The need for fast and reliable sensing at millimeter-wave frequencies has been increasing dramaticallyin recent years for a wide range of applications. Imaging has been of particular interest since the wavelengths at millimeter-wave frequencies provide good resolution and are capable to propagate through obscurants such as smoke, clouds, and clothing with negligible attenuation. While various implementations for millimeter-wave imaging have been developed, the new technique of active incoherent millimeter-wave (AIM) imaging is of particular interest because it solves fundamental limitations inherent in other approaches. Furthermore, AIM enables imaging with significantly fewer elements than phased arrays and costs less than passive imagers. This is enabled by actively transmitting noise signals, allowing the system to capture scene information in the spatial frequency domain. In this work, I explore the use of array dynamics to further reduce the hardware requirements of AIM imaging by introducing a new degree of freedom in the array design. By dynamically changing the locations of receiving antennas in a sparse array through motions, the spatial frequency domain can be efficiently sampled using as few as two antennas. In this thesis, I demonstrate the use of array dynamics to generate imagery and show a new concept for imageless object identification based on sampling unique spatial frequency features associated with physical shapes of objects. This non-imaging approach further reduces the required number of antennas. The designed rotational dynamic antenna array operates at 38 GHz and leverages noise transmitting sources as required by the AIM technique. Two receivers are designed with adjustable distance in-between, enabling a sparse linear array to be synthesized. Simulation and experimental measurements using the AIM based rotational dynamic antenna array are discussed for imaging and imageless classification.
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