Dynamic Modeling for Land Mobile Navigation Using Low-Cost Inertial Sensors and Least Squares Support Vector Machine Learning

摘要

Traditional algorithms used to determine a vehicle's navigation state (e.g. Kalman filter) has as one of its prerequisites, a model that describes how the vehicle is expected to move over time. The accuracy of this dynamic model is important, as it allows for optimization of the navigation solution, particularly when dealing with low cost sensors which typically exhibit significant errors and biases. Unfortunately, for land vehicles, apriori knowledge of the true dynamic model is very difficult to achieve by virtue of the random dynamic variations that exist and that there is no general navigation case. This situation is even further complicated in many navigation applications where non-linearity and demanding environments characterize the motion and challenge the assumptions of most filtering methods (e.g. linear dynamics behaviour and Gaussian white noise).