Name
Abstract | ||
|---|---|---|
Efficient target tracking applications require active sensor nodes to track a cluster of moving targets. Clustering could lead to significant cost improvement as compared to tracking individual targets. This paper presents accurate clustering of targets for both coherent and incoherent movement patterns. We propose a novel clustering algorithm that utilises an implicit dynamic time frame to assess the relational history of targets in creating a weighted graph of connected components. The proposed algorithm employs key features of localisation algorithms in target tracking, namely, estimated current and predicted locations to determine the relational directions and distances of moving targets. Our simulation results show a significant improvement on the clustering accuracy and computation time by dynamically adjusting the history-window size and predicting the relationships among targets. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/VTCFall.2012.6399265 | Vehicular Technology Conference |
Keywords | Field | DocType |
graph theory,image sensors,object detection,pattern clustering,target tracking,wireless sensor networks,WSN,active sensor node,clustering accuracy,clustering algorithm,cost improvement,dynamic clusters graph,history-window size,incoherent movement pattern,localisation algorithm,moving target detection,moving target location,moving target tracking,target clustering,weighted graph | Graph theory,Data mining,Object detection,Canopy clustering algorithm,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Connected component,Cluster analysis | Conference |
ISSN | ISBN | Citations |
1090-3038 E-ISBN : 978-1-4673-1879-2 | 978-1-4673-1879-2 | 1 |
PageRank | References | Authors |
0.35 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Farzaneh R. Armaghani | 1 | 11 | 1.88 |
| Iqbal Gondal | 2 | 316 | 48.05 |
| Joarder Kamruzzaman | 3 | 410 | 49.22 |
| David G. Green | 4 | 153 | 25.63 |