Nonlinear Attractor Dynamics: A New Approach to Sensor Fusion

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..

A Robust Self-Calibrating Data Fusion Architecture

We present a general mathematical framework for the fusion of noisy sensor data. On the basis of the mathematical theory of dynamical systems we couple the outputs of the sensors to obtain a nonlinearly averaged overall estimate of the physical quantity to measure which automatically discards outliers from the averaging process. Drifts within the time series of single sensors can be compensated through a recalibration by the method of time scale inversion. By means of a unified way of representing information as stable states of a dynamical system it is possible to integrate di#erent sorts of information such as expert knowledge and sensor information smoothly within the data fusion system. We verify the feasibility of our approach on the basis of simulated stochastic data sets and on the basis of data from a study in which the brightness temperature of oil films on sea water was measured. The proposed self-calibrating sensor fusion architecture extends the work we presented at IGARSS ..

Self-calibration based on invariant view recognition: Dynamic approach to navigation

We extend a dynamic approach of behavior generation to the representation of spatial information. Two levels of dynamics integrate dead-reckoning, dominant far from home bases, and piloting, dominant near home bases. When the view-based piloting system recognizes a home base, visual place information recalibrates the dead-reckoning system, inverting the hierarchical ordering of the two dynamic levels by time scale inversion. Reference views taken at discrete home bases are recognized invariantly under rotation of views. This process yields compass information. Continuous translational information is obtained as a neural place representation built from view correlations with a scattered set of local views. This self-calibrating cognitive map couples into a dynamics of heading direction integrating the behaviors of obstacle avoidance and target acquisition. Targets can be designated in terms of the cognitive map. We demonstrate the dynamical model in simulation. Keywords: ffl navigatio..