Research on Sate Recognition of a Human Pilot and Decision Making Model

飞行员状态识别及决策模型研究

基本信息

批准号：
10450370
负责人：
SUZUKI Shinji
金额：
$ 7.1万
依托单位：
The University of Tokyo, Graduate School of Engineering
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B).
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 2000
项目状态：
已结题

项目摘要

This research is aiming for improving flight safety through the analysis of human pilot behavior at landing operation. In the fiscal year of 1998, we measured the viewpoint of a pilot operating a flight simulator and analyzed where the pilot put attention through a landing flight. This measurement revealed that the pilot cannot monitor the instrument panel under about 100 ft height. In the fiscal year of 1999, we made a numerical model of the human pilot. This model contains the Kalman Filter which can estimate the state variables from the input data and the output data to minimize the estimation error. In this analysis, the distribution ratio of a pilot attentiveness are dealt with unknown parameters and are optimized to minimize the estimation error index in the Kalman Filter. It is found that the visual cue of the runway side edges is more important than the others in the state estimation in the final approach phase, and that attention should be distributed to several visual cues to minimize the total sum of the estimation errors. These results are confirmed from experimental data obtained by using a flight simulator. In the final year of this research, the pilot model is automatically constructed using neural network from the results of the simulator tests. The main inputs to this model are observation cues such as the horizon and the runway, and the output is the elevator angle. The data on two cases (with wind, or with no wind) are used for learning. Next, the constructed pilot models are simulated in various cases and we check their validity and generalization. Finally, the models are evaluated using factor analysis.

这项研究旨在通过分析着陆运行时的人类飞行员行为来提高飞行安全。在1998财政年度，我们测量了一名飞行模拟器的飞行员的观点，并分析了飞行员通过着陆飞行引起注意的地方。该测量表明，飞行员无法在约100英尺高的高度下监视仪表板。在1999财政年度，我们制作了人类飞行员的数值模型。该模型包含Kalman滤波器，该过滤器可以估算输入数据和输出数据的状态变量，以最大程度地减少估计误差。在此分析中，试验专注力的分布比涉及未知参数，并进行了优化以最大程度地减少卡尔曼滤波器中的估计误差指数。发现跑道侧边缘的视觉提示比在最终进近阶段的状态估计中更重要，并且应将注意力分布到几种视觉提示中，以最大程度地减少估计错误的总和。通过使用飞行模拟器获得的实验数据证实了这些结果。在这项研究的最后一年中，通过模拟器测试结果使用神经网络自动构建了试点模型。该模型的主要输入是观察提示，例如地平线和跑道，输出是电梯角度。两种情况（有风或无风）的数据用于学习。接下来，在各种情况下模拟了构建的试点模型，我们检查了它们的有效性和概括。最后，使用因子分析评估模型。