KEYWORDS: Target detection, Monte Carlo methods, Automatic tracking, Digital filtering, Process modeling, Electronic filtering, Gaussian filters, Data modeling, Detection and tracking algorithms, Bayesian inference, Estimation theory
The δ-generalized labeled multi-Bernoulli (δ-GLMB) tracker is the first multiple hypothesis tracking (MHT)-like
tracker that is provably Bayes-optimal. However, in its basic form, the δ-GLMB provides no mechanism for
adaptively initializing targets at their first appearance from unlabeled measurements. By introducing a new
multitarget likelihood function that accounts for new target appearance, a data-driven δ-GLMB tracker is derived
that automatically initializes new targets in the tracker measurement update. Monte Carlo results of simulated
multitarget tracking problems demonstrate improved multitarget tracking accuracy over comparable adaptive
birth methods.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.