权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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.

本研究的目的是通过分析人类驾驶员在着陆操作中的行为来提高飞行安全性。在1998年的财政年度，我们测量了飞行员操作飞行模拟器的观点，并分析了飞行员在着陆飞行中的注意力。测量结果显示，飞行员无法在100英尺高度以下监控仪表板。在1999年的财政年度，我们做了一个人类飞行员的数值模型。该模型包含卡尔曼滤波器，它可以从输入数据和输出数据中估计状态变量，以最小化估计误差。在该分析中，驾驶员注意力的分配率处理未知参数，并优化，以最小化卡尔曼滤波器的估计误差指标。结果表明，在最后进近阶段的状态估计中，跑道侧边缘的视觉线索比其他视觉线索更重要，应将注意力分散到多个视觉线索上，以使估计误差的总和最小。这些结果证实了通过使用飞行模拟器获得的实验数据。在本研究的最后一年，驾驶员模型自动构建使用神经网络从模拟器测试的结果。该模型的主要输入是观察线索，如地平线和跑道，输出是电梯角度。两种情况下（有风或无风）的数据用于学习。接下来，构建的飞行员模型在各种情况下进行仿真，我们检查其有效性和通用性。最后，使用因子分析对模型进行了评价。