Adaptive Filtering and Change Detection

Gustafsson, Fredrik

doi:10.1002/0470841613

Cited by 595 publications

(724 citation statements)

References 145 publications

(201 reference statements)

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“…In that setting surveillance is highly required while reliability and timeliness in decision-making is of paramount importance. Thus decentralized rapid detection based on fusion technology and intelligent algorithms play a key role in the proposed model (Gustaffson, 2008). In particular decentralized detection is an active research discipline imposing serious research problems and design issues, [Bassevile & Nikiforov, 1993;Chamberland & Veeravali, 2006 and (Tsitsiklis, 1993;Veeravali et al, 1993).…”

Section: Distributed Event Detection Strategy Methodsmentioning

confidence: 99%

Design Issues of an Operational Fire Detection System Integrated with Observation Sensors

Halikias¹,

Leventakis²,

Kontoes³

et al. 2011

Advances in Satellite Communications

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Section: Distributed Event Detection Strategy Methodsmentioning

confidence: 99%

Design Issues of an Operational Fire Detection System Integrated with Observation Sensors

Halikias¹,

Leventakis²,

Kontoes³

et al. 2011

Advances in Satellite Communications

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“…Though we estimated a density ratio between two consecutive segments, some earlier researches (Basseville and Nikiforov, 1993;Gustafsson, 1996Gustafsson, , 2000 introduced a hyperparameter that controls the size of a margin between two segments. In our preliminary experiments, however, we did not observe significant improvement by changing the margin.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

“…Detecting abrupt changes in time-series data, called change-point detection, has attracted researchers in the statistics and data mining communities for decades (Basseville and Nikiforov, 1993;Gustafsson, 2000;Brodsky and Darkhovsky, 1993). Depending on the delay of detection, change-point detection methods can be classified into two categories: Real-time detection (Adams and MacKay, 2007;Garnett et al, 2009;Paquet, 2007) and retrospective detection (Basseville and Nikiforov, 1993;Takeuchi and Yamanishi, 2006;Moskvina and Zhigljavsky, 2003a).…”

Section: Introductionmentioning

confidence: 99%

Change-point detection in time-series data by relative density-ratio estimation

Yamada²,

et al. 2013

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The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical changepoint detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the relative Pearson divergence as a divergence measure, and it is accurately and efficiently estimated by a method of direct density-ratio estimation. Through experiments on artificial and real-world datasets including human-activity sensing, speech, and Twitter messages, we demonstrate the usefulness of the proposed method.

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“…In general, application dependent safety measures share a generic structure that is depicted in Fig. 2 and already well established in literature [1,9]. The signal or state Ý´Øµ, to be monitored, is compared to its estimate, generated for instance by a model also called filter.…”

Section: Functional Safetymentioning

confidence: 99%

“…Mean, variance and other statistical properties could be used as distance measures. We refer to [1] for a state of the art overview of such techniques. Criteria for chosing one method over another are the trade-off between mean time to detection and mean time to false alarm, but also computational load etc.…”

Section: Functional Safetymentioning

confidence: 99%