Simulated Performance Comparison of Staring, Scanning and Multiplexed Imagers
Wide area surveillance using imaging sensors is a challenging problem. Traditional systems use either a wide field of view (WFOV) imager, or a relatively narrow field of view (NFOV) sensor with a scanning system. While WFOV sensors offer ideal area coverage and performance, they are generally associated with high data bandwidth and large amounts of pixels. On the other hand, scanning systems are less data intensive, but also relatively slow. We present a multiplexed imaging concept that offers a performance trade between the two conventional imaging solutions. We discuss results based on emulating the different sensing modalities using data obtained with commercially available infra-red cameras. It is shown that using a single FPA, a FOV multiplexing imager can provide better performance than a scanning system for finding targets over a wide field of view.
Dr. Abhijit Mahalanobis is currently an Associate Professor in the Department of Electrical and Computer Engineering at the University of Arizona. His primary research areas are in machine vision and computational imaging. He has over 180 journal and conference publications in these areas. He also holds six patents, co-authored a book on pattern recognition, contributed several book chapters, and edited special issues of several journals. Abhijit completed his B.S. degree with Honors at the University of California, Santa Barbara in 1984. He then joined the Carnegie Mellon University and received the MS. and Ph.D. degrees in 1985 and 1987, respectively. Prior to joining UCF, Abhijit was a faculty at the University of Central Florida in the Center for Research in Computer Vision (CRCV). He was also a former Senior Fellow at Lockheed Martin in Orlando and worked at Raytheon in Tucson before that. Abhijit was elected a Fellow of SPIE in 1997, and a Fellow of OSA 2004 for his work on optical pattern recognition and automatic target recognition. He was elected Fellow of IEEE in 2015 for his work on the theory of correlation filters.