An ADMM-Based Geometric Configuration Optimization in RSSD-Based Source Localization By UAVs with Spread Angle Constraint
arxiv(2023)
摘要
Deploying multiple unmanned aerial vehicles (UAVs) to locate a
signal-emitting source covers a wide range of military and civilian
applications like rescue and target tracking. It is well known that the
UAVs-source (sensors-target) geometry, namely geometric configuration,
significantly affects the final localization accuracy. This paper focuses on
the geometric configuration optimization for received signal strength
difference (RSSD)-based passive source localization by drone swarm. Different
from prior works, this paper considers a general measuring condition where the
spread angle of drone swarm centered on the source is constrained. Subject to
this constraint, a geometric configuration optimization problem with the aim of
maximizing the determinant of Fisher information matrix (FIM) is formulated.
After transforming this problem using matrix theory, an alternating direction
method of multipliers (ADMM)-based optimization framework is proposed. To solve
the subproblems in this framework, two global optimal solutions based on the
Von Neumann matrix trace inequality theorem and majorize-minimize (MM)
algorithm are proposed respectively. Finally, the effectiveness as well as the
practicality of the proposed ADMM-based optimization algorithm are demonstrated
by extensive simulations.
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