Human Detection and Characterization through Barriers

Abstract: This talk will address the phenomenology and characteristics of wave propagation through barriers, such as ground, walls, foliage, etc. behind which targets may be hidden, as a function of frequency and material properties. Techniques for detecting such obscured targets using radar techniques will be discussed. Both conventional and emerging waveforms will be presented. The tutorial will be subdivided into the following main sections.

We will discuss different types of radar waveforms in general use, both conventional and emerging. These will include pulse, impulse, dual-frequency, FMCW, noise, and noise-like (chaotic), with special reference to the unique requirements for through-the-barrier imaging applications. We will then discuss how specific waveforms are affected by the EM environment and assess the limitations of current techniques and how the waveforms can be tailored to suit the unique needs of through-the-barrier detection. Next, we will consider emerging waveform design for optimal target detection in enclosed structures behind barriers. In particular, techniques based on signature exploitation, such as matched illumination theory, as well as mutual information formulation, will be presented for both monostatic and multistatic operation. We will conclude by providing directions for further research.

Bio: Ram Narayanan received his B.Tech. from IIT Madras in 1976 and his Ph.D. from UMass Amherst in 1988. He served as R&D Engineer at Bharat Electronics Limited, India (1976–1983), as Graduate Research Assistant at UMass (1983–1988), and as a faculty member at the University of Nebraska-Lincoln (1988–2003). He is currently a professor of electrical engineering at Penn State. He has coauthored over 180 journal papers and more than 500 conference publications. His current research interests include image analysis, radar detection of mines and IEDs, nonlinear radar, noise radar, cognitive radar, medical radar, quantum radar, radar networks, compressive sensing, and machine learning. He is a Fellow of IEEE, SPIE, and IETE.

 

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Media Contact: Iam-Choon Khoo