"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)."
This research program is motivated by the recognition that the volume of sensor data is expected to overwhelm even the enormous performance improvements in silicon technology expressed by Moore's Law. The focus is the development of a low-complexity alternative to non-adaptive image formation through innovations in signal design. The objectives support closer monitoring of weather patterns and may lead to a more detailed understanding of climate change. They also impact a wider range of surveillance applications from microwave landing systems to through-wall imaging. The research program is highly interdisciplinary with signal processing as the bridge between application domains and the mathematics of sequence design. Current hardware allows transmission of wavefields that vary across space, polarization, time and frequency and which can be changed in rapid succession. However, sensing resolution is limited, not by hardware, but by the complexity of remote image formation. This research program develops new signal design principles that enable fast and reliable active sensing with minimal receiver signal processing complexity. The basic unit of transmission is a unitary matrix of phase coded waveforms indexed by array element and by pulse repetition interval, where the polarization of constituent waveforms may vary. Golay Complementary waveforms appear as entries of these matrices. Appropriate sequencing of unitary waveform matrices in time eliminates Doppler induced range sidelobes and provides resilience to multipath without compromising the simplicity of signal processing. OFDM signaling of complementary waveforms improves performance beyond conventional matched filtering by introducing nonlinear signal processing that exchanges static sidelobes for more dynamic cross-terms. The development of a new mathematical framework based on group theory enables the systematic construction of new complementary sequences.
