Bounded-error target localization and tracking in presence of decoys using a fleet of UAVs

Ibenthal, Julius; Meyer, Luc; Kieffer, Michel; Piet-Lahanier, Hélène

doi:10.1016/j.ifacol.2020.12.2429

Cited by 3 publications

(12 citation statements)

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“…Issues related to false detection and misidentification of false targets, as well as potential non-identification of true targets are considered. The distinction between true and false targets relies on some deterministic observation conditions generalizing that introduced in [17,18]. The distributed MPC approach introduced here accounts for limited and possibly delayed communications between UAVs, extending previous results in [17,29].…”

Section: Introductionmentioning

confidence: 74%

“…The distinction between true and false targets relies on some deterministic observation conditions generalizing that introduced in [17,18]. The distributed MPC approach introduced here accounts for limited and possibly delayed communications between UAVs, extending previous results in [17,29]. Finally, a better management of set estimates leads to a more efficient and computationally less demanding control law design compared to that of [17].…”

Section: Introductionmentioning

confidence: 82%

“…The distributed MPC approach introduced here accounts for limited and possibly delayed communications between UAVs, extending previous results in [17,29]. Finally, a better management of set estimates leads to a more efficient and computationally less demanding control law design compared to that of [17]. Some related work in the context of cooperative search, acquisition, and track (CSAT) is provided in Section 2.…”

Section: Introductionmentioning

confidence: 87%

“…Set estimates containing all possible states for a target are then derived, see [30,29]. This type of bounded noise has been considered in the context of MPC, e.g., in [3], [7], and [19], and considered in a CSAT context in [17], to the best of our knowledge.…”

Section: Related Workmentioning

confidence: 99%

“…Some results have been obtained in [5], where simultaneous target tracking and identification from electro-optical and infrared sensors is considered. In [17] the presence of static decoys is taken into account, and the bounded-error approach introduced in [30] is adapted to design a distributed set-membership estimator able to discriminate decoys from true targets.…”

Section: Related Workmentioning

confidence: 99%

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Bounded-error target localization and tracking using a fleet of UAVs

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Section: Introductionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 87%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Bounded-error target localization and tracking using a fleet of UAVs

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Set-membership vs. stochastic approaches for target localization with UAVs

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Localization of partially hidden targets using a fleet of UAVs via robust bounded-error estimation

Ibenthal

Meyer

Piet-Lahanier

et al. 2021

2021 60th IEEE Conference on Decision and Control (CDC)

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This paper addresses the cooperative search of static ground targets by a group of Unmanned Aerial Vehicles (UAVs) over some region of interest. The search strategy dependents on the availability and accuracy of the information collected. When a target is detected, a probabilistic description of the measurement noise is usually considered, as well as probabilities of false alarm and non-detection, which may prove difficult to characterize a priori.An alternative modeling is introduced here. The ability to detect and identify a target depends deterministically on the point of view from which the target is observed. Introducing the notion of detectability sets for targets, we propose a robust distributed set-membership estimator to provide set estimates of target locations. The obtained set estimates are guaranteed to contain all target locations when the search is completed. The target search is formulated as a multi-agent cooperative control problem where the control inputs are obtained using a Model Predictive Control (MPC) approach minimizing a measure of the set estimates representing the detection performance. The proposed set estimator and cooperative control scheme are distributed, i.e., accounting only for information from neighbors within communication range. The effectiveness of the proposed algorithm is illustrated by simulation.

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Bounded-error target localization and tracking in presence of decoys using a fleet of UAVs

Cited by 3 publications

References 12 publications

Bounded-error target localization and tracking using a fleet of UAVs

Bounded-error target localization and tracking using a fleet of UAVs

Set-membership vs. stochastic approaches for target localization with UAVs

Localization of partially hidden targets using a fleet of UAVs via robust bounded-error estimation

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