Inertial Navigation Algorithm Based on Modified Kalman Filter and Wavelet Technique for Intelligent Vehicle

Aimed at solving the problems of GPS outage or deterioration of accuracy, the strap-down inertial navigation system (SINS) is proposed to provide reliable positioning or attitude information for intelligent vehicle. A set of mathematical transformations and integrations with respect to time are applied to these measurements of SINS to calculate position, velocity and attitude information. More importantly, in allusion to the deterioration with time of SINS accuracy, caused by the possible noise, this paper analyzes the stochastic mathematical model of SINS, uses two de-noising methods to preprocess the raw experimental data of the intelligent vehicle for eliminating integrated error. By comparing the position errors calculated with three sets of data: the raw data, the wavelet de-noised data and the adaptive fading Kalman filtered data based on the current statistical model of acceleration in the experiment, we draw a conclusion that inertial navigation algorithm with data preprocessed by the two de-noising methods can improve the position accuracy considerably. And the proposed adaptive fading Kalman filter method based on the current statistical model of acceleration is better than the wavelet de-noising method.