Vehicle-mounted Beidou positioning front fusion method and system

Abstract

In order to solve the problems of time delay and deviation caused by the fact that satellite positioning signals need to be transmitted to the rear portion of a vehicle-mounted main control chip and can be fused with vehicle information in the prior art, the core of the application lies in that fusion calculation of multi-source data is executed from the front portion of the vehicle-mounted main control chip to the interior of a Beidou positioning module. According to the method, a positioning module directly receives satellite signals and obtains vehicle operation parameters, then fusion calculation is completed in real time in the module, and then a result is output. A fusion computing node is built in, satellite signals and vehicle information are fused at the starting point of a transmission path, and communication and processing time delay generated when the satellite signals and the vehicle information are respectively transmitted to a main chip is directly eliminated from a system architecture. Through the arrangement, the positioning result can closely follow the instantaneous motion state of the vehicle, so that the positioning deviation caused by time delay is remarkably reduced, the positioning real-time performance and precision of the vehicle in a high-dynamic driving scene are effectively improved, and a key technical support is provided for high-order automatic driving.

Description

Technical Field

[0001] This application relates to the field of intelligent connected vehicle technology, specifically to a vehicle-mounted high-precision positioning technology, and in particular to a vehicle-mounted BeiDou positioning front-end fusion method and system. Background Technology

[0002] With the rapid development of autonomous driving technology, high-precision and highly reliable real-time vehicle positioning has become a core supporting technology. While the BeiDou Navigation Satellite System, my country's independently developed global positioning system, is widely used in vehicle navigation, its satellite signals are easily blocked or interfered with by reflections in complex environments such as urban canyons, tunnels, and under overpasses, leading to decreased positioning accuracy or even failure.

[0003] Current mainstream technologies typically employ a "positioning module + vehicle-mounted main control chip" architecture: the positioning module receives and processes raw satellite observation data, sending the preliminary positioning results to the vehicle-mounted terminal control chip via a serial port (such as UART); the control chip then integrates vehicle motion information from sensors such as the vehicle's CAN bus and inertial measurement unit (IMU), performing backend fusion filtering (such as Kalman filtering) to finally output the positioning result. This architecture has inherent drawbacks: there is a communication delay in the transmission of satellite signals from the module to the control chip, and vehicle motion information also needs to be collected and transmitted before it can be used for correction, resulting in a lag in fusion timing. When the vehicle is moving at high speed or undergoing drastic dynamic changes (such as rapid acceleration or emergency lane changes), this delay introduces significant positioning errors, failing to meet the stringent real-time requirements of high-level autonomous driving.

[0004] Therefore, reducing the latency of signal transmission and processing links and achieving real-time and accurate fusion of satellite signals and vehicle motion information has become a key technical challenge for improving vehicle positioning performance. Summary of the Invention

[0005] The purpose of this application is to overcome the shortcomings of existing technologies and provide a vehicle-mounted BeiDou positioning front-end fusion method and system. Its core concept lies in moving the fusion computing node of multi-source data (satellite signals and vehicle motion information) from the traditional "rear-end" processing of the vehicle terminal control chip to the front end of the satellite positioning module. This achieves shortest path fusion in the hardware architecture, fundamentally reducing data transmission latency and improving the real-time performance and dynamic accuracy of positioning. To achieve the above objectives, the technical solution adopted in this application includes:

[0006] In a first aspect, embodiments of this application provide a vehicle-mounted BeiDou positioning front-end fusion method, including:

[0007] The positioning module receives BeiDou satellite signals and obtains multi-source operating parameters from the vehicle.

[0008] In the positioning module, the BeiDou satellite signal and the multi-source operating parameters are fused and calculated to obtain fused positioning data;

[0009] The fused positioning data is sent to the vehicle terminal control chip for vehicle control and location display.

[0010] Furthermore, the multi-source operating parameters include:

[0011] The first velocity and direction parameters are from the onboard inertial measurement unit, the second velocity and direction parameters are from the vehicle analog signal interface, and the third velocity and direction parameters are from the vehicle communication interface.

