Tracking algorithm analysis for the PCL-PET fusion system

We discuss the problem of poor initial estimation of altitude of distant and low located objects in PCL-PET data fusion. We focus here on tracking algorithm showing how one can make the convergence of the estimate to the real value faster. The main result is that on having replaced extended Kalman f...

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Bibliographic Details
Published in2015 Signal Processing Symposium (SPSympo) pp. 1 - 6
Main Authors Lamentowski, Leszek, Mularzuk, Roman, Brenner, Tadeusz, Nieszporski, Maciej
Format Conference Proceeding
LanguageEnglish
Published Warsaw University of Technology 01.06.2015
Subjects
bistatic measurements
Estimation
Kalman filters
multiband
passive radar
PCL-PET data fusion
Positron emission tomography
Radar tracking
Technological innovation
Tracking
Velocity measurement
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Summary:We discuss the problem of poor initial estimation of altitude of distant and low located objects in PCL-PET data fusion. We focus here on tracking algorithm showing how one can make the convergence of the estimate to the real value faster. The main result is that on having replaced extended Kalman filter with unscented Kalman filter, we are able to choose from scenarios with various initial conditions the one which gives (statistically) the best estimation of position and velocity of an object.
DOI:10.1109/SPS.2015.7168274