Model-based multi-sensor fusion for reconstructing wall-bounded turbulence

Wall-bounded turbulence

Model - based sensor fusion for

We describe a sensor fusion algorithm based on a set of simple assumptions about the relationship among the sensors. Under these assumptions we estimate the common signal in each sensor, and the optimal fusion is then approximated by a weighted sum of the common component in each sensor output at each pixel. We then examine a variety of techniques to map the sensor signals onto perceptual dimen...

Coherent structures in wall-bounded turbulence.

The inherent difficulty of understanding turbulence has led to researchers attacking the topic in many different ways over the years of turbulence research. Some approaches have been more successful than others, but most only deal with part of the problem. One approach that has seen reasonable success (or at least popularity) is that of attempting to deconstruct the complex and disorganised tur...

Phenomenology of wall-bounded Newtonian turbulence.

We construct a simple analytic model for wall-bounded turbulence, containing only four adjustable parameters. Two of these parameters are responsible for the viscous dissipation of the components of the Reynolds stress tensor. The other two parameters control the nonlinear relaxation of these objects. The model offers an analytic description of the profiles of the mean velocity and the correlat...

A Model Based Approach to Statistical, Multi-Modal Sensor Fusion

We propose a framework for obtaining statistical inferences from multi-modal and multisensor data. In particular, we consider a military battlefield scene and address problems that arise in tactical decision-making while using a wide variety of sensors (an infrared camera, an acoustic sensor array, a human scout, and a seismic sensor array). Outputs of these sensors vary widely, from 2D images ...