Bayesian Distributed Detection by Sensor Networks with Missing Data

• We consider detection and identication of a moving target, using a network of sensors. The target emits a signal; a stationary stochastic process corrupted by additive noise, independently across sensors. Before intersensor communication, all sensors reduce external data as energy over disjoint frequency bands and time blocks. One sensor, the internal fusion center (IFC), gathers feature vectors from the other sensors, possibly after message passing. Using Bayesian decision theory, it decides for presence or absence of the target and computes a maximum posterior estimate of target (trajectory and spectral) parameters. The main novelties of the paper are: 1) To apply statistical theory of missing data to an inter-sensor communication protocol which censors weak signals before transmission and an imperfect channel in which some transmitted signals are lost. A Naive Bayes approximate detector is dened, which requires recursive computation of reception probabilities. 2) To derive asymptotic approximations of the distribution of the spectral feature vectors and a Laplace type approximation of the detector. The performance of the proposed Bayesian detector is shown in a simulation study to be only slightly inferior to that of an ideal Bayesian detector (with no missing data), as well as superior to a naive detector.

Ämnesord

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Nyckelord

Bayesian detection

data reduction

distributed detection

internal fusion center

missing data

routing

periodogram

sensor network

Publikations- och innehållstyp

vet (ämneskategori)

rap (ämneskategori)