[0012] Furthermore, it also includes:

[0013] The validity of the multi-source operating parameters is judged, and invalid parameters are removed. The average value of the valid parameters is then calculated as the reference operating parameters.

[0014] Furthermore, the validity determination includes:

[0015] Dynamic thresholds are set according to vehicle type to differentiate the parameters under different motion states.

[0016] Furthermore, prior to the fusion computation, the following is also included:

[0017] Quality prediction of BeiDou satellite signals is performed to eliminate unreliable satellite data.

[0018] Furthermore, the quality prediction includes:

[0019] Dynamic threshold determination is based on at least one of the following indicators: signal strength, multipath effect, and carrier phase continuity.

[0020] Furthermore, it also includes:

[0021] The multi-source operating parameters are spatiotemporally registered and unified to the same spatiotemporal reference before fusion calculation.

[0022] Furthermore, the spatiotemporal registration includes:

[0023] The noise model parameters are dynamically adjusted based on the vehicle's motion state for Kalman filter estimation.

[0024] Furthermore, the noise model employs an adaptive estimation algorithm based on the innovation sequence to adjust the noise covariance matrix in real time.

[0025] Secondly, embodiments of this application provide a system capable of implementing the pre-fusion method described in any of the foregoing claims, comprising:

[0026] The positioning module is used to receive BeiDou satellite signals;

[0027] The first acquisition module is set in the positioning module and is used to acquire the first vehicle operating parameters from the vehicle-mounted inertial measurement unit;

[0028] The second acquisition module is connected to the vehicle controller and is used to acquire the second vehicle operating parameters from the vehicle controller;

[0029] The fusion computing module, integrated within the positioning module, is used to perform fusion computing on the BeiDou satellite signal, the first vehicle operating parameters, and the second vehicle operating parameters to generate fused positioning data.

[0030] The output module is used to send the fused positioning data to the vehicle terminal control chip.

[0031] To address the issue of latency and inaccuracies caused by the requirement of transmitting satellite positioning signals to the vehicle's main control chip before fusion with vehicle information in existing technologies, this application proposes a pre-fusion method for vehicle-mounted BeiDou positioning. The core of this method lies in moving the fusion calculation of multi-source data from the vehicle's main control chip to the BeiDou positioning module itself. This method involves the positioning module directly receiving satellite signals and acquiring vehicle operating parameters, then performing the fusion calculation in real-time within the module and outputting the result. By embedding the fusion calculation node, satellite signals and vehicle information are fused at the beginning of the transmission path, directly eliminating the communication and processing latency caused by separate transmissions to the main chip. This setup allows the positioning results to closely follow the vehicle's instantaneous movement, significantly reducing positioning errors caused by latency and effectively improving the real-time performance and accuracy of vehicle positioning in highly dynamic driving scenarios, providing key technical support for advanced autonomous driving. Attached Figure Description

[0032] Figure 1 A schematic diagram of the structure of a vehicle-mounted BeiDou positioning front-end fusion system provided in this application embodiment;

[0033] Figure 2 This is a core flowchart of a vehicle-mounted BeiDou positioning front-end fusion method provided in an embodiment of this application. Detailed Implementation

[0034] To enable those skilled in the art to better understand the technical solutions of this application, exemplary embodiments of this application are described below with reference to the accompanying drawings, including various details of the embodiments of this application to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description. Unless otherwise specified, the various embodiments of this application and the features within those embodiments can be combined with each other.

[0035] As used herein, the term “and / or” includes any and all combinations of one or more of the associated enumerated entries. The terminology used herein is for describing particular embodiments only and is not intended to limit the application. As used herein, the singular forms “a” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated features, integrals, steps, operations, elements, and / or components is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0036] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It should also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and this application, and will not be interpreted as having an idealized or overly formal meaning, unless expressly so defined herein.

