Abstract
In multiple sensor extended target tracking problems, asynchronous measurements are inevitable, since sensors usually have distinct sampling rates and initial sampling times. This paper presents a new distributed extended target tracking algorithm with asynchronous measurements for multiple sensor scenarios. A distributed Bayesian estimation scheme for asynchronous measurements using random matrix framework is derived. We also proposed an effective implementation using particle filtering. Compressed Gaussian Mixture approximations of extended state distributions are exchanged and fused between neighbor sensors. The temporal evolution of elliptic extent parameters can be obtained explicitly in our algorithm. Simulations show reasonable performance with a significant reduction of communication costs for small size systems compared with the centralized algorithm.
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.
