Fusion algorithm for data including kinematic and attribute

Yi Nung Chung; Joy I. Chen

Fusion algorithm for data including kinematic and attribute

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

Fingerprint Dive into the research topics of 'Fusion algorithm for data including kinematic and attribute'. Together they form a unique fingerprint.

Cite this

@inproceedings{04e4581bb6994cae9817b74144bcb02f,

title = "Fusion algorithm for data including kinematic and attribute",

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.",

author = "Chung, {Yi Nung} and Chen, {Joy I.}",

year = "1995",

month = dec,

day = "1",

language = "English",

isbn = "0819419206",

volume = "2561",

pages = "395--405",

booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",

note = "Signal and Data Processing of Small Targets 1995 ; Conference date: 11-07-1995 Through 13-07-1995",

}

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

T2 - Signal and Data Processing of Small Targets 1995

Y2 - 11 July 1995 through 13 July 1995

ER -