretations of this.

[0063] Digits 4-8: Vehicle Characteristics Code (VDS), describing the general characteristics of the vehicle, such as car type, body type, engine type, etc.

[0064] The 9th digit: check digit, calculated by a specific algorithm, used to verify the correctness of the VIN code;

[0065] The 10th digit: Model year code, which indicates the year of manufacture of the vehicle, but not necessarily the actual year of production. The difference from the actual year of production shall not exceed 1 year.

[0066] The 11th digit: assembly plant code, indicating the assembly plant of the vehicle;

[0067] Digits 12-17: Vehicle production sequence number, used to identify the unique serial number of the vehicle on the production line;

[0068] These coding rules together ensure that each vehicle's VIN code is unique worldwide and contains key information about the vehicle, allowing for smooth vehicle identification, management, sales and repair. Through these rules, we can parse out important information such as the vehicle's manufacturer, model year, chassis type, etc. from the VIN code.

[0069] S2, obtaining the working state parameters of the vehicle in real time through the vehicle-mounted sensors and the vehicle bus system, and then using a digital signal processor or a microcontroller to dynamically adjust the power amplifier parameters according to the working state parameters of the vehicle type or user instructions;

[0070] Further, the vehicle's operating state parameters are obtained in real time through the vehicle-mounted sensors and the vehicle bus system, and also include: using a method based on GS-EKF (hierarchical cascade extended Kalman filter) to estimate the vehicle state parameters;

[0071] The method based on GS-EKF (hierarchical cascade extended Kalman filter) to estimate vehicle state parameters specifically includes:

[0072] S21. Based on the vehicle's dynamic model and sensor characteristics, a state space model is established, including the vehicle's lateral and longitudinal positions, driving trajectory, yaw angle, vehicle speed, acceleration, yaw rate, etc. as state variables, and the error between the measurement model and the actual state is considered;

[0073] S22, using the established state space model, the extended Kalman filter algorithm is used to estimate the state of the vehicle, and the EKF recursively estimates the state through prediction and update. In the prediction step, the vehicle's motion model is used to predict the next state estimate value based on the current state estimate value and the system's dynamic equation; in the update step, the vehicle's measurement model is used to correct the state based on the predicted state estimate value and the measurement value;

[0074] S23, adjusting the parameters in the EKF to improve the accuracy and robustness of the state estimation, these parameters include the covariance matrix between states, the process noise covariance matrix and the measurement noise covariance matrix, etc.;

[0075] S24. Based on the hierarchical series connection idea, the result of the primary extended Kalman filter is used as the measurement control input signal of the secondary state estimation. In the primary EKF, a rough state of the vehicle is estimated once; in the secondary EKF, a more refined secondary state estimation is performed using the primary result.

[0076] GS-EKF (hierarchical series extended Kalman filter) is an efficient multi-stage filtering technology. It improves the accuracy and robustness of state estimation by making a rough state estimate in the primary extended Kalman filter and then using the result as the input of the secondary filter for a more refined estimate, while maintaining a high computational efficiency. It is particularly suitable for state monitoring and control of intelligent vehicles and automation systems with high real-time and accuracy requirements. GS-EKF takes less time than traditional EKF, but compared with the unscented Kalman filter (UKF) and particle filter (PF) algorithms, the simulation time is shortened, which helps reduce the computational complexity of vehicle control chips.

[0077] Specifically, by establishing a state space model based on the vehicle dynamics model and sensor characteristics, the extended Kalman filter (EKF) algorithm is used to recursively estimate the key state parameters of the vehicle, such as the lateral and longitudinal position, driving trajectory, and yaw angle, through prediction and update steps. In this process, we fine-tune the parameters in the EKF, including the covariance matrix between states, the process noise covariance matrix, and the measurement noise covariance matrix, to enhance the accuracy and robustness of state estimation. Furthermore, using the hierarchical cascade extended Kalman filter (GS-EKF) method, the primary EKF provides a rough state estimate of the vehicle, while the secondary EKF makes a more refined estimate based on the primary result, ultimately achieving high-precision and high-robustness monitoring of the vehicle state, providing reliable data support for vehicle control and decision-making.

