FLIR & MMW Algorithms to Detect and Classify Stationary Targets
Department of Defense
Agency Tracking Number:
Solicitation Topic Code:
Small Business Information
I-math Associates, Inc.
P.o. Box 560788, Orlando, FL, 32856
Socially and Economically Disadvantaged:
Alexander Akerman Iii
AbstractSix multisensor (FLIR/MMW) data bases will be accessed to include all those at CNVEOD including the VISION 1 and subsequent field tests, particularly those involving the RIMS and BWI MMW radars, along with the IIDAS and SAIRS FLIRS. Data from other government agencies will include that in TABILS and at HDL, MICOM, and AATD. A Phase II plan will be produced for training and testing three ATR algorithms not heretofore considered for tank-mounted applications but proven in related contexts: 1. An adaptive Bayesian statistical classifier, which in its operational implementation would be continually retrained upon the actual ground clutter within which the tank is operating. 2. A "genetic" algorithm, which uses the same feature sets as #1, but evolves a different polynomial discriminant function. Both the "genetic" and "adaptive" classifier algorithms are under development for the LONGBOW MMW radar by Systems Dynamics, who would be subcontracted to I-MATH for this SBIR. 3. Geometric hashing algorithms, which I-MATH has recently developed using a CNVEOD-provided FLIR imagery under a Phase II Air Force SBIR. Such algorithms provide a robust mechanism for multisensor fusion as well, since each feature is indexed by its geometric position. This greatly facilitates the incorporation of MMW range (z) profiles as a third coordinate to FLIR (x,y) has points.
* information listed above is at the time of submission.