Adaptive Search and Tracking of Sparse Dynamic Targets Under Resource Constraints

Newstadt, Gregory E.; Wei, Dennis; Hero, Alfred O.

doi:10.1109/tsp.2015.2405493

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…Different performance metric can be chosen depending on the particular tracking scenario and relevant constraints. In other works, the goal of sensor scheduling is to minimize the tracking error subject to usage cost constraints, eg, computational and memory resources limited, dwell time and activation time constrained, and sensor resource constrained and noise limited . However, on the other hand, the tracking accuracy is often considered as a constraint to minimize the usage cost in some special applications, especially in the wireless sensor networks with minimizing the energy consumption …”

Section: Introductionmentioning

confidence: 99%

Nonmyopic sensor scheduling for target tracking with emission control

Qiao

Shan

Yan

et al. 2019

Adaptive Control & Signal

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Summary Active sensors obtain the measurements of targets by emitting energy that can be intercepted by enemy surveillance sensors. To satisfy the target tracking requirement and control the whole system emission, we propose a nonmyopic sensor scheduling to minimize the emission cost while maintaining a desired tracking accuracy. The processes of target tracking and emission control are formulated as a partially observable Markov decision process. Then, we translate our scheduling problem to a discrete unconstrained optimization problem, which consists of multistep emission cost and multistep tracking accuracy cost. Furthermore, the cubature Kalman filter is utilized to update the target belief state and predict the multistep tracking accuracy cost, whereas the multistep emission cost is obtained by hidden Markov model filter. Scheduling is implemented efficiently by constructing a decision tree and using a search algorithm, which combines uniform cost search with augmented branch and bound technique. Simulation results demonstrate the effectiveness of our proposed method.

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Section: Introductionmentioning

confidence: 99%

Nonmyopic sensor scheduling for target tracking with emission control

Qiao

Shan

Yan

et al. 2019

Adaptive Control & Signal

View full text Add to dashboard Cite

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“…Networks of multiple agents have been widely applied to execute a variety of spatially distributed tasks in the recent literature. A multi-agent network can effectively expand the cover range and increase the efficiency compared with a single agent when the agents are applied for environmental exploration (Nestmeyer et al, 2017;Baranzadeh and Savkin, 2017;Li et al, 2017Li et al, , 2018, navigation (Liu et al, 2018;Geng et al, 2016;Fazlollahtabar, 2016), tracking (Moon et al, 2015;Newstadt et al, 2015;Cui et al, 2017) and target searching (Lanillos et al, 2014;Lanillos, 2013). In most applications, the connectivity maintenance of the network is a required precondition (Cortés, 2008;Olfati-Saber and Murray, 2004).…”

Section: Introductionmentioning

confidence: 99%

Cooperative multi-agent search using Bayesian approach with connectivity maintenance

Xiao

Cui

2019

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Purpose This paper aims to present a distributed Bayesian approach with connectivity maintenance to manage a multi-agent network search for a target on a two-dimensional plane. Design/methodology/approach The Bayesian framework is used to compute the local probability density functions (PDFs) of the target and obtain the global PDF with the consensus algorithm. An inverse power iteration algorithm is introduced to estimate the algebraic connectivity λ2 of the network. Based on the estimated λ2, the authors design a potential field for the connectivity maintenance. Then, based on the detection probability function, the authors design a potential field for the search target. The authors combine the two potential fields and design a distributed gradient-based control for the agents. Findings The inverse power iteration algorithm can distributed estimate the algebraic connectivity by the agents. The agents can efficient search the target with connectivity maintenance with the designed distributed gradient-based search algorithm. Originality/value Previous study has paid little attention to the multi-agent search problem with connectivity maintenance. Our algorithm guarantees that the strongly connected graph of the multi-agent communication topology is always established while performing the distributed target search problem.

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“…It mainly solves the optimal allocation of search resources to make it possible to successfully detect the targets with the least resources and the greatest possibility, and it is also an optimization theory and method to find the specific targets by means of detection. Many complex search problems are derived from basic search problems (Newstadt et al, 2015;Senanayake et al, 2016;Han et al, 2011;Campbell and Whitacre, 2007;Cho et al, 2016). Since it is relatively easy to solve the problem of the static target search, a large number of literature have studied it (Jevtic et al, 2012;Bourgault et al, 2006;Pugh and Martinoli, 2007;Lanillos et al, 2014;Kassem and El-Hadidy, 2014), and the certain research results are obtained.…”

Section: Introductionmentioning

confidence: 99%

“…https://doi.org/10.1016/j.oceaneng.2018.12.035 Received 14 September 2018; Received in revised form 12 November 2018; Accepted 13 December 2018 However, solving the search problem of the dynamic target is still immature, and further research is needed (Newstadt et al, 2015;Esmailifar and Saghafi, 2015;Li et al, 2017;Lee et al, 2010). In the search process of multiple UAVs (unmanned aerial vehicles), the rolling time domain method is applied to the target search in the literature (Lanillos et al, 2014;Polycarpout et al, 2001;Xiao et al, 2012;Hu et al, 2014;Bertuccelli and How, 2006;Trodden and Richards, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…In the search process of multiple UAVs (unmanned aerial vehicles), the rolling time domain method is applied to the target search in the literature (Lanillos et al, 2014;Polycarpout et al, 2001;Xiao et al, 2012;Hu et al, 2014;Bertuccelli and How, 2006;Trodden and Richards, 2008). The different cooperative control laws are designed by other scholars from the perspective of information sharing to realize the target search and tracking (Newstadt et al, 2015;Esmailifar and Saghafi, 2015;Li et al, 2017;Lavis et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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