IV.R.06. Real Aperture Target Discrimination. Develop innovative technologies to detect, discriminate, and classify stationary targets with a real beam radar. By FY95, complete conversion of primary clutter database to match Longbow resolution. By FY96, complete real beam radar algorithm training in geographically and seasonally diverse environments. By FY98, develop and demonstrate target/clutter discrimination techniques and algorithms that increase probability of target detection in these diverse environments. Provide quantitative assessment using a Longbow equivalent data set as to the improvement of the existing capability. The algorithm suite will be capable of autonomous adaptation to various clutter backgrounds. Performance capabilities will be demonstrated using a Longbow equivalent data set. By FY99, develop more effective classification of tactical vehicles using a twofold approach: (1) Improve underlying fidelity of target signatures using super-resolution techniques and (2) Apply data compression technique such as a wavelet-based approach to vehicle template storage for efficiently cataloging additional signatures.
Supports: Apache-Longbow, Comanche, Mounted Battle Space Battle Lab, Target Acquisition ATD.
|STO Manager:||TSO:||TRADOC POC:|
|Jeffrey Sichina||Catherine Kominos||Charles Campbell|