Optimal control of measurement subsystems

Meier, L.; Peschon, J.; Dressler, R. M.

doi:10.1109/tac.1967.1098668

Cited by 196 publications

(117 citation statements)

References 5 publications

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“…Die lineare Einsatzplanung besitzt dabei den Vorteil, dass sich für kovarianzbasierte Gütemaße die Entwicklung der Güte über den Beobachtungszeitraum unabhängig von den tatsächlichen Messwerten und somit bereits im Voraus berechnen lässt [14]. Zur Bestimmung einer optimalen Sequenz von Messpositionen muss daher nur die Güte aller Messsequenzen der Länge L miteinander verglichen werden.…”

Section: Introductionunclassified

Modellbasierte Quellenverfolgung in räumlich ausgedehnten Phänomenen mittels Sensoreinsatzplanung

Kuwertz¹,

Huber

Sawo

et al. 2010

Tm - Technisches Messen

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Summary Space-time continuous phenomena such as pollution loads or temperature distributions often originate from unknown and possibly movable sources. In many realworld scenarios, however, information about the location of such sources can only be gained indirectly by monitoring the induced physical phenomena using distributed sensing systems, e. g., stationary or mobile sensors. In this article, a modelbased approach for real-time source localization and target tracking is introduced. To maximize specific information gained from the measurements, this approach strongly relies on a non-myopic sensor management methodology, which allows for tracking moving sources in an efficient and accurate manner.

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Section: Introductionunclassified

Modellbasierte Quellenverfolgung in räumlich ausgedehnten Phänomenen mittels Sensoreinsatzplanung

Kuwertz¹,

Huber

Sawo

et al. 2010

Tm - Technisches Messen

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“…We restrict our attention in this paper to single-stage (myopic) scheduling. Multi-stage extensions to the Rényi Divergence approach using a partially observable Markov decision process (POMDP) [21], [22] approach and approximation techniques have been discussed elsewhere [23], [24]. Others have used POMDP approaches with other metrics and approximation methods for related problems, e.g., [25], [26], [27], [28].…”

Section: Introductionmentioning

confidence: 99%

An Information-Based Approach to Sensor Management in Large Dynamic Networks

et al. 2007

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Abstract-This paper addresses the problem of sensor management for a large network of agile sensors. Sensor management, as defined here, refers to the process of dynamically retasking agile sensors in response to an evolving environment. Sensors may be agile in a variety of ways, e.g., the ability to reposition, point an antenna, choose sensing mode, or waveform. The goal of sensor management in a large network is to choose actions for individual sensors dynamically so as to maximize overall network utility. An effective sensor management algorithm must combine prior knowledge, sensor models, environment models, and measurements to predict the best actions to take.Sensor management in the multiplatform setting is a challenging problem for several reasons. First, the state space required to characterize an environment is typically of very high dimension and poorly represented by a parametric form. Second, the network must simultaneously address a number of competing goals. Third, the number of potential taskings grows exponentially with the number of sensors. Finally, in low communication environments, decentralized methods are required.The approach we present in this paper addresses these challenges through a novel combination of particle filtering for nonparametric density estimation, information theory for comparing actions, and physicomimetics for computational tractability. The efficacy of the method is illustrated in a realistic surveillance application by simulation, where an unknown number of ground targets are to be detected and tracked by a network of mobile sensors.Index Terms-multiplatform sensor management, information theory, particle filtering, joint multitarget probability density, multitarget tracking.

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“…, A N }. When the dynamics of the state and the observation processes are both linear and Gaussian, then the optimal solution to the sensor scheduling problem (2) (when gives a quadratic cost) can be computed off line (Meier et al, 1967); this is not surprising given that the Kalman filter covariance is also independent of the actual realisation of observations. In the general setting studied in this paper, the dynamics can be both nonlinear and non-Gaussian, which means that the filtering density n , and integration w.r.t.…”

Section: Introductionmentioning

confidence: 99%

Simulation-based optimal sensor scheduling with application to observer trajectory planning

Singh

Kantas

et al. 2007

Automatica

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The sensor scheduling problem can be formulated as a controlled hidden Markov model and this paper solves the problem when the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. The aim is to minimise the variance of the estimation error of the hidden state w.r.t. the action sequence. We present a novel simulation-based method that uses a stochastic gradient algorithm to find optimal actions. ᭧

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Optimal control of measurement subsystems

Cited by 196 publications

References 5 publications

Modellbasierte Quellenverfolgung in räumlich ausgedehnten Phänomenen mittels Sensoreinsatzplanung

Modellbasierte Quellenverfolgung in räumlich ausgedehnten Phänomenen mittels Sensoreinsatzplanung

An Information-Based Approach to Sensor Management in Large Dynamic Networks

Simulation-based optimal sensor scheduling with application to observer trajectory planning

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