Inferring Flight Performance Under Different Maneuvers With Pilot's Multi-Physiological Parameters

Edmond Q. Wu, Meng Chu Zhou, Pengwen Xiong, Zhi Ri Tang, Ruihan Hu, Yu Wen Jie

Research output: Contribution to journalArticlepeer-review

Abstract

The relationship between flight performance and multi-physiological parameters under different flight operating patterns is unknown. This work proposes a Stacked Gaussian Process Network (SGPN) to reveal it. SGPN is a multi-layer network model formed by recursion from a regular Gaussian process and random disturbance. This work constructs an auxiliary variable strategy with the induced points to improve its learning efficiency, thus leading to a sparse SGPN model. In it, a Gaussian process acts as an activation function of each node, but the entire model is no longer a Gaussian process and thus very challenging to solve it. This work presents its solution via variational approximate inference. Experimental results of pilot flight performance evaluation show that the proposed model has stronger learning and generalization ability than its seven competitive peers. It is able to approximate non-linear coupling relationship between multi-physiological parameters and flight height differences.

Original languageEnglish (US)
JournalIEEE Transactions on Intelligent Transportation Systems
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

Keywords

  • Flight performance
  • Gaussian process
  • physiological parameters
  • sparse stacked Gaussian process network
  • workload.

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