Abstract
The main advantage of a multi-sensor fusion approach is to complement the data of one sensor with that of another sensor in order to obtain better target measurement information and to make a more accuracy estimation. In this paper, one image processing along with neural network algorithm is applied to solve the attribute information. The fundamental idea of this paper is that one decentralized estimation approach for a sensor fusion problems in which a Bayesian mathematical structure denoted 1-step maximum a posteriori estimate algorithm is applied for the data association. In such an algorithm, one also applied a techniques to combine the target attribute data to enhance the tracking results.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
| Pages | 395-405 |
| Number of pages | 11 |
| Volume | 2561 |
| Publication status | Published - 1995 Dec 1 |
| Event | Signal and Data Processing of Small Targets 1995 - San Diego, CA, USA Duration: 1995 Jul 11 → 1995 Jul 13 |
Other
| Other | Signal and Data Processing of Small Targets 1995 |
|---|---|
| City | San Diego, CA, USA |
| Period | 95-07-11 → 95-07-13 |
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All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering
Cite this
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Fusion algorithm for data including kinematic and attribute. / Chung, Yi Nung; Chen, Joy I.
Proceedings of SPIE - The International Society for Optical Engineering. Vol. 2561 1995. p. 395-405.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Fusion algorithm for data including kinematic and attribute
AU - Chung, Yi Nung
AU - Chen, Joy I.
PY - 1995/12/1
Y1 - 1995/12/1
N2 - The main advantage of a multi-sensor fusion approach is to complement the data of one sensor with that of another sensor in order to obtain better target measurement information and to make a more accuracy estimation. In this paper, one image processing along with neural network algorithm is applied to solve the attribute information. The fundamental idea of this paper is that one decentralized estimation approach for a sensor fusion problems in which a Bayesian mathematical structure denoted 1-step maximum a posteriori estimate algorithm is applied for the data association. In such an algorithm, one also applied a techniques to combine the target attribute data to enhance the tracking results.
AB - The main advantage of a multi-sensor fusion approach is to complement the data of one sensor with that of another sensor in order to obtain better target measurement information and to make a more accuracy estimation. In this paper, one image processing along with neural network algorithm is applied to solve the attribute information. The fundamental idea of this paper is that one decentralized estimation approach for a sensor fusion problems in which a Bayesian mathematical structure denoted 1-step maximum a posteriori estimate algorithm is applied for the data association. In such an algorithm, one also applied a techniques to combine the target attribute data to enhance the tracking results.
UR - http://www.scopus.com/inward/record.url?scp=0029547012&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0029547012&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0029547012
SN - 0819419206
SN - 9780819419200
VL - 2561
SP - 395
EP - 405
BT - Proceedings of SPIE - The International Society for Optical Engineering
ER -