.png){kind=icon}
.png){kind=icon}
Return
Environmental-Adaptive RSSI-Based Indoor Localization
.png){kind=icon}
PRE
AI
DOI：10.1109/TASE.2008.2009126.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper addresses a novel method for localizing a stationary object in an indoor office environment. The proposed method utilizes the received-signal-strength index (RSSI) of radio signals radiating from fixed reference nodes and reference tags placed at known positions to generate a precise signal propagation model. Signal attenuation parameters are updated online according to environmental variation; thus, the proposed method has environmental-adaptation capabilities. Subsequent experiments were conducted to demonstrate the superiority of the proposed technique over a commercial location-based service (LBS) chipset. Note to Practitioners-The practical motivation of this paper is summarized as follows. First, we would like to develop an indoor wireless localization network that is particularly useful for the position estimation of stationary and/or slowly moving objects. The sensor network developed in this paper can be variously used for factory automation, home automation, robot navigation/guidance, and pedestrian tracking. Second, the environmental-adaptive RSSI-based indoor localization (EARIL) algorithm developed in this paper is based on received signal strength; so the system is very simple and the implementation cost is much cheaper than typical TOA and TDOA-based methods.
Keywords:
Indoor localization
location-based service (LBS)
received-signal-strength index (RSSI)
wireless sensor network
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.4
Papers:
4.9K
Citations:
1.6W