Stochastic Geometric Filter and Its Application to Shape Estimation for Target Objects
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...
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| Veröffentlicht in: | IEEE transactions on signal processing 2011-10, Vol.59 (10), p.4971-4984 |
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| container_title | IEEE transactions on signal processing |
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| creator | Saito, H. Tanaka, S. Shioda, S. |
| description | 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. |
| doi_str_mv | 10.1109/TSP.2011.2161476 |
| format | Article |
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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.</description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/TSP.2011.2161476</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Angles (geometry) ; Applied sciences ; Boundaries ; Detection, estimation, filtering, equalization, prediction ; Estimates ; Estimation ; Exact sciences and technology ; Filter ; Geographic information systems ; Geometry ; Global Positioning System ; Information, signal and communications theory ; Object recognition ; pair-line composite sensor node ; Pattern recognition ; Satellite navigation systems ; sensor network ; Sensors ; Shape ; shape estimation ; Signal and communications theory ; Signal processing ; Signal, noise ; stochastic geometric filter ; Stochasticity ; Telecommunications and information theory ; Wireless sensor networks</subject><ispartof>IEEE transactions on signal processing, 2011-10, Vol.59 (10), p.4971-4984</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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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. 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| subjects | Angles (geometry) Applied sciences Boundaries Detection, estimation, filtering, equalization, prediction Estimates Estimation Exact sciences and technology Filter Geographic information systems Geometry Global Positioning System Information, signal and communications theory Object recognition pair-line composite sensor node Pattern recognition Satellite navigation systems sensor network Sensors Shape shape estimation Signal and communications theory Signal processing Signal, noise stochastic geometric filter Stochasticity Telecommunications and information theory Wireless sensor networks |
| title | Stochastic Geometric Filter and Its Application to Shape Estimation for Target Objects |
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