"Inferring Flight Performance Under Different Maneuvers With Pilot's Mu" by Edmond Q. Wu, Mengchu Zhou et al.
 

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

Document Type

Article

Publication Date

8-1-2022

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.

Identifier

85114734250 (Scopus)

Publication Title

IEEE Transactions on Intelligent Transportation Systems

External Full Text Location

https://doi.org/10.1109/TITS.2021.3103068

e-ISSN

15580016

ISSN

15249050

First Page

11338

Last Page

11348

Issue

8

Volume

23

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