Vehicle speed measurement method and system based on Beidou

Abstract

This invention relates to the field of vehicle navigation and speed measurement technology, specifically a vehicle speed measurement method and system based on BeiDou navigation satellite system. The method includes the following steps: S1: In environments where satellite signals are disturbed or interrupted, based on a quantum positioning architecture, a quantum inertial measurement unit (QIMU) and a quantum clock are used for autonomous motion reference and time reference, respectively, to acquire the original observation dataset. The fidelity of the original observation dataset is then verified in real time using the QIMU. This invention achieves high reliability, high accuracy, and high reliability in vehicle speed measurement under complex environments by providing highly reliable original data and performing fidelity verification when satellite signals are disturbed / interrupted. This meets the high requirements for robustness and reliability in speed measurement in scenarios such as autonomous driving and intelligent transportation.

Description

Technical Field

[0001] This invention relates to the field of vehicle navigation and speed measurement technology, specifically to a vehicle speed measurement method and system based on BeiDou. Background Technology

[0002] "BeiDou," short for BeiDou Navigation Satellite System (BDS), is a global satellite navigation system independently developed and operated by China. It is one of the four major global satellite navigation systems, along with the US GPS, Russia's GLONASS, and the EU's Galileo. Through the coordinated operation of its space segment (BeiDou satellite constellation), ground segment (monitoring and control stations), and user segment (terminal equipment), it provides high-precision, highly reliable positioning, navigation, and timing services to users worldwide. It also possesses the unique short message communication capability that distinguishes it from other navigation systems. It has been widely applied in transportation, surveying and mapping, agriculture, and emergency rescue, and is particularly important in vehicle navigation and speed measurement scenarios, where it has become one of the core technologies supporting the acquisition of vehicle spatiotemporal information.

[0003] However, in the actual driving process, the traditional Beidou vehicle speed measurement system has low data reliability, easily broken trajectory, lack of speed measurement accuracy and uncertainty quantification when the satellite signal is disturbed or interrupted. In addition, the lack of multi-source information verification leads to insufficient speed measurement robustness and reliability in complex environments, making it difficult to meet the high requirements of autonomous driving and intelligent transportation scenarios.

[0004] Based on this, the present invention provides a vehicle speed measurement method and system based on BeiDou to solve the aforementioned technical problems. Summary of the Invention

[0005] The purpose of this invention is to provide a vehicle speed measurement method and system based on BeiDou. This invention provides highly reliable raw data and completes fidelity verification when satellite signals are disturbed / interrupted, and generates an uninterrupted trajectory. Then, based on a hybrid noise model and adaptive filtering, it calculates high-precision instantaneous speed with statistical uncertainty. Finally, it completes reliability assessment and adaptive calibration by fusing wheel speed and map information. This invention achieves uninterrupted, high-precision and high-reliability vehicle speed measurement in complex environments, meeting the high requirements for speed measurement robustness and reliability in scenarios such as autonomous driving and intelligent transportation.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] This invention provides a vehicle speed measurement method based on BeiDou, comprising the following steps:

[0008] S1: In the event of satellite signal interference or interruption, based on the quantum positioning architecture, the quantum inertial measurement unit and the quantum clock are used as autonomous motion reference and time reference respectively to obtain the original observation dataset, and the fidelity of the original observation dataset is verified in real time based on the quantum inertial measurement unit.

[0009] S2: The BeiDou pseudorange and carrier phase observations in the verified original observation dataset are fused with the angular increment and velocity increment information output by the quantum inertial measurement unit under a unified spatiotemporal reference to generate an uninterrupted vehicle motion trajectory sequence.

[0010] S3: Based on the fused trajectory and the joint state model including the noise characteristics of quantum sensors, the instantaneous speed of the vehicle is calculated using an adaptive filtering algorithm, and the speed measurement result and its corresponding uncertainty index are output according to the hybrid covariance propagation mechanism.

[0011] S4: Combining wheel speed pulse signals with map matching information, the speed measurement results are checked for consistency and evaluated for dynamic reliability. The final vehicle speed information is then output after adaptive calibration of the evaluation results.

[0012] Based on the above method, this invention also proposes a BeiDou-based vehicle speed measurement system, comprising a quantum-enhanced observation unit, a data fusion processing unit, an uncertainty-aware speed measurement unit, and a multi-source verification and calibration unit, wherein:

[0013] The quantum-enhanced observation unit is used to acquire the original observation dataset based on the quantum positioning mechanism, using a quantum inertial measurement unit and a quantum clock as autonomous motion reference and high-stability time reference, respectively, in the event of satellite signal interference or interruption. The data fidelity is also verified in real time based on the quantum inertial measurement unit.

[0014] The data fusion processing unit is used to fuse the verified BeiDou pseudorange and carrier phase observations with the angular increment and velocity increment information output by the quantum inertial measurement unit under a unified spatiotemporal reference to generate an uninterrupted vehicle motion trajectory sequence.

[0015] The uncertainty-aware speed measurement unit: based on the fused trajectory and the joint state model including the noise characteristics of quantum sensors, it calculates the instantaneous speed of the vehicle through an adaptive filtering algorithm, and synchronously outputs the speed measurement result and its statistical uncertainty index according to the hybrid covariance propagation mechanism.

[0016] The multi-source verification and calibration unit is used to fuse wheel speed pulse signals and map matching information, perform consistency verification and dynamic reliability assessment on the speed measurement results, and perform adaptive calibration based on the assessment results to output the final vehicle speed information.

[0017] The quantum-enhanced observation unit includes a quantum reference module, a multi-source data acquisition module, and a data fidelity verification module, wherein:

[0018] The quantum reference module is used to invoke the quantum inertial measurement unit and the quantum clock to generate autonomous motion reference signals and precision time reference signals, respectively.

