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CN-121521139-B - Multi-source data fusion vehicle monitoring and positioning data processing method

CN121521139BCN 121521139 BCN121521139 BCN 121521139BCN-121521139-B

Abstract

The invention relates to the technical field of electronic data digital processing, and discloses a vehicle monitoring and positioning data processing method for multi-source data fusion, which comprises the steps of acquiring GNSS, IMU and odometer data; the method comprises the steps of constructing four-dimensional eigenvectors comprising satellite quantity, carrier-to-noise ratio statistics and vertical acceleration vibration energy, identifying running environment states in real time through a decision tree classifier, and dynamically activating corresponding fusion processing pipelines (such as tight coupling, loose coupling, dead reckoning or enhancement type pipelines with vibration compensation) according to state types, wherein a gyroscope bias compensation and self-adaptive Butterworth filtering mechanism based on a lookup table is introduced in the vibration interference state. The system comprises a data acquisition, environment classification, pipeline selection and fusion processing module. The invention improves the positioning robustness and the full scene adaptability in the bumpy scene, optimizes the balance of power consumption and precision through an on-demand activation strategy, and ensures the track continuity by means of hysteresis switching and covariance transfer.

Inventors

  • ZHANG XINJIE

Assignees

  • 上海科络达云软件技术有限公司

Dates

Publication Date
20260505
Application Date
20260113

Claims (9)