[0037] With the rapid development of intelligent connected vehicle technology, high-precision and high-reliability vehicle positioning technology has become a core support for realizing autonomous driving. Currently, the BeiDou Navigation Satellite System, as my country's independently developed global positioning system, has, after years of development and iterative upgrades and continuous optimization of its satellite positioning algorithms, met the basic application requirements for vehicle navigation and positioning. However, its positioning accuracy decreases or even fails when satellite signals are easily blocked or weak. In existing technologies, positioning data is mostly transmitted from satellite data to the vehicle positioning terminal control chip via a positioning module, where the chip performs fusion calculations. This results in a lag in fusion, easily leading to positioning result deviations. This application, through hardware design and software optimization, moves the fusion calculation to the satellite communication module, enabling the satellite signal to be directly fused with the vehicle correction signal in real time, thereby minimizing positioning deviations caused by time delays.

[0038] refer to Figure 1 This application proposes a vehicle-mounted BeiDou positioning front-end fusion system, which designs the transmission of vehicle information as two paths: 1. Speed ​​and direction signals are directly transmitted to the positioning module via analog pulse signals (i.e., Figure 1 1. A pre-fusion unit. 2. The vehicle terminal control chip communicates with the positioning module via UART serial port, transmitting speed and direction of travel to the positioning module. The IMU sensing unit on the vehicle terminal controller is transferred to the positioning module. The fusion calculation of satellite positioning signals and vehicle signals is completed directly in the positioning module, thereby reducing the latency of actual satellite signal transmission. The fused positioning position is transmitted to the vehicle terminal control chip via UART serial port communication for subsequent platform control, intelligent network control, and location display.

[0039] refer to Figure 1 One embodiment of this application proposes a vehicle-mounted BeiDou positioning front-end fusion system, whose hardware architecture is built around a high-performance positioning module, which mainly integrates the following four parts.

[0040] (1) Satellite signal processing unit: used to receive and process signals from multiple frequency points such as Beidou B1I, B1C*, and B2a, complete signal acquisition, tracking, and extraction of raw observation values ​​(pseudorange and carrier phase), and perform real-time tracking and conditioning of the signal through front-end processing to obtain real-time satellite positioning signals.

[0041] (2) Multi-source data acquisition interface unit: including:

[0042] A high-speed SPI interface is directly connected to a microelectromechanical system inertial measurement unit (IMU). This IMU, acting as the "first sensing unit," directly provides the positioning module with high-frequency (e.g., 100 Hz) information on the vehicle's three-axis acceleration and angular velocity.

[0043] An analog signal input interface is used to directly acquire analog pulse signals (such as vehicle speed pulses) from vehicle sensors.

[0044] A UART or CAN FD interface is used to communicate with the vehicle domain controller to obtain digital vehicle speed, steering wheel angle, gear position signals, etc., summarized through the vehicle bus (such as CAN).

[0045] (3) Front-end fusion computing unit (i.e. Figure 1The front-end data processing unit (FAR) is a core processor (such as an ARM Cortex-R series real-time core) that runs the fusion positioning algorithm software. Based on traditional satellite data parsing algorithms, the FAR acquires typical vehicle information input from the vehicle controller and performs fusion calculations in advance within the positioning module. This reduces the transmission delay of satellite-derived signals and the errors caused by indirectly acquiring vehicle speed information. Vehicle information is acquired through two channels, forming a redundant design. This ensures the real-time performance of the direct analog signal input while avoiding the problem of not receiving vehicle signals due to frequent failures in the analog transmission path. Simultaneously, addressing the issue of multiple input sources for the same signal, the FAR performs signal priority processing and error correction in the fusion algorithm, making the positioning data more accurate and reliable.

[0046] (4) Data output unit: Send the fused high-precision positioning, speed, attitude and other information to the autonomous driving domain controller or instrument display unit through another UART or Ethernet interface.

[0047] The innovation of this system lies in the fact that the IMU sensing unit is separated from the traditional main control board and integrated or tightly coupled with the satellite signal receiver (i.e., the satellite signal processing unit) within the positioning module. The two communicate via an internal bus, resulting in extremely low latency. Vehicle information is input through redundant analog and digital dual channels, ensuring the real-time performance and reliability of data acquisition. The vehicle-mounted BeiDou positioning front-end fusion system proposed in this application directly fuses satellite position signals, its own inertial sensing signals, and standard signals collected by the vehicle, thereby reducing satellite signal transmission delay and increasing cross-correction of the same signal from different signal sources, resulting in higher positioning real-time performance and more accurate data.

