Improvements to RADAR location classification

Zhili Wu*, Chun Hung Li, Joseph K Y NG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

3 Citations (Scopus)

Abstract

Location estimation has been a backbone for location-aware services as wireless networks and mobile devices are more pervasively available. By operating on the signal strength space, nearest neighbor methods like RADAR have proved to be simple yet effective for location estimation. It has been common to take locations as classes, and then to infer location classes based on signal strength measurements. Under such a location classification setting, this paper investigates in detail the k-nearest neighbor approach in RADAR, and demonstrates that considering more neighboring signal strength measurements usually cannot help. Instead the orientations in which the signal strength is taken should be more carefully treated. This paper also develops a refinement step for RADAR, by building nearest neighbor classifiers to further clarify several top location estimates by RADAR. At a very economic cost, our refinement step can significantly boost the accuracy.

Original languageEnglish
Title of host publication2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008
DOIs
Publication statusPublished - 2008
Event2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008 - Dalian, China
Duration: 12 Oct 200814 Oct 2008

Publication series

Name2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008

Conference

Conference2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008
Country/TerritoryChina
CityDalian
Period12/10/0814/10/08

Scopus Subject Areas

  • Computer Networks and Communications
  • Software
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Improvements to RADAR location classification'. Together they form a unique fingerprint.

Cite this