Track maintenance using the SMC-intensity filter

Abstract

The so-called lack of memory is an inherent challenge of the probability hypothesis density (PHD) filter and leads to the fact that only targets which rely on a currently available measurement can securely be reported as present in the respective iteration. Yet there is no method presented that enables the sequential Monte Carlo (SMC) version of the intensity filter (iFilter) to manage failure of measurements. In this paper we develop a procedure and a complete implementation scheme within the SMC-iFilter to detect failure of measurements and to generate so-called pseudo measurements, which are used to estimate the state of targets, belonging to missing measurements. To assess the developed method with respect to accuracy a numerical study is carried out, using a simulation of a linear multi-object scenario.