82 search results - page 4 / 17 » Nonlinear signal processing vs. Kalman filtering |
ICIP
2006
IEEE
14 years 9 months ago
2006
IEEE
Particle filters have been introduced as a powerful tool to estimate the posterior density of nonlinear systems. These filters are also capable of processing data online as requir...
ICASSP
2009
IEEE
14 years 2 months ago
2009
IEEE
In recent work, Kalman Filtered Compressed Sensing (KF-CS) was proposed to causally reconstruct time sequences of sparse signals, from a limited number of “incoherent” measure...
NIPS
1998
13 years 8 months ago
1998
We propose a novel strategy for training neural networks using sequential Monte Carlo algorithms. This global optimisation strategy allows us to learn the probability distribution...
ICASSP
2009
IEEE
14 years 2 months ago
2009
IEEE
In recent work, we studied the problem of causally reconstructing time sequences of spatially sparse signals, with unknown and slow time-varying sparsity patterns, from a limited ...
ICASSP
2008
IEEE
14 years 1 months ago
2008
IEEE
We consider state estimation of a Markov stochastic process using an ad hoc wireless sensor network (WSN) based on noisy linear observations. Due to power and bandwidth constraint...