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

本研究旨在通过分析飞行员在着陆操作时的行为来提高飞行安全性。在1998财年，我们测量了操作飞行模拟器的飞行员的视点，并分析了飞行员在着陆飞行中的注意力集中在哪里。该测量表明飞行员无法在大约 100 英尺高度下监控仪表板。 1999财年，我们制作了人类飞行员的数值模型。该模型包含卡尔曼滤波器，可以根据输入数据和输出数据估计状态变量，以最小化估计误差。在该分析中，飞行员注意力的分配比率被处理为未知参数并被优化以最小化卡尔曼滤波器中的估计误差指数。研究发现，在最后进场阶段的状态估计中，跑道侧边缘的视觉提示比其他视觉提示更重要，并且应该将注意力分散到多个视觉提示上，以最小化估计误差的总和。这些结果通过使用飞行模拟器获得的实验数据得到证实。在这项研究的最后一年，根据模拟器测试的结果，使用神经网络自动构建试点模型。该模型的主要输入是地平线和跑道等观测线索，输出是升降舵角度。两种情况（有风或无风）的数据用于学习。接下来，在各种情况下对构建的试点模型进行模拟，并检查其有效性和泛化性。最后，使用因子分析对模型进行评估。

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.