KEYWORDS: Data modeling, Information fusion, Machine learning, Data fusion, Sensors, Systems modeling, Telecommunications, Artificial intelligence, Video, Image fusion
During the 2019 SPIE DSS conference, panelists were invited to highlight the trends and use of artificial intelligence and machine learning (AI/ML) for information fusion. The common themes between the panelists include leveraging AI/ML coordinated with Information Fusion for: (1) knowledge reasoning, (2) model building, (3) object recognition and tracking, (4) multimodal learning, and (5) information processing. The opportunity for machine learning exists within all the fusion levels of the Data Fusion Information Group model.
David Rouse, Adam Watkins, David Porter, John Harer, Paul Bendich, Nate Strawn, Elizabeth Munch, Jonathan DeSena, Jesse Clarke, Jeff Gilbert, Sang Chin, Andrew Newman
This paper introduces a method to integrate target behavior into the multiple hypothesis tracker (MHT) likelihood ratio. In particular, a periodic track appraisal based on behavior is introduced that uses elementary topological data analysis coupled with basic machine learning techniques. The track appraisal adjusts the traditional kinematic data association likelihood (i.e., track score) using an established formulation for classification-aided data association. The proposed method is tested and demonstrated on synthetic vehicular data representing an urban traffic scene generated by the Simulation of Urban Mobility package. The vehicles in the scene exhibit different driving behaviors. The proposed method distinguishes those behaviors and shows improved data association decisions relative to a conventional, kinematic MHT.
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