Multiple Model BLE-based Tracking via Validation of RSSI Fluctuations under Different Conditions

Atashi, Mohammadamin; Salimibeni, Mohammad; Malekzadeh, Parvin; Bojincă, Mihai; Plataniotis, Konstantinos N.; Mohammadi, Arash

doi:10.23919/fusion43075.2019.9011367

Cited by 2 publications

(2 citation statements)

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“…We adopt a discretized variant of the formation model proposed in [37], [56]. Let p i and q i be the position and velocity of i th UAV evolving in time as 8 :…”

Section: Formation Scenariomentioning

confidence: 99%

See 1 more Smart Citation

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

Doostmohammadian

Taghieh

Zarrabi

2022

IEEE Trans. Automat. Sci. Eng.

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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, morecostly 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. Note to Practitioners-Inspired by recent development in indus-trial UAVs along with emerging progress in low-cost processing units, fog computing systems, and wireless communications, this paper considers mobile tracking of a moving target via a group of wireless-connected autonomous drones. In the classical tracking methods, which are prone to single-point-of-failure, all the sensors need to send their information to a ground central station over a long-range and costly data-transmission channel. In contrast, by collaborative tracking the processing and decision-making are distributed among a swarm of drones Manuscript

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“…We adopt a discretized variant of the formation model proposed in [37], [56]. Let p i and q i be the position and velocity of i th UAV evolving in time as 8 :…”

Section: Formation Scenariomentioning

confidence: 99%

“…The target position is estimated based on some measurements. The typical measurements are based on time-of-arrival (TOA) [6], angle-of-arrival (AOA), received-signal-strength (RSS) [7], [8], image-processing-based [9], [10], or a combination of these. A survey of different measurement techniques is given in [3].…”

Section: Introductionmentioning

confidence: 99%