Exploiting Ultrasound Sensitivity Patterns to Accurately Localize Mobile Motes in Real Time using Particle Filtering

We investigate the problem of using contact sensors to estimate the pose of an object during planar pushing by a fixed-shape hand. Contact sensors are unique because they inherently discriminate between “contact” and “no-contact” configurations. As a result, the set of object configurations that activates a sensor constitutes a lower-dimensional contact manifold in the configuration space of th...

In this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the ...

Positioning of moving platforms has been a technical driver for real-time applications of the particle filter (PF) in both the signal processing and the robotics communities. For this reason, we will spend some time to explain several such applications in detail, and to summarize the experiences of using the PF in practice. The applications concern positioning of underwater vessels, surface shi...

The problem of tracking an object in an image sequence involves challenges like translation, in-plane and out-of-plane rotations, scaling, variations in ambient light and occlusions. A model of an object to be tracked is built off-line by making a training set with images of the object with different poses. A dimensionality reduction technique is used to capture the variations in the training i...

Particle probability hypothesis density filtering has become a promising means for multi-target tracking due to its capability of handling an unknown and time-varying number of targets in non-linear non-Gaussian system. However, its computational complexity grows linearly with the number of measurements and particles assigned to each target, and this can be very time consuming especially when n...

This report introduces the ideas behind particle filters, looking at the Kalman filter and the SIS and SIR filters to learn about the latent state of state space models. It then introduces particle MCMC as a way of learning about the parameters behind these models. Finally, the SIR filter and particle MCMC algorithms are applied to reaction networks, in particular the Lotka Volterra model.

The core tenet of Bayesian modeling is that subjects represent beliefs as distributions over possible hypotheses. Such models have fruitfully been applied to the study of learning in the context of animal conditioning experiments (and analogously designed human learning tasks), where they explain phenomena such as retrospective revaluation that seem to demonstrate that subjects entertain multip...

A large class of proven discrete-time branching particle filters with Bayesian model selection capabilities and effective resampling is analyzed mathematically. The particles interact weakly in the branching procedure through the total mass process in such a way that the expected number of particles can remain constant. The weighted particle filter, which has no resampling, and the fullyresampl...

Particle filters are fully non-linear data assimilation techniques that aim to represent the probability distribution of the model state given the observations (the posterior) by a number of particles. In high-dimensional geophysical applications the number of particles required by the sequential importance resampling (SIR) particle filter in order to capture the high probability region of the ...