BAYESIAN AND CRAMER-RAO BOUNDS FOR SINGLE SENSOR TARGET LOCALIZATION VIA MULTIPATH EXPLOITATION

In urban scenarios, target localization may be achieved using a single sensor via multipath exploitation. The multipath generating mechanisms such as building walls creates virtual radar sensors aiding in localization. For a wide class of radar-target geometries, specialized functions termed multipath preservers are derived to ensure that multipath is physically observable in the radar returns, and therefore these functions assist in evaluating the potential of multi path exploitation in urban sensing. The single sensor system performance is studied by deriving the Cramer-Rao and the Bayesian Cramer-Rao bounds (BCRBs). Given a reflecting geometry, these lower bounds and multi path preservers allow the radar operator to anticipate blind spots, place confidence levels on the localization results, and permit sensor positioning to optimally aid in exploiting multipath for target localization. It is shown here that Cramer-Rao bounds (CRBs) on the location parameters improve with additional multipath.