Swarm technology provides a new opportunity for sensor systems in which the movement of the agents can be leveraged to enhance the joint capacity of the sensors and subsequent signal processing algorithms. In the Localizing Urban Swarm Technology (LocUST) project for DARPA's OFFensive Swarm-Enabled Tactics (OFFSET) program, the authors developed a complex fading model for the virtual radio frequency (RF) environment, decentralized search and localization tasking, and movement-enhanced time difference of arrival (TDOA) localization. The system was also implemented in hardware using Bluetooth 5.0 modules. This paper reports on the fading model, localization algorithm, and hardware testing results, with the companion paper reporting on the swarm coordination, localization-enhancing movement, and associated experimentation.
Swarms of inexpensive, robotic sensors have the potential for revolutionizing intelligence gathering. They self-organize to provide wide apertures, redundancy, attritability, with low probability of detect over a wide area. Coordinating swarm behaviors to provide the necessary apertures and spatial configurations requires novel methods of distributed control that can maintain the positioning accuracy in the face of arbitrary threats and obstacles. In this paper we describe the algorithms to control a swarm of air vehicles with radio frequency receivers that cooperatively search an urban area for radio frequency emitters, self-organize into teams to localize each emitter, and perform coordinated maneuvers to maximize the information gain during the localization operation. The swarm is able to adapt to attrition, performs collision avoidance, and adjusts its trajectories based on the urban terrain. These behaviors were implemented in a ROS-based swarm deployment environment suitable for execution on a small drone and simulated in a 3D model of a small urban area. This paper describes the search and localization tactics employed, the algorithms for implementing those tactics in the swarm, and experimental results. Our companion paper describes the algorithms used for localization.
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