Sensor integration for classification
Steven Kay, Quan Ding, Muralidhar Rangaswamy · 2010
In the problem of sensor integration, an important issue is to estimate the joint PDF of the measurements of sensors. However in practice, we may not have enough training data to have a good estimate. In this paper, we have constructed the joint PDF using an exponential family for classification. This method only requires the PDF under a reference hypothesis. Its performance has shown to be as good as the estimated maximum a posteriori probability classifier which requires more information. This shows a wide application of our method in classification because less information is needed than existing methods.