[0048] refer to Figure 2 One embodiment of this application proposes a vehicle-mounted BeiDou positioning pre-fusion method, including:

[0049] Step 1: Synchronous acquisition and validity preprocessing of multi-source data. This involves receiving BeiDou satellite signals through the positioning module and acquiring multi-source operating parameters from the vehicle.

[0050] Specifically, the positioning module can perform the following operations simultaneously:

[0051] (1) Solve satellite observation data and call the signal quality prediction module. This signal quality prediction module calculates the carrier-to-noise ratio (C / N0), multipath error (MP), and geometrically independent combination (GF) of each satellite in real time, and compares each indicator with a preset threshold or a dynamic threshold. For example, when a vehicle enters under an overpass, if a satellite's C / N0 drops sharply and its MP surges, it is marked as "suspicious," and its weight is reduced or it is temporarily removed in subsequent calculations to avoid unreliable observations contaminating the calculation. Quality prediction includes dynamic threshold judgment based on at least one of the following indicators: signal strength, multipath effect, and carrier phase continuity. For example, C / N0 and multipath error are calculated in real time and compared with dynamic thresholds to mark the satellite status. In this way, real-time evaluation and adaptive filtering of signal quality can be achieved.

[0052] (2) Read acceleration, angular velocity or velocity V1 data from the built-in IMU sensing unit via SPI.

[0053] (3) Read the vehicle speed pulse V2 through the analog channel and convert it into instantaneous speed.

[0054] (4) Read information such as digital vehicle speed V3 and steering wheel angle from the vehicle controller via UART.

[0055] Before fusion, the validity of V1 (obtained by integration from the IMU sensing unit), V2, and V3 is verified: the difference between each speed value and the previous value is calculated at a fixed period (e.g., 500 ms). If the rate of change exceeds the threshold set according to the vehicle dynamics model (e.g., the threshold for rapid acceleration of a car is set to 3 m / s²), the instantaneous value is considered to be possibly affected by noise interference or transmission error, and is temporarily marked as invalid and not included in this fusion cycle. The retained valid speed values ​​proceed to the next step.

[0056] Step 2: Spatiotemporal registration and adaptive fusion calculation. This involves fusing the BeiDou satellite signal with the multi-source operating parameters within the positioning module to obtain the fused positioning data. Step 2 is completed in the pre-processing unit of the positioning module.

[0057] (1) Time Registration: A high-precision 1PPS pulse signal output from the GNSS receiver is used as the hardware time reference to assign a unified timestamp (microsecond level) to IMU data, analog and digital vehicle signals. For non-strictly synchronized data, spline interpolation is used to align it to the main time axis of the fusion algorithm. Specifically, the time registration methods include:

[0058] Hardware synchronization: This is the best solution. Use a unified hardware clock source (such as a 1PPS pulse per second provided by a GNSS receiver) to trigger all sensors, fundamentally ensuring time alignment.

[0059] Software synchronization:

[0060] Interpolation / Extrapolation: Using the time of a high-frequency sensor (such as an IMU) as a reference, the observations of a low-frequency sensor (such as a GNSS) are unified onto the high-frequency time axis through interpolation (such as Lagrange interpolation, spline interpolation) or fitting (such as polynomial fitting).

[0061] Timestamp alignment: Each data packet is given a precise timestamp (as accurate as possible to the microsecond level), and data from the same or closest timestamps are paired during data processing.

[0062] (2) Spatial Registration: Through pre-calibration, the arm vector and installation angle deviation matrix between the IMU and the phase center of the BeiDou antenna are obtained. During data fusion of each frame, the specific force and angular velocity measured by the IMU are accurately compensated to the antenna phase center using the above parameters, eliminating calculation errors caused by different physical positions of the sensors. Specifically, the spatial registration method includes:

[0063] Lever compensation:

[0064] Precisely measure the relative position vector (lever vector) L between sensors b .

[0065] Transform observations from non-reference sensors to the center of a reference sensor. For example, transform acceleration / angular velocity measurements from the IMU center to the GNSS antenna phase center: a GNSS =a IMU +ω˙×L b +ω×(ω×L b ) ; where ω and ω˙ are angular velocity and angular acceleration, respectively.

