94,111 research outputs found
Distributed Hypothesis Testing with Social Learning and Symmetric Fusion
We study the utility of social learning in a distributed detection model with
agents sharing the same goal: a collective decision that optimizes an agreed
upon criterion. We show that social learning is helpful in some cases but is
provably futile (and thus essentially a distraction) in other cases.
Specifically, we consider Bayesian binary hypothesis testing performed by a
distributed detection and fusion system, where all decision-making agents have
binary votes that carry equal weight. Decision-making agents in the team
sequentially make local decisions based on their own private signals and all
precedent local decisions. It is shown that the optimal decision rule is not
affected by precedent local decisions when all agents observe conditionally
independent and identically distributed private signals. Perfect Bayesian
reasoning will cancel out all effects of social learning. When the agents
observe private signals with different signal-to-noise ratios, social learning
is again futile if the team decision is only approved by unanimity. Otherwise,
social learning can strictly improve the team performance. Furthermore, the
order in which agents make their decisions affects the team decision.Comment: 10 pages, 7 figure
Model Selection Approach for Distributed Fault Detection in Wireless Sensor Networks
Sensor networks aim at monitoring their surroundings for event detection and
object tracking. But, due to failure, or death of sensors, false signal can be
transmitted. In this paper, we consider the problems of distributed fault
detection in wireless sensor network (WSN). In particular, we consider how to
take decision regarding fault detection in a noisy environment as a result of
false detection or false response of event by some sensors, where the sensors
are placed at the center of regular hexagons and the event can occur at only
one hexagon. We propose fault detection schemes that explicitly introduce the
error probabilities into the optimal event detection process. We introduce two
types of detection probabilities, one for the center node, where the event
occurs and the other one for the adjacent nodes. This second type of detection
probability is new in sensor network literature. We develop schemes under the
model selection procedure, multiple model selection procedure and use the
concept of Bayesian model averaging to identify a set of likely fault sensors
and obtain an average predictive error.Comment: 14 page
An excess power statistic for detection of burst sources of gravitational radiation
We examine the properties of an excess power method to detect gravitational
waves in interferometric detector data. This method is designed to detect
short-duration (< 0.5 s) burst signals of unknown waveform, such as those from
supernovae or black hole mergers. If only the bursts' duration and frequency
band are known, the method is an optimal detection strategy in both Bayesian
and frequentist senses. It consists of summing the data power over the known
time interval and frequency band of the burst. If the detector noise is
stationary and Gaussian, this sum is distributed as a chi-squared (non-central
chi-squared) deviate in the absence (presence) of a signal. One can use these
distributions to compute frequentist detection thresholds for the measured
power. We derive the method from Bayesian analyses and show how to compute
Bayesian thresholds. More generically, when only upper and/or lower bounds on
the bursts duration and frequency band are known, one must search for excess
power in all concordant durations and bands. Two search schemes are presented
and their computational efficiencies are compared. We find that given
reasonable constraints on the effective duration and bandwidth of signals, the
excess power search can be performed on a single workstation. Furthermore, the
method can be almost as efficient as matched filtering when a large template
bank is required. Finally, we derive generalizations of the method to a network
of several interferometers under the assumption of Gaussian noise.Comment: 22 pages, 6 figure
Optimal and Suboptimal Detection of Gaussian Signals in Noise: Asymptotic Relative Efficiency
The performance of Bayesian detection of Gaussian signals using noisy
observations is investigated via the error exponent for the average error
probability. Under unknown signal correlation structure or limited processing
capability it is reasonable to use the simple quadratic detector that is
optimal in the case of an independent and identically distributed (i.i.d.)
signal. Using the large deviations principle, the performance of this detector
(which is suboptimal for non-i.i.d. signals) is compared with that of the
optimal detector for correlated signals via the asymptotic relative efficiency
defined as the ratio between sample sizes of two detectors required for the
same performance in the large-sample-size regime. The effects of SNR on the ARE
are investigated. It is shown that the asymptotic efficiency of the simple
quadratic detector relative to the optimal detector converges to one as the SNR
increases without bound for any bounded spectrum, and that the simple quadratic
detector performs as well as the optimal detector for a wide range of the
correlation values at high SNR.Comment: To appear in the Proceedings of the SPIE Conference on Advanced
Signal Processing Algorithms, Architectures and Implementations XV, San
Diego, CA, Jul. 1 - Aug. 4, 200
A bayesian approach to adaptive detection in nonhomogeneous environments
We consider the adaptive detection of a signal of interest embedded in colored noise, when the environment is nonhomogeneous, i.e., when the training samples used for adaptation do not share the same covariance matrix as the vector under test. A Bayesian framework is proposed where the covariance matrices of the primary and the secondary data are assumed to be random, with some appropriate joint distribution. The prior distributions of these matrices require a rough knowledge about the environment. This provides a flexible, yet simple, knowledge-aided model where the degree of nonhomogeneity can be tuned through some scalar variables. Within this framework, an approximate generalized likelihood ratio test is formulated. Accordingly, two Bayesian versions of the adaptive matched filter are presented, where the conventional maximum likelihood estimate of the primary data covariance matrix is replaced either by its minimum mean-square error estimate or by its maximum a posteriori estimate. Two detectors require generating samples distributed according to the joint posterior distribution of primary and secondary data covariance matrices. This is achieved through the use of a Gibbs sampling strategy. Numerical simulations illustrate the performances of these detectors, and compare them with those of the conventional adaptive matched filter
Bayesian detection of unmodeled bursts of gravitational waves
The data analysis problem of coherently searching for unmodeled
gravitational-wave bursts in the data generated by a global network of
gravitational-wave observatories has been at the center of research for almost
two decades. As data from these detectors is starting to be analyzed, a renewed
interest in this problem has been sparked. A Bayesian approach to the problem
of coherently searching for gravitational wave bursts with a network of
ground-based interferometers is here presented. We demonstrate how to
systematically incorporate prior information on the burst signal and its source
into the analysis. This information may range from the very minimal, such as
best-guess durations, bandwidths, or polarization content, to complete prior
knowledge of the signal waveforms and the distribution of sources through
spacetime. We show that this comprehensive Bayesian formulation contains
several previously proposed detection statistics as special limiting cases, and
demonstrate that it outperforms them.Comment: 18 pages, 3 figures, revisions based on referee comment
Classification of chirp signals using hierarchical bayesian learning and MCMC methods
This paper addresses the problem of classifying chirp signals using hierarchical Bayesian learning together with Markov chain Monte Carlo (MCMC) methods. Bayesian learning consists of estimating the distribution of the observed data conditional on each class from a set of training samples. Unfortunately, this estimation requires to evaluate intractable multidimensional integrals. This paper studies an original implementation of hierarchical Bayesian learning that estimates the class conditional probability densities using MCMC methods. The performance of this implementation is first studied via an academic example for which the class conditional densities are known. The problem of classifying chirp signals is then addressed by using a similar hierarchical Bayesian learning implementation based on a Metropolis-within-Gibbs algorithm
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