Distributed fusion of local probability data association filters in multi-sensor environment

Authors:
Kyungmin Lee;Vladimir Shin
Affiliations:
School of Information and Mechatronics, Gwangu Institute of Science and Technology, Gwangju, South Korea;School of Information and Mechatronics, Gwangu Institute of Science and Technology, Gwangju, South Korea
Venue:
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Year:
2009

Citing 3
Cited 0

Estimation with Applications to Tracking and Navigation

Estimation with Applications to Tracking and Navigation
Generalized Millman's formula and its application for estimation problems

Signal Processing
A new fusion formula and its application to continuous-time linear systems with multisensor environment

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

The problem of data association for target tracking in a multi-sensor cluttered environment is discussed. The probabilistic data association filter (PDAF) is useful to obtain proper estimate of state in this environment. We propose two distributed algorithms for PDAF to acquire high accuracy system and reduce computation burden caused by clutter. The distributed process and its modified fusion algorithm for the PDAF is introduced, such as the optimal fusion formula (OFF) and covariance intersection (CI). The OFF is optimal in view of each local sensor and it has the great accuracy among the distributed fusion algorithms. On the other hands, the CI has weighted convex combination without cross-covariance, so it has the advantage of fastness. Finally, the simulation results show that the proposed algorithms have advantages over robustness and lower computation burden.