A review of some exchange algorithms for constructing discrete D-optimal designs

Nguyen, Nam-Ky; Miller, A. J.

doi:10.1016/0167-9473(92)90064-m

Cited by 141 publications

(78 citation statements)

References 17 publications

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“…These include genetic algorithms [YSK93], and application specific local search methods. Local optimization techniques, similar to the one we describe, are summarized in [NM92], [JD75]. While these heuristics can produce good suboptimal sensor selections, they do not yield any guarantees or bounds on the performance that is achievable.…”

Section: Introductionmentioning

confidence: 99%

Sensor Selection via Convex Optimization

Joshi

Boyd

2009

IEEE Trans. Signal Process.

1,149

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.

1,149

1,025

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“…The existing software products, see, for instance, Mitchell (1974), Nguen and Miller (1992), Nachtsheim (1987), SAS/QC Software (1995), Wheeler (1994), confirm this statement. Unfortunately, there are a few hurdles, which do not allow the direct use of the results reported above.…”

Section: Xexmentioning

confidence: 65%

16 Design of spatial experiments: Model fitting and prediction

Fedorov¹

1996

Handbook of Statistics

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“…20,21 Several heuristic techniques have been proposed to search for approximation of global optima. 6,22,23 However, these methods are still time-consuming due to the nature of combinatorial searching and consequently difficult to perform real-time sensor selection for tracking. Hence, we propose a decomposed sensor selection method in the following which is computationally tractable and convenient to implement by taking advantage of the multi-Bernoulli filtering.…”

Section: U2okmentioning

confidence: 99%

Multitarget tracking in sensor networks via efficient information-theoretic sensor selection

Wang

Xue

2017

International Journal of Advanced Robotic Systems

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In networks composed of moving robots or static sensing nodes, multitarget tracking is critical and fundamental for highlevel applications, such as scene analysis or event detection. However, tracking multiple targets in the sensor network is challenging for two reasons: multisensor multitarget fusion itself is difficult and dynamic sensor scheduling is necessary to balance the tracking accuracy and energy consumption of the sensor network. In this article, we present a novel information-theoretic sensor selection method for multitarget tracking via the multi-Bernoulli filter. The sensor selection is based on the multi-Bernoulli filtering and a collection of subselection problems for individual target to avoid the combinatorial optimization. A subselection problem for each target is investigated under the framework of partially observed Markov decision process, and we propose to solve it by maximizing the information gain of the probability hypothesis density. Simulation results validate the effectiveness and efficiency of our method for multitarget tracking in sensor networks.

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A review of some exchange algorithms for constructing discrete D-optimal designs

Cited by 141 publications

References 17 publications

Sensor Selection via Convex Optimization

Sensor Selection via Convex Optimization

16 Design of spatial experiments: Model fitting and prediction

Multitarget tracking in sensor networks via efficient information-theoretic sensor selection

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