@conference {12648,
	title = {Algorithmic and architectural design methodology for particle filters in hardware},
	booktitle = {Computer Design: VLSI in Computers and Processors, 2005. ICCD 2005. Proceedings. 2005 IEEE International Conference on},
	year = {2005},
	month = {2005/10//},
	pages = {275 - 280},
	abstract = {In this paper, we present algorithmic and architectural methodology for building particle filters in hardware. Particle filtering is a new paradigm for filtering in presence of nonGaussian nonlinear state evolution and observation models. This technique has found wide-spread application in tracking, navigation, detection problems especially in a sensing environment. So far most particle filtering implementations are not lucrative for real time problems due to excessive computational complexity involved. In this paper, we re-derive the particle filtering theory to make it more amenable to simplified VLSI implementations. Furthermore, we present and analyze pipelined architectural methodology for designing these computational blocks. Finally, we present an application using the bearing only tracking problem and evaluate the proposed architecture and algorithmic methodology.},
	keywords = {(numerical, algorithmic, architectural, architectures;, bearing, complexity;, computational, design, digital, evolution;, Filtering, filtering;, filters;, implementation;, methodology;, methods);, nonGaussian, nonlinear, only, Parallel, particle, pipeline, pipelined, problem;, processing;, state, tracking, VLSI, VLSI;},
	doi = {10.1109/ICCD.2005.20},
	author = {Sankaranarayanan,A. C and Chellapa, Rama and Srivastava, A.}
}
@conference {13198,
	title = {Kernel-based Bayesian filtering for object tracking},
	booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
	volume = {1},
	year = {2005},
	month = {2005/06//},
	pages = {227 - 234 vol. 1 - 227 - 234 vol. 1},
	abstract = {Particle filtering provides a general framework for propagating probability density functions in nonlinear and non-Gaussian systems. However, the algorithm is based on a Monte Carlo approach and sampling is a problematic issue, especially for high dimensional problems. This paper presents a new kernel-based Bayesian filtering framework, which adopts an analytic approach to better approximate and propagate density functions. In this framework, the techniques of density interpolation and density approximation are introduced to represent the likelihood and the posterior densities by Gaussian mixtures, where all parameters such as the number of mixands, their weight, mean, and covariance are automatically determined. The proposed analytic approach is shown to perform sampling more efficiently in high dimensional space. We apply our algorithm to real-time tracking problems, and demonstrate its performance on real video sequences as well as synthetic examples.},
	keywords = {approach;, approximation;, Bayes, Bayesian, Carlo, density, detection;, filtering;, functions;, Gaussian, interpolation;, kernel-based, methods;, mixtures;, Monte, nonGaussian, nonlinear, object, particle, probability, probability;, processes;, recognition;, sampling, sampling;, sequences;, system;, tracking;, video},
	doi = {10.1109/CVPR.2005.199},
	author = {Han,Bohyung and Zhu,Ying and Comaniciu, D. and Davis, Larry S.}
}
