A neural network based algorithm for precise transformation between GPS height and pressure altitude

Author

Jiachuan, Lv ; Xuejun, Zhang

Author_Institution

Sch. of Electron. Inf. Eng., Beijing Univ. of Aeronaut. & Astronaut., Beijing

fYear

2008

fDate

26-30 Oct. 2008

Abstract

This paper proposes a neural network based algorithm for precise transformation between GPS height and pressure altitude. In order to accomplish the transformation, the algorithm adopts geometric height referenced to MSL as a mid-transfer parameter by which the entire process is divided into two components: the transformation between two reference surfaces and the transformation between two height measurements. The algorithm can be used in applications that require high accuracy position information, such as the monitoring of aircraft height keeping performance in reduced vertical separation minimum (RVSM) airspace and precise positioning for aerostat.

Keywords

Global Positioning System; backpropagation; height measurement; neural nets; GPS height; Global Positioning System; aircraft height; geometric height; height measurements; neural network; pressure altitude; reduced vertical separation minimum airspace; reference surfaces; Air traffic control; Aircraft; Ellipsoids; Extraterrestrial measurements; Global Positioning System; Neural networks; Sea level; Sea measurements; Sea surface; Space technology;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Avionics Systems Conference, 2008. DASC 2008. IEEE/AIAA 27th

Conference_Location

St. Paul, MN

Print_ISBN

978-1-4244-2207-4

Electronic_ISBN

978-1-4244-2208-1

Type

conf

DOI

10.1109/DASC.2008.4702866

Filename

4702866