Multistatic Bayesian Extended Target Tracking
Artikel i vetenskaplig tidskrift, 2016
To track an extended target presents challenges because the hypothesis of "one target means one detection" is not valid. Several approaches to extended target tracking (ETT) have been found promising, and in particular those involving random matrices have demonstrated their appeal. When targets are extended and the data is multistatic the issues are compounded; the random matrix model has continued appeal and offers a way to avoid enumerative data association. In this paper, a bistatic Bayesian ETT approach integrated into the random matrix framework is proposed. Furthermore, a closed-form solution for fusing multistatic radar system data into the same framework is presented. The proposed approaches are tested on both simulated data and real data.
probability hypothesis density