Parameter Estimation and the CRLB with Uncertain Origin Measurements

Abstract

Parameter estimation in the presence of false measurements due to false alarms and missed true detections, i.e., in the presence of measurement origin uncertainty, is a difficult problem because of the need for data association, the process of deciding which, if any, is the true measurement and which are false. An additional aspect of estimation is performance evaluation via, for example, the Cramer-Rao Lower Bound (CRLB), which quantifies the achievable performance. With measurement origin uncertainty and the ensuing data association, the CRLB has to be modified to account for the loss of information due to false alarms and missed true detections. This is the focus of our paper—we show that the loss of information can be accounted for by a single scalar, known as the information reduction factor, under certain conditions. We illustrate the evaluation of the generalized CRLB on parameter estimation from direction-of-arrival measurements with applications to target tracking, communications and signal processing. Simulation results on a realistic scenario show that the lower bounds quantified via the information reduction factor are statistically compatible with the observed errors.