Model-Based Identification of Moving Time Critical Targets using Wideband Signal Processing
Small Business Information
6 New England Executive Park, Burlington, MA, 01803
Lead Research Engineer
Lead Research Engineer
AbstractA broad range of surveillance technologies is required for the successful prosecution of time-critical targets (TCTs). These technologies include detection, tracking, identification (ID), sensor resource management (SRM), and battle damage assessment.For mobile missile launcher TCTs (e.g. SCUD TELs), ID is a critical component because these targets remain in deep hide for most of their life cycle. During Phase I, we will develop new model-based approaches for identifying moving TCTs. We focus onmoving TCTs as opposed to stationary TCTs for three reasons: (1) there are more opportunities to identify these targets while they are moving than while they are stationary, (2) stationary ID may be too late to prevent launch, and (3) early ID is animportant input to SRM. We will apply and extend our existing clutter cancellation and STAP algorithms to extract signatures from real phase-history data (MTFP experiments) and will leverage our current model-based algorithms for stationary targets(MSTAR) to develop new model-based moving-target ID algorithms. We will perform a thorough parametric evaluation of these algorithms using real phase-history data and we will emulate current and near-term operational sensors by appropriately modifying thebandwidth, coherent processing interval, and signal-to-noise ratio of the MTFP phase-history data. The technology developed under this program will contribute directly to the overall military objective of identifying advanced time critical targets. Inaddition, model-based moving-target identification is directly applicable to other moving ground targets as well as airborne targets. We anticipate that moving-target identification methods could be used in peacetime applications such as treaty complianceassessment and monitoring. Moreover, these techniques could be used for disease detection and diagnosis in modern medical imagery, including tomographic radiography (CAT scans) and magnetic resonance imagery (MRI).
* information listed above is at the time of submission.