United States of America As Represented by the Secretary of the Air Force

Robert L Ewing

Siyang Cao

David Brendel

Jacob Allen

Hoda S Abdel-Aty-Zohdy

Yuan Zheng

The present disclosure addresses a novel feedback design methodology to meet the emerging frontiers of beamforming radio frequency (RF) technology in the areas of machine learning and surveillance. The feasibility of developing adaptive waveform modulation schemes for spectrum management in radars via orthogonal wavelet concepts. With the increasing prevalence of RF spectrum bandwidth limitations, this approach of adaptive feedback waveforms addresses advanced signal processing beamforming technique for phase array RF improving overall sensing performance. The adaptive illumination waveform algorithms for enhancing detection, discrimination, and tracking is motivated from the analogy drawn between the cellular wireless communication systems and the general multi-static radar automotive systems. The present innovation has developed signal processing schemes of adaptive illumination waveforms for enhancing RF detection performance and have developed a unified system architecture of the adaptive radar waveform design for various scenarios including multi-static radars and multiple targets.

Robust feedback design methodology for transform sensing using radio frequency (RF) phase array detection

An odor discrimination method and device for an electronic nose system including olfactory pattern classification based on a binary spiking neural network with the capability to handle many sensor inputs in a noise environment while recognizing a large number of potential odors. The spiking neural networks process a large number of inputs arriving from a chemical sensor array and implemented with efficient use of chip surface area.

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Kettering, OH, United States of America

Robert L Ewing