Title :
Multi-sensor estimation using CKF and DKF
Author :
Mirzazadeh, Ehsan ; Moshiri, Behzad
Author_Institution :
Fac. of Electr. & Comput. Eng., K.N. Toosi Univ. of Technol., Tehran, Iran
fDate :
Oct. 31 2013-Nov. 1 2013
Abstract :
This paper compares two methods of multi-sensor estimation using Kalman filter in a sensor network. We focused on group-sensor method and track-to-track fusion approach, as representatives of typical centralized and decentralized architectures, to obtain a final estimation for the states in an instant sensor network. Each strategy is clearly illustrated and simulated for a simple network containing two sensors. The results of estimations in each one presented in two distinct situations; the conditions in those the two sensors benefit from either same or different measurement noise variances. The MSE values as a criterion for assessing the error between the estimations´ and the states´ real values are obtained using a procedure containing the Monte-Carlo technique.
Keywords :
Kalman filters; Monte Carlo methods; sensor fusion; state estimation; wireless sensor networks; CKF; DKF; MSE values; Monte-Carlo technique; centralized Kalman filters; centralized architectures; decentralized Kalman filters; decentralized architectures; group-sensor method; measurement noise variances; multisensor estimation; track-to-track fusion approach; wireless sensor network; Estimation; Monte Carlo methods; Group-Sensor Method; Kalman Filter; Monte-Carlo; Multi-Sensor Estimation; Track-to-Track Fusion;
Conference_Titel :
Computer and Knowledge Engineering (ICCKE), 2013 3th International eConference on
Conference_Location :
Mashhad
Print_ISBN :
978-1-4799-2092-1
DOI :
10.1109/ICCKE.2013.6682870