The networking capabilities of tactical mobile adhoc
networks (MANETs) provide the basis to enhance robustness
and accuracy of Blue Force Tracking (BFT) where existing BFT
mechanisms are unavailable, unreliable, or simply not sufficiently
accurate (owing to factors such as update frequencies and the
need for back-link availability). BFT is an essential element to any
tactical environment given its ability to contribute to situational
awareness at all levels.
Tactical environments are characterized by spectrum contention,
jamming and other factors limiting the ability of na¨ıve
approaches, e. g. in urban environments and broken terrain.
Unlike previous work this paper aims to provide MANET-based
BFT without the requirement of line-of-sight (LOS) links or backend
infrastructure which is robust against temporal disruption of
network connectivity. These results are achieved by distributedly
fusing sensor data and additional information sources across the
tactical MANET using techniques also employed in robotics and
object tracking.
Our contribution is the provision of enhanced BFT mechanisms
exploiting networking capabilities of tactical MANETs
and data fusion mechanisms based on Sequential Monte Carlo
methods, specifically particle filters, incorporating additional
information such as mission information (e. g. mobility models)
and topographic data. We demonstrate that the use of these
techniques enhances both accuracy and robustness as compared
to standard BFT by using a simulation environment with various
mobility and radio propagation characteristics.