A Modified PDAF Algorithm For Measurement Covariances that Vary within a Time Scan

This paper presents a modified version of the probabilistic data association filter (PDAF) algorithm for single target tracking which allows for measurement covariances to vary within, as well as between, time scans. This modification enhances the ability to track targets in situations where the measurement errors within a time scan are highly variable. The potential for improved tracking is demonstrated by comparing the performance of the modified and the standard algorithm in tracking simulated intensity detector measurements.