[0019] The multi-source data acquisition module is used to simultaneously acquire pseudorange / carrier phase observation data from BeiDou satellites and raw inertial data output by the quantum inertial measurement unit.

[0020] The data fidelity verification module performs real-time credibility verification and anomaly marking on the acquired raw observation dataset based on the internal physical constraints and data consistency of the quantum inertial measurement unit.

[0021] The data fidelity verification module, based on the internal physical constraints and data consistency of the quantum inertial measurement unit, performs real-time credibility verification and anomaly marking on the acquired raw observation dataset. The specific operations are as follows:

[0022] A1: Extract the angular increment sequence and velocity increment sequence from the continuous output of the quantum inertial measurement unit;

[0023] A2: Based on Newtonian mechanics constraints, construct the geometric consistency relationship of the incremental sequence within a short time window and obtain the theoretical constraint boundary;

[0024] A3: The incremental information calculated from the actual observed BeiDou carrier phase change rate is synchronously compared with the incremental information output by the quantum inertial measurement unit;

[0025] A4: When the comparison deviation exceeds the theoretical constraint boundary, the original observation data at the corresponding time is determined to be abnormal and marked.

[0026] The specific formulas for A2-A4 are as follows:

[0027] A2 angular increment antisymmetry constraint:

[0028] ;

[0029] Velocity increment time continuity constraint:

[0030] ;

[0031] in, The angular increment of the i-axis relative to the j-axis. The angular increment of the j-axis relative to the i-axis. The maximum permissible rate of change of the speed increment. The sampling interval is... The angular increment antisymmetry threshold, The velocity increment output by the quantum inertial measurement unit at time t;

[0032] A3 contains the BeiDou carrier phase change rate. Estimated speed increment :

[0033] ;

[0034] ;

[0035] in, For the carrier wavelength of BeiDou satellite signals, Let t be the carrier phase observation value output by the Beidou receiver at time t;

[0036] Calculate the comparison deviation:

[0037] ;

[0038] ;

[0039] in, Let be the deviation between the quantum inertial velocity increment at time t and the velocity increment calculated by BeiDou. The deviation between the quantum inertial angle increment at time t and the angle increment calculated by BeiDou is given. To calculate the angle increment for BeiDou, This represents the quantum inertial angle increment.

[0040] A4: Calculate the standard deviation of the total system noise. ,in, The standard deviation of the velocity increment noise of the quantum inertial measurement unit. The standard deviation of the velocity increment noise in BeiDou observation data;

[0041] ①When If so, the velocity increment data at time t is determined to be abnormal;

[0042] ②When If the angle increment data at time t is found to be abnormal, the original observation data at that time will be marked.

[0043] The data fusion processing unit includes a spatiotemporal reference alignment module, a tightly coupled fusion solution module, and a trajectory generation and optimization module, wherein:

[0044] The spatiotemporal reference alignment module is used to unify BeiDou observation data and quantum inertial data into a spatiotemporal coordinate system driven by a quantum clock.

[0045] The tightly coupled fusion solution module is used to input the aligned multi-source heterogeneous observation data into the fusion filter for joint solution to estimate the continuous position and attitude of the vehicle.

[0046] The trajectory generation and optimization module is used to smooth and interpolate the discrete position points calculated by fusion to generate a continuous, smooth, and data-interruption-resistant vehicle motion trajectory sequence.

[0047] The uncertainty-aware velocity measurement unit includes a hybrid noise modeling module, an adaptive state estimation module, and an uncertainty quantification output module, wherein:

[0048] The hybrid noise modeling module is used to construct a joint state-space model that includes the characteristics of quantized quantum sensor noise and BeiDou observation noise.

[0049] The adaptive state estimation module is used to take the motion trajectory sequence as a measurement input and recursively estimate the motion state of the vehicle through an adaptive filtering algorithm.

[0050] The uncertainty quantification output module is used to calculate and output the statistical uncertainty index of the instantaneous velocity estimate based on the mixed noise model and the filter covariance.

[0051] The hybrid noise modeling module constructs a joint state-space model that incorporates the noise characteristics of quantized quantum sensors and BeiDou observations. The specific operations are as follows:

[0052] B1: Quantitative analysis of quantum sensor noise characteristics and construction of a noise model for the quantum inertial measurement unit: The decoherence noise of the quantum state in the quantum inertial measurement unit is modeled as a Poisson distributed random process, and its angular increment noise formula is:

[0053] ;

[0054] in, This represents the actual angle increment of the vehicle. This is quantum state decoherent noise, which follows a Poisson distribution. , k is the proportionality constant, and F is the quantum state fidelity. ;

[0055] B2: Quantifying the characteristics of BeiDou observation noise and constructing a BeiDou observation noise model: The BeiDou pseudorange / carrier phase observation noise is decomposed into a Gaussian distribution process, and its carrier phase observation noise formula is:

[0056] ;

[0057] in, Let t be the carrier phase observation value output by the Beidou receiver. This represents the actual carrier phase between the satellite and the vehicle. Noise from BeiDou observations;

[0058] B3: Construct a joint state-space model: based on the vehicle's position ,speed acceleration As state variables, by integrating quantum sensor noise and BeiDou observation noise, state equations and observation equations are constructed:

[0059] Equations of state:

[0060] ;

[0061] Observation equation:

[0062] ;

[0063] in, To measure the noise vector, Here is the state transition matrix. For the input matrix, For the observation matrix, This is the system noise vector.

