5,178 research outputs found
Theoretical Bounds in Minimax Decentralized Hypothesis Testing
Minimax decentralized detection is studied under two scenarios: with and
without a fusion center when the source of uncertainty is the Bayesian prior.
When there is no fusion center, the constraints in the network design are
determined. Both for a single decision maker and multiple decision makers, the
maximum loss in detection performance due to minimax decision making is
obtained. In the presence of a fusion center, the maximum loss of detection
performance between with- and without fusion center networks is derived
assuming that both networks are minimax robust. The results are finally
generalized.Comment: Submitted to IEEE Trans. on Signal Processin
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
On optimal quantization rules for some problems in sequential decentralized detection
We consider the design of systems for sequential decentralized detection, a
problem that entails several interdependent choices: the choice of a stopping
rule (specifying the sample size), a global decision function (a choice between
two competing hypotheses), and a set of quantization rules (the local decisions
on the basis of which the global decision is made). This paper addresses an
open problem of whether in the Bayesian formulation of sequential decentralized
detection, optimal local decision functions can be found within the class of
stationary rules. We develop an asymptotic approximation to the optimal cost of
stationary quantization rules and exploit this approximation to show that
stationary quantizers are not optimal in a broad class of settings. We also
consider the class of blockwise stationary quantizers, and show that
asymptotically optimal quantizers are likelihood-based threshold rules.Comment: Published as IEEE Transactions on Information Theory, Vol. 54(7),
3285-3295, 200
Distributed Detection in Sensor Networks with Limited Range Sensors
We consider a multi-object detection problem over a sensor network (SNET)
with limited range sensors. This problem complements the widely considered
decentralized detection problem where all sensors observe the same object.
While the necessity for global collaboration is clear in the decentralized
detection problem, the benefits of collaboration with limited range sensors is
unclear and has not been widely explored. In this paper we develop a
distributed detection approach based on recent development of the false
discovery rate (FDR). We first extend the FDR procedure and develop a
transformation that exploits complete or partial knowledge of either the
observed distributions at each sensor or the ensemble (mixture) distribution
across all sensors. We then show that this transformation applies to
multi-dimensional observations, thus extending FDR to multi-dimensional
settings. We also extend FDR theory to cases where distributions under both
null and positive hypotheses are uncertain. We then propose a robust
distributed algorithm to perform detection. We further demonstrate scalability
to large SNETs by showing that the upper bound on the communication complexity
scales linearly with the number of sensors that are in the vicinity of objects
and is independent of the total number of sensors. Finally, we deal with
situations where the sensing model may be uncertain and establish robustness of
our techniques to such uncertainties.Comment: Submitted to IEEE Transactions on Signal Processin
Rate Allocation for Decentralized Detection in Wireless Sensor Networks
We consider the problem of decentralized detection where peripheral nodes
make noisy observations of a phenomenon and send quantized information about
the phenomenon towards a fusion center over a sum-rate constrained multiple
access channel. The fusion center then makes a decision about the state of the
phenomenon based on the aggregate received data. Using the Chernoff information
as a performance metric, Chamberland and Veeravalli previously studied the
structure of optimal rate allocation strategies for this scenario under the
assumption of an unlimited number of sensors. Our key contribution is to extend
these result to the case where there is a constraint on the maximum number of
active sensors. In particular, we find sufficient conditions under which the
uniform rate allocation is an optimal strategy, and then numerically verify
that these conditions are satisfied for some relevant sensor design rules under
a Gaussian observation model.Comment: Accepted at SPAWC 201
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