Compressive imaging for difference image formation and wide-field-of-view target tracking.

摘要

Use of imaging systems for performing various situational awareness tasks in military and commercial settings has a long history. There is increasing recognition, however, that a much better job can be done by developing non-traditional optical systems that exploit the task-specific system aspects within the imager itself. In some cases, a direct consequence of this approach can be real-time data compression along with increased measurement fidelity of the task-specific features. In others, compression can potentially allow us to perform high-level tasks such as direct tracking using the compressed measurements without reconstructing the scene of interest. In this dissertation we present novel advancements in feature-specific (FS) imagers for large field-of-view surveillence, and estimation of temporal object-scene changes utilizing the compressive imaging paradigm. We develop these two ideas in parallel. In the first case we show a feature-specific (FS) imager that optically multiplexes multiple, encoded sub-fields of view onto a common focal plane. Sub-field encoding enables target tracking by creating a unique connection between target characteristics in superposition space and the target's true position in real space. This is accomplished without reconstructing a conventional image of the large field of view. System performance is evaluated in terms of two criteria: average decoding time and probability of decoding error. We study these performance criteria as a function of resolution in the encoding scheme and signal-to-noise ratio. We also include simulation and experimental results demonstrating our novel tracking method. In the second case we present a FS imager for estimating temporal changes in the object scene over time by quantifying these changes through a sequence of difference images. The difference images are estimated by taking compressive measurements of the scene. Our goals are twofold. First, to design the optimal sensing matrix for taking compressive measurements. In scenarios where such sensing matrices are not tractable, we consider plausible candidate sensing matrices that either use the available a priori information or are non-adaptive. Second, we develop closed-form and iterative techniques for estimating the difference images. We present results to show the efficacy of these techniques and discuss the advantages of each.