Klaas Gadeyne, a Ph.D. student in the Mechanical Engineering Robotics Research Group at K.U.Leuven, has developed a C++ Bayesian Filtering Library that includes software for Sequential Monte Carlo methods, Kalman filters, particle filters, etc.
Aiming at the application of passive trackinn based on sensor array, a new passive trackinn usinn sensor array
based on particle filter was proposed. Firstly, the“fake points" could be almost entirely and exactly deleted with the aids of the
sensor array at the expense of an additional sensor. Secondly, considered the fact that the measurements notten from each array
were independent in passive trackinn system, a novel sequential particle filter usinn sensor array with improved distribution was proposed. At last, in a simulation study we compared this approach a壇orithm with traditional trackinn methods. The simulation re-sups show that the proposed method can nreatly improve the state estimation precision of sensor array passive trackinn system.
In this paper, we consider the problem of filtering in relational
hidden Markov models. We present a compact representation for
such models and an associated logical particle filtering algorithm. Each
particle contains a logical formula that describes a set of states. The
algorithm updates the formulae as new observations are received. Since
a single particle tracks many states, this filter can be more accurate
than a traditional particle filter in high dimensional state spaces, as we
demonstrate in experiments.
％ PURPOSE : Demonstrate the differences between the following
％ filters on a simple DBN.
％
％ 3) particle Filter (PF)
％ 4) PF with Rao Blackwellisation (RBPF)
This a collection of MATLAB functions for extended Kalman filtering, unscented Kalman filtering, particle filtering, and miscellaneous other things. These utilities are designed for reuse and I have found them very useful in many projects. The code has been vectorised for speed and is stable and fast.
The algorithms are coded in a way that makes it trivial to apply them to other problems. Several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar -xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "demo_MC" for the demo.
