1 research outputs found
Enhanced RSS-based UAV Localization via Trajectory and Multi-base Stations
To improve the localization precision of unmanned aerial vehicle (UAV), a
novel framework is established by jointly utilizing multiple measurements of
received signal strength (RSS) from multiple base stations (BSs) and multiple
points on trajectory. First, a joint maximum likelihood (ML) of exploiting both
trajectory information and multi-BSs is proposed. To reduce its high
complexity, two low-complexity localization methods are designed. The first
method is from BS to trajectory (BST), called LCSL-BST. First, fixing the nth
BS, by exploiting multiple measurements along trajectory, the position of UAV
is computed by ML rule. Finally, all computed positions of UAV for different
BSs are combined to form the resulting position. The second method reverses the
order, called LCSL-TBS. We also derive the Cramer-Rao lower boundary (CRLB) of
the joint ML method. From simulation results, we can see that the proposed
joint ML and separate LCSL-BST methods have made a significant improvement over
conventional ML method without use of trajectory knowledge in terms of location
performance. The former achieves the joint CRLB and the latter is of
low-complexity