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Machine Learning for Object Recognition from High Volume Radio Frequency Data
May 5, 2017 @ 11:30 am - 1:00 pm
Much research efforts have been devoted to applying machine learning algorithms in video imagery for object recognition. However, very limited open literatures can be found on machine learning in radio frequency data. Hence, this research explores application of deep learning algorithms to the task of Automatic Target Recognition (ATR) in synthetic aperture radar (SAR) imagery. Radar enables imaging ground objects at far greater standoff distances. However, the false-alarm rate of both human and machine-based radar image recognition is unacceptably high. Existing ATR algorithms also require impractically large computing resources for airborne applications. The goal of our research is to advance the state-of-the-art ATR capability by developing a more accurate, real-time, and low-power object recognition system. We implemented Convolution Neural Network (CNN) based SAR object recognition algorithms in GPU and energy efficient computing systems. We received acceptable classification accuracy on relevant SAR data. We will discuss technical challenges and future research on radio frequency object recognition.
Speaker(s): Dr. Uttam Majumder,
11:30 AM Social/Pizza
12:00 PM Presentation
Bldg: Golisano Hall (GOL)
152 Lamb Memorial Dr.
Rochester, New York