Talks and Poster Presentations (with Proceedings-Entry):
E. Leitinger, F. Meyer, P. Meissner, K. Witrisal, F. Hlawatsch:
"Belief Propagation Based Joint Probabilistic Data Association for Multipath-Assisted Indoor Navigation and Tracking";
Poster: International Conference on Localization and GNSS,
- 06-30-2016; in: "ICL-GNSS-2016",
We apply joint probabilistic data association (JPDA) to multipath-assisted indoor navigation and tracking (MINT). In MINT, position-related information in multipath components (MPCs) is exploited to increase the accuracy and robustness of indoor tracking. Conventional MINT algorithms are based on deterministic data association and perform a global nearest-neighbor "hard" association of MPC-related delays with the room geometry. In such a setup, incorrect associations may lead to severe tracking errors and to divergence of the Bayesian filter. Here, we propose a JPDA-MINT algorithm that is able to handle difficult situations where MPC delays overlap and data association is ambiguous. The algorithm is based on a recently introduced loopy belief propagation scheme that performs probabilistic data association jointly with agent state
estimation, scales well in all relevant systems parameters, and
has a very low computational complexity. Using data from an
ultra-wideband indoor measurement campaign, we demonstrate
that the proposed JPDA-MINT algorithm is highly accurate and
more robust than the conventional MINT algorithms based on
deterministic data association.
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.