[0066] Installation angle compensation:

[0067] Calibration: Determine the rotation matrix R between the sensor coordinate system and the carrier coordinate system using static or dynamic calibration methods. bs .

[0068] Transformation: Transform all observations to the carrier coordinate system: vb=Rsbvs.

[0069] Coordinate System 1: Unify all spatial data under the same Earth reference frame (such as WGS-84 or CGCS2000), which involves the transformation from latitude, longitude, and altitude to geocentric and geofixed coordinates.

[0070] (3) The overall steps of the spatiotemporal registration process include:

[0071] Calibration: Offline precise measurement of lever arm vector and installation angle rotation matrix.

[0072] Time alignment: Using hardware synchronization or software interpolation methods to generate data with a unified time series.

[0073] Spatial transformation: For the data at each moment, the installation angle rotation, lever arm compensation, and coordinate system transformation are performed in sequence, and finally all data are registered to a unified spatiotemporal reference.

[0074] Spatiotemporal registration includes dynamically adjusting noise model parameters based on vehicle motion states for Kalman filter estimation. For example, the process noise and observation noise matrices are dynamically adjusted based on the motion states identified by the IMU (stationary, constant speed, acceleration, turning). This improves the adaptability and estimation accuracy of the filter in dynamic environments.

[0075] (4) Combination filtering based on dynamic noise model:

[0076] Traditional fusion filters use fixed process noise (Q) and observation noise (R) matrices. Observation noise is mainly caused by the inherent uncertainties in GNSS observations, including multipath propagation, ionospheric / tropospheric delay residuals, and receiver noise. This noise is related to the vehicle's dynamics; for example, the multipath effect becomes more complex during sharp turns and acceleration / deceleration. Process noise is used in the state equation to simulate the uncertainty in state prediction, reflecting model errors. For instance, vehicle motion models (such as constant velocity (CV) and constant acceleration (CA) models) cannot fully describe real, complex motion. This application introduces a motion state recognition and noise adaptation module.

[0077] Motion state recognition: Real-time analysis of high-frequency data from the IMU, using threshold judgment or a lightweight classifier to identify whether the vehicle is currently in a state such as "stationary", "uniform speed", "straight-line acceleration / deceleration" or "turning".

[0078] Dynamic adjustment of the noise model: A set of optimized Q and R matrix parameters is preset for each motion state. For example:

[0079] "Uniform speed" state: Set the process noise (especially acceleration noise) low, and the weight of observation noise (satellite) can be appropriately increased.

[0080] "Sharp Turn" state: Process noise (angular velocity, lateral acceleration noise) increases. At the same time, due to the possibility of satellite signals being blocked by the vehicle body, the observation noise also increases accordingly, and the short-term reliability weight of the IMU is increased.

[0081] Dynamic adjustment: Based on the identified motion state, the Q and R matrices used at the current moment are dynamically switched or interpolated. For example, during the acceleration phase, the variance of the acceleration component in the process noise Q is increased.

[0082] Innovation Adaptive: Simultaneously, the filter continuously monitors the "inspiration" sequence (the difference between the observed and predicted values) between the predicted and observed values. The filter's "inspiration" sequence contains information about the statistical characteristics of the noise; if the noise model is accurate, the "inspiration" sequence should be zero-mean white noise.

[0083] If the new information covariance continues to exceed the theoretical value, it indicates that the pre-defined noise model may still be mismatched. In this case, the Q and R matrices are adjusted in reverse according to a certain strategy (such as maximum likelihood estimation or variance matching), and an online fine-tuning of the Q and R matrices is initiated using an algorithm such as the Sage-Husa algorithm. The simplified formula is as follows:

[0084]

[0085] in, For the new interest, This is the fading factor, which enables the filter to adapt to the complex and ever-changing actual driving environment.

[0086] Step 3: Redundant signal selection and output. The fused positioning data is then sent to the vehicle-mounted terminal control chip for vehicle control and location display.

[0087] Preliminary fused positioning results (position, velocity, heading) are obtained after filtering. Finally, redundant vehicle signals are used for cross-validation and optimization at the result level.

