Implementation of Particle Filters: Algorithms and Hardware Architectures

Particle filters outperform most of the traditional filtering methods, such as Kalman filters, offering excellent performance in very difficult problems including communications, signal processing, navigation, and computer vision. Hence, particle filters have been the focus of wide research recently and immense literature can be found on their theory. Most of these works recognize the complexity and computational intensity of particle filters, but there has been little effort directed toward real-time implementation of these filters. The objective of this monograph is to develop and design efficient hardware for particle filters, and thereby bring them closer to practical applications. To achieve this objective, algorithmic transformations that lead to improved scalability of design and reduced hardware resources have been performed. Architectures for particle filters have been proposed and implemented on an FPGA platform. The issues tackled include reduction of computational complexity, improvement of scalability of parallel implementation and reduction of memory requirements.

Miodrag Bolic is an assistant professor at the University of Ottawa, Canada. He received his Ph.D. degree in electrical engineering from Stony Brook University, USA in 2004. His research interests include statistical signal processing and computer architectures. He was one of the first researchers to work on hardware design of particle filters.