[0078] Furthermore, a digital signal processor or a microcontroller is used to dynamically adjust the power amplifier parameters according to the working state parameters of the vehicle type or the user instructions, specifically including:

[0079] A real-time loop power monitoring system is constructed, including a power amplifier (PA), a ring coupler, an AD8362 power true value detection module and a microcontroller (MCU). Through the ring coupler and the attenuator, part of the energy output by the RF power amplifier is coupled to the RF input port of the AD8362 power true value detection module. The AD8362 power true value detection module outputs the detected power in the form of voltage to the analog-to-digital conversion module connected to the microcontroller (MCU) to perform real-time monitoring of the power amplifier output power.

[0080] Through the "calibration" program embedded in the MCU, the digital attenuator at the front end of the power amplifier is adjusted to change the size of the source signal to adjust the power amplifier output or change the gate (MOS transistor) voltage of the power amplifier to change its working state, so as to achieve fine-tuning of the maximum output power;

[0081] According to the working state parameters of the vehicle or user instructions, the power amplifier parameters are dynamically adjusted, and the adaptive vehicle state prediction based on the model and dynamic adjustment of parameters is carried out. The vehicle model parameters are dynamically updated through the recursive least squares method to correct the vehicle dynamics model.

[0082] Specifically, by building a real-time loop power monitoring system including a power amplifier (PA), a ring coupler, an AD8362 power truth detection module and a microcontroller (MCU), combined with the "calibration" program inside the MCU, accurate monitoring and fine-tuning of the power amplifier output power is achieved. At the same time, based on the vehicle operating status parameters or user instructions, the recursive least squares method is used to dynamically update the vehicle model parameters. This comprehensive approach not only improves the performance and efficiency of the power amplifier, but also enhances the accuracy and adaptability of the vehicle dynamics model, thereby ensuring a perfect match between the audio output and the vehicle status and user needs, and improving the overall driving experience and system responsiveness.

[0083] Furthermore, the vehicle model parameters are dynamically updated by the recursive least squares method to correct the vehicle dynamics model, which is specifically expressed as:

[0084] ,in

[0085] is at the time step The updated parameter vector, is at the time step The parameter vector estimate of is at the time step The gain vector, is the time step The observed output is is the time step The transpose of the regression vector containing the input variables;

[0086] Also includes covariance matrix updates:

[0087] ,in is the time step The covariance matrix of is the time step The covariance matrix of is the forgetting factor, which is used to balance the weight of historical data and new data, and its value is between 0 and 1. represents the regression vector at time step k.

[0088] Specifically, the method of dynamically updating the parameters of the vehicle model through the recursive least squares (RLS) method can realize the real-time correction and optimization of the vehicle dynamics model. The update mechanism enables the algorithm to quickly adapt to new observation data while maintaining a certain memory of historical data, thereby providing more accurate parameter estimation in a dynamic environment. In general, the RLS-based method can effectively track the changes in vehicle dynamics parameters, improve the accuracy of model prediction, enhance the responsiveness and robustness of the vehicle control system, and ultimately improve the performance and safety of the vehicle.

[0089] S3, obtaining audio signal data of the music device, preprocessing the audio signal data, performing real-time analysis on the input audio signal through a dynamic power driving algorithm, and dynamically adjusting the power output of the amplifier;

[0090] Furthermore, the audio signal data is preprocessed, specifically including: using DSP technology to filter, reduce noise and enhance the audio signal,

[0091] Filtering an audio signal through a FIR filter is expressed as:

[0092] ,in It's in time The filter output is is the filter input signal In time The value delay at m time unit, are the coefficients of the filter, is the order of the filter, which indicates the number of filter coefficients;

[0093] The noise reduction of the audio signal is expressed as: ,in is the frequency response of the filter, is the cross-correlation function between the input signal and the desired signal, is the autocorrelation function of the input signal, is the variance of the noise.

[0094] Preprocessing the audio signal through DSP technology, including applying FIR filter for frequency selective filtering and Wiener filter for noise reduction, can effectively remove unnecessary frequency components and background noise in the audio, while retaining and enhancing useful signals, thereby significantly improving the quality of the audio signal, making the output sound clearer and purer, and enhancing the listening experience.

