Name
Abstract | ||
|---|---|---|
Radio tomographic localization (RTL) is an emerging technology for estimating the position of objects by analyzing received signal strength (RSS) variation in wireless sensor networks (WSNs). This method can localize targets which do not need to carry any electronic devices or tags, so that it is of great importance in applications such as surveillance, rescue operations and smart-buildings. This paper introduces a fast and effective RTL method. In this method, we propose a histogram-based target feature and a cascaded detection strategy. This method can localize an unknown number of objects with high accuracy in different environments, and the computational complexity is low enough to be used for real-time systems. First, we present a series of single-link experiments to demonstrate the proposed target feature. Then a cascaded detection process is introduced to locate an unknown number of targets while reducing much computation. To evaluate the proposed RTL method, experiments including both outdoor and indoor environment are conducted and the performance is carefully demonstrated. |
Year | DOI | Venue |
|---|---|---|
2013 | null | WPMC |
Keywords | Field | DocType |
histogram-based target feature detection strategy,wsn,rtl,object position estimation,tomography,electronic device,wireless sensor network,rescue operation,surveillance,radio direction-finding,computational complexity,single-link experiment,feature extraction,histogram-based cascade detector,received signal strength,object detection,wireless sensor networks,rss,radio equipment,radio tomographic localization,smart-building,smart building,signal processing | Object detection,Key distribution in wireless sensor networks,Computer vision,Histogram,Radio equipment,Computer science,Feature extraction,Real-time computing,Artificial intelligence,Wireless sensor network,Detector,Computational complexity theory | Conference |
Volume | Issue | ISSN |
null | null | 1347-6890 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Yang | 1 | 76 | 33.15 |
| YuQian Pan | 2 | 0 | 0.34 |
| DongWei Bai | 3 | 0 | 0.34 |
| Hang Liu | 4 | 835 | 90.79 |
| Bo Yang | 5 | 67 | 13.56 |