Adaptive High-Resolution Sensor Waveform Design for Tracking (Synthesis Lectures on Algorithms and Software in Engineering)

Recent innovations in modern radar for designing transmitted waveforms, coupled with new algorithms for adaptively selecting the waveform parameters at each time step, have resulted in improvements in tracking performance. Of particular interest are waveforms that can be mathematically designed to have reduced ambiguity function sidelobes, as their use can lead to an increase in the target state estimation accuracy. Moreover, adaptively positioning the sidelobes can reveal weak target returns by reducing interference from stronger targets. The manuscript provides an overview of recent advances in the design of multicarrier phase-coded waveforms based on Bjorck constant-amplitude zero-autocorrelation (CAZAC) sequences for use in an adaptive waveform selection scheme for mutliple target tracking. The adaptive waveform design is formulated using sequential Monte Carlo techniques that need to be matched to the high resolution measurements. The work will be of interest to both practitionersand researchers in radar as well as to researchers in other applications where high resolution measurements can have significant benefits. Table of Contents: Introduction / Radar Waveform Design / Target Tracking with a Particle Filter / Single Target tracking with LFM and CAZAC Sequences / Multiple Target Tracking / Conclusions

Ioannis Kyriakides received his B.S. degree in Electrical Engineering in 2003 from Texas A&M University. He received his M.S. and Ph.D. degrees in 2005 and 2008, respectively, from Arizona State University. His research interests include Bayesian target tracking, sequential Monte Carlo methods, radar waveform design, and compressive sensing and processing. He is currently a lecturer at the Electrical and Computer Engineering Department of the University of Nicosia. Darryl Morrell received his B.S., M.S., and Ph.D. degrees in Electrical Engineering in 1984, 1986, and 1988 from Brigham Young University. He is currently an Associate Professor at Ari[1]zona State University in the Department of Engineering; as the Associate Chair, he is participating in the implementation of a multi-disciplinary undergraduate engineering program using innova[1]tive, research-based pedagogical and curricular approaches. His technical research interests include stochastic decision theory applied to sensor scheduling and information fusion and application of research based pedagogy to engineering education. Antonia Papandreou-Suppappola is a Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. Her research interests and expertise are in the areas of Adaptive Waveform Design for Agile Sensing, Time-varying Signals and Systems Processing, and Stochastic Processing for Detection, Estimation and Tracking. Her funded research work on sensing and information processing includes the development of optimal waveform selection and config[1]uration algorithms using sequential Monte Carlo and stochastic approximation techniques for the detection and tracking of targets in diverse environments; these include underwater, wideband, or dispersive environments, environments with high noise or clutter, urban terrain or requiring multiple or multi-modal sensors.