[0095] Furthermore, the input audio signal is analyzed in real time through a dynamic power driving algorithm to dynamically adjust the power output of the amplifier, specifically including:

[0096] By continuously comparing the actual signal output by the power amplifier with the expected signal, the error between the two is calculated, and then the filter coefficients are updated using the LMS algorithm based on the error and the input signal to construct a pre-distortion filter.

[0097] Its purpose is to process the audio signal before the power amplifier to offset the nonlinear distortion generated during the power amplifier process, achieve real-time signal processing and sound quality optimization, and ultimately output audio closer to the original signal. This process is iterative, and the filter coefficients are continuously adjusted to minimize the mean square error and improve the system's adaptive performance and correction accuracy.

[0098] S4. After preprocessing the audio signal, the audio signal is amplified using a digital signal processor according to the adjusted amplifier parameters to ensure that the audio signal can be properly amplified according to the specific needs of the vehicle and the user's preferences while maintaining the clarity and dynamic range of the signal.

[0099] Further, according to the adjusted power amplifier parameters, a digital signal processor is used to amplify the audio signal, specifically including: using an LMS algorithm to dynamically adjust the gain coefficient so that the amplified signal is close to the expected sound quality effect, and the LMS algorithm is expressed as: ,in It's time The filter coefficients of , which correspond to the gain coefficients in the amplification process, is the learning rate, is the error signal, which is the difference between the expected signal and the actual output signal, Indicates time The input signal.

[0100] By applying the LMS algorithm, the digital signal processor can dynamically adjust the gain coefficient of the audio signal to minimize the difference between the expected signal and the actual output signal. The LMS algorithm can adaptively adjust the gain to optimize the amplification effect of the audio signal. This adaptive adjustment makes the amplified audio signal closer to the ideal sound quality, optimizes the loudness and clarity of the audio, and reduces possible distortion, thereby significantly improving the overall auditory experience and system performance.

[0101] The vehicle-mounted device power amplifier control method of this embodiment realizes intelligent optimization and dynamic adjustment of audio output to meet the needs of different vehicle models and users. First, the vehicle type and configuration information are automatically detected through the vehicle communication interface (such as OBD-II) and the vehicle identification system (VIN code reading device), the vehicle diagnostic information is parsed using the CAN protocol, and the VIN code is parsed in combination with image processing and OCR technology, so as to quickly obtain detailed vehicle information, which not only improves the efficiency of vehicle information management, but also provides accurate data support for subsequent audio adjustments;

[0102] Then, the vehicle's working state parameters are acquired in real time through the on-board sensors and vehicle bus system, and the GS-EKF-based method is used to estimate the vehicle's state parameters. By establishing a state space model and an extended Kalman filter algorithm, an accurate estimation of the vehicle's dynamic state is achieved, which not only improves the accuracy and robustness of state estimation, but also reduces the amount of calculation, which is particularly suitable for intelligent vehicles and automation systems with high requirements for real-time and accuracy.

[0103] The audio signal is pre-processed through DSP technology, including filtering, noise reduction and enhancement operations, which significantly improves the quality of the audio signal. The FIR filter is used for frequency selective filtering and the Wiener filter is used for noise reduction, which effectively removes unnecessary frequency components and background noise while retaining useful signals, making the output sound clearer and purer.

[0104] According to the adjusted amplifier parameters, the digital signal processor is used to amplify the audio signal, and the gain coefficient is dynamically adjusted through the LMS algorithm, so that the amplified signal is close to the expected sound quality effect, optimizing the loudness and clarity of the audio, while reducing possible distortion, significantly improving the overall auditory experience and system performance;

[0105] Ultimately, by controlling the polarity of the output signal to match different audio systems, we ensure that the audio signal can be properly amplified according to the specific needs of the vehicle and the user's preferences, while maintaining the clarity and dynamic range of the signal. This approach not only improves the performance and efficiency of the amplifier, but also enhances the accuracy and adaptability of the vehicle dynamics model, improves the overall driving experience and system responsiveness, and intelligently controls the device amplifier according to different models and the working status of the car, achieving low-cost and high-quality amplifier performance.

