Next Article in Journal
Evaluation of Aboveground Nitrogen Content of Winter Wheat Using Digital Imagery of Unmanned Aerial Vehicles
Next Article in Special Issue
Optimal Target Assignment with Seamless Handovers for Networked Radars
Previous Article in Journal
Terrain Feature Estimation Method for a Lower Limb Exoskeleton Using Kinematic Analysis and Center of Pressure
Previous Article in Special Issue
DOA Tracking Based on Unscented Transform Multi-Bernoulli Filter in Impulse Noise Environment
Open AccessArticle

GLMB Tracker with Partial Smoothing

1
School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Bentley 6102, Australia
2
School of Engineering, RMIT University, Melbourne 3000, Australia
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(20), 4419; https://doi.org/10.3390/s19204419
Received: 9 August 2019 / Revised: 9 October 2019 / Accepted: 10 October 2019 / Published: 12 October 2019
In this paper, we introduce a tracking algorithm based on labeled Random Finite Sets (RFS) and Rauch–Tung–Striebel (RTS) smoother via a Generalized Labeled Multi-Bernoulli (GLMB) multi-scan estimator to track multiple objects in a wide range of tracking scenarios. In the forward filtering stage, we use the GLMB filter to generate a set of labels and the association history between labels and the measurements. In the trajectory-estimating stage, we apply a track management strategy to eliminate tracks with short lifespan compared to a threshold value. Subsequently, we apply the information of trajectories captured from the forward GLMB filtering stage to carry out standard forward filtering and RTS backward smoothing on each estimated trajectory. For the experiment, we implement the tracker with standard GLMB filter, the hybrid track-before-detect (TBD) GLMB filter, and the GLMB filter with objects spawning. The results show improvements in tracking performance for all implemented trackers given negligible extra computational effort compared to standard GLMB filters. View Full-Text
Keywords: labeled RFS; RTS smoother; GLMB filter labeled RFS; RTS smoother; GLMB filter
Show Figures

Figure 1

MDPI and ACS Style

Nguyen, T.T.D.; Kim, D.Y. GLMB Tracker with Partial Smoothing. Sensors 2019, 19, 4419.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop