44,100 research outputs found
Asymptotic Optimality Theory For Decentralized Sequential Multihypothesis Testing Problems
The Bayesian formulation of sequentially testing hypotheses is
studied in the context of a decentralized sensor network system. In such a
system, local sensors observe raw observations and send quantized sensor
messages to a fusion center which makes a final decision when stopping taking
observations. Asymptotically optimal decentralized sequential tests are
developed from a class of "two-stage" tests that allows the sensor network
system to make a preliminary decision in the first stage and then optimize each
local sensor quantizer accordingly in the second stage. It is shown that the
optimal local quantizer at each local sensor in the second stage can be defined
as a maximin quantizer which turns out to be a randomization of at most
unambiguous likelihood quantizers (ULQ). We first present in detail our results
for the system with a single sensor and binary sensor messages, and then extend
to more general cases involving any finite alphabet sensor messages, multiple
sensors, or composite hypotheses.Comment: 14 pages, 1 figure, submitted to IEEE Trans. Inf. Theor
Decentralized sequential change detection using physical layer fusion
The problem of decentralized sequential detection with conditionally
independent observations is studied. The sensors form a star topology with a
central node called fusion center as the hub. The sensors make noisy
observations of a parameter that changes from an initial state to a final state
at a random time where the random change time has a geometric distribution. The
sensors amplify and forward the observations over a wireless Gaussian multiple
access channel and operate under either a power constraint or an energy
constraint. The optimal transmission strategy at each stage is shown to be the
one that maximizes a certain Ali-Silvey distance between the distributions for
the hypotheses before and after the change. Simulations demonstrate that the
proposed analog technique has lower detection delays when compared with
existing schemes. Simulations further demonstrate that the energy-constrained
formulation enables better use of the total available energy than the
power-constrained formulation in the change detection problem.Comment: 10 pages, two-column, 10 figures, revised based on feedback from
reviewers, accepted for publication in IEEE Trans. on Wireless Communication
Information bounds and quickest change detection in decentralized decision systems
The quickest change detection problem is studied in decentralized decision systems, where a set of sensors receive independent observations and send summary messages to the fusion center, which makes a final decision. In the system where the sensors do not have access to their past observations, the previously conjectured asymptotic optimality of a procedure with a monotone likelihood ratio quantizer (MLRQ) is proved. In the case of additive Gaussian sensor noise, if the signal-to-noise ratios (SNR) at some sensors are sufficiently high, this procedure can perform as well as the optimal centralized procedure that has access to all the sensor observations. Even if all SNRs are low, its detection delay will be at most pi/2-1 approximate to 57% larger than that of the optimal centralized procedure. Next, in the system where the sensors have full access to their past observations, the first asymptotically optimal procedure in the literature is developed. Surprisingly, the procedure has the same asymptotic performance as the optimal centralized procedure, although it may perform poorly in some practical situations because of slow asymptotic convergence. Finally, it is shown that neither past message information nor the feedback from the fusion center improves the asymptotic performance in the simplest model
A New Approach to Linear/Nonlinear Distributed Fusion Estimation Problem
Disturbance noises are always bounded in a practical system, while fusion
estimation is to best utilize multiple sensor data containing noises for the
purpose of estimating a quantity--a parameter or process. However, few results
are focused on the information fusion estimation problem under bounded noises.
In this paper, we study the distributed fusion estimation problem for linear
time-varying systems and nonlinear systems with bounded noises, where the
addressed noises do not provide any statistical information, and are unknown
but bounded. When considering linear time-varying fusion systems with bounded
noises, a new local Kalman-like estimator is designed such that the square
error of the estimator is bounded as time goes to . A novel
constructive method is proposed to find an upper bound of fusion estimation
error, then a convex optimization problem on the design of an optimal weighting
fusion criterion is established in terms of linear matrix inequalities, which
can be solved by standard software packages. Furthermore, according to the
design method of linear time-varying fusion systems, each local nonlinear
estimator is derived for nonlinear systems with bounded noises by using Taylor
series expansion, and a corresponding distributed fusion criterion is obtained
by solving a convex optimization problem. Finally, target tracking system and
localization of a mobile robot are given to show the advantages and
effectiveness of the proposed methods.Comment: 9 pages, 3 figure
- …