Information Fusion and Performance Modeling with Distributed Sensor Networks

Multi-sensor fusion is founded on the principle that combining information from multiple sources will enable a better understanding of the surroundings. However, it would be desirable to evaluate how much one gains by integrating different sensors in a fusion system, even before implementing it. This presentation describes our latest research on fusion performance modeling. Specifically, we have developed a framework for quantifying the classification performance of a set of sensors with varying qualities based on local confusion matrix and global confusion matrix using the Bayesian network model. We have developed a theoretical model to analyze the convergence property of the methodology.  We have also developed a software tool based on UnBBayes open source environment (http://unbbayes.sourceforge.net/index.html) to validate the performance modeling. A demonstration with an example model for air target tracking and classification using the developed tool will be given.  

Beyond The PHD Filters

 

In recent years Mahler’s Finite Set Statistics (FISST) approach to multi-target tracking has

attracted substantial interest mainly through the developments of the PHD/cardinalized PHD (CPHD)

filters. However, FISST is much more powerful and extends beyond the PHD/CPHD filters. This talk

explores a number of tractable tracking solutions derived from FISST, including, joint detection and

tracking (of a single target) using Bernoulli random sets, multi-target tracking using multi-Bernoulli random

sets, and multi-target smoothing with the PHD.

 

 

HaesunPark_03Sep_09.pdf

 

Nonnegative Matrix Factorization (NMF) has attracted much attention during the past decade as

a dimension reduction method in machine learning and data mining. NMF is considered for high

dimensional data where each element has a nonnegative value, and it provides a lower rank

approximation formed by factors whose elements are also nonnegative. Numerous success

stories were reported in application areas including text clustering, computer vision, and

chemometrics.