433,040 research outputs found
Sequential Detection and Identification of a Change in the Distribution of a Markov-Modulated Random Sequence
The problem of detection and identification of an unobservable change in the distribution of a random sequence is studied via a hidden Markov model (HMM) approach. The formulation is Bayesian, on-line, discrete-time, allowing both single- and multiple- disorder cases, dealing with both independent and identically distributed (i.i.d.) and dependent observations scenarios, allowing for statistical dependencies between the change-time and change-type in both the observation sequence and the risk structure, and allowing for general discrete-time disorder distributions. Several of these factors provide useful new generalizations of the sequential analysis theory for change detection and/or hypothesis testing, taken individually. In this paper, a unifying framework is provided that handles each of these considerations not only individually, but also concurrently. Optimality results and optimal decision characterizations are given as well as detailed examples that illustrate the myriad of sequential change detection and identification problems that fall within this new framework
Fusing Censored Dependent Data for Distributed Detection
In this paper, we consider a distributed detection problem for a censoring
sensor network where each sensor's communication rate is significantly reduced
by transmitting only "informative" observations to the Fusion Center (FC), and
censoring those deemed "uninformative". While the independence of data from
censoring sensors is often assumed in previous research, we explore spatial
dependence among observations. Our focus is on designing the fusion rule under
the Neyman-Pearson (NP) framework that takes into account the spatial
dependence among observations. Two transmission scenarios are considered, one
where uncensored observations are transmitted directly to the FC and second
where they are first quantized and then transmitted to further improve
transmission efficiency. Copula-based Generalized Likelihood Ratio Test (GLRT)
for censored data is proposed with both continuous and discrete messages
received at the FC corresponding to different transmission strategies. We
address the computational issues of the copula-based GLRTs involving
multidimensional integrals by presenting more efficient fusion rules, based on
the key idea of injecting controlled noise at the FC before fusion. Although,
the signal-to-noise ratio (SNR) is reduced by introducing controlled noise at
the receiver, simulation results demonstrate that the resulting noise-aided
fusion approach based on adding artificial noise performs very closely to the
exact copula-based GLRTs. Copula-based GLRTs and their noise-aided counterparts
by exploiting the spatial dependence greatly improve detection performance
compared with the fusion rule under independence assumption
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey
The Byzantine attack in cooperative spectrum sensing (CSS), also known as the
spectrum sensing data falsification (SSDF) attack in the literature, is one of
the key adversaries to the success of cognitive radio networks (CRNs). In the
past couple of years, the research on the Byzantine attack and defense
strategies has gained worldwide increasing attention. In this paper, we provide
a comprehensive survey and tutorial on the recent advances in the Byzantine
attack and defense for CSS in CRNs. Specifically, we first briefly present the
preliminaries of CSS for general readers, including signal detection
techniques, hypothesis testing, and data fusion. Second, we analyze the spear
and shield relation between Byzantine attack and defense from three aspects:
the vulnerability of CSS to attack, the obstacles in CSS to defense, and the
games between attack and defense. Then, we propose a taxonomy of the existing
Byzantine attack behaviors and elaborate on the corresponding attack
parameters, which determine where, who, how, and when to launch attacks. Next,
from the perspectives of homogeneous or heterogeneous scenarios, we classify
the existing defense algorithms, and provide an in-depth tutorial on the
state-of-the-art Byzantine defense schemes, commonly known as robust or secure
CSS in the literature. Furthermore, we highlight the unsolved research
challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral
Bayesian Design of Tandem Networks for Distributed Detection With Multi-bit Sensor Decisions
We consider the problem of decentralized hypothesis testing under
communication constraints in a topology where several peripheral nodes are
arranged in tandem. Each node receives an observation and transmits a message
to its successor, and the last node then decides which hypothesis is true. We
assume that the observations at different nodes are, conditioned on the true
hypothesis, independent and the channel between any two successive nodes is
considered error-free but rate-constrained. We propose a cyclic numerical
design algorithm for the design of nodes using a person-by-person methodology
with the minimum expected error probability as a design criterion, where the
number of communicated messages is not necessarily equal to the number of
hypotheses. The number of peripheral nodes in the proposed method is in
principle arbitrary and the information rate constraints are satisfied by
quantizing the input of each node. The performance of the proposed method for
different information rate constraints, in a binary hypothesis test, is
compared to the optimum rate-one solution due to Swaszek and a method proposed
by Cover, and it is shown numerically that increasing the channel rate can
significantly enhance the performance of the tandem network. Simulation results
for -ary hypothesis tests also show that by increasing the channel rates the
performance of the tandem network significantly improves
A Binary Control Chart to Detect Small Jumps
The classic N p chart gives a signal if the number of successes in a sequence
of inde- pendent binary variables exceeds a control limit. Motivated by
engineering applications in industrial image processing and, to some extent,
financial statistics, we study a simple modification of this chart, which uses
only the most recent observations. Our aim is to construct a control chart for
detecting a shift of an unknown size, allowing for an unknown distribution of
the error terms. Simulation studies indicate that the proposed chart is su-
perior in terms of out-of-control average run length, when one is interest in
the detection of very small shifts. We provide a (functional) central limit
theorem under a change-point model with local alternatives which explains that
unexpected and interesting behavior. Since real observations are often not
independent, the question arises whether these re- sults still hold true for
the dependent case. Indeed, our asymptotic results work under the fairly
general condition that the observations form a martingale difference array.
This enlarges the applicability of our results considerably, firstly, to a
large class time series models, and, secondly, to locally dependent image data,
as we demonstrate by an example
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