Consensus CPHD Filter for Distributed Multitarget Tracking 论文
2013IEEE Journal of Selected Topics in Signal Processing引用 342
Target Tracking and Data Fusion in Sensor NetworksDistributed Sensor Networks and Detection AlgorithmsNeural Networks and Applications
摘要
The paper addresses distributed multitarget tracking over a network of heterogeneous and geographically dispersed nodes with sensing, communication and processing capabilities. The contribution has been to develop a novel consensus Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filter that provides a fully distributed, scalable and computationally efficient solution to the problem. The effectiveness of the proposed approach is demonstrated via simulation experiments on realistic scenarios.