Our research focus is on the application machine learning for automatic target recognition (ATR) and the design of next-generation radar systems. This includes fundamental research in neural network architectures and training methodologies that incorporate physical sensor and target models for improved classification accuracy, robustness, and reduced computational complexity. Exploiting advances in adaptive antennas and RF electronics, we strive to incorporate biologically-inspired machine learning and artificial intelligence algorithms into radar system design for the goal of developing cognitive radars of the future. Specific applications of interest include RF-sensing for the design of human motion sensitive ambient environments, remote health monitoring, fall detection, fall risk assessment, gait analysis, as well as applications to human-computer interaction, including gesture recognition, and natural language processing, including ASL recognition and the design of smart Deaf spaces. We also work on applications of signal processing and machine learning to a variety of defense and security applications.
