Optimal Measurement of Drone Swarm in RSS-based Passive Localization with Region Constraints

TL;DR

This paper investigates optimal UAV placement for RSS-based passive localization considering real-world region constraints, deriving closed-form solutions to improve localization accuracy in practical scenarios.

Contribution

It introduces a novel optimization framework using D-optimal criteria for UAV placement under geographical constraints, with explicit closed-form solutions.

Findings

Optimal UAV configurations improve localization accuracy.

Closed-form solutions are practical for real-world deployment.

Simulation confirms effectiveness of the proposed methods.

Abstract

Passive geolocation by multiple unmanned aerial vehicles (UAVs) covers a wide range of military and civilian applications including rescue, wild life tracking and electronic warfare. The sensor-target geometry is known to significantly affect the localization precision. The existing sensor placement strategies mainly work on the cases without any constraints on the sensors locations. However, UAVs cannot fly/hover simply in arbitrary region due to realistic constraints, such as the geographical limitations, the security issues, and the max flying speed. In this paper, optimal geometrical configurations of UAVs in received signal strength (RSS)-based localization under region constraints are investigated. Employing the D-optimal criteria, i.e., minimizing the determinate of Fisher information matrix (FIM), such optimal problem is formulated. Based on the rigorous algebra and geometrical derivations, optimal and also closed form configurations of UAVs under different flying states are proposed. Finally, the effectiveness and practicality of the proposed configurations are demonstrated by simulation examples.