UAV Based Distributed ATR under Realistic Simulated Environmental Effects

Over the past several years, the military has grown increasingly reliant upon the use of unattended aerial vehicles (UAVs) for surveillance missions. There is an increasing trend towards fielding swarms of UAVs operating as large-scale sensor networks in the air. Such systems tend to be used primarily for the purpose of acquiring sensory data with the goal of automatic detection, identification, and tracking objects of interest. These trends have been paralleled by advances in both distributed detection, image/signal processing and data fusion techniques. Furthermore, swarmed UAV systems must operate under severe constraints on environmental conditions and sensor limitations. In this work, we investigate the effects of environmental conditions on target detection and recognition performance in a UAV network. We assume that each UAV is equipped with an optical camera, and use a realistic computer simulation to generate synthetic images. The detection algorithm relies on Haar-based features while the automatic target recognition (ATR) algorithm relies on Bessel K features. The performance of both algorithms is evaluated using simulated images that closely mimic data acquired in a UAV network under realistic environmental conditions. We design several fusion techniques and analyze both the case of a single observation and the case of multiple observations of the same target.