Effective missile warning and countermeasures are an unfulfilled goal for the Air Force and DOD community. To make the expectations a reality, sensors exhibiting the required sensitivity, field of regard, and spatial resolution are needed. The largest concern is in the first stage of a missile warning system, detection, in which all targets need to be detected with a high confidence and with very few false alarms. Typical sensors are limited in their detection capability by the presence of heavy background clutter, sun glints, and inherent sensor noise. The objective of this proposal is to demonstrate improved discrimination methods and algorithms for detection and declaration of missile threats for aircraft. M&M Aviation will develop and assess the feasibility of two innovative and fundamentally different approaches to clutter suppression, track-before-detect processing and threat declaration for missile warning sensors. This effort will also characterize such algorithm suites for suitable implementation on system-level hardware to include: massively parallel arrays (SIMD and MIMD), ASIC, FPGA, distributed processing and conventional general-purpose platforms. Evaluation of implemented algorithms will include processing cost in regards to clock cycles and end-to-end system tests compared with current AFRL spatio-temporal algorithms.
Benefits: Successful implementation of the new processing suite will allow for more cost effective missile warning systems, whether centered in the infrared, visible, or ultraviolet spectrums, for inclusion on-board civil aircraft.
Keywords: missile warning, Automatic Target Recognition, Automatic Target Tracking, Bayes Classifier, image procesing, Mathematical Morphology, Neural Network, Sensor Fusion
