Wavelets feature aided tracking (WFAT) using GMTI/HRR data

Authors:
Lang Hong;Shan Cong;Mark T. Pronobis;Stephen Scott
Affiliations:
Department of Electrical Engineering, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH;Department of Electrical Engineering, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH;Information Directorate, Air Force Research Laboratory, Rome, NY;Information Directorate, Air Force Research Laboratory, Rome, NY
Venue:
Signal Processing
Year:
2003

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Abstract

This paper presents a research development of wavelets feature aided tracking, which effectively combines information from both high-resolution range (HRR) radar profiles and ground moving target indication (GMTI) radar reports. The state-of-the-art wavelets-based statistical signal processing technique: wavelets domain hidden Markov trees is used to extract robust features from HRR profiles. With the assistance of HRR wavelets features, a GMTI tracker based on a probabilistic data association logic can effectively track ground moving targets in confusing scenarios.