Distributed Estimation Approach for Tracking a Mobile Target via Formation of UAVs

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.

Automated summary

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.