Dynamic Object Tracking by Multi-UAV with Time-variant Radio Maps

As aerial mobile receivers, unmanned aerial vehicles (UAVs) possess the capability to localize and track dynamically moving objects that broadcast signals. In practice, the performance of localization and tracking is heavily reliant on the observation sequence of time-variant received signal strength (RSS) from the dynamic object on UAVs. However, this sequence can be significantly distorted by complex land layouts. In this paper, we propose dynamic object tracking by muti-UAV with time-variant radio maps, which contain rich information on the impact of land layouts on RSS. We generate the time-variant radio maps from real-world topographic data, which can be highly complex, from the multi-UAV perspectives. Then, a grid-based method and a particle filter based method, both tailored to the observed RSS from the dynamic object and the generated time-variant radio maps, are derived for the case where both the UAVs and the object are dynamic. Simulations validate the two proposed algorithms and show that time-variant radio maps can significantly improve tracking precision and efficiency compared to other benchmarks for both the mountainous area and the urban area with highly complex layouts. In addition, the particle filter based method can reduce the computational complexity via Monte Carlo sampling of the time-variant radio maps with little sacrifice in tracking precision.