Optimal sensor selection strategy for discrete-time state estimators

Oshman, Yaakov

doi:10.1109/7.272256

Cited by 80 publications

(39 citation statements)

References 11 publications

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“…The sensor selection problem arises in various applications, including robotics [HM97], sensor placement for structures [Kam91,KP02], target tracking [WYPE04,IB05], chemical plant control [KP99], and wireless networks [ZG04]. Sensor selection in the context of dynamical systems is studied in, e.g., [GCHM06,KP98,Osh94]. Sensor selection, with a rather different setup from ours, has been studied in sensor network management [REJ + 07], hypothesis testing in a sensor network [DLT02], and discrete-event systems [JKG03].…”

Section: Introductionmentioning

confidence: 99%

Sensor Selection via Convex Optimization

Joshi

Boyd

2009

IEEE Trans. Signal Process.

1,121

1,025

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We consider the problem of choosing a set of k sensor measurements, from a set of m possible or potential sensor measurements, that minimizes the error in estimating some parameters. Solving this problem by evaluating the performance for each of the m k possible choices of sensor measurements is not practical unless m and k are small. In this paper we describe a heuristic, based on convex optimization, for approximately solving this problem. Our heuristic gives a subset selection as well as a bound on the best performance that can be achieved by any selection of k sensor measurements. There is no guarantee that the gap between the performance of the chosen subset and the performance bound is always small; but numerical experiments suggest that the gap is small in many cases. Our heuristic method requires on the order of m 3 operations; for m = 1000 possible sensors, we can carry out sensor selection in a few seconds on a 2 GHz personal computer.

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Section: Introductionmentioning

confidence: 99%

Sensor Selection via Convex Optimization

Joshi

Boyd

2009

IEEE Trans. Signal Process.

1,121

1,025

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“…Greedy sensor scheduling techniques, only concerned with maximizing the information gain of the next measurement, are another way to keep the complexity of the sensor management feasible [12], [13].…”

Section: Introductionmentioning

confidence: 99%

A Log-Ratio Information Measure for Stochastic Sensor Management

Lyons

Noack

Hanebeck

2010

2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing

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Abstract-In distributed sensor networks, computational and energy resources are in general limited. Therefore, an intelligent selection of sensors for measurements is of great importance to ensure both high estimation quality and an extended lifetime of the network. Methods from the theory of model predictive control together with information theoretic measures have been employed to pick sensors yielding measurements with high information value. We present a novel information measure that originates from a scalar product on a class of continuous probability densities and apply it to the field of sensor management. Aside from its mathematical justifications for quantifying the information content of probability densities, the most remarkable property of the measure, an analogon of the triangle inequality under Bayesian information fusion, is deduced. This allows for deriving computationally cheap upper bounds for the model predictive sensor selection algorithm and for comparing the performance of planning over different lengths of time horizons.

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“…[13] applies V-lambda filtering to choose between a priori selection matrices, determined to adequately represent the sensor space. [11] and [6] perform a greedy entropy minimization problem using Bayesian filtering to select an optimal sensor set for object tracking.…”

Section: Introductionmentioning

confidence: 99%