With the recent surge in autonomous driving vehicles, the need for accurate vehicle
detection and tracking is critical now more than ever. Detecting vehicles from visual sensors …

Human monitoring applications in indoor environments depend on accurate human
identification and activity recognition (HIAR). Single modality sensor systems have shown to …

In the Information Age, the widespread usage of blackbox algorithms makes it difficult to
understand how data is used. The practice of sensor fusion to achieve results is widespread …

Machine learning (ML) algorithms have been widely used in many critical automated
systems, including as a technique in Dynamic Data Driven Applications Systems (DDDAS) …

For intelligence and surveillance methods, information fusion techniques typically increase
the accuracy and reliability of results through uncertainty reduction. Recently, artificial …

Since the rise of deep learning (DL), methods are being proposed daily for all kinds of
applications such as systems that include radar, infrared (IR), and electro-optical (EO) …

A modern, contested environment produces exponential amounts of data from a vast array of
multimodal sensory inputs for intelligence actively updating our situational awareness (SA) …

Since the rise of deep learning (DL), methods are being proposed daily for all kinds of
applications such as systems that include radar, infrared (IR), and electro-optical (EO) …

This paper presents an explainable late-stage decision fusion model for Electro-Optical (EO)
and Passive Radio Frequency (P-RF) target detection via hybrid Explainable AI model …

Increasingly, target tracking systems are comprised of distributed heterogeneous sensors as
part of a system that is not necessarily optimized for a specific target. In order to increase the …