9,338 research outputs found
Performance Bounds for Finite Moving Average Change Detection: Application to Global Navigation Satellite Systems
Due to the widespread deployment of Global Navigation Satellite Systems
(GNSSs) for critical road or urban applications, one of the major challenges to
be solved is the provision of integrity to terrestrial environments, so that
GNSS may be safety used in these applications. To do so, the integrity of the
received GNSS signal must be analyzed in order to detect some local effect
disturbing the received signal. This is desirable because the presence of some
local effect may cause large position errors, and hence compromise the signal
integrity. Moreover, the detection of such disturbing effects must be done
before some pre-established delay. This kind of detection lies within the field
of transient change detection. In this work, a finite moving average stopping
time is proposed in order to approach the signal integrity problem with a
transient change detection framework. The statistical performance of this
stopping time is investigated and compared, in the context of multipath
detection, to other different methods available in the literature. Numerical
results are presented in order to assess their performance.Comment: 12 pages, 2 figures, transaction paper, IEEE Transaction on Signal
Processing, 201
Quick Search for Rare Events
Rare events can potentially occur in many applications. When manifested as
opportunities to be exploited, risks to be ameliorated, or certain features to
be extracted, such events become of paramount significance. Due to their
sporadic nature, the information-bearing signals associated with rare events
often lie in a large set of irrelevant signals and are not easily accessible.
This paper provides a statistical framework for detecting such events so that
an optimal balance between detection reliability and agility, as two opposing
performance measures, is established. The core component of this framework is a
sampling procedure that adaptively and quickly focuses the
information-gathering resources on the segments of the dataset that bear the
information pertinent to the rare events. Particular focus is placed on
Gaussian signals with the aim of detecting signals with rare mean and variance
values
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