Moving target search optimization – A literature review

Raap, Manon; Preuß, Michael; Meyer-Nieberg, Silja

doi:10.1016/j.cor.2019.01.004

Cited by 20 publications

(11 citation statements)

References 22 publications

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“…A recent and extensive review of probabilistic search algorithms for moving targets can be found in (Raap et al, 2019).…”

Section: State Of the Artmentioning

confidence: 99%

Multi-UAS Minimum Time Search in Dynamic and Uncertain Environments

Carabaza

2021

Springer Theses

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La tesis es una obra original que no infringe los derechos de propiedad intelectual ni los derechos de propiedad industrial u otros, de acuerdo con el ordenamiento jurídico vigente, en particular, la Ley de Propiedad Intelectual (R.D. legislativo 1/1996, de 12 de abril, por el que se aprueba el texto refundido de la Ley de Propiedad Intelectual, modificado por la Ley 2/2019, de 1 de marzo, regularizando, aclarando y armonizando las disposiciones legales vigentes sobre la materia), en particular, las disposiciones referidas al derecho de cita.Del mismo modo, asumo frente a la Universidad cualquier responsabilidad que pudiera derivarse de la autoría o falta de originalidad del contenido de la tesis presentada de conformidad con el ordenamiento jurídico vigente.En Madrid, a ____ de _________________________ de 20___ Fdo.: _______________________________ Esta DECLARACIÓN DE AUTORÍA Y ORIGINALIDAD debe ser insertada en la primera página de la tesis presentada para la obtención del título de Doctor.

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“…A recent and extensive review of probabilistic search algorithms for moving targets can be found in (Raap et al, 2019).…”

Section: State Of the Artmentioning

confidence: 99%

Multi-UAS Minimum Time Search in Dynamic and Uncertain Environments

Carabaza

2021

Springer Theses

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“…Efficient search requires the design of control schemes to move the cooperative UAVs in order to optimize some criterion quantifying the performance of the search, e.g., minimizing uncertainty of estimated target locations. A review of path-constrained approaches to design sets of trajectories is presented in [28]. These approaches rely on strong a priori knowledge on the search zone and on targets.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2021

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“…Fig. 3 illustrates the fusion step after communications resulting from (15) and (16). The figure highlights the fact that information on target locations is not differentiated for each target, since they are collected in a single set X i,k+1 .…”

Section: Fusion Step After Communicationsmentioning

confidence: 99%

“…Their search trajectories are then evaluated to optimize the target state estimation accuracy. These trajectories can be obtained using explicit planning methods, which determine predicted environmental states, estimated target movements, and potential sensor observations (see, e.g., [16] for a review of path-constrained approaches). Determination of such UAVs trajectories has also been addressed through optimal control [5], [6] or derived from optimal strategies in a cooperative game approach [12].…”

Section: Introductionmentioning

confidence: 99%

Target search and tracking using a fleet of UAVs in presence of decoys and obstacles

Ibenthal¹,

Meyer²,

Piet-Lahanier³

et al. 2020

2020 59th IEEE Conference on Decision and Control (CDC)

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This paper addresses the problem of searching and tracking of an a priori unknown number of indistinguishable targets spread over some geographical area using a fleet of UAVs. State perturbations and measurement noises are assumed to belong to bounded sets. In the monitored geographical area, some false targets (decoys) are present and may be erroneously considered as targets when observed under specific conditions. Moreover, obstacles in the search area constrain the displacements of the targets, alter the UAVs' trajectories, reduce their fields of view, and limit their communications. While the UAVs can detect targets or decoys when observation conditions are satisfied, they cannot identify them individually.The search process relies on a robust bounded-error estimation approach which aim is to evaluate a set guaranteed to contain the actual states of already localized true targets and a set containing the states of targets still to be discovered. These two sets are used by each UAV to determine their control inputs in a distributed way to minimize future estimation uncertainty.Simulations involving several UAVs illustrate that the proposed robust set-membership estimator and distributed control laws make it possible to efficiently search and track targets in the presence of decoys in a cluttered area.

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Moving target search optimization – A literature review

Cited by 20 publications

References 22 publications

Multi-UAS Minimum Time Search in Dynamic and Uncertain Environments

Multi-UAS Minimum Time Search in Dynamic and Uncertain Environments

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

Target search and tracking using a fleet of UAVs in presence of decoys and obstacles

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