Observability-based sensor sampling

Abstract

Systems with a rich array of sensors but limited power and/or processing resources have more potential information available than they can use and are forced to subsample the data. In this work, we build on observability analysis for general nonlinear systems to provide a basis for a framework to investigate dynamic sensor selection to optimize a measure of observability, specifically the condition number of an observability Gramian. This optimization is then applied to a sample system of natural Frenet Frames with sensing allowed to alternate between bearing and range measurements relative to a fixed beacon.