  1. 1. The vehicle monitoring and positioning data processing method based on multi-source data fusion is characterized by comprising the following steps of: Acquiring multi-source real-time sensor data, wherein the multi-source real-time sensor data comprises global navigation satellite system data, inertial measurement unit data and vehicle odometer data, the global navigation satellite system data comprises pseudo-range, carrier phase, doppler frequency shift and satellite ephemeris information, the inertial measurement unit data comprises triaxial acceleration and triaxial angular velocity, and the vehicle odometer data comprises wheel rotating speed pulse signals; constructing a driving environment feature vector based on the multi-source real-time sensor data, and classifying the current driving environment state of the vehicle into a preset driving environment state type according to the driving environment feature vector, wherein the preset driving environment state type comprises an open sky state, an urban canyon state, a tunnel or shielding state and a vibration interference state; Selecting and activating a target data processing pipeline uniquely corresponding to the current running environment state type from a preset processing pipeline set according to the classified running environment state type, wherein the processing pipeline set is an independent data fusion processing unit preconfigured for different running environment state types; Adopting the activated target data processing pipeline to perform fusion calculation on the multi-source real-time sensor data so as to generate and output real-time three-dimensional position, three-dimensional speed and three-dimensional posture information of the vehicle; Selecting and activating a target data processing pipeline, comprising: When the driving environment state type is an open sky state, activating a fusion processing pipeline of a tightly coupled global navigation satellite system and an inertial measurement unit, wherein the pipeline adopts an extended Kalman filter, directly measures the pseudo range and carrier phase observation value of the global navigation satellite system as quantity, and corrects the position, speed and attitude error solved by the inertial navigation system, and at the moment, the vibration suppression and compensation module is in an inactive state; When the driving environment state type is an urban canyon state, activating a loosely coupled global navigation satellite system and inertial measurement unit fusion processing pipeline, wherein the pipeline firstly independently calculates a positioning result of the global navigation satellite system and a positioning result of the inertial navigation system, and then takes a difference value of the position and the speed of the two as a quantity measurement, and inputs the quantity measurement to an extended Kalman filter for state estimation; When the driving environment state type is a tunnel or shielding state, activating an inertial measurement unit and a vehicle odometer dead reckoning processing pipeline, wherein the pipeline calculates the vehicle advancing speed by using a wheel rotating speed pulse signal from a vehicle controller area network bus as an external speed observation value, and restrains the speed integral drift of the inertial measurement unit; when the driving environment state type is a vibration interference state, activating an enhanced fusion processing pipeline with vibration self-adaptive compensation; the enhanced fusion processing assembly line with vibration self-adaptive compensation comprises a vibration-induced attitude drift compensation module and a self-adaptive vibration suppression filtering module.
  2. 2. The method for processing the vehicle monitoring and positioning data by multi-source data fusion according to claim 1, wherein constructing the driving environment feature vector and classifying the driving environment state in real time comprises: Setting a sliding time window with fixed duration, and extracting the number of satellites in the global navigation satellite system data and the signal carrier-to-noise ratio mean and variance of all visible satellites in the time window; In the same sliding time window, carrying out power spectrum density analysis on the vertical axis acceleration signals in the inertial measurement unit data, and extracting signal energy integral values in a preset vibration frequency band; Constructing a four-dimensional driving environment feature vector comprising the number of satellites, a signal carrier-to-noise ratio mean value, a signal carrier-to-noise ratio variance and a vertical axis acceleration signal energy integral value; And inputting the driving environment characteristic vector into a preset rule-based decision tree classifier, and outputting a unique driving environment state type code through multistage comparison with a preset threshold value.
  3. 3. The method for processing vehicle monitoring and positioning data based on multi-source data fusion according to claim 2, wherein the preset vibration frequency band is 2 hz to 20 hz, and the signal energy integration value is obtained by integrating the power spectral density of the vertical axis acceleration signal in the preset vibration frequency band.
  4. 4. The method for processing vehicle monitoring and positioning data based on multi-source data fusion according to claim 1, wherein the specific processing flow of the vibration-induced attitude drift compensation module is as follows: performing a short-time fourier transform on the vertical axis acceleration signal acquired by the inertial measurement unit to identify a primary vibration frequency and amplitude; Acquiring dynamic bias compensation vectors for the pitch angle and the roll angle gyroscopes by inquiring a two-dimensional lookup table generated by pre-calibration according to the identified main vibration frequency and the identified amplitude, wherein the two-dimensional lookup table is established by performing off-line calibration on gyroscope output biases under vibration input of different frequencies and amplitudes on a six-degree-of-freedom vibration platform; in the state prediction step of the extended Kalman filter, the dynamic bias compensation vector is superimposed onto the static bias estimate of the gyroscope, thereby actively cancelling the cross-coupling error and angular rate integration error caused by mechanical vibration in the state propagation model.
  5. 5. The method for processing vehicle monitoring and positioning data based on multi-source data fusion according to claim 4, wherein the specific processing flow of the adaptive vibration suppression filtering module is as follows: The adaptive vibration suppression filter module is implemented as an eighth order butterworth digital low pass filter; Selecting a group of filter coefficients corresponding to the main vibration frequency from a preset filter coefficient library according to the main vibration frequency identified by the vibration-induced gesture drift compensation module, wherein the filter coefficient library prestores a plurality of groups of Butterworth filter coefficients aiming at different cut-off frequencies; And configuring the eight-order Butterworth digital low-pass filter by using the selected filter coefficient, filtering three-axis acceleration and three-axis angular velocity original data of the inertial measurement unit, and then using the filtered data for subsequent inertial navigation calculation.
  6. 6. The method for processing vehicle monitoring and positioning data according to claim 5, wherein the cut-off frequencies pre-stored in the filter coefficient library include 5 hz, 10 hz, 15 hz and 20 hz, and the selected filter cut-off frequency is slightly higher than the main vibration frequency.
  7. 7. The method for processing vehicle monitoring and positioning data according to claim 1, further comprising a running environment state switching management mechanism for ensuring smoothness and stability of switching of a processing pipeline; the driving environment state switching management mechanism specifically comprises: setting a state entry threshold and a state exit threshold, wherein the state exit threshold is lower than the state entry threshold so as to form a hysteresis interval of state switching; setting a state duration counter, and executing switching to a corresponding target data processing pipeline after the triggering condition of the new running environment state type continuously meets the preset time length; At the instant of the processing pipeline switching, the state covariance matrix of the extended Kalman filter in the previous pipeline is passed to the filter of the newly activated pipeline as its initial covariance matrix.
  8. 8. The method for processing vehicle monitoring and positioning data based on multi-source data fusion according to claim 7, wherein the preset time length is 2 seconds, the entering threshold of the vibration interference state is 0.1 meter per square second square hertz of the vertical axis acceleration signal energy integrated value, and the exiting threshold is 0.07 meter per square second square hertz.
  9. 9. The method for processing vehicle monitoring and positioning data according to claim 1, wherein the multisource real-time sensor data are each provided with a high-precision time stamp, and nanosecond time alignment is achieved through a hardware synchronization circuit.

