Multi-scan smoothing for tracking manoeuvering target trajectory in heavy cluttered environment

Abstract

An automatic target tracking algorithm must be capable of dealing with an unknown number of targets and their trajectory behaviour inside the surveillance region. However, due to target motion uncertainties, heavily populated clutter measurements and low detection probabilities of targets, the smoothing algorithms often fail to detect the true number of target trajectories. In this study, the authors discussed some deficiencies and insignificances of existing smoothing algorithms and proposed a new smoothing data association based algorithm called fixed-interval integrated track splitting smoothing (ITS-S). The proposed algorithm employ smoothing data association algorithm and compared with existing smoothing algorithms outperform in terms of target trajectory accuracy and false-track discrimination (FTD). However, existing algorithms fail to generate smoothed target trajectory and provides insignificant FTD performance in such difficult environments as illustrated in this simulation study. The ITS-S shows improved smoothing performance compared with that of existing algorithms for a manoeuvering target tracking in a heavily populated cluttered environment and low detection probabilities.