[0088] Speed ​​selection: Compare the speed V of the filter output. fusion V2 and V3 are retained after validity verification. If V... fusion If the difference between V2 (analog direct signal, highest real-time performance) and V2 is less than a preset threshold, then the final speed is based on V2. fusion If the difference is too large, the fusion result will be constrained or corrected using V2 or V3 (judged according to signal quality).

[0089] Direction confirmation: Similarly, by integrating the heading angle, digital steering wheel angle, and IMU integral heading, arbitration is conducted based on signal reliability priority (e.g., steering wheel angle > IMU short-term integral > integrated heading) when the directions are inconsistent.

[0090] Finally, the optimized and highly reliable positioning, speed, and heading data packets are sent to the vehicle host through the output unit.

[0091] In some embodiments, the parameter error processing procedure specifically includes:

[0092] (1) First, determine the validity of the vehicle speed V.

[0093] V1, V2, and V3 calculate V 500 ms ago.f and after V b The difference, when (V b -V f When the value is greater than or equal to ΔV (which can be calibrated based on the vehicle's purpose, categorized as B2B, B2C, and racing, etc.), it is considered invalid and not included in the fusion calculation. Dynamic thresholds are set according to vehicle type to differentiate parameters under different motion states. Different acceleration change thresholds ΔV are set for different vehicle types, such as B2B, B2C, and racing. This adapts to the dynamic characteristics of different vehicles, improving the accuracy and adaptability of the judgment.

[0094] The effective average vehicle speed V = (V1 + V2 + V3) / 3 is used as the reference speed for fusion calculation.

[0095] (2) Secondly, the error between effective vehicle speeds is compared simultaneously.

[0096] ΔV1 = ABS(V1 - V2), ΔV2 = ABS(V2 - V3), ΔV3 = ABS(V1 - V3). Compare ΔV1, ΔV2, and ΔV3. When the speed exceeds 1 km / h (which can be calibrated based on the actual vehicle), refer to the effective vehicle speed and determine the reference speed for fusion calculation according to the acceptance order V2, V3, and V1.

[0097] (3) Third, confirm the direction.

[0098] The corresponding directional parameters F1, F2, and F3 are determined based on the effective vehicle speed. If the three parameters are consistent in direction, they are directly adopted. If the directions are inconsistent, the reference direction for fusion calculation is determined according to the adoption order F2, F3, and F1.

[0099] In one embodiment where the system operates in a tunnel scenario, the satellite signal completely fails when the vehicle enters the tunnel. In conventional solutions, the positioning module stops outputting due to the lack of signal, and the main control chip can only rely on the IMU for dead reckoning (DR), leading to rapid error accumulation. In the system proposed in this application:

[0100] (1) The signal quality prediction module immediately detects that the C / N0 of all satellites has dropped below the loss of lock threshold, triggering the "pure inertial / vehicle signal assistance mode".

[0101] (2) The fusion computing unit automatically switches the process noise model to the "high dynamic uncertainty" mode and increases the weight of the IMU data.

[0102] (3) The system continuously acquires real vehicle speeds V2 and V3 with high real-time performance through analog and digital dual channels, as well as steering wheel angle from the vehicle's EPS.

[0103] (4) The fusion algorithm uses the precise vehicle speed (rather than the IMU integral speed) as the observation value, combined with the angular velocity of the IMU, to perform rigorous dead reckoning. Because the vehicle speed information is accurate and has no cumulative error, the position reckoning error is strictly controlled within the linear growth range in the tunnel, which lasts for tens of seconds.

[0104] (5) The satellite signal is restored the moment the vehicle exits the tunnel. The system quickly reacquires the signal and uses the accurate state information accumulated during the inertial calculation to achieve a smooth and seamless connection with satellite positioning, thereby avoiding the "positioning jump" phenomenon commonly seen in traditional schemes.

[0105] The main advantages of this application compared to the prior art include:

[0106] 1. Significantly improved real-time performance: By embedding the fusion computing hardware unit into the positioning module, satellite signals and vehicle information are fused instantly with "zero forwarding" within the module, completely eliminating the millisecond-level latency introduced by serial communication and data queuing in the traditional architecture. This greatly improves the synchronization between the positioning output and the vehicle's real status, making it particularly suitable for high-speed and high-dynamic scenarios.

