Parallel Hardware for Sampling Based Nonlinear Filters in FPGAs
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Particle filters are a class of sequential Monte-Carlo methods which are used commonly when estimating various unknowns of the time-varying signals presented in real time, especially when dealing with nonlinearity and non-Gaussianity in BOT applications. This thesis work is designed to perform one such estimate involving tracking a person using the road information available from an IR surveillance video. In this thesis, a parallel custom hardware is implemented in Altera cyclone IV E FPGA device utilizing SIRF type of particle filter. This implementation has accounted how the algorithmic aspects of this sampling based filter relate to possibilities and constraints in a hardware implementation. Using 100MHz clock frequency, the synthesised hardware design can process almost 50 Mparticles/s. Thus, this implementation has resulted in tracking the target, which is defined by a 5-dimensional state variable, using the noisy measurements available from the sensor.
Place, publisher, year, edition, pages
2014. , 77 p.
Particle filters, Sampling Importance Resampling Filter, Hardware architectures, Bearings-only tracking.
Other Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:liu:diva-112926ISRN: LiTH-ISY-EX--14/4821--SEOAI: oai:DiVA.org:liu-112926DiVA: diva2:774383
Subject / course
2014-12-18, Nollstället, B-Huset, Campus Valla., Linköping, 17:12 (English)
Gustafsson, Oscar, Dr.