Maximum Mutual Information Principle for Dynamic Sensor Query Problems

Ertin, Emre; Fisher, John W.; Potter, Lee C.

doi:10.1007/3-540-36978-3_27

Cited by 113 publications

(95 citation statements)

References 6 publications

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“…Research on estimating the location of a fixed target, given measurements from a subset of sensors has been reported in [1], [3], [5], [6], [7], [8], [9], [10], and [11]. These differ from each other on the basis of (a) the number of sensors required, (b) the nature of measurements, and (c) the technique for estimating the location.…”

Section: Related Workmentioning

confidence: 99%

Sensor Selection Heuristic in Sensor Networks

Sadaphal

Jain

2005

Lecture Notes in Computer Science

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Abstract. We consider the problem of estimating the location of a moving target in a 2-D plane. In this paper, we focus attention on selecting an appropriate 3 rd sensor, given two sensors, with a view to minimize the estimation error. Only the selected sensors need to measure distance to the target and communicate the same to the central "tracker". This minimizes bandwidth and energy consumed in measurement and communication while achieving near minimum estimation error. In this paper, we have proposed that the 3 rd sensor be selected based on three measures viz. (a) collinearity, (b) deviation from the ideal direction in which the sensor should be selected, and (c) proximity of the sensor from the target. We assume that the measurements are subject to multiplicative error. Further, we use least square error estimation technique to estimate the target location. Simulation results show that using the proposed algorithm it is possible to achieve near minimum error in target location.

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Section: Related Workmentioning

confidence: 99%

Sensor Selection Heuristic in Sensor Networks

Sadaphal

Jain

2005

Lecture Notes in Computer Science

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“…(4.20) consists of calculating two determinants of matrices sized m × m, instead of calculating two n × n determinants where usually m << n. Using different reasoning, the same Eq. (4.20) expression was reached in [32] and [55], using a Bayesian formulation.…”

Section: Efficient Closed-form Solution For Calculating the Observatimentioning

confidence: 99%

Efficient Topology Estimation for Large Scale Optical Mapping

Elibol¹,

Gracias²,

García³

2013

Springer Tracts in Advanced Robotics

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ADVERTIMENT. La consulta d'aquesta tesi queda condicionada a l'acceptació de les següents condicions d'ús: La difusió d'aquesta tesi per mitjà del servei TDX ha estat autoritzada pels titulars dels drets de propietat intel·lectual únicament per a usos privats emmarcats en activitats d'investigació i docència. No s'autoritza la seva reproducció amb finalitats de lucre ni la seva difusió i posada a disposició des d'un lloc aliè al servei TDX. No s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant al resum de presentació de la tesi com als seus continguts. En la utilització o cita de parts de la tesi és obligat indicar el nom de la persona autora.ADVERTENCIA. La consulta de esta tesis queda condicionada a la aceptación de las siguientes condiciones de uso: La difusión de esta tesis por medio del servicio TDR ha sido autorizada por los titulares de los derechos de propiedad intelectual únicamente para usos privados enmarcados en actividades de investigación y docencia. No se autoriza su reproducción con finalidades de lucro ni su difusión y puesta a disposición desde un sitio ajeno al servicio TDR. No se autoriza la presentación de su contenido en una ventana o marco ajeno a TDR (framing). Esta reserva de derechos afecta tanto al resumen de presentación de la tesis como a sus contenidos. En la utilización o cita de partes de la tesis es obligado indicar el nombre de la persona autora. WARNING.On having consulted this thesis you're accepting the following use conditions: Spreading this thesis by the TDX service has been authorized by the titular of the intellectual property rights only for private uses placed in investigation and teaching activities. Reproduction with lucrative aims is not authorized neither its spreading and availability from a site foreign to the TDX service. Introducing its content in a window or frame foreign to the TDX service is not authorized (framing). This rights affect to the presentation summary of the thesis as well as to its contents. In the using or citation of parts of the thesis it's obliged to indicate the name of the author. PhD AbstractLarge scale image mosaicing methods are in great demand among scientists who study different aspects of the seabed, and have been fostered by impressive advances in the capabilities of underwater robots in gathering optical data from the seafloor. Cost and weight constraints mean that low-cost Remotely operated vehicles (ROVs) usually have a very limited number of sensors. When a low-cost robot carries out a seafloor survey using a downlooking camera, it usually follows a predefined trajectory that provides several non time-consecutive overlapping image pairs. Finding these pairs (a process known as topology estimation) is indispensable to obtaining globally consistent mosaics and accurate trajectory estimates, which are necessary for a global view of the surveyed area, especially when optical sensors are the only data source.This thesis presents a set of consistent methods a...

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“…(20) is independent of the control decision (u), it is clear that minimization of expected conditional entropy is equivalent to maximization of mutual information [12]. Sensor management strategies which select the action that minimizes the conditional entropy or that maximizes the mutual information over the next time step are sometimes referred to as greedy or myopic, as they only consider information which is obtainable in the next time step.…”

Section: Sensor Managementmentioning

confidence: 99%

An approximate dynamic programming approach for communication constrained inference

Williams

Fisher

Willsky

2005

IEEE/SP 13th Workshop on Statistical Signal Processing, 2005

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Resource management in distributed sensor networks is a challenging problem. This can be attributed to the fundamental tradeoff between the value of information contained in a distributed set of measurements versus the energy costs of acquiring the measurements, fusing them into a model of uncertainty, and transmitting the resulting model. Communications is commonly the highest contributor among these costs, typically by orders of magnitude. Failure to consider this tradeoff can significantly reduce the operational lifetime of a sensor network. While a variety of methods have been proposed that treat a subset of these issues, the approaches are indirect and usually consider at most a single time step. In the context of target tracking with a distributed sensor network we propose an approximate dynamic programming approach which integrates the value of information and the cost of transmitting data over a rolling time horizon. Specifically, we consider tracking a single target and constrain the problem such that, at any time, a single sensor, referred to as the leader node, is activated to both sense and update the probabilistic model. The issue of selecting which sensor should be the leader at each time is of primary interest, as it directly impacts the trade-off between the estimation accuracy and the cost of communicating the probabilistic model from old leader node to new leader node. We formulate this trade-off as a dynamic program, and use an approximation based on a linearization of the sensor model about a nominal trajectory to find a tractable solution. Simulation results demonstrate that the resulting algorithm can provide similar estimation performance to that of the common most informative sensor election method at a fraction of the communication energy cost.

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Maximum Mutual Information Principle for Dynamic Sensor Query Problems

Cited by 113 publications

References 6 publications

Sensor Selection Heuristic in Sensor Networks

Sensor Selection Heuristic in Sensor Networks

Efficient Topology Estimation for Large Scale Optical Mapping

An approximate dynamic programming approach for communication constrained inference

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