[0064] The adaptive state estimation module uses the motion trajectory sequence as a measurement input and recursively estimates the vehicle's motion state through an adaptive filtering algorithm. The specific operation is as follows:

[0065] C1: Initialization and Input: Receives the vehicle motion trajectory sequence output by the trajectory generation and optimization modules as the main measurement, and receives the joint state space model and its initial noise covariance matrix output by the mixed noise modeling module;

[0066] C2: Dual-source adaptive filtering recursion: For each time k, execute:

[0067] ①State prediction: Based on the model's state equation, predict the current state and the prediction error covariance from the state at the previous time step;

[0068] ② Quantum weight adaptation: The verification mark of Beidou data at time k is queried by the data fidelity verification module; if the data is marked as low confidence, the element in the measurement noise covariance matrix corresponding to the trajectory observation at that time is multiplied by the penalty factor, otherwise the original value is used;

[0069] ③State update: Calculate the Kalman gain and fuse the trajectory measurements to update the optimal state estimate and the estimation error covariance;

[0070] ④ Noise covariance adaptation: Based on the statistical properties of the innovation sequence, the Sage-Husa algorithm is used to estimate and update the process noise covariance matrix;

[0071] C3: State Output: Extract the instantaneous velocity vector of the vehicle as the output, and pass the corresponding velocity state covariance to the uncertainty quantification output module.

[0072] The multi-source verification and calibration unit includes a multi-source information synchronization module, a consistency verification and evaluation module, and an intelligent calibration and decision output module, wherein:

[0073] The multi-source information synchronization module is used to receive and time-synchronize external auxiliary navigation information such as wheel speed pulse signals and high-precision map matching information.

[0074] The consistency verification and evaluation module is used to cross-compare the instantaneous velocity estimate with multi-source auxiliary information to evaluate its logical consistency and dynamic reliability.

[0075] The intelligent calibration and decision output module is used to adaptively select the optimal speed source based on the credibility assessment results and output the final vehicle speed information calibrated with quality labels.

[0076] Compared with the prior art, the beneficial effects of the present invention are:

[0077] This invention provides highly reliable raw data and performs fidelity verification when satellite signals are disturbed / interrupted, generates an uninterrupted trajectory, and then calculates high-precision instantaneous speed with statistical uncertainty based on a hybrid noise model and adaptive filtering. Finally, it completes credibility assessment and adaptive calibration by fusing wheel speed and map information, thus achieving uninterrupted, high-precision, and high-reliability vehicle speed measurement in complex environments. This meets the high requirements for speed measurement robustness and reliability in scenarios such as autonomous driving and intelligent transportation. Attached Figure Description

[0078] Figure 1 This is a system diagram of a vehicle speed measurement system based on BeiDou according to the present invention.

[0079] Figure 2 This is a flowchart of a vehicle speed measurement method based on BeiDou according to the present invention.

[0080] Explanation of icon numbers:

[0081] 1. Quantum Enhanced Observation Unit; 11. Quantum Reference Module; 12. Multi-Source Data Acquisition Module; 13. Data Fidelity Verification Module; 2. Data Fusion Processing Unit; 21. Spatiotemporal Reference Alignment Module; 22. Tightly Coupled Fusion Solving Module; 23. Trajectory Generation and Optimization Module; 3. Uncertainty Perception Velocity Measurement Unit; 31. Hybrid Noise Modeling Module; 32. Adaptive State Estimation Module; 33. Uncertainty Quantification Output Module; 4. Multi-Source Verification and Calibration Unit; 41. Multi-Source Information Synchronization Module; 42. Consistency Verification and Evaluation Module; 43. Intelligent Calibration and Decision Output Module. Detailed Implementation

[0082] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0083] Example 1:

[0084] like Figure 1 As shown, this embodiment provides a BeiDou-based vehicle speed measurement system, including a quantum-enhanced observation unit 1, a data fusion processing unit 2, an uncertainty-aware speed measurement unit 3, and a multi-source verification and calibration unit 4. Specifically: the quantum-enhanced observation unit 1 is used to acquire the original observation dataset based on a quantum positioning mechanism, utilizing a quantum inertial measurement unit and a quantum clock for autonomous motion reference and high-stability time reference, respectively, in environments where satellite signals are disturbed or interrupted. The data fidelity is verified in real time based on the quantum inertial measurement unit. The data fusion processing unit 2 is used to combine the verified BeiDou pseudorange and carrier phase observations with the quantum inertial measurement unit... The angular increment and velocity increment information output by the measurement unit are fused under a unified spatiotemporal reference to generate an uninterrupted vehicle motion trajectory sequence; Uncertainty-aware speed measurement unit 3: Based on the fused trajectory and a joint state model including quantum sensor noise characteristics, it calculates the instantaneous vehicle speed through an adaptive filtering algorithm, and synchronously outputs the speed measurement result and its statistical uncertainty index according to the hybrid covariance propagation mechanism; Multi-source verification and calibration unit 4: Used to fuse wheel speed pulse signals and map matching information, perform consistency verification and dynamic reliability assessment on the speed measurement results, and perform adaptive calibration based on the assessment results to output the final vehicle speed information.

[0085] It should be noted that the quantum-enhanced observation unit 1 provides highly reliable raw data and completes fidelity verification when the BeiDou signal is disturbed. The data fusion processing unit 2 generates an anti-interruption trajectory based on this data and quantum inertial navigation information. The uncertainty-aware speed measurement unit 3 calculates the instantaneous speed with statistical uncertainty based on this. Finally, the multi-source verification and calibration unit 4 fuses wheel speed and map information to perform reliability assessment and adaptive calibration of the speed result, and outputs highly reliable and robust final vehicle speed information.

