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.

Tracking and Estimation of Multiple Cross-Over Targets in Clutter

“…The FIsJITS algorithm is the extension of ITS-S [25] in MTT environment which employs JITS with the smoothing data association for tracking multiple cross-over targets in heavy clutter.…”

“…fJITS track forms a validation gate using Equation (22) and selects bJITS validation component prediction for fusion to obtain the predicted smoothing component bold-italicXfalse¯N\k,jψ using an information fusion (IF) filter [25] in Equation (23a). However, if the backward track (labelled by j th) is not validated, the smoothing component prediction becomes a forward component as expressed in Equation (23b).…”

“…With the track management including track component pruning and merging [26,27,28,29,30], FIsJITS removes majority of unwanted fJITS and bJITS components. FIsJITS employs track management technique [25] which compares component measurement histories computed in last four scans to merge the identical fJITS and bJITS components respectively. For example, an identical component measurement is repeating in the last four scans as depicted by blue-circle in the ellipsoid fJITS track validation gates from scan K th to K th + 3 as shown in Figure 4.…”

“…Figure 4 also illustrates the component propagation and formation of a new component with a feasible selected measurement outcome in each forward-time scan. FIsJITS applies a same pruning threshold as employed in [25] to remove the fJITS and bJITS components which have a low component existence probability.…”

“…In (22), a fixed-lag smoothing data association based on IPDA [24] algorithm is extended in MTT environment using JIPDA. Another version of ITS family is known as fixed-interval ITS smoothing (ITS-S) [25,26] but it is developed only for single target tracking (STT).…”