Architectures for efficient implementation of particle filters
Architectures for efficient implementation of particle filters
Resampling algorithms for particle filters: a computational complexity perspective
EURASIP Journal on Applied Signal Processing
A new class of particle filters for random dynamic systems with unknown statistics
EURASIP Journal on Applied Signal Processing
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
Guest editorial special issue on monte carlo methods for statistical signal processing
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Marginalized particle filters for mixed linear/nonlinear state-space models
IEEE Transactions on Signal Processing
Resampling algorithms and architectures for distributed particle filters
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
| Hi-index | 0.08 |
Particle filters are a state-of-the-art method for the state estimation of non-linear stochastic systems. Recent many-core architectures and cellular processor arrays offer a new paradigm for algorithm development, which provides not only high performance, but also theoretical advances for parallel implementations. We have developed a new variant of the particle filter algorithm, which suits ideally implementation on a cellular processor array. The new algorithm often performs better than the classical one and a significant gain in running time can be achieved, especially when there is a large number of particles to be simulated.