[0086] In this embodiment, it should also be noted that the quantum-enhanced observation unit 1 includes a quantum reference module 11, a multi-source data acquisition module 12, and a data fidelity verification module 13, wherein: the quantum reference module 11 is used to call the quantum inertial measurement unit and the quantum clock to generate autonomous motion reference signals and precise time reference signals, respectively; the multi-source data acquisition module 12 is used to synchronously acquire pseudorange / carrier phase observation data of Beidou satellites and raw inertial data output by the quantum inertial measurement unit; the data fidelity verification module 13 performs real-time reliability verification and anomaly marking on the acquired raw observation dataset based on the internal physical constraints and data consistency of the quantum inertial measurement unit. The specific operations are as follows: A1: Extract the angular increment sequence and velocity increment sequence from the continuous output of the quantum inertial measurement unit; A2: Construct the geometric consistency relationship of the increment sequence within a short time window according to Newtonian mechanics constraints, and obtain the theoretical constraint boundary; angular increment antisymmetry constraint:

[0087] ;

[0088] Velocity increment time continuity constraint:

[0089] ;

[0090] in, The angular increment of the i-axis relative to the j-axis. The angular increment of the j-axis relative to the i-axis. The maximum permissible rate of change of the speed increment. The sampling interval is... The angular increment antisymmetry threshold, A3: The increment of velocity output by the quantum inertial measurement unit at time t; The increment information calculated from the actual observed BeiDou carrier phase change rate is synchronously compared with the increment information output by the quantum inertial measurement unit; The BeiDou carrier phase change rate... Estimated speed increment :

[0091] ;

[0092] ;

[0093] in, For the carrier wavelength of BeiDou satellite signals, Let t be the carrier phase observation value output by the Beidou receiver; calculate the comparison deviation:

[0094] ;

[0095] ;

[0096] in, Let be the deviation between the quantum inertial velocity increment at time t and the velocity increment calculated by BeiDou. The deviation between the quantum inertial angle increment at time t and the angle increment calculated by BeiDou is given. To calculate the angle increment for BeiDou, A4: When the comparison deviation exceeds the theoretical constraint boundary, the original observation data at the corresponding time is judged to be abnormal and marked. Calculate the standard deviation of the total system noise. ,in, The standard deviation of the velocity increment noise of the quantum inertial measurement unit. The standard deviation of the velocity increment noise of BeiDou observation data; ① When If the velocity increment data at time t is abnormal, then it is determined that the velocity increment data at time t is abnormal; ② When If the angle increment data at time t is found to be abnormal, the original observation data at that time will be marked.

[0097] It should be noted that the quantum reference module 11 provides a highly stable motion and time reference. The multi-source data acquisition module 12 synchronously acquires the original observation data from BeiDou and quantum inertial navigation. The data fidelity verification module 13 then constructs a short-term geometric consistency boundary based on the internal physical constraints of quantum inertial navigation. Through cross-source comparison of angular / velocity increments and noise adaptive threshold judgment, the original observation data is evaluated for reliability and anomaly is marked in real time.

[0098] Furthermore, it should be noted that the maximum permissible rate of change of velocity increment in A2... To obtain This corresponds to the extreme scenarios of rapid acceleration / deceleration of a vehicle (the maximum acceleration of a typical sedan is approximately...). Sampling interval The sampling time is 10ms (sampling frequency 100Hz), balancing real-time performance and computational load, with an antisymmetric threshold for angular increment. The value is 0.01 rad (approximately 0.57°), set based on the rigid body motion characteristics of the quantum inertial measurement unit to ensure that the angular increment conforms to physical constraints.

[0099] Short-time window setting: Short-time window length N = 20 sampling points (corresponding to a time of 200ms, to avoid constraint failure due to excessively long windows and noise sensitivity due to excessively short windows).

[0100] A3 Beidou satellite signal carrier wavelength It is 0.1903m.

[0101] The velocity increment noise standard deviation of the quantum inertial measurement unit in A4 The value is 0.001 m / s. The markings include: "Anomaly type (velocity increment anomaly / angular increment anomaly), anomaly time t, and deviation value". ".

[0102] In this embodiment, it should also be noted that the data fusion processing unit 2 includes a spatiotemporal reference alignment module 21, a tightly coupled fusion solution module 22, and a trajectory generation and optimization module 23, wherein: the spatiotemporal reference alignment module 21 is used to unify BeiDou observation data and quantum inertial data into a spatiotemporal coordinate system driven by a quantum clock; the tightly coupled fusion solution module 22 is used to input the aligned multi-source heterogeneous observation data into the fusion filter for joint solution to estimate the continuous position and attitude of the vehicle; the trajectory generation and optimization module 23 is used to smooth and interpolate the discrete position points obtained by fusion solution to generate a continuous, smooth, and data interruption-resistant vehicle motion trajectory sequence.

[0103] It should be noted that the spatiotemporal reference alignment module 21 uses the quantum clock as a reference to unify the spatiotemporal coordinate system of BeiDou and quantum inertial data. The tightly coupled fusion solution module 22 performs joint filtering and solution on multi-source heterogeneous observations under this unified framework to obtain high-precision continuous pose estimation. Then, the trajectory generation and optimization module 23 smooths and interpolates the discrete solution results, and finally outputs a continuous, smooth vehicle motion trajectory sequence with anti-interruption capability.

[0104] Furthermore, it should be noted that the time alignment in the spatiotemporal reference alignment module 21 is based on a precise timestamp using a quantum clock (precision). The time delay between the BeiDou receiver and the quantum inertial measurement unit is corrected using the following formula: ,in, For the measured delay ( Spatial alignment: via a pre-calibrated mounting matrix (3×3 rotation matrix) converts the volume coordinate system data of the quantum inertial measurement unit to the BeiDou ENU coordinate system. The conversion formula is as follows: The installation matrix calibration accuracy is ≤0.1°.

