Journal
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
Volume 19, Issue 4, Pages 3765-3776Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TASE.2021.3135834
Keywords
Target tracking; Estimation; Sensors; Protocols; Observability; Drones; Computational modeling; Target tracking; distributed estimation; observability; structural analysis; multilateration; formation; consensus
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This paper explores distributed estimation methods for enabling UAV formations to track a moving target by sharing information and processing received messages effectively. The approach aims to improve observability assumption and reduce communication traffic while maintaining accurate target tracking.
This paper considers distributed estimation methods to enable the formation of Unmanned-Aerial-Vehicles (UAVs) that track a moving target. The UAVs (or agents) are equipped with communication devices to receive a beacon signal from the target and share information with neighboring UAVs. The shared information includes the time-of-arrival (TOA) of the beacon signal from the target and estimates on the target's location. Every UAV processes the received information from the neighbors using a single-time-scale distributed estimation protocol. This differs from multi-time-scale protocols that require (i) many consensus iterations on a-priori estimates, (ii) fast communication among agents (in general, much faster than the sampling rate of the target dynamics), and thus, more-costly communication equipment and processing units. Further, our approach outperforms most single-time-scale methods in terms of observability assumption as these methods assume that the target is observable via the measurement data received from neighboring UAVs (referred to as local-observability). This requires more communications among the sensors. In contrast, our approach is only based on global-observability assumption, and thus, requires less networking (only strong-connectivity) and communication traffic along with less computational load by data-processing once at the same time-scale of sampling target dynamics. We consider modified time-difference-of-arrival (TDOA) measurements with a constant output matrix for the linearized model. UAVs make a pre-specified formation, and by estimating the target's location via these measurements, move along with the target.
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