Architectures for Particle Filtering - Signal Processing Systems - page 625

Architectures for Particle Filtering
Sangjin Hong and Seong-Jun Oh
Abstract There are many applications in which particle filters outperform
traditional signal processing algorithms. Some of these applications include track-
ing, joint detection and estimation in wireless communication, and computer
vision. However, particle filters are not used in practice for these applications
mainly because they cannot satisfy real-time requirements. This chapter discusses
several important issues in designing an efficient resampling architecture for high
throughput parallel particle filtering. The resampling algorithm is developed in
order to compensate for possible error caused by finite precision quantization
in the resampling step. Communication between the processing elements after
resampling is identified as an implementation bottleneck, and therefore, concurrent
buffering is incorporated in order to speed up communication of particles among
processing elements. The mechanism utilizes a particle-tagging scheme during
quantization to compensate possible loss of replicated particles due to the finite
precision effect. Particle tagging divides replicated particles into two groups for
systematic redistribution of particles to eliminate particle localization in parallel
processing. The mechanism utilizes an efficient interconnect topology for guaran-
teeing complete redistribution of particles even in case of potential weight unbalance
among processing elements. The architecture supports high throughput and ensures
that the overall parallel particle filtering execution time scales with the number of
processing elements employed.
S. Hong ( )
Department of Electrical and Computer Engineering, Stony Brook University,
Stony Brook, NY 11794, USA
e-mail: [e-mail]
S.-J. Oh
College of Information & Communication, Division of Computer and Communications
Engineering, Korea University, Seoul 136-701, Korea
e-mail: [e-mail]