[0105] The fusion filter in the tightly coupled fusion solution module 22 employs an "extended Kalman filter" to adapt to the nonlinear motion characteristics of the vehicle; state vector: It includes position, velocity, angular rate and acceleration zero bias (considering the influence of sensor zero drift); Anti-interruption logic: when the BeiDou signal is interrupted (no valid BeiDou data for 3 consecutive sampling points), it automatically switches to "quantum inertial pure inertial navigation mode" and predicts the current position based on the state at the previous moment, with a prediction error ≤0.1m / s (within 10s of interruption).

[0106] The smoothing algorithm in the trajectory generation and optimization module 23 uses a sliding window Kalman filter with a window length of 20 sampling points. The smoothing formula is as follows: ,in, Gaussian weights (maximum weight at the center, decreasing at the edges); Interpolation algorithm: cubic spline interpolation is used, and the interpolation function is... ( To ensure the continuity of the first and second derivatives of the trajectory, the interpolation error is ≤0.05m.

[0107] In this embodiment, it should also be noted that the uncertainty sensing velocity measurement unit 3 includes a hybrid noise modeling module 31, an adaptive state estimation module 32, and an uncertainty quantization output module 33, wherein: the hybrid noise modeling module 31 is used to construct a joint state-space model that includes the characteristics of quantized quantum sensor noise and BeiDou observation noise; the specific operation is as follows: B1: Quantize the noise characteristics of the quantum sensor and construct the noise model of the quantum inertial measurement unit: model the quantum state decoherence noise of the quantum inertial measurement unit as a Poisson distributed random process, and its angular increment noise formula is:

[0108] ;

[0109] in, This represents the actual angle increment of the vehicle. This is quantum state decoherent noise, which follows a Poisson distribution. , k is the proportionality constant, and F is the quantum state fidelity. B2: Quantifying the characteristics of BeiDou observation noise and constructing a BeiDou observation noise model: The BeiDou pseudorange / carrier phase observation noise is decomposed into a Gaussian distribution process, and its carrier phase observation noise formula is:

[0110] ;

[0111] in, Let t be the carrier phase observation value output by the Beidou receiver. This represents the actual carrier phase between the satellite and the vehicle. B3: Constructing a joint state-space model based on the vehicle's position, to reduce BeiDou observation noise; ,speed acceleration As state variables, by integrating quantum sensor noise and BeiDou observation noise, state equations and observation equations are constructed: State equation:

[0112] ;

[0113] Observation equation:

[0114] ;

[0115] in, To measure the noise vector, Here is the state transition matrix. For the input matrix, For the observation matrix, This is the system noise vector. Adaptive state estimation module 32: Used to take the motion trajectory sequence as measurement input and recursively estimate the vehicle's motion state using an adaptive filtering algorithm; specific operations are as follows: C1: Initialization and Input: Receives the vehicle motion trajectory sequence output by trajectory generation and optimization module 23 as the main measurement, and receives the joint state space model and its initial noise covariance matrix output by mixed noise modeling module 31; C2: Dual-source adaptive filtering recursion: For each time k, executes: ① State prediction: Based on the model state equation, predicts the current state and prediction error covariance from the state at the previous time; ② Quantum weight adaptation: Queries the data fidelity verification module 13 on k... Verification markers for BeiDou data at specific times; if data is marked as low confidence, the element in the measurement noise covariance matrix corresponding to the trajectory observation at that time moment is multiplied by a penalty factor; otherwise, the original value is used; ③ State update: calculate the Kalman gain and fuse the trajectory measurements to update the optimal state estimate and estimation error covariance; ④ Noise covariance adaptation: based on the statistical characteristics of the innovation sequence, the Sage-Husa algorithm is used to estimate and update the process noise covariance matrix; C3: State output: extract the instantaneous velocity vector of the vehicle as the output, and pass the corresponding velocity state covariance to the uncertainty quantification output module 33. Uncertainty quantification output module 33: used to calculate and output the statistical uncertainty index of the instantaneous velocity estimate based on the mixed noise model and filter covariance.

[0116] It should be noted that the hybrid noise modeling module 31 constructs a joint state-space model that integrates the decoherence characteristics of the quantum sensor and the noise of BeiDou observation. Based on this model, the adaptive state estimation module 32 combines the trajectory measurement and data fidelity verification results, and uses a dual-source adaptive filtering mechanism to recursively estimate the vehicle motion state and dynamically adjust the noise covariance. Then, the uncertainty quantification output module 33 calculates and outputs the instantaneous speed and its statistical uncertainty index based on the filter covariance and the hybrid noise model.

[0117] Furthermore, it should be noted that the state transition matrix A (6×6, based on the uniform acceleration motion model) in the mixed noise modeling module 31 is as follows:

[0118] Observation matrix H (2×6, extracting only position state): .

[0119] The state prediction formula in the adaptive state estimation module 32 is as follows: , ; ( For the input matrix, To control the input, we take... , .

[0120] Kalman gain formula: ;

[0121] Quantification of penalty factors: (When dealing with low-reliability data) (significantly reduces measurement weight).

[0122] Definition of new information sequence: (Used for adaptive adjustment of noise covariance).

[0123] The velocity uncertainty quantification formula in uncertainty quantification output module 33:

[0124] ( , To estimate the error covariance matrix Chinese correspondence , (Diagonal elements); Output index definition: The final output is a combination of "instantaneous velocity + uncertainty index", such as ( (This refers to the statistical uncertainty of the synthesis rate).

[0125] In this embodiment, it should also be noted that the multi-source verification and calibration unit 4 includes a multi-source information synchronization module 41, a consistency verification and evaluation module 42, and an intelligent calibration and decision output module 43, wherein: the multi-source information synchronization module 41 is used to receive and time-synchronize external auxiliary navigation information such as wheel speed pulse signals and high-precision map matching information; the consistency verification and evaluation module 42 is used to cross-compare instantaneous speed estimates with multi-source auxiliary information to evaluate their logical consistency and dynamic reliability; the intelligent calibration and decision output module 43 is used to adaptively select the optimal speed source based on the reliability evaluation result and output the final vehicle speed information calibrated with quality labels.

