Multispectral target recognition using adaptive radar and infrared data integration

Author

Woo-Yong Jang ; Park, James ; Fuchs, Zachariah ; Parada, Francisco ; Hanna, Philip ; Derov, John ; Noyola, Michael

Author_Institution

Sensors Directorate, Air Force Res. Lab., Wright-Patterson AFB, OH, USA

fYear

2014

fDate

13-18 July 2014

Firstpage

189

Lastpage

190

Abstract

We report a RF and IR data-integration strategy based on a probabilistic (or a distribution) model. At the heart of our approach is the ability to extract the probability density functions (pdfs) from the sensed dataset for RF and IR respectively followed by the detection or target identification process based on posterior fusion (i.e., the product of individual pdfs) and Bayesian decision process. The pdf-acquisition processes in RF and IR modules have been further refined with clutter models and data-compression techniques.

Keywords

Bayes methods; data acquisition; data compression; data integration; object detection; sensor fusion; statistical distributions; Bayesian decision process; IR module; RF module; RF-IR data-integration strategy; adaptive radar-infrared data integration; clutter models; data-compression techniques; distribution model; multispectral target recognition; posterior fusion; probabilistic model; probability density function-acquisition processes; target identification process; Bayes methods; Clutter; Doppler effect; Feature extraction; Radar; Radio frequency; Sensors; Data compression; Data integration; Multispectral target recognition; Posterior fusion;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International

Conference_Location

Quebec City, QC

Type

conf

DOI

10.1109/IGARSS.2014.6946388

Filename

6946388