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The networking capabilities of tactical mobile ad-hoc 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 naive 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.