Selective measurement transmission in distributed estimation with data association

Authors:
Paolo Braca;Marco Guerriero;Stefano Marano;Vincenzo Matta;Peter Willett
Affiliations:
Dipartimento di Ingegneria dell'Informazione e Ingegneria Elettrica, Università degli Studi di Salerno, Fisciano, SA, Italy;Electrical and Computer Engineering Department, University of Connecticut, Storrs, CT;Dipartimento di Ingegneria dell'Informazione e Ingegneria Elettrica, Università degli Studi di Salerno, Fisciano, SA, Italy;Dipartimento di Ingegneria dell'Informazione e Ingegneria Elettrica, Università degli Studi di Salerno, Fisciano, SA, Italy;Electrical and Computer Engineering Department, University of Connecticut, Storrs, CT
Venue:
IEEE Transactions on Signal Processing
Year:
2010

Citing 8
Cited 0

Elements of information theory

Elements of information theory
Optimal Design of Experiments (Classics in Applied Mathematics) (Classics in Applied Mathematics, 50)

Optimal Design of Experiments (Classics in Applied Mathematics) (Classics in Applied Mathematics, 50)
Quantization for Maximin ARE in Distributed Estimation

IEEE Transactions on Signal Processing - Part II
Some approaches to quantization for distributed estimation with data association

IEEE Transactions on Signal Processing
Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case

IEEE Transactions on Signal Processing
The CEO problem [multiterminal source coding]

IEEE Transactions on Information Theory
Distributed source coding using syndromes (DISCUS): design and construction

IEEE Transactions on Information Theory
An upper bound on the sum-rate distortion function and its corresponding rate allocation schemes for the CEO problem

IEEE Journal on Selected Areas in Communications

Quantified Score

Hi-index	35.68

Visualization

Abstract

In distributed multisensor estimation/tracking the problem of fusion is complicated by that of data association (i.e., with false alarms and missed detections): not only is it of concern to provide an estimation-efficient sensor level quantization of the "target-originated" measurement, but it is also unclear which among each sensor's measurements this might be, if any at all. The former issue has been studied previously; in this paper we address only the latter concern. At first we assume that each sensor is tasked to communicate exactly one of its observations to a Fusion Center (FC) for a global estimate, and we work in one dimension. Via order statistics we show that, surprisingly, the nearest neighbor (NN) is not always the most appropriate measurement to share. We also expand our bandwidth to allow for transmission of multiple measurements, for example the nearest and third-nearest: it turns out that a single-measurement transmission is more bandwidth efficient than multiple. The analysis and results are further extended to two dimensions, but the moral-that sharing of the NNs is not always a good idea-remains.