Distributed Noise Covariance Matrices Estimation in Sensor Networks

Jiahong Li, Nan Ma, Fang Deng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Adaptive algorithms based on in-network processing over networks are useful for online parameter estimation of historical data (e.g., noise covariance) in predictive control and machine learning areas. This paper focuses on the distributed noise covariance matrices estimation problem for multi-sensor linear time-invariant (LTI) systems. Conventional noise covariance estimation approaches, e.g., auto-covariance least squares (ALS) method, suffers from the lack of the sensor's historical measurements and thus produces high variance of the ALS estimate. To solve the problem, the distributed auto-covariance least squares (D-ALS) algorithm is proposed based on the batch covariance intersection (BCI) method by enlarging the innovations from the neighbors. The accuracy analysis of DALS algorithm is given to show the decrease of the variance of the D-ALS estimate. The numerical results of cooperative target tracking tasks in static and mobile sensor networks are demonstrated to show the feasibility and superiority of the proposed D-ALS algorithm.

Original languageEnglish
Title of host publication2020 59th IEEE Conference on Decision and Control, CDC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1158-1163
Number of pages6
ISBN (Electronic)9781728174471
DOIs
Publication statusPublished - 14 Dec 2020
Externally publishedYes
Event59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of
Duration: 14 Dec 202018 Dec 2020

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2020-December
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference59th IEEE Conference on Decision and Control, CDC 2020
Country/TerritoryKorea, Republic of
CityVirtual, Jeju Island
Period14/12/2018/12/20

Fingerprint

Dive into the research topics of 'Distributed Noise Covariance Matrices Estimation in Sensor Networks'. Together they form a unique fingerprint.

Cite this