Self-Tuning Information Fusion Wiener Smoother for ARMA Signals
2011 ◽
Vol 48-49
◽
pp. 1018-1023
Keyword(s):
For the multisensor autoregressive moving average (ARMA) signals, based on the modern time series analysis method, a self-tuning information fusion Wiener smoother is presented when both model parameters and noise variances are unknown. The principle is that substituting the estimators of unknown parameters and noise variances into the corresponding optimal fusion Wiener smoother will yield a self-tuning fuser. Further, applying the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning fused Wiener smoother converges to the optimal fused Wiener smoother in a realization, i.e. it has asymptotic optimality. A simulation example shows its effectiveness.
2012 ◽
Vol 229-231
◽
pp. 1768-1771
2013 ◽
Vol 274
◽
pp. 579-582
2013 ◽
Vol 2013
◽
pp. 1-12
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2014 ◽
Vol 538
◽
pp. 439-442
Keyword(s):
1994 ◽
Vol 21
(5)
◽
pp. 559-563
2014 ◽
Vol 136
(4)
◽
2014 ◽
Vol 29
(6)
◽
pp. 725-740
◽
Keyword(s):
2012 ◽
Vol 6
(12)
◽
pp. 1899-1908
◽
Keyword(s):