Abstract
Most conventional data association based multi-target tracking (MTT) algorithms typically suffer from intractable computational complexities and could not perform in an environment where large number of closely spaced multiple targets move across each other in clutters. Unlike to the existing MTT systems, the linear multi-target (LM) algorithm modifies the measurement detection followed by neighbored tracks as a clutter, hence, it updates the track without the influence of other tracks. Thus, LM technique is a computationally efficient algorithm that allows the multi-target system to play like a single target tracking algorithm. Smoothing maximizes the state estimation accuracy and reduces estimation error based on future scan measurement. However, only few research paper focused on the LM algorithm without utilizing the benefits of the smoothing. This paper presents Rauch-Tung-Striebel Smoothing in the linear multi-target based on integrated probabilistic data association (RTS-LMIPDA). The RTS-LMIPDA algorithm fuses forward and backward LM track predictions to obtain the smoothing prediction which is required to calculate the smoothing multi-target state estimates in the forward track. Numerical analysis is presented to illustrate the estimation accuracy and false track discrimination (FTD) performances of RTS-LMIPDA in comparison to the existing MTT algorithms using the simulations.
Highlights
A radar detector returns uncertain measurements from multi-targets and various random sources to a multi-target tracking system
The measurements that originate from unknown object sources are referred to as clutter measurements
A target tracking algorithm initializes and updates the false tracks as well as the true tracks using the measurements received from a radar detector in each time scan
Summary
A radar detector returns uncertain measurements from multi-targets and various random sources to a multi-target tracking system. The MTT algorithms such as joint probabilistic data association (JPDA) [3], joint integrated PDA (JIPDA) [4][5], joint create all feasible joint measurement-to-track association assignment hypotheses and recursively calculate their joint a-posteriori probabilities in each scan In this situation, a cluster is formed that groups the shared tracks with similar measurements of detection. This made the smoothing algorithm efficient for target tracking but increased the smoothing time delay due to the time consumed in the initialization and estimation of the backward multi-tracks These algorithms were extended for smoothing MTT by utilizing a joint data association algorithm, such as fixed interval smoothing based on JIPDA (FIsJIPDA) [16]. 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].
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