Concurrent multi-target localization, data association, and navigation for a swarm of flying sensors

Deming, Ross; Perlovsky, Leonid

doi:10.1016/j.inffus.2005.11.001

Cited by 42 publications

(27 citation statements)

References 29 publications

(83 reference statements)

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“…In [4], [5], [6] and [7] authors presented cooperative vision based object tracking methods with multiple UAVs based on the KF, EKF, particle filters and sigma point information filter, respectively. Information filters are more suitable for multi-sensor (UAV) object tracking compared to the conventional Bayesian filters due to their inherent information fusion mechanism [8].…”

Section: Introductionmentioning

confidence: 99%

Distributed object tracking based on information weighted UAV selection with priory objects

Bhuvana

Tonello

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this work, we propose a distributed cubature information filter based multi-object tracking method with an information weighted selection for unmanned aerial vehicle (UAV) networks. In an UAV network, multiple UAVs can observe multiple objects in the region of interest. Further, the UAVs can exchange the objects local information among themselves and fuse them together to obtain the global state of the objects. As the number of UAVs in the network increases, the information exchange among the UAVs suffers from scalability, bandwidth and energy limitations. Thus, it is usually desirable to allow only a desired number of UAVs with highly relevant information to participate in the information exchange. In our approach, the innovation vector within the information filtering framework is used to calculate the amount of information associated with each UAV. Further, a threshold based selection mechanism is proposed to facilitate the UAVs to take independent decisions on whether to participate in the information exchange or not. In the proposed method, the UAVs take the decision to participate in the information exchange based on the information associated with a dynamic subset of objects known as priory objects while keeping the total number of information exchanges in the network to a desired number (on average).

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Section: Introductionmentioning

confidence: 99%

Distributed object tracking based on information weighted UAV selection with priory objects

Bhuvana

Tonello

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…Specifically, Deming and Perlovsky (2007) tried to develop a concurrent multi-target tracking and navigation based on a probabilistic technique by using a swarm of flying sensors. Looney and Liang (2003) developed an integrated multi-phase approach focusing on middle and high level data fusion for situation and threat assessments of ground battle spaces using a fuzzy belief network.…”

Section: Introductionmentioning

confidence: 99%

Behaviour recognition of ground vehicle using airborne monitoring of unmanned aerial vehicles

Kim

Shin

et al. 2013

International Journal of Systems Science

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“…Then, their results are arranged on a graph where associations are propagated and conflicts are solved. The work in [7], from the target tracking literature, simultaneously considers the association of all local maps. It uses an expectation-maximization method for both computing the data association and the final global map.…”

Section: Introductionmentioning

confidence: 99%

Consistent data association in multi-robot systems with limited communications

Aragüés

Montijano

Sagüés

2010

Robotics: Science and Systems VI

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Abstract-In this paper we address the data association problem of features observed by a robot team with limited communications. At every time instant, each robot can only exchange data with a subset of the robots, its neighbors. Initially, each robot solves a local data association with each of its neighbors. After that, the robots execute the proposed algorithm to agree on a data association between all their local observations which is globally consistent. One inconsistency appears when chains of local associations give rise to two features from one robot being associated among them. The contribution of this work is the decentralized detection and resolution of these inconsistencies. We provide a fully decentralized solution to the problem. This solution does not rely on any particular communication topology. Every robot plays the same role, making the system robust to individual failures. Information is exchanged exclusively between neighbors. In a finite number of iterations, the algorithm finishes with a data association which is free of inconsistent associations.In the experiments, we show the performance of the algorithm under two scenarios. In the first one, we apply the resolution and detection algorithm for a set of stochastic visual maps. In the second, we solve the feature matching between a set of images taken by a robotic team.

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Concurrent multi-target localization, data association, and navigation for a swarm of flying sensors

Cited by 42 publications

References 29 publications

Distributed object tracking based on information weighted UAV selection with priory objects

Distributed object tracking based on information weighted UAV selection with priory objects

Behaviour recognition of ground vehicle using airborne monitoring of unmanned aerial vehicles

Consistent data association in multi-robot systems with limited communications

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