[0126] It should be noted that the multi-source information synchronization module 41 performs time alignment between the wheel speed pulse and the high-precision map matching information. The consistency verification and evaluation module 42 cross-compares the instantaneous speed output by the uncertainty perception speed measurement unit 3 with the synchronized auxiliary information to dynamically evaluate its logical consistency and credibility. Then, the intelligent calibration and decision output module 43 adaptively fuses or switches the speed source based on the credibility result, and finally outputs high-reliability vehicle speed information with quality labels.

[0127] Furthermore, it should be noted that the consistency verification threshold in the consistency verification and evaluation module 42 is as follows:

[0128] Wheel speed deviation threshold: (Wheel speed sensor accuracy range);

[0129] Map matching deviation threshold: (Straight road section) (Curved section) The instantaneous speed estimate of the vehicle output by the uncertainty-aware speed measurement unit 3;

[0130] Dynamic credibility calculation: ,in, The score is determined by the wheel speed verification (1 for passing, 0 for failing). The map matching validation score is 1 for a pass and 0 for a fail. Score for uncertainty indicator ( =1, (If it is 0.5, otherwise it is 0).

[0131] The reliability calibration algorithm in the intelligent calibration and decision output module 43 ( Weighted fusion formula (The higher the credibility, the better) (The larger the weight)

[0132] Quality labeling rules: Excellent: ;good: To be verified: Multiple frames of data are required for verification.

[0133] Example 2:

[0134] like Figure 2 As shown in this embodiment, a vehicle speed measurement method based on BeiDou specifically includes the following steps:

[0135] S1. Quantum-enhanced observation and data fidelity verification:

[0136] S1.1: Reference provision and synchronous data acquisition:

[0137] The quantum inertial measurement unit and quantum clock are invoked to generate an autonomous motion reference and a highly stable time reference, respectively.

[0138] Simultaneously collect pseudorange and carrier phase observation data from BeiDou satellites, as well as raw angular increment and velocity increment data output by the quantum inertial measurement unit, to form the raw observation dataset;

[0139] S1.2: Internal verification based on physical constraints:

[0140] Extract the angular increment sequence from the continuous output of the quantum inertial measurement unit. With velocity increment sequence ;

[0141] Based on Newtonian mechanics constraints, its geometric consistency is verified within a short time window (e.g., N=20 points, corresponding to 200ms):

[0142] ① Angular increment antisymmetry constraint: ,in, ;

[0143] ② Velocity increment continuity constraint: ,in, ΔT = 10 ms;

[0144] S1.3: Quantum-BeiDou cross-source comparison: Comparison based on velocity increments calculated from the BeiDou carrier phase change rate.

[0145] ① Calculate the BeiDou velocity increment: Where λ = 0.1903m, ;

[0146] ② Calculate the comparison deviation: , ;

[0147] S1.4: Anomaly Comprehensive Judgment and Labeling: Judgment based on physical boundaries and statistical noise boundaries:

[0148] I. Calculate the standard deviation of the total system noise: ;

[0149] II. Judgment Rules:

[0150] ①If If so, the velocity increment data at time t is determined to be abnormal;

[0151] ②If If so, the angle increment data at time t is determined to be abnormal;

[0152] Add tags to outlier data that include type, time, and deviation value;

[0153] S2. Tightly Coupled Data Fusion and Trajectory Generation:

[0154] S2.1: Spacetime Reference Alignment: Based on a quantum clock (precision) Time delay compensation for BeiDou and quantum inertial data ( , ) and spatial coordinate system one (transformed through a pre-calibrated installation matrix M, with calibration accuracy ≤0.1°);

[0155] S2.2: Tightly Coupled Fusion Solution: The aligned multi-source data is input into the extended Kalman filter for joint solution. The filter state vector includes position, velocity, angular velocity, and zero-bias acceleration.

[0156] When the BeiDou signal is interrupted for more than 3 consecutive sampling points, the system automatically switches to pure quantum inertial navigation mode;

[0157] S2.3: Trajectory Smoothing and Interpolation: The discrete pose points calculated by fusion are smoothed using a sliding window Kalman filter (20 points), and cubic spline interpolation is used to generate a continuous, smooth, and uninterrupted vehicle motion trajectory sequence. ;

[0158] S3, Uncertainty-aware instantaneous velocity calculation:

[0159] S3.1: Hybrid Noise Modeling: Constructing a Joint State-Space Model

[0160] Quantizing quantum noise: Modeling the decoherence noise of the quantum inertial measurement unit as a Poisson process. , , Where F is the real-time quantum state fidelity;

[0161] Quantifying BeiDou Noise: Modeling BeiDou Observation Noise as a Gaussian Process :

[0162] Model building: State equations Observation equation , where matrix A is designed based on a uniform acceleration model;

[0163] S3.2: Adaptive State Estimation: For measurement, perform adaptive filtering:

[0164] ①State prediction: , ,in, This is the vehicle control input; if there is no input, it is taken. ;

[0165] ② Quantum weight adaptation: Query the verification flag for the BeiDou data at time k in step S1.4; if the flag is marked as low confidence, adjust the measurement noise covariance matrix. (Penalty factor α=3), otherwise ;

[0166] ③ State Update: Calculate Kalman Gain Update status With covariance ;

[0167] ④ Noise covariance adaptive: based on innovation sequence The Sage-Husa algorithm is used to update Q(k);

[0168] S3.3: Uncertainty quantification output: from the updated state Extracting instantaneous velocity , and from The statistical uncertainty is calculated as follows: Output speed estimate and its synthesis uncertainty ;

[0169] S4. Multi-source calibration and final output:

[0170] S4.1: Multi-source information synchronization: Receives and time-aligns wheel speed pulse signals and high-precision map matching information;

[0171] S4.2: Consistency Verification and Reliability Assessment: [The following text appears to be incomplete and requires further context: Cross-reference with multi-source information:

[0172] ① Wheel speed consistency: deviation If so, then it passes;

[0173] ② Map matching consistency: On straight / curved road sections, the deviation does not exceed [percentage missing]. 5% / 10% will pass;

[0174] ③ Overall credibility score: ;in, , The score is used to verify the pass rate (1 for pass, 0 otherwise). according to Values ​​( =1, (If it is 0.5, otherwise it is 0).