[0106] Example 2

[0107] See also Figure 4 This embodiment provides a vehicle-mounted device power amplifier control system, which is used to implement a vehicle-mounted device power amplifier control method, specifically including:

[0108] The vehicle information identification module is responsible for automatically detecting the type and configuration information of the vehicle, exchanging data with the vehicle's ECU through the vehicle communication interface, parsing the vehicle's diagnostic information using the CAN protocol, and scanning, identifying and parsing the VIN code through the VIN code reader combined with image processing and OCR technology;

[0109] The vehicle power amplifier parameter adjustment module obtains the vehicle's working state parameters in real time through the vehicle-mounted sensors and the vehicle bus system, and adopts the GS-EKF-based method to estimate the vehicle state parameters. By establishing a state space model, the extended Kalman filter algorithm is used to estimate the vehicle state, and the power amplifier parameters are dynamically adjusted to adapt to the vehicle's working state and user instructions;

[0110] The audio signal preprocessing module is responsible for acquiring the audio signal data of the music device and performing preprocessing, including filtering, noise reduction and enhancement operations. It uses FIR filters for frequency selective filtering, Wiener filters for noise reduction, and dynamic power drive algorithms to perform real-time analysis of input audio signals and dynamically adjust the power output of the amplifier.

[0111] The audio signal amplification processing module, after preprocessing the audio signal, uses a digital signal processor to amplify the audio signal according to the adjusted power amplifier parameters, wherein the gain coefficient is dynamically adjusted through the LMS algorithm so that the amplified signal is close to the expected sound quality.

[0112] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for controlling a power amplifier of an on-vehicle device, characterized in that: The steps include: Using a vehicle communication interface or a vehicle identification system to automatically detect vehicle type and configuration information, receiving a user's mode command through a user interface of an in-vehicle infotainment system, and the user setting the audio mode according to personal preference; The vehicle's operating state parameters are acquired in real time through the vehicle's onboard sensors and vehicle bus system, and then a digital signal processor or microcontroller is used to dynamically adjust the power amplifier parameters according to the vehicle's operating state parameters or user instructions; Acquiring the working state parameters of the vehicle in real time through the vehicle-mounted sensors and the vehicle bus system, and also including: estimating the vehicle state parameters using a GS-EKF-based method; The GS-EKF-based method for estimating vehicle state parameters specifically includes: Based on the vehicle's dynamic model and sensor characteristics, a state space model is established, including the vehicle's lateral and longitudinal positions, driving trajectory, yaw angle, vehicle speed, acceleration, and yaw rate as state variables; The state of the vehicle is estimated by using the established state space model and the extended Kalman filter algorithm. The EKF recursively estimates the state through prediction and update. In the prediction process, the vehicle's motion model is used to predict the next state estimate based on the current state estimate and the system's dynamic equations. In the update process, the vehicle's measurement model is used to correct the state based on the predicted state estimate and the measured value. Adjust the parameters in the EKF, including the covariance matrix between states, the process noise covariance matrix, and the measurement noise covariance matrix; Based on the hierarchical series connection idea, the result of the primary extended Kalman filter is used as the measurement control input signal of the secondary state estimation. In the primary EKF, the vehicle state is estimated once; in the secondary EKF, the primary result is used to perform secondary state estimation; Acquire audio signal data from music equipment, pre-process the audio signal data, analyze the input audio signal in real time through a dynamic power driving algorithm, and dynamically adjust the power output of the amplifier; Utilize the vehicle communication interface or vehicle identification system to automatically detect vehicle type and configuration information, including: Use the vehicle communication interface to exchange data with the vehicle's ECU, parse the vehicle's diagnostic information through the controller area network protocol, and obtain the vehicle type and configuration information; Scan the vehicle's VIN code with a vehicle identification code reader, separate each character in the VIN code using an image processing algorithm, recognize the VIN code in the image using optical character recognition technology, convert the segmented character image into text, and parse the vehicle information based on the VIN code encoding rules; Use a digital signal processor or microcontroller to dynamically adjust the power amplifier parameters according to the operating status parameters of the vehicle type or user instructions, including: A real-time loop power monitoring system is constructed, including a power amplifier, a ring coupler, an AD8362 power true value detection module and a microcontroller. Through the ring coupler and the attenuator, a part of the energy output by the RF power amplifier is coupled to the RF input port of the AD8362 power true value detection module. The AD8362 power true value detection module outputs the detected power in the form of voltage to the analog-to-digital conversion module connected to the microcontroller to perform real-time monitoring of the power amplifier output power. Through the calibration program embedded in the MCU, the digital attenuator at the front end of the power amplifier is adjusted to change the size of the source signal to adjust the power amplifier output or change the gate voltage of the power amplifier to change its working state, so as to achieve fine-tuning of the maximum output power; Dynamically adjust the power amplifier parameters according to the vehicle's working state parameters or user instructions, adaptive vehicle state prediction based on model and parameter dynamic adjustment, dynamically update the vehicle model parameters through recursive least squares method, and use it to correct the vehicle dynamics model; The vehicle model parameters are dynamically updated through the recursive least squares method to correct the vehicle dynamics model, which is specifically expressed as: ,in is at the time step The updated parameter vector, is at the time step The parameter vector estimate of is at the time step The gain vector, is the time step The observed output is is the time step The transpose of the regression vector containing the input variables; Also includes covariance matrix updates: ,in is the time step The covariance matrix of is the time step The covariance matrix of is the forgetting factor, which is used to balance the weights of historical data and new data. represents the regression vector at time step k; The preprocessing of the audio signal data specifically includes: using DSP technology to filter, reduce noise and enhance the audio signal, Filtering an audio signal through a FIR filter is expressed as: ,in It's in time The filter output is is the filter input signal In time The value delay at m time unit, are the coefficients of the filter, is the order of the filter, which indicates the number of filter coefficients; The noise reduction of the audio signal is expressed as: ,in is the frequency response of the filter, is the cross-correlation function between the input signal and the desired signal, is the autocorrelation function of the input signal, is the variance of the noise.

