Abstract
In order to solve the target joint detection, tracking, and classification (JDTC) for multi-target, a recursive algorithm based on the labeled multi-Bernoulli (LMB) filter was presented under the framework of the conditional joint decision and estimation (JDE). A new generalized Bayesian risk is defined for the LMB variables involving the costs of target existence probability estimation, state estimation, and classification. Then the optimal solution is obtained according to the generalized Bayesian risk. The Gaussian mixture implementation of the proposed recursive JDTC algorithm is developed. The parameter selection for the new Bayesian risk is discussed, too. Simulation results show that the proposed method outperforms the traditional methods.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
