Multi-target Track-Before-Detect using labeled random finite set
MetadataShow full item record
Multi-target tracking requires the joint estimation of the number of target trajectories and their states from a sequence of observations. In low signal-to-noise ratio (SNR) scenarios, the poor detection probability and large number of false observations can greatly degrade the tracking performance. In this case an approach called Track-Before-Detect (TBD) that operates on the pre-detection signal, is needed. In this paper we present a labeled random finite set solution to the multitarget TBD problem. To the best of our knowledge this is the first provably Bayes optimal approach to multi-target tracking using image data. Simulation results using realistic radar-based TBD scenarios are also presented to demonstrate the capability of the proposed approach.
Showing items related by title, author, creator and subject.
Mahler, Ronald (2013)This tutorial paper summarizes the motivations, concepts and techniques of finite-set statistics (FISST), a system-level, 'top-down,' direct generalization of ordinary single-sensor, single-target engineering statistics ...
Mallick, M.; Vo, Ba-Ngu; Kirubarajan, T.; Arulampalam, S. (2013)Multitarget tracking has a long history spanning over 50 years and it refers to the problem of jointly estimating the number of targets and their states from sensor data. Today, multitarget tracking has found applications ...
Visual tracking of multiple targets by Multi-Bernoulli filtering of background subtracted image dataHoseinnezhad, R.; Vo, Ba-Ngu; Vu, T.N. (2011)Most visual multi-target tracking techniques in the literature employ a detection routine to map the image data to point measurements that are usually further processed by a filter. In this paper, we present a visual ...