Ghost Track Detection in Multitarget Tracking using LSTM Network

This paper analyses the track-level detection of ghost tracks in multitarget tracking with a known reflection surface. In a real-world target tracking problem, the number of targets in surveillance is unknown to the platform. Thus, the tracker will be inadequate to distinguish the direct target return from the multipath return during the track initialization. Therefore, ghost tracks can be created with multipath measurements when they are considered direct path measurements. Even though the possible multipath measurement could be predicted for the existing tracks at a given instance, it is hard to decide whether the detected track is a multipath or a new target. Thus, a sequence of time instances needs to be considered to determine the track status. In this work, we propose a classification model to classify a track as either a multipath or direct path using an LSTM network with sequential data. Additionally, the performance of the proposed approach is compared with four other algorithms using a simulation-based dataset.