Spatio-Temporal Interceptor Image Filtering for Threat Cloud RV/Clutter Discrimination

In Phase II, Computational Sensors Corporation (CSC) proposes to continue developing mid-course threat cloud target identification and target/background clutter discrimination using spatio-temporal filtering motion analysis techniques initiated during Phase I. Our novel approach leverages the natural ability of massively parallel analog circuitry to perform important computationally intensive image processing tasks in real-time, and therefore has potential for handling intercept engagements containing numerous targets. During Phase I, CSC explored the applicability of analog spatio-temporal (S-T) filtering and scene motion analysis in a proof-of-concept demonstration applied to visible band data from a PENAID data acquisition program. Phase I results indicate potential for S-T filtering based target/clutter discrimination and tracking, and guides our Phase II approach to improving and tailoring the data association and tracking code applied during Phase I as well as the development of wavelet based discrimination. Hardware-in-the-loop testing and analysis using the existing Thin Film Analog Image Processor (TAIP) spatial filter in conjunction with temporal image processing will proceed throughout Phase II to validate our simulated results.