Multistatic Bayesian Extended Target Tracking
Journal article, 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.

phd

model

kalman-filter

filter

Telecommunications

random matrices

objects

probability hypothesis density

Engineering

Author

G. Vivone

NATO Undersea Research Centre

P. Braca

NATO Undersea Research Centre

Karl Granström

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

P. Willett

University of Connecticut

IEEE Transactions on Aerospace and Electronic Systems

0018-9251 (ISSN)

Vol. 52 6 2626-2643

Subject Categories

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/taes.2016.150724

More information

Created

10/8/2017