[0175] S4.3: Intelligent Calibration and Decision Output: Based on Perform adaptive calibration:

[0176] ①If Direct output The label is "Excellent";

[0177] ②If Perform weighted fusion: The label is "Good";

[0178] ③If Marked as "Unverified", it needs to be verified in conjunction with subsequent frame data;

[0179] The final output is highly reliable vehicle speed information with a quality label.

[0180] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0181] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A Beidou-based vehicle speed measurement method, characterized in that, Includes the following steps: S1: In the event of satellite signal interference or interruption, based on the quantum positioning architecture, the quantum inertial measurement unit and the quantum clock are used as autonomous motion reference and time reference respectively to obtain the original observation dataset, and the fidelity of the original observation dataset is verified in real time based on the quantum inertial measurement unit. S2: The BeiDou pseudorange and carrier phase observations in the verified original observation dataset are fused with the angular increment and velocity increment information output by the quantum inertial measurement unit under a unified spatiotemporal reference to generate an uninterrupted vehicle motion trajectory sequence. S3: Based on the fused trajectory and the joint state model including the noise characteristics of quantum sensors, the instantaneous speed of the vehicle is calculated using an adaptive filtering algorithm, and the speed measurement result and its corresponding uncertainty index are output according to the hybrid covariance propagation mechanism. S4: Combining wheel speed pulse signals with map matching information, the speed measurement results are checked for consistency and evaluated for dynamic reliability. The final vehicle speed information is then output after adaptive calibration of the evaluation results.

2. A Beidou-based vehicle speed measurement system, according to the Beidou-based vehicle speed measurement method of claim 1, characterized in that, It includes a quantum-enhanced observation unit (1), a data fusion processing unit (2), an uncertainty-aware velocimetry unit (3), and a multi-source verification and calibration unit (4), wherein: The quantum-enhanced observation unit (1) is used to obtain the original observation dataset based on the quantum positioning mechanism, using a quantum inertial measurement unit and a quantum clock, respectively, for autonomous motion reference and high-stability time reference, in the event of satellite signal interference or interruption. The data fidelity is verified in real time based on the quantum inertial measurement unit. The data fusion processing unit (2) is used to fuse the verified BeiDou pseudorange and carrier phase observations with the angular increment and velocity increment information output by the quantum inertial measurement unit under a unified spatiotemporal reference to generate an uninterrupted vehicle motion trajectory sequence. The uncertainty-sensing speed measurement unit (3) calculates the instantaneous speed of the vehicle through an adaptive filtering algorithm based on the fused trajectory and the joint state model containing the noise characteristics of quantum sensors, and outputs the speed measurement results and their statistical uncertainty indicators synchronously according to the hybrid covariance propagation mechanism. The multi-source verification and calibration unit (4) is used to fuse wheel speed pulse signals and map matching information, perform consistency verification and dynamic reliability evaluation on the speed measurement results, and perform adaptive calibration based on the evaluation results to output the final vehicle speed information.

3. The Beidou-based vehicle speed measurement system according to claim 2, characterized in that, The quantum-enhanced observation unit (1) includes a quantum reference module (11), a multi-source data acquisition module (12), and a data fidelity verification module (13), wherein: The quantum reference module (11) is used to call the quantum inertial measurement unit and the quantum clock to generate autonomous motion reference signal and precision time reference signal respectively; The multi-source data acquisition module (12) is used to synchronously acquire pseudorange / carrier phase observation data of Beidou satellites and raw inertial data output by the quantum inertial measurement unit; The data fidelity verification module (13) performs real-time credibility verification and anomaly marking on the collected original observation dataset based on the internal physical constraints and data consistency of the quantum inertial measurement unit.

4. A vehicle speed measurement system based on BeiDou according to claim 3, characterized in that, The data fidelity verification module (13) performs real-time credibility verification and anomaly marking on the collected original observation dataset based on the internal physical constraints and data consistency of the quantum inertial measurement unit. The specific operations are as follows: A1: Extract the angular increment sequence and velocity increment sequence from the continuous output of the quantum inertial measurement unit; A2: Based on Newtonian mechanics constraints, construct the geometric consistency relationship of the incremental sequence within a short time window and obtain the theoretical constraint boundary; A3: The incremental information calculated from the actual observed BeiDou carrier phase change rate is synchronously compared with the incremental information output by the quantum inertial measurement unit; A4: When the comparison deviation exceeds the theoretical constraint boundary, the original observation data at the corresponding time is determined to be abnormal and marked.