2. A vehicle-mounted device power amplifier control method according to claim 1, characterized in that: The dynamic power drive algorithm analyzes the input audio signal in real time and dynamically adjusts the power output of the amplifier, including: By continuously comparing the actual signal output by the power amplifier with the expected signal, the error between the two is calculated, and then the filter coefficients are updated using the LMS algorithm based on the error and the input signal to construct a pre-distortion filter.

3. The method for controlling a power amplifier of an in-vehicle device according to claim 1, characterized in that: Also includes: After preprocessing the audio signal, the audio signal is amplified using a digital signal processor according to the adjusted power amplifier parameters.

4. A vehicle-mounted device power amplifier control method according to claim 3, characterized in that: According to the adjusted amplifier parameters, the audio signal is amplified using a digital signal processor, specifically including: dynamically adjusting the gain coefficient using an LMS algorithm so that the amplified signal is close to the desired sound quality. The LMS algorithm is expressed as: ,in It's time The filter coefficients of , which correspond to the gain coefficients in the amplification process, is the learning rate, is the error signal, which is the difference between the expected signal and the actual output signal, Indicates time The input signal.

5. A vehicle-mounted device power amplifier control system, used to implement a vehicle-mounted device power amplifier control method as claimed in any one of claims 1 to 4, characterized in that: include: The vehicle information recognition module is responsible for automatically detecting the type and configuration information of the vehicle, exchanging data with the vehicle's ECU through the vehicle communication interface, parsing the vehicle's diagnostic information using the CAN protocol, and scanning, identifying and parsing the VIN code through the vehicle identification code reader combined with image processing and optical character recognition technology; The vehicle power amplifier parameter adjustment module obtains the vehicle's working state parameters in real time through the vehicle-mounted sensors and the vehicle bus system, and adopts the GS-EKF-based method to estimate the vehicle state parameters. By establishing a state space model, the extended Kalman filter algorithm is used to estimate the vehicle state, and the power amplifier parameters are dynamically adjusted to adapt to the vehicle's working state and user instructions; The audio signal preprocessing module is responsible for acquiring the audio signal data of the music device and performing preprocessing, including filtering, noise reduction and enhancement operations. It uses FIR filters for frequency selective filtering, Wiener filters for noise reduction, and dynamic power drive algorithms to perform real-time analysis of input audio signals and dynamically adjust the power output of the amplifier. The audio signal amplification processing module, after preprocessing the audio signal, uses a digital signal processor to amplify the audio signal according to the adjusted power amplifier parameters, wherein the gain coefficient is dynamically adjusted through the LMS algorithm so that the amplified signal is close to the expected sound quality.

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