A novel method for CFAR data fusion

Author

Liu, Weixian ; Lu, Yilong ; Fu, J.S.

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore

Volume

2

fYear

2000

fDate

2000

Firstpage

711

Abstract

Detection systems with distributed sensors and data fusion are increasingly being used by surveillance systems. There has been a great deal of theoretical study into decentralized detection networks that are composed of similar independent sensors. To solve the resulting nonlinear system, an exhaustive search and some approximation methods are usually adopted. However, these often either cause the system to be insensitive to some parameters or they lead to suboptimal results. In this paper, a genetic algorithm is investigated in order to obtain optimal results on constant false alarm rate (CFAR) data fusion

Keywords

distributed sensors; genetic algorithms; nonlinear systems; sensor fusion; surveillance; CFAR data fusion; CFAR detection systems; approximation methods; constant false alarm rate; decentralized detection networks; distributed sensors; exhaustive search; genetic algorithm; nonlinear system; optimal results; parameter insensitivity; similar independent sensors; suboptimal results; surveillance systems; Detectors; Genetic algorithms; Inference algorithms; Nonlinear systems; Radar detection; Sensor fusion; Sensor phenomena and characterization; Sensor systems; Surveillance; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop

Conference_Location

Sydney, NSW

ISSN

1089-3555

Print_ISBN

0-7803-6278-0

Type

conf

DOI

10.1109/NNSP.2000.890150

Filename

890150