CN-120043518-B - GNSS signal quality self-adaptive integrated navigation method

Abstract

A GNSS signal quality self-adaptive integrated navigation method relates to the technical field of vehicle navigation. Reading the IMU, GNSS positioning information and positioning states, realizing combined navigation through ESKF pine combination algorithm, changing a measurement covariance matrix in real time according to the GNSS positioning states and DOP factors, finally estimating the value of an error state, feeding back the value into a combined navigation system, feeding back position and speed information errors and attitude Euler angle errors under a navigation coordinate system into INS mechanical programming, feeding back the angular acceleration zero offset errors of the IMU to the IMU, and finishing updating of state quantity. According to the method, an IMU and GNSS data are subjected to a ESKF fusion algorithm in a loose combination mode, a measurement covariance matrix is changed in real time according to the GNSS positioning state and DOP factors, an error state is estimated, and the error state is fed back to the integrated navigation system, so that the positioning accuracy can be effectively improved.

Inventors

• ZHAO LINHUI
• ZHANG BINGHONG

Assignees

• 哈尔滨工业大学

Dates

Publication Date

20260512

Application Date

20250126

Claims (1)

1. 1. A GNSS signal quality self-adaptive integrated navigation method is characterized by comprising the following steps: step one, reading IMU and GNSS positioning information and positioning state The original data reading of the IMU comprises three-axis acceleration, three-axis angular velocity and time stamp information, the GNSS is connected with a Cors base station through a 4G module to realize an RTK positioning mode, a positioning precision factor DOP, a position and time stamp information are obtained after the GNSS is converged, wherein the positioning mode information comprises an RTK fixed solution, an RTK floating point solution, a DGPS and a GPS, the position information comprises longitude, latitude and altitude, the information data of the IMU and the GNSS are stored through an SD card through a singlechip, and subsequent data offline processing is carried out; Step two, ESKF pine combination algorithm for realizing combination navigation The error state Kalman filtering method is designed, a north-east-earth coordinate system is adopted as a navigation coordinate system, a front-right-down is adopted as a carrier coordinate system, and an error state vector model is defined as follows: In the formula, For the position information error in the navigation coordinate system, For the speed information error in the navigational coordinate system, For the attitude euler angle error, Is the angular velocity zero offset error of the IMU, The acceleration zero offset error of the IMU; the rotation matrix from the carrier coordinate system to the navigation coordinate system is defined as follows: In the formula, Representing the calculated rotation matrix estimate, Is used for the transverse rolling angle, and the transverse rolling angle is used for the transverse rolling angle, Is used as a pitch angle of the light beam, Is a course angle; INS mechanical orchestration slave From moment to moment Updating the time of day state, wherein: Pose update, equation is expressed as follows: In the formula, Representation of The attitude euler angle of the moment in time, Representation of Three-axis angular velocity information measured by the IMU at the moment, Representation of The rotation matrix estimate of the time instant, And The projected components of the earth rotation angular velocity and the traction angular velocity in the navigation coordinate system respectively represent the current geographic position, Representing a sampling time interval; speed update, equation is expressed as follows: In the formula, Representation of Speed information in a time-of-day navigation coordinate system, Representation of From moment to moment The projected component of the velocity increment of time in the carrier coordinate system, Gravitational acceleration representing a current geographic location; The location update, equation is expressed as follows: In the formula, Representation of The height of the moment in time is set, Representation of The latitude of the moment in time, Representation of The longitude of the moment of time, The velocity of the ground direction is indicated, The north-direction velocity is indicated as such, Indicating the speed of the east direction, And Radius values of the earth's prime circle and the meridian circle are respectively represented; modeling an error state vector of a navigation coordinate system, wherein the attitude error model is expressed as follows: In the formula, And Respectively representing the position information estimated value and the speed information estimated value under the navigation coordinate system, And Respectively representing the position information and the speed information of the INS under the navigation coordinate system of mechanical arrangement calculation, Representing the identity matrix of the cell, Representing an antisymmetric matrix of deviation angles between the true euler angles and the calculated euler angles, Representing the true value of the rotation matrix; the error model of the IMU accelerometer and the angular velocity meter is expressed as follows, taking into account zero bias and white noise of the IMU: Wherein, the In the formula, And Representing the error of the angular velocity meter and the accelerometer respectively in the carrier coordinate system of the IMU output, And Modeling is performed as a first order gaussian markov model, And Representing the white noise of the IMU, And Is the time constant of the model and, And Is the bias of the model; The state transfer matrix is obtained by composing a state propagation model through error states The following are provided: In the formula, Representation of An antisymmetric matrix formed by projection components of specific force output by the IMU at the moment under a navigation coordinate system, An antisymmetric matrix composed of the rotation angular velocity and the traction angular velocity of the earth is shown, 、 、 、 、 The method comprises the following steps: In the formula, Representing an earth rotation speed constant; the state prediction equation of the integrated navigation system is as follows: Wherein, the In the formula, Representation of A discrete state transition matrix of time of day, Representation of A continuous state transition matrix of time instants, Representation of The IMU noise matrix at the moment in time, Representing the IMU noise drive matrix, A noise covariance matrix representing IMU measurements, Is shown in Time of day prediction The error state vector of the moment in time, Is shown in Time of day prediction A state covariance matrix of the moment; step three, changing the measurement covariance matrix in real time according to the GNSS positioning state and DOP factor Reading positioning mode information of GNSS at each moment, initializing the value of a measurement covariance matrix according to a preset value according to different positioning mode information, and calculating according to the HDOP factor and the VDOP factor The measurement covariance matrix of the time instant is expressed as follows: In the formula, Values representing an initial measurement covariance matrix; The position information of the navigation coordinate system output by the GNSS is taken as the observed value of ESKF algorithm, and the observation equation of the combined navigation system is expressed as follows: In the formula, Representing the position information estimation value under the navigation coordinate system after compensating the lever arm effect, Vectors representing the spatial positions of the GNSS and IMU, Representing a deviation vector between the predicted position information and the GNSS measured position information in the navigation coordinate system, Representing position vector information in the navigation coordinate system of the GNSS measurements, Representing the observation matrix of the integrated navigation system, The conversion matrix representing the conversion of longitude and latitude into meters is represented as follows: Constructing Kalman filtering equation, and modeling error state vector Sum-state covariance matrix The estimation is performed as follows: In the formula, Representation of A Kalman gain at time; Estimating the value of the error state and feeding back the value to the integrated navigation system At the position of In the error state vector obtained by time calculation, the position information error under the navigation coordinate system And speed information error Euler angle error of attitude The composition navigation error is fed back to INS mechanical arrangement, and the angular velocity of IMU is zero offset error And acceleration zero offset error And forming an IMU error feedback to the IMU itself, completing the update of the state quantity, and finally resetting the error state vector.