Description

Multi-source data fusion vehicle monitoring and positioning data processing method Technical Field The invention belongs to the technical field of electronic data digital processing, and particularly relates to a vehicle monitoring and positioning data processing method based on multi-source data fusion. Background With the rapid development of intelligent traffic systems and high-level automatic driving technologies, high-precision positioning of vehicles has become a core support for realizing safe navigation, fleet coordination and real-time monitoring. In urban complex road environments, global Navigation Satellite System (GNSS) signals are extremely susceptible to high-rise obstruction, overpass group reflection or multipath effect interference, resulting in severely compromised positioning continuity. In order to make up for the positioning blank in the period of GNSS signal deficiency, the existing scheme generally adopts an Inertial Navigation System (INS), a wheel speed meter and other sensors to carry out dead reckoning, and the track integrity is maintained by relying on a multi-source data fusion strategy. However, such methods still face a number of fundamental challenges in practical applications. Vertical dynamic disturbance generated when a vehicle runs on a bumpy road surface can obviously influence the attitude resolving precision of an Inertial Measurement Unit (IMU), particularly the dynamic drift of a pitch angle and a roll angle is not effectively modeled and compensated, and the position and heading errors are directly accumulated in the dead reckoning process. The existing fusion algorithm mostly assumes that the vehicle is in a stable motion state, and lacks explicit perception and utilization of instantaneous vibration signals caused by road surface excitation, so that attitude estimation is rapidly misaligned after short-time GNSS interruption. In addition, the main stream Kalman filtering or factor graph optimizing framework usually adopts fixed noise covariance parameters, and the trust degree of IMU data cannot be dynamically adjusted according to the current road conditions, so that the system is excessively dependent on polluted acceleration and angular velocity observation under severe jolt, and the positioning deviation is further amplified. The problem that the energy efficiency and the precision are difficult to be compatible is that the vibration suppression or error compensation module often works in a full-time operation mode, and calculation resources and electric energy are continuously consumed no matter whether a vehicle is in a highway cruising or urban congestion low-vibration scene, so that unnecessary power consumption expenditure is caused. Meanwhile, the fusion strategy lacks active perceptibility of environmental contexts (such as GNSS availability, road types and building density), and cannot be switched to a more robust processing mode in advance in typical weak signal areas such as under an overhead bridge, tunnel entrance or dense urban areas. The defects are intensively exposed in key road sections such as an interchange intersection area, an underground ramp connection section and the like, track jump, positioning drift and even service interruption are often caused, and the deployment efficiency of a vehicle monitoring system in high-reliability application scenes such as automatic driving decision, emergency dispatching response, safety early warning and the like is severely restricted. Disclosure of Invention The invention aims to provide a vehicle monitoring and positioning data processing method for multi-source data fusion, and aims to solve the problems that an inertial navigation module dynamically drifts at an attitude angle of a bumpy road section, a sensor fusion strategy lacks environmental context sensing capability and a vibration suppression model operates at full time to cause unbalance of power consumption and precision in the prior art. To achieve the above object, according to an aspect of the present invention, there is provided a vehicle monitoring and positioning data processing method of multi-source data fusion, comprising the steps of: Acquiring multi-source real-time sensor data, wherein the multi-source real-time sensor data comprises global navigation satellite system data, inertial measurement unit data and vehicle odometer data, the global navigation satellite system data comprises pseudo-range, carrier phase, doppler frequency shift and satellite ephemeris information, the inertial measurement unit data comprises triaxial acceleration and triaxial angular velocity, and the vehicle odometer data comprises wheel rotating speed pulse signals; constructing a driving environment feature vector based on the multi-source real-time sensor data, and classifying the current driving environment state of the vehicle into a preset driving environment state type according to the driving environment feature vector, wherein the preset dr