Fusion architectures with Extended KALMAN Filter for locate wheelchair position using sensors measurements

Abstract

Tow different architectures are presented to fuse measurements coming from odometers, compass and accelerometer to locate wheelchair position in 2D Cartesian coordinates, with Extended KALMAN Filter (EKF). The performance of these architectures is checked with simulated data. Detailed mathematical expressions are provided which could be useful for algorithm implementation. Comparative studies between these two methods shows that the MF architecture (measurement fusion) provides estimates of states relatively less uncertainty followed by SVF (state vector fusion). The odometers measures give the position with relatively high uncertainty followed by the accelerometer measurements. It shows the need for fusion in navigation system.