Multisensor data fusion in nonlinear Bayesian filtering

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Communications and Electronics (ICCE), 2012 Fourth International Conference on, 2012, pp. 351 - 354
Issue Date:
2012-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2012004270OK.pdf Published version2.72 MB
Adobe PDF
In this paper, an optimal multisensor data fusion method is proposed to estimate the state of a highly nonlinear dynamic model. Data fusion from spatially distributed sensors is expressed as a semi definite program (SDP) that aims at minimizing mean-squared error (MSE) of the state estimate under total transmit power constraints. Furthermore, a Bayesian filtering technique, based on unscented transformations and linear fractional transformations (LFT), is presented under multisensor framework to implement the SDP. Extensive simulations are performed to justify effectiveness of the proposed multisensor scheme over a single sensor supplied with the same power budget as that of the entire sensor network.
Please use this identifier to cite or link to this item: