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Integrated Waveform-Agile Multi-Modal Track-before-Detect Algorithms for Tracking Low Observable Targets

abstract: In this thesis, an integrated waveform-agile multi-modal tracking-beforedetect sensing system is investigated and the performance is evaluated using an experimental platform. The sensing system of adapting asymmetric multi-modal sensing operation platforms using radio frequency (RF) radar and electro-optical (EO) sensors allows for integration of complementary information from different sensors. However, there are many challenges to overcome, including tracking low signal-to-noise ratio (SNR) targets, waveform configurations that can optimize tracking performance and statistically dependent measurements. Address some of these challenges, a particle filter (PF) based recursive waveformagile track-before-detect (TBD) algorithm is developed to avoid information loss caused by conventional detection under low SNR environments. Furthermore, a waveform-agile selection technique is integrated into the PF-TBD to allow for adaptive waveform configurations. The embedded exponential family (EEF) approach is used to approximate distributions of parameters of dependent RF and EO measurements and to further improve target detection rate and tracking performance. The performance of the integrated algorithm is evaluated using real data from three experimental scenarios. / Dissertation/Thesis / M.S. Electrical Engineering 2012

Identiferoai:union.ndltd.org:asu.edu/item:14953
Date January 2012
ContributorsLiu, Shubo (Author), Papandreou-Suppappola, Antonia (Advisor), Duman, Tolga (Committee member), Kovvali, Narayan (Committee member), Arizona State University (Publisher)
Source SetsArizona State University
LanguageEnglish
Detected LanguageEnglish
TypeMasters Thesis
Format75 pages
Rightshttp://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved

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