Abstract
For heavily cluttered environments with low target detection probabilities, tracking filters may fail to estimate the true number of targets and their trajectories. Smoothing may be needed to refine the estimates based on collected measurements. However, due to uncertainties in target motions, heavy clutter, and low target detection probabilities, the forward prediction and the backward prediction may not be properly matched in the smoothing algorithms, so that the smoothing algorithms may fail to detect the true target trajectories. In this paper, we propose a new smoothing algorithm to overcome such difficulties. This algorithm employs two independent integrated probabilistic data association (IPDA) tracking filters: one running forward in time (fIPDA) and the other running backward in time (bIPDA). The proposed algorithm utilizes bIPDA multi-tracks in each fIPDA path track for fusing through data association to obtain the smoothing innovation in a fixed-lag interval. The smoothing innovation is used to obtain the smoothing data association probabilities which update the target trajectory state and the probability of target existence. The fIPDA tracks are updated after smoothing using the smoothing data association probabilities, which makes the fIPDA path tracks robust for maneuvering target tracking in clutter. This significantly improves the target state estimation accuracy compared to the IPDA. The proposed algorithm is called fixed-lag smoothing data association based on IPDA (FLIPDA-S). A simulation study shows that the proposed algorithm improves false track discrimination performance for maneuvering target tracking in clutter.
Highlights
In target tracking, sensors detect targets as well as various unwanted objects in the surveillance area
We propose a new smoothing algorithm called fixed-lag smoothing data association based on Integrated probabilistic data association (IPDA) (FLIPDA-S)
6 Conclusions This paper presents a new smoothing algorithm called fixed-lag smoothing data association algorithm based on IPDA (FLIPDA-S) for tracking maneuvering targets in clutter using a constant-velocity model
Summary
Sensors detect targets as well as various unwanted objects in the surveillance area. At each scan in the fixed-lag interval, the forward track state prediction of FLIPDA-S is used to produce smoothing innovations associating with the multiple tracks generated from bIPDA In this fusion, each track in the forward path treats the backward path tracks as the measurements for data association. In the fixed-lag interval, the FLIPDA-S uses each updated forward track and new forward tracks (initialized by fIPDA) to fuse with the backward path multiple tracks to smooth the target trajectory state and the probability of target existence. This procedure continues in each subsequent interval, which significantly improves both the target trajectory state estimation accuracy and the FTD.
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