Phillip K. Poon

Static compressive tracking

This paper presents the Static Computational Optical Undersampled Tracker (SCOUT), an architecture for compressive motion tracking systems. The architecture uses compressive sensing techniques to track moving targets at significantly higher resolution than the detector array, allowing for low cost, low weight design and a significant reduction in data storage and bandwidth requirements. Using two amplitude masks and a standard focal plane array, the system captures many projections simultaneously, avoiding the need for time-sequential measurements of a single scene. Scenes with few moving targets on static backgrounds have frame differences that can be reconstructed using sparse signal reconstruction techniques in order to track moving targets. Simulations demonstrate theoretical performance and help to inform the choice of design parameters. We use the coherence parameter of the system matrix as an efficient predictor of reconstruction error to avoid performing computationally intensive reconstructions over the entire design space. An experimental SCOUT system demonstrates excellent reconstruction performance with 16X compression tracking movers on scenes with zero and nonzero backgrounds.

Calibration Challenges and Initial Experimental Demonstration of an Adaptive, Feature-Specific Spectral Imaging Classifier

We describe the calibration challenges associated with a computational spectral imaging classifier and present initial experimental results. We also discuss planned system improvements to address these calibration challenges and observed performance issues.

Experimental Demonstration of Compressive Target Tracking

We present an experimental demonstration of compressive target tracking—detection of mover locations with a spatial resolution finer than that provided by the detector pixel dimensions. The tracking performance is evaluated with a customized metric.

Computational hyperspectral unmixing using the AFSSI-C

We have previously introduced a high throughput multiplexing computational spectral imaging device. The device measures scalar projections of pseudo-arbitrary spectral filters at each spatial pixel. This paper discusses simulation and initial experimental progress in performing computational spectral unmixing by taking advantage of the natural sparsity commonly found in the fractional abundances. The simulation results show a lower unmixing error compared to traditional spectral imaging devices. Initial experimental results demonstrate the ability to directly perform spectral unmixing with less error than multiplexing alone.

Experimental demonstration of an adaptive architecture for direct spectral imaging classification

Spectral imaging is a powerful tool for providing in situ material classification across a spatial scene. Typically, spectral imaging analyses are interested in classification, though often the classification is performed only after reconstruction of the spectral datacube. We present a computational spectral imaging system, the Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C), which yields direct classification across the spatial scene without reconstruction of the source datacube. With a dual disperser architecture and a programmable spatial light modulator, the AFSSI-C measures specific projections of the spectral datacube which are generated by an adaptive Bayesian classification and feature design framework. We experimentally demonstrate multiple order-of-magnitude improvement of classification accuracy in low signal-to-noise (SNR) environments when compared to legacy spectral imaging systems.

Hyperspectral Unmixing using the AFSSI-C

We investigate the use of the Adaptive Feature Specific Spectral Imaging Classifier (AFSSI-C) architecture to perform spectral unmixing. Through simulations using a variety of static coding schemes, we demonstrate that the AFSSI-C outperforms traditional hyperspectral imaging.

A static architecture for compressive target tracking

Traditional approaches to persistent surveillance generate prodigious amounts of data, stressing storage, communication, and analysis systems. As such, they are well suited for compressed sensing (CS) concepts. Existing demonstrations of compressive target tracking have utilized time-sequences of random patterns, an approach that is sub-optimal for real world dynamic scenes. We have been investigating an alternative architecture that we term SCOUT-the Static Computational Optical Undersampled Tracker-which uses a pair of static masks and a defocused detector to acquire a small number of measurements in parallel. We will report on our working prototypes that have demonstrated successful target tracking at 16x compression.

Fabrication and Testing of Computer-Generated Volume Holograms in the Terahertz

Advances in rapid prototyping technology have allowed for successful fabrication of terahertz computer-generated volume holograms. We report on our progress in the design, fabrication, and measurement of our first successful holograms.