Abstract
This paper deals with target classification by using both feature data and kinematic measurements. The problem is tackled by multihypothesis sequential testing with embedded target tracking. We implement an Armitage sequential test for nonmaneuvering and maneuvering targets. Both (centralized and distributed) fusion architectures are used for the embedded tracking. The contributions of the kinematic measurements to classification are analyzed, and classification performance improvement is shown analytically for a special case. Numerical results are provided to demonstrate the performance of our algorithms.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IEEE Transactions on Aerospace and Electronic Systems
Disclaimer: 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.