EconPapers    
Economics at your fingertips  
 

Study on High Sensitivity GPS Signal Acquisition Techniques Indoor Positioning

Liu Yu (), Liu Ding-xing () and Tan Ze-fu ()
Additional contact information
Liu Yu: Chongqing Three Gorges University
Liu Ding-xing: Chongqing Three Gorges University
Tan Ze-fu: Chongqing Three Gorges University

A chapter in 2012 International Conference on Information Technology and Management Science(ICITMS 2012) Proceedings, 2013, pp 697-703 from Springer

Abstract: Abstract Global Position System is more and more close with people’s life from start run till now. It has already been applied to various fields. Compared with the common circumstance, as outdoors, GPS signals power will be weaken much. So researches on HS-GPS indoor position technology are necessary in the extreme to implement high accuracy position in indoor environment. Weak GPS signals acquisition algorithm in low signal to noise ratio environment is studied, signals process flow and acquisition performance of algorithm, such as coherent integration, non-coherent integration and differentially-coherent integration. It can be seen from theoretical analysis and simulation that differentially-coherent integration is capable of detecting GPS signals in low signal to noise ratio environment.

Keywords: GPS signals; Low signal to noise; High sensitivity; Detection (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-34910-2_79

Ordering information: This item can be ordered from
http://www.springer.com/9783642349102

DOI: 10.1007/978-3-642-34910-2_79

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-04-02
Handle: RePEc:spr:sprchp:978-3-642-34910-2_79