Title :
Maximum a posteriori estimation of multichannel Bernoulli-Gaussian sequences
Author :
Dai, Guan-Zhong ; Mendel, Jerry M.
fDate :
1/1/1989 12:00:00 AM
Abstract :
General problems and solutions are described for maximum a posteriori estimation of multichannel Bernoulli-Gaussian sequences, which are inputs to a linear discrete-time multivariable system. The authors first develop a separation principle, which indicates that one can estimate multichannel Gaussian amplitudes and Bernoulli events separately. They then discuss approaches for estimation of these quantities
Keywords :
discrete time systems; estimation theory; information theory; linear systems; multivariable systems; Bernoulli events; information theory; linear discrete-time multivariable system; maximum a posteriori estimation; multichannel Bernoulli-Gaussian sequences; multichannel Gaussian amplitudes; separation principle; Amplitude estimation; Computer science; Deconvolution; Gaussian noise; Helium; MIMO; Maximum a posteriori estimation; Noise measurement; State estimation; Vectors;
Journal_Title :
Information Theory, IEEE Transactions on