Graduate School and Research Center in Digital Sciences

Mean estimation MSE for Kalman filtering of large dimensional sources sent over fading channels

Parseh, R; Slock, Dirk TM; Kansanen, K

SPAWC 2014, 15th IEEE International Workshop on Signal Processing Advances in Wireless Communications, 22-25 June 2014, Toronto, Canada


Uncoded transmission of a large dimensional Gauss-Markov vector process over a fading channel is considered. This problem is of interest in sensor network applications with data processing at the fusion center or in control and real-time monitoring where this method could be useful due to its simplicity and zero delay property. Assuming perfect channel knowledge at the receiver, the optimal estimator is the Kalman filter. In contrast to the classical Kalman filter, the prediction and estimation error covariance matrices are random. In this paper, by using Stieltjes transform analysis, we find an approximation to the pdf of the eigenvalue distribution of the estimation error covariance matrix for the high channel SNR regime. The approximated pdf is then used to obtain the mean estimation MSE of the Kalman filter.

Doi Bibtex

Title:Mean estimation MSE for Kalman filtering of large dimensional sources sent over fading channels
Type:Conference
Language:English
City:Toronto
Country:CANADA
Date:
Department:Communication systems
Eurecom ref:6155
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Bibtex: @inproceedings{EURECOM+6155, doi = {http://dx.doi.org/10.1109/SPAWC.2014.6941922}, year = {2014}, title = {{M}ean estimation {MSE} for {K}alman filtering of large dimensional sources sent over fading channels}, author = {{P}arseh, {R} and {S}lock, {D}irk {TM} and {K}ansanen, {K}}, booktitle = {{SPAWC} 2014, 15th {IEEE} {I}nternational {W}orkshop on {S}ignal {P}rocessing {A}dvances in {W}ireless {C}ommunications, 22-25 {J}une 2014, {T}oronto, {C}anada\&\#13;\&\#10;}, address = {{T}oronto, {CANADA}}, month = {06}, url = {http://www.eurecom.fr/publication/6155} }
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