Title :
MCMC methods for tracking two closely spaced targets using monopulse radar channel signals
Author :
Isaac, A. ; Willett, P. ; Bar-Shalom, Y.
Author_Institution :
Dept. of Electr. & Comput. Eng., Connecticut Univ., Storrs, CT
fDate :
6/1/2007 12:00:00 AM
Abstract :
Four techniques to successfully track two closely spaced and unresolved targets using monopulse radar measurements have been discussed, the quality of such tracking being a determinant of successful detection of target spawn. The paper explores statistical estimation techniques based on the maximum likelihood criterion and Gibbs sampling, and addresses concerns about the accuracy of the measurements delivered thereby. In particular, the Gibbs approach can deliver joint measurements (and the associated covariances) from both targets, and it is therefore natural to consider a joint filter. The ideas are compared, and among the various strategies discussed, a particle filter that operates directly on the monopulse measurements seems to be the best.
Keywords :
Markov processes; Monte Carlo methods; matrix algebra; maximum likelihood estimation; particle filtering (numerical methods); radar tracking; target tracking; Gibbs sampling; Markov chain Monte Carlo method; joint filter; maximum likelihood criterion; monopulse radar channel signals; particle filter; statistical estimation techniques; tracking targets;
Journal_Title :
Radar, Sonar & Navigation, IET