Analysis of high dynamic car manoeuvres using two types of lever-arm correction

As navigation algorithms using Kalman filters, fuzzy or adaptive algorithms, interacting multiple model (IMM) algorithms and other possible solutions combining data from several sensors, have been progressively used in the last decade, there has been little advance in developing a robust and accurate device available for car manufacturers. The most solutions fail in long-term reliability and/or use too generalized linearization models. This is why in this paper we have examined some high dynamic manoeuvres which are usually a part of automotive tests. Some major issues during these manoeuvres were identified and a modified Kalman filter solution is presented. The problem of positioning of an inertial device within a vehicle is addressed and a transformation of measured data to the centre of gravity (COG) or rotation point (RP) of the vehicle is introduced. We also propose a few methods to identify the start and the stop of a brake test and show distance difference between conventional and modified Kalman algorithm during driving in circles. Finally, a direct and indirect lever-arm correction is introduced and real road tests are made to present an improvement in outputs using one-device sensor setup.