Title :
Fusion of local filters
Author :
Choi, Daebum ; Shin, Vladimir ; Ahn, Byung-Ha ; Ahn, Jun Il
Author_Institution :
Dept. of Mechatronics, Gwangju Inst. of Sci. & Technol., South Korea
Abstract :
This paper considers the problem of fusion of local filters. We derive an optimal mean square combination of arbitrary number of correlated estimates. In particular, for two sensors this combination represents the well-known Millman and Bar-Shalom-Campo formulae for uncorrelated and correlated estimation errors, respectively. The new combination is applied to an adaptive filtering problem and fusion of multisensor estimates. Two suboptimal filters with a parallel structure are herein proposed. The equation for error covariance characterizing the mean square accuracy of these filters is derived. In consequence of parallel structure of the filters, parallel computers can be used for their design. The examples demonstrate the effect of the common process noise on the fusion of the state estimates of a target based on measurements obtained by two different sensors.
Keywords :
Kalman filters; adaptive filters; correlation methods; mean square error methods; parameter estimation; sensor fusion; Bar-Shalom-Campo formula; Kalman filtering; Millman formula; adaptive filtering; correlated estimates; correlated estimation errors; error covariance equation; filter design; local filter fusion; mean square accuracy; multisensor estimate fusion; optimal mean square combination; parallel computers; parallel structure suboptimal filters; process noise; sensor measurements; target state estimates; uncorrelated estimation errors; Adaptive filters; Data processing; Equations; Estimation error; Filtering; Mechatronics; Sensor fusion; Sensor phenomena and characterization; Signal processing algorithms; Target tracking;
Conference_Titel :
Intelligent Signal Processing and Communication Systems, 2004. ISPACS 2004. Proceedings of 2004 International Symposium on
Print_ISBN :
0-7803-8639-6
DOI :
10.1109/ISPACS.2004.1439008