Abstract
When a low-cost micro-electro-mechanical system inertial measurement unit is used for a vehicle navigation system, errors will quickly accumulate because of the large micro-electro-mechanical system sensor measurement noise. To solve this problem, an automotive sensor–aided low-cost inertial navigation system is proposed in this article. The error-state model of the strapdown inertial navigation system has been derived, and the measurements from the wheel speed sensor and steer angle sensor are used as the new observation vector. Then, the micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor–integrated system is established based on adaptive Kalman filtering. The experimental results show that the positioning error of micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor is 94.67%, 98.88%, and 97.88% less than the values using pure strapdown inertial navigation system in the east, north, and down directions, respectively. The yaw angle error is reduced to less than 1°, and the vehicle velocity estimation of micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor–integrated navigation system is closer to the reference value. These results show the precision of the integrated navigation solution.
Highlights
A micro-electro-mechanical system (MEMS) sensor has the advantages of small size, low energy consumption and low price, but it has a large measurement error.[1]
The results show that the MEMS inertial measurement unit (MIMU)/wheel speed sensor (WSS)/SASintegrated navigation system can well estimate the vehicle roll angle and pitch angle
When the low-cost inertial navigation system (INS) with the MIMU is used for the strapdown inertial navigation system (SINS), considering the positioning accuracy of such INSs and the fact that the gyroscope cannot eliminate the integral error in the process of using the gyroscope, an MIMU/WSS/steering wheel angle sensor (SAS)-integrated navigation system is proposed in this article
Summary
A micro-electro-mechanical system (MEMS) sensor has the advantages of small size, low energy consumption and low price, but it has a large measurement error.[1]. The estimation of the attitude, velocity, and position of the vehicle can be obtained by integrating the acceleration and angular velocity of the body in the navigation frame. The AKF can estimate and correct the unknown system model parameters and noise statistics parameters.[15] In this article, this type of filtering method is used to fuse the signal data of the MIMU/ WSS/SAS vehicle-integrated navigation system.
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