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Skip to Search Results- 8Particle filters
- 2Bayesian Model Selection
- 2Branching Process
- 2Law of large numbers
- 2Non-linear filtering
- 1Artificial learning
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2014-06-05
The classical particle filter, introduced in 1993, approximates the normalized filter directly. It has two defiencies, over resampling and the inability to distinguish models, the former of which was overcome but the later is fundamental. Conversely, the weighted particle filter, motivated by the...
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2004
McCrosky, Jesse, Kouritzin, Michael, Blount, Douglas
A hybrid weighted/interacting particle filter, the selectively resampling particle (SERP) filter, is used to detect and track an unknown number of independent targets on a one-dimensional \"racetrack\" domain. The targets evolve in a nonlinear manner. The observations model a sensor positioned...
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2009
Newton, Fraser, Kouritzin, Michael, Wu, Biao, Luo, Dandan
Military tanks, cargo or troop carriers, missile carriers or rocket launchers often hide themselves from detection in the forests. This plagues the detection problem of locating these hidden targets. An electro-optic camera mounted on a surveillance aircraft or unmanned aerial vehicle is used to...
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2008
Target Obscuration, including foliage or building obscuration of ground targets and landscape or horizon obscuration of airborne targets, plagues many real world filtering problems. In particular, ground moving target identification Doppler radar, mounted on a surveillance aircraft or unattended...
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2004
Kouritzin, Michael, Long, H., Sun, W.
Herein, we consider direct Markov chain approximations to the Duncan–Mortensen–Zakai equations for nonlinear filtering problems on regular, bounded domains. For clarity of presentation, we restrict our attention to reflecting diffusion signals with symmetrizable generators. Our Markov chains are...
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2003
Kouritzin, Michael, Ma, Xinjian, Long, Hongwei, Sun, Wei
Nonlinear filtering is an important and effective tool for handling estimation of signals when observations are incomplete, distorted, and corrupted. Quite often in real world applications, the signals to be estimated contain unknown parameters which need to be determined. Herein, we develop and...
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2015-04-20
A large class of discrete-time branching particle filters with Bayesian model selection ca-pabilities and effective resampling is introduced in algorithmic form, shown empirically to outperform the popular bootstrap algorithm and analyzed mathematically. The particles interact weakly in the...