Aircraft Ship Integrated Secure and Traverse (ASIST) is a system designed to
arrest helicopters safely and efficiently on ships. Originally, a precision
Helicopter Position Sensing Equipment (HPSE) tracked and monitored the position
of the helicopter relative to the Rapid Securing Device (RSD). However, using
the HPSE component was determined to be infeasible in the transition of the
ASIST system due to the hardware installation requirements. As a result,
sailors track the position of the helicopters with their eyes with no sensor or
artificially intelligent decision aid. Manually tracking the helicopter takes
additional time and makes recoveries more difficult, especially at high sea
states. Performing recoveries without the decision aid leads to higher
uncertainty and cognitive load. PETA (Pose Estimation and Tracking for ASIST)
is a research effort to create a helicopter tracking system prototype without
hardware installation requirements for ASIST system operators. Its overall goal
is to improve situational awareness and reduce operator uncertainty with
respect to the aircrafts position relative to the RSD, and consequently
increase the allowable landing area. The authors produced a prototype system
capable of tracking helicopters with respect to the RSD. The software included
a helicopter pose estimation component, camera pose estimation component, and a
user interface component. PETA demonstrated the potential for state-of-the-art
computer vision algorithms Faster R-CNN and HRNet (High-Resolution Network) to
be used to estimate the pose of helicopters in real-time, returning ASIST to
its originally intended capability. PETA also demonstrated that traditional
methods of encoder-decoders could be used to estimate the orientation of the
helicopter and could be used to confirm the output from HRNet.

Aircraft Ship Integrated Secure and Traverse (ASIST) 航空器船舶综合安全遍历系统中的 Pose Estimation and Tracking for ASIST (PETA) 研究了一种不需要硬件安装的直升机跟踪系统原型，其目标是提高操作员对飞行器相对于快速固定装置的位置的认知，减少操作员的不确定性，并从而扩大可允许的着陆区域。PETA 证明了先进的计算机视觉算法 Faster R-CNN 和 HRNet 可用于实时估计直升机的姿态，恢复 ASIST 到其最初的预期能力，并展示了编码器 - 解码器的传统方法可用于确认 HRNet 的输出。