Distributed data fusion using iterative covariance intersection

Author

Hlinka, Ondrej ; Sluciak, Ondrej ; Hlawatsch, Franz ; Rupp, Markus

Author_Institution

Inst. of Telecommun., Vienna Univ. of Technol., Vienna, Austria

fYear

2014

fDate

4-9 May 2014

Firstpage

1861

Lastpage

1865

Abstract

We propose an iterative extension of the covariance intersection (CI) algorithm for distributed data fusion. Our iterative CI (ICI) algorithm is able to disseminate local information throughout the network. We show that the ICI algorithm converges asymptotically to a consensus across all network nodes. We furthermore apply the ICI algorithm to distributed sequential Bayesian estimation and propose an ICI-based distributed particle filter (DPF). This DPF allows for spatially correlated measurement noises with unknown cross-correlations and does not require knowledge of the network size. The performance of the proposed DPF is assessed experimentally for a target tracking problem.

Keywords

Bayes methods; convergence of numerical methods; correlation theory; covariance analysis; information dissemination; iterative methods; measurement errors; particle filtering (numerical methods); sensor fusion; sequential estimation; target tracking; DPF; ICI algorithm; asymptotic convergence; distributed data fusion; distributed particle filter; distributed sequential Bayesian estimation; iterative covariance intersection; iterative extension; local information dissemination; network node; spatially correlated measurement noise; target tracking problem; unknown cross-correlation; Distributed data fusion; covariance intersection; distributed estimation; distributed particle filter; sensor network;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on

Conference_Location

Florence

Type

conf

DOI

10.1109/ICASSP.2014.6853921

Filename

6853921