5. The Beidou-based vehicle speed measurement system according to claim 4, characterized in that, The specific formulas for A2-A4 are as follows: A2 angular increment antisymmetry constraint: ； Velocity increment time continuity constraint: ； in, This represents the angular increment of the i-axis relative to the j-axis. The angular increment of the j-axis relative to the i-axis. The maximum permissible rate of change of the speed increment. The sampling interval is... The angular increment antisymmetry threshold, The velocity increment output by the quantum inertial measurement unit at time t; A3 in the BeiDou carrier phase rate of change estimated velocity increment : ； ； wherein, is the wavelength of the carrier signal of the BeiDou satellite, is the carrier phase observation value output by the BeiDou receiver at time t; Calculate the comparison deviation: ； ； in, Let be the deviation between the quantum inertial velocity increment at time t and the velocity increment calculated by BeiDou. The deviation between the quantum inertial angle increment at time t and the angle increment calculated by BeiDou is given. To calculate the angle increment for BeiDou, This represents the quantum inertial angle increment. A4: Calculate the standard deviation of the total system noise. ,in, The standard deviation of the velocity increment noise of the quantum inertial measurement unit. The standard deviation of the velocity increment noise in BeiDou observation data; ①When If so, the velocity increment data at time t is determined to be abnormal; ②When If the angle increment data at time t is found to be abnormal, the original observation data at that time will be marked.

6. A vehicle speed measurement system based on BeiDou according to claim 2, characterized in that, The data fusion processing unit (2) includes a spatiotemporal reference alignment module (21), a tightly coupled fusion solution module (22), and a trajectory generation and optimization module (23), wherein: The spatiotemporal reference alignment module (21) is used to unify BeiDou observation data and quantum inertial data into a spatiotemporal coordinate system driven by a quantum clock. The tightly coupled fusion solution module (22) is used to input the aligned multi-source heterogeneous observation data into the fusion filter for joint solution to estimate the continuous position and attitude of the vehicle. The trajectory generation and optimization module (23) is used to smooth and interpolate the discrete position points calculated by fusion to generate a continuous, smooth and data-interruption-resistant vehicle motion trajectory sequence.

7. A vehicle speed measurement system based on BeiDou according to claim 2, characterized in that, The uncertainty-aware velocity measurement unit (3) includes a hybrid noise modeling module (31), an adaptive state estimation module (32), and an uncertainty quantification output module (33), wherein: The hybrid noise modeling module (31) is used to construct a joint state-space model that includes the characteristics of quantized quantum sensor noise and BeiDou observation noise. The adaptive state estimation module (32) is used to take the motion trajectory sequence as a measurement input and recursively estimate the motion state of the vehicle through an adaptive filtering algorithm. The uncertainty quantification output module (33) is used to calculate and output the statistical uncertainty index of the instantaneous velocity estimate based on the mixed noise model and the filter covariance.

8. A vehicle speed measurement system based on BeiDou according to claim 7, characterized in that, The hybrid noise modeling module (31) constructs a joint state-space model that includes the noise characteristics of the quantized quantum sensor and the BeiDou observation noise. The specific operation is as follows: B1: Quantitative analysis of quantum sensor noise characteristics and construction of a noise model for the quantum inertial measurement unit: The decoherence noise of the quantum state in the quantum inertial measurement unit is modeled as a Poisson distributed random process, and its angular increment noise formula is: ； in, This represents the actual angle increment of the vehicle. This is quantum state decoherent noise, which follows a Poisson distribution. , k is the proportionality constant, and F is the quantum state fidelity. ; B2: Quantifying the characteristics of BeiDou observation noise and constructing a BeiDou observation noise model: The BeiDou pseudorange / carrier phase observation noise is decomposed into a Gaussian distribution process, and its carrier phase observation noise formula is: ； in, Let t be the carrier phase observation value output by the Beidou receiver. This represents the actual carrier phase between the satellite and the vehicle. Noise from BeiDou observations; B3: Construct a joint state-space model: based on the vehicle's position ,speed acceleration As state variables, by integrating quantum sensor noise and BeiDou observation noise, state equations and observation equations are constructed: Equations of state: ； Observation equation: ； in, To measure the noise vector, Here is the state transition matrix. For the input matrix, For the observation matrix, This is the system noise vector.

9. A vehicle speed measurement system based on BeiDou according to claim 7, characterized in that, The adaptive state estimation module (32) uses the motion trajectory sequence as the measurement input and recursively estimates the motion state of the vehicle through an adaptive filtering algorithm. The specific operation is as follows: C1: Initialization and Input: Receive the vehicle motion trajectory sequence output by the trajectory generation and optimization module (23) as the main measurement, and receive the joint state space model and its initial noise covariance matrix output by the mixed noise modeling module (31); C2: Dual-source adaptive filtering recursion: For each time k, execute: ①State prediction: Based on the model's state equation, predict the current state and the prediction error covariance from the state at the previous time step; ② Quantum weight adaptation: Query data fidelity verification module (13) verifies the BeiDou data at time k; If the data is marked as low confidence, the elements in the measurement noise covariance matrix corresponding to the trajectory observation at that time moment are multiplied by a penalty factor; otherwise, the original values ​​are used. ③State update: Calculate the Kalman gain and fuse the trajectory measurements to update the optimal state estimate and the estimation error covariance; ④ Noise covariance adaptation: Based on the statistical properties of the innovation sequence, the Sage-Husa algorithm is used to estimate and update the process noise covariance matrix; C3: State output: Extract the instantaneous velocity vector of the vehicle as the output, and pass the corresponding velocity state covariance to the uncertainty quantification output module (33).

10. A vehicle speed measurement system based on BeiDou according to claim 2, characterized in that, The multi-source verification and calibration unit (4) includes a multi-source information synchronization module (41), a consistency verification and evaluation module (42), and an intelligent calibration and decision output module (43), wherein: The multi-source information synchronization module (41) is used to receive and time-synchronize external auxiliary navigation information such as wheel speed pulse signals and high-precision map matching information. The consistency verification and evaluation module (42) is used to cross-compare the instantaneous velocity estimate with multi-source auxiliary information to evaluate its logical consistency and dynamic reliability. The intelligent calibration and decision output module (43) is used to adaptively select the optimal speed source based on the credibility assessment results and output the final vehicle speed information calibrated with quality labels.

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