Infrared model development for a high-speed imaging fuze

Abstract

Development and generation of high-fidelity JR scenes to support testing requirements at the Kinetic Kill VehicleHardware-in-the-Loop Simulator (KHILS) facility at Eglin AFB, Florida has been the mission for the Air Force Research Laboratory's (AFRL) scene generation team throughout the past ten years. During that time scene generation efforts have supported operational scenarios ranging from surveillance through terminal homing. Recent programs have required the development of JR target and background models to support the testing needs of a high-speed fuze. Development of JR models and techniques to support high-speed fuze applications required advancing the state-of-the-art in JR scene generation. This effort required the development of several target models not available from other sources. In addition, due to the unusual proximity fuze seeker configuration that utilizes a wide angle lens to encompass a full 360 degree fieldof-view (FOV) and very fast frame rate requirements, normal scene generation techniques were not adequate. Hundreds of scenarios consisting of hundreds of image frames were needed to develop the fuzing algorithms. This scene generation requirement necessitated that realistic scene sequences be produced in minutes rather than hours. This paper discusses the JR model development path to generate JR scene sequences to support the algorithm development for this fuzing program. The discussion describes the process and unique modeling techniques that were implemented to build foreign target models that include fighter and bomber aircraft, low-flying cruise missiles, and helicopters. Jmplementation of appropriate rendering techniques to support the generation of backgrounds that include atmospherics, terrain, and sea for realistic target engagements are also discussed. Finally, a description of the process utilized in merging JR model and commercial hardware solutions to satisfy the JR scene generation requirements for this program is presented.

FPGA-based processor for high framerate target detection on cluttered backgrounds using LVASITMsensors

“…Because of this, Nova had to reduce the number of in-cell spatial configuration bits from two to one [3], [10].…”

“…Image "compression" means the reduction of the total number of effective pixel values that are multiplexed off of the focal plane array (FPA). The full resolution a)-image was generated by the CHAMP infrared scene generation program [10], [11]. Both a) and d) images (see Figure 1.a and Figure 1.d) contain a point-target and a resolved target image.…”

Advanced image processing package for FPGA-based re-programmable miniature electronics

“…Use of Zero Crossing Technique for target acquisition and for edge detection. The left figure is the original image that contains a point-target and an air-target image [3], [4]. The right image is the ZCT's output.…”

“…Both figures contain a point-target and a resolved target. The full resolution image on the left was generated by the CHAMP infrared scene generation program [3], [4], and contains 16384 pixel values (128x128). The size of the full resolution frame has been reduced from 16384 pixel values to 718 pixel values in the "compressed" superpixellated image on the right.…”