Mitigation of System Latency in Next Generation Helmet Mounted Display Systems (Nghmds)

Abstract

The ability of helmet mounted display (HMD) systems to increase effectiveness in operational aircraft has been well documented over the last several years. Now that advanced aircraft are committed to using HMDs in combat operations, issues associated with their use in simulators must be addressed. A major factor associated with the onset of simulator sickness is system latency. Simulator sickness can be a significant distraction during training and may result in ineffective training, negative training, reduced user acceptance, and a reduction in simulator usage. Innovative solutions to address system latency, as well as many other issues, must be developed so that training can be optimized. The current project developed a number of innovative strategies that effectively mitigate both system latency and image alignment error. The strategies developed include: 1) a customized Kalman predictive filter, 2) a learning neural network, and 3) “Warper Board” technology. These strategies operate independently yet in concert to continually sample, compare, and adjust their outputs to produce the most accurate prediction of future image placement possible using current available head movement and position data. Experimental results indicate that effective system latency was reduced up to 90% from the baseline system state. Implications for training systems and improved training will be discussed.