[0107] 2. Enhanced Positioning Accuracy and Reliability: Direct fusion within the positioning module allows for the use of more complex fusion algorithms with high real-time requirements (such as dynamic noise models based on motion state recognition). Simultaneously, the multi-path redundant vehicle signal acquisition mechanism, combined with pre-positioned signal quality assessment and data validity verification, can intelligently switch or weight signals when a single signal source fails or malfunctions, thus ensuring the system's ability to continuously output reliable positioning results.

[0108] 3. System Architecture Optimization: This simplifies the computational burden on the vehicle's main control chip, allowing it to focus more on upper-level application decisions. The modular design also facilitates integration and updates, thereby improving the maintainability and scalability of the entire vehicle electronic system.

[0109] The aforementioned embodiments of the vehicle-mounted BeiDou positioning front-end fusion method and the embodiments of the vehicle-mounted BeiDou positioning front-end fusion system are technically related, and they can be referred to each other in terms of technical details and technical effectiveness, which will not be repeated here.

[0110] The flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and / or operation of possible implementations of systems, methods, and / or computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0111] Exemplary embodiments have been disclosed herein, and while specific terminology has been used, it is used and should be interpreted only in a general illustrative sense and is not intended to be limiting. In some embodiments, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this application as set forth by the appended claims.

Claims

1. A vehicle-mounted BeiDou positioning front-end fusion method, characterized in that, include: The positioning module receives BeiDou satellite signals and obtains multi-source operating parameters from the vehicle. In the positioning module, the BeiDou satellite signal and the multi-source operating parameters are fused and calculated to obtain fused positioning data; The fused positioning data is sent to the vehicle terminal control chip for vehicle control and location display.

2. The pre-fusion method according to claim 1, characterized in that, The multi-source operating parameters include: The first velocity and direction parameters are from the onboard inertial measurement unit, the second velocity and direction parameters are from the vehicle analog signal interface, and the third velocity and direction parameters are from the vehicle communication interface.

3. The pre-fusion method according to claim 2, characterized in that, Also includes: The validity of the multi-source operating parameters is judged, invalid parameters are removed, and the average value of the valid parameters is calculated as the reference operating parameters.

4. The pre-fusion method according to claim 3, characterized in that, The validity judgment includes: Dynamic thresholds are set according to vehicle type to differentiate the parameters under different motion states.

5. The pre-fusion method according to claim 1, characterized in that, Prior to the fusion calculation, the following is also included: Quality prediction of BeiDou satellite signals is performed to eliminate unreliable satellite data.

6. The pre-fusion method according to claim 5, characterized in that, The quality prediction includes: Dynamic threshold determination is based on at least one of the following indicators: signal strength, multipath effect, and carrier phase continuity.

7. The pre-fusion method according to claim 1, characterized in that, Also includes: The multi-source operating parameters are spatiotemporally registered and unified to the same spatiotemporal reference before fusion calculation.

8. The pre-fusion method according to claim 7, characterized in that, The spatiotemporal registration includes: The noise model parameters are dynamically adjusted based on the vehicle's motion state for Kalman filter estimation.

9. The pre-fusion method according to claim 8, characterized in that, The noise model employs an adaptive estimation algorithm based on the innovation sequence to adjust the noise covariance matrix in real time.

10. A system capable of implementing the pre-fusion method according to any one of claims 1-9, characterized in that, include: The positioning module is used to receive BeiDou satellite signals; The first acquisition module is set in the positioning module and is used to acquire the first vehicle operating parameters from the vehicle-mounted inertial measurement unit; The second acquisition module is connected to the vehicle controller and is used to acquire the second vehicle operating parameters from the vehicle controller. The fusion computing module, integrated within the positioning module, is used to perform fusion computing on the BeiDou satellite signal, the first vehicle operating parameters, and the second vehicle operating parameters to generate fused positioning data. The output module is used to send the fused positioning data to the vehicle terminal control chip.

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