Nonlinear Attractor Dynamics: A New Approach to Sensor Fusion

Abstract

Fusing information of multiple sensors is particularly di#cult if the sensor systems which provide the information have very di#erent characteristics such as di#erent data formats, reliabilities, signal to noise ratios, sampling rates and so on. Furthermore, the information is often provided on di#erent levels of abstraction such as the direct sensor output in contrast to expert knowledge or a priori information. We propose a new approach to sensor fusion which accounts for these problems. The basic idea is to represent the quantity to estimate as the state variable of a nonlinear dynamical system. The sensor signals act on this dynamics by specifying attractors with limited range of influence. The dynamics relaxes into a stable state which results from the superposition of the attractors. By means of the limited attractor ranges, the dynamics automatically averages nonlinearly over corresponding sensor signals while outliers stemming from temporarily de-calibrated or erroneous sensors..