Tracking and Estimation of Multiple Cross-Over Targets in Clutter

Abstract

Tracking problems, including unknown number of targets, target trajectories behaviour and uncertain motion of targets in the surveillance region, are challenging issues. It is also difficult to estimate cross-over targets in heavy clutter density environment. In addition, tracking algorithms including smoothers which use measurements from upcoming scans to estimate the targets are often unsuccessful in tracking due to low detection probabilities. For efficient and better tracking performance, the smoother must rely on backward tracking to fetch measurement from future scans to estimate forward track in the current time. This novel idea is utilized in the joint integrated track splitting (JITS) filter to develop a new fixed-interval smoothing JITS (FIsJITS) algorithm for tracking multiple cross-over targets. The FIsJITS initializes tracks employing JITS in two-way directions: Forward-time moving JITS (fJITS) and backward-time moving JITS (bJITS). The fJITS acquires the bJITS predictions when they arrive from future scans to the current scan for smoothing. As a result, the smoothing multi-target data association probabilities are obtained for computing the fJITS and smoothing output estimates. This significantly improves estimation accuracy for multiple cross-over targets in heavy clutter. To verify this, numerical assessments of the FIsJITS are tested and compared with existing algorithms using simulations.

Rauch-Tung-Striebel Smoothing Linear Multi-Target Tracking in Clutter

“…The simulation analysis compares the estimation accuracy, root-mean square errors (RMSEs), FTD track quality measure, and track retention of the RTS-LMIPDA, FIsJIPDA [16], FIsJITS [17], sJIPDA [18], sJITS [19], and LMIPDA [10] algorithms. The RTS-LMIPDA algorithm is designed to track six targets that are crossing near the position (387, 300) m and are closely moving in a twodimensional surveillance region that is 700 m wide along the x-and y-axes, as shown in Fig.…”

“…These algorithms were extended for smoothing MTT by utilizing a joint data association algorithm, such as fixed interval smoothing based on JIPDA (FIsJIPDA) [16]. Recently, fixed interval smoothing was utilized in multi-scan, multi-target joint integrated track splitting to calculate the smoothing a-posteriori probabilities of the multiple track components associated in a cluster for smoothing state estimation [17]. RTS smoothing equations in a JIPDA algorithm (sJIPDA) use the forward track validated measurements to obtain a backward track prediction [18].…”

Smoothing Linear Multi-Target Tracking Using Integrated Track Splitting Filter

“…This implies that each forward track is conditioned on a set of backward multi-components. Each forward state component forms a certain number of true pairs in association with backward multi-tracks components using the validation measurement selection criterion expressed in (20). This procedure is illustrated in Figure 2.…”

“…As an example of smoothing based on JITS (sJITS), the authors have applied the two-way pass filtering method of the Fraser and Potter algorithm [18] in [19]. In [20], the fixed interval smoothing based on JITS (FIsJITS) was developed for MTT, which outperformed the algorithms developed in [14,17,19]. However, due to involvement of clusters as well as ITS multi-track components, the joint data association procedure in FIsJITS and sJITS is computationally intensive and complex.…”

Extended smoothing joint data association for multi‐target tracking in cluttered environments

“…In [18], FLIPDA‐S employed JIPDA to propose fixed‐interval smoothing JIPDA (JIPDAS) in MTT environments. Later, FLIPDA‐S is extended to a multi‐scan algorithm known as fixed‐interval integrated track splitting smoothing for tracking a single target in [19, 20].…”