Description

GNSS signal quality self-adaptive integrated navigation method Technical Field The invention relates to the technical field of vehicle navigation, in particular to a GNSS signal quality self-adaptive combined navigation method. Background The Global Navigation Satellite System (GNSS) is widely used to provide real-time positioning navigation services in the vehicle-mounted field, and in order to achieve accurate positioning, a GNSS receiver needs to receive signals from at least four satellites, and the four satellites should be distributed to cover different orientations as much as possible, so as to ensure higher positioning accuracy and reliability. The quality of GNSS signals may be affected by many aspects, such as the natural environment, geometric position arrangement of satellites, electromagnetic field interference, etc. The difference in positioning modes of GNSS directly affects positioning accuracy, where single point positioning (SPS) is the most basic GNSS positioning mode, positioning is performed using only signals from satellites of GNSS, with relatively low accuracy, typically in the range of a few meters, real-time kinematic (RTK) can provide positioning accuracy on the order of centimeters, and differential positioning (DGPS) corrects errors in satellite signals by using known position information of one or more ground reference stations. An Inertial Measurement Unit (IMU) consists of accelerometers, gyroscopes, for measuring and reporting physical properties of the vehicle, including speed, direction, acceleration, etc., in real time. In strapdown inertial navigation of a vehicle, dead Reckoning (DR) can be performed according to original data of an IMU, and the basic idea is that velocity information is obtained by integrating acceleration information of the IMU, then displacement can be estimated by integrating the velocity data again, and the direction of an object is corrected or updated through gyroscope data, so that accuracy of displacement calculation is improved. However, small measurement errors are amplified over time due to the accumulation of errors in the IMU, affecting the accuracy of the positioning. Meanwhile, the original data of the IMU is affected by factors such as zero offset and scale factors, and proper calibration and compensation mechanisms are needed to ensure the accuracy of the data. At present, under the condition that the initial pose is known, IMU and GNSS data are processed separately, and the calculation results of the IMU and the GNSS are fused. And predicting by dead reckoning of the IMU, calculating a navigation solution, taking GNSS measurement as an observation value, and combining the measurement value with the observation value in an error state Kalman filtering mode to improve the positioning accuracy of the whole system. However, in practical applications, the positioning accuracy of GNSS may be affected by the positioning mode and the geometric position of the satellite, so that the observed noise in the fusion algorithm is increased, in unreliable GNSS positioning information, the integrated navigation system may be affected, the current navigation strategy usually ignores the effect, and ignores the change of the GNSS positioning mode in practical applications, so that the noise is indirectly introduced into the integrated navigation system, and the accuracy may be reduced. Disclosure of Invention In order to solve the defects in the background art, the invention provides a GNSS signal quality self-adaptive integrated navigation method, which aims at the IMU and GNSS data, and by means of a ESKF fusion algorithm of loose combination, a measurement covariance matrix is changed in real time according to a GNSS positioning state and DOP factors, and an error state is estimated and fed back to an integrated navigation system, so that the positioning precision can be effectively improved. In order to achieve the purpose, the technical scheme adopted by the invention is that the GNSS signal quality self-adaptive integrated navigation method comprises the following steps: step one, reading IMU and GNSS positioning information and positioning state The original data reading of the IMU comprises three-axis acceleration, three-axis angular velocity and time stamp information, the GNSS is connected with a Cors base station through a 4G module to realize an RTK positioning mode, a positioning precision factor DOP, a position and time stamp information are obtained after the GNSS is converged, wherein the positioning mode information comprises an RTK fixed solution, an RTK floating point solution, a DGPS and a GPS, the position information comprises longitude, latitude and altitude, the information data of the IMU and the GNSS are stored through an SD card through a singlechip, and subsequent data offline processing is carried out; Step two, ESKF pine combination algorithm for realizing combination navigation The error state Kalman filtering method is de