Paper Info
Title
Stochastic Geometric Filter and Its Application to Shape Estimation for Target Objects
Abstract
We investigated how to estimate the shape of a target object. For this problem, we propose pair-line composite sensor nodes consisting of multiple sensors on a pair of line segments, where each sensor generates binary information whether it detects the target object or not. We show that the proposed pair-line composite sensor nodes, which are randomly placed, can detect a certain range of angles; therefore, we also call them stochastic geometric filters. By random distribution of pair-line composite sensor nodes without GPS functions or careful placement at known locations, the information sent from the nodes enables us to estimate the boundary angles of the target object as well as its size and perimeter length. A composite sensor node can be conceptualized as between a sensor node equipped with GPS functions, or carefully placed sensors at known locations, and randomly deployed simple sensors without GPS functions.
Year
DOI
Venue
2011
10.1109/TSP.2011.2161476
IEEE Transactions on Signal Processing
Keywords
Field
DocType
stochastic geometric filter,shape estimation,sensor node,gps function,target objects,pair-line composite sensor node,proposed pair-line composite sensor,multiple sensor,known location,target object,pair-line composite sensor,composite sensor node,simple sensor,wireless sensor network,sensors,stochastic processes,wireless sensor networks,shape,filter,sensor network,object recognition,global positioning system,geometry,estimation
Sensor node,Line segment,Computer vision,Binary information,Stochastic process,Perimeter,Artificial intelligence,Global Positioning System,Wireless sensor network,Mathematics,Cognitive neuroscience of visual object recognition
Journal
Volume
Issue
ISSN
59
10
1053-587X
Citations 
PageRank 
References 
13
0.85
15
Authors
3
Name
Order
Citations
PageRank
Hiroshi Saito116416.39
Sadaharu Tanaka2322.58
Shigeo Shioda316526.69