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.
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.
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.