Tracking algorithm analysis for the PCL-PET fusion system

Autor:	Tadeusz Brenner, Leszek Lamentowski, Maciej Nieszporski, Roman Mularzuk
Rok vydání:	2015
Předmět:	Radar tracker business.industry Tracking system Kalman filter Sensor fusion Extended Kalman filter Geography Fast Kalman filter Computer vision Artificial intelligence business Focus (optics) Algorithm Alpha beta filter
Zdroj:	2015 Signal Processing Symposium (SPSympo).
DOI:	10.1109/sps.2015.7168274
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::dcda4bbfc2ea8d8b96faa79d7b15b2da https://doi.org/10.1109/sps.2015.7168274 Zobrazit plný text záznamu