TY - GEN
T1 - Heterogeneous Gaussian and Student's t Fusion for Distributed Target Tracking
AU - Qin, Haowen
AU - Li, Tiancheng
AU - Li, Hongfei
AU - Li, Guchong
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In this paper, the heterogeneous fusion of the Kalman and Student's t filters is considered in the context of distributed filter fusion for target tracking. This problem is involved in a multi-sensor tracking scenario where each sensor runs either a Kalman or Student's t filter and they cooperate with each other via fusing the posterior density in a peer-to-peer fashion. This type of heterogeneous fusion has never been investigated before without closed-form solution. What is more, these sensors/filters are inherently correlated with each other to an unknown degree which raises a significant challenge for robust fusion. To address these challenges, both the arithmetic and geometric average fusion approaches are extended based on the appreciated moment matching strategies, in order to maintain the Gaussian or Student's t distribution of the local posterior. The effectiveness and robustness of the proposed methods are verified through simulations which have demonstrated the superiority of arithmetic average fusion method over covariance intersection fusion and augmented measurement fusion.
AB - In this paper, the heterogeneous fusion of the Kalman and Student's t filters is considered in the context of distributed filter fusion for target tracking. This problem is involved in a multi-sensor tracking scenario where each sensor runs either a Kalman or Student's t filter and they cooperate with each other via fusing the posterior density in a peer-to-peer fashion. This type of heterogeneous fusion has never been investigated before without closed-form solution. What is more, these sensors/filters are inherently correlated with each other to an unknown degree which raises a significant challenge for robust fusion. To address these challenges, both the arithmetic and geometric average fusion approaches are extended based on the appreciated moment matching strategies, in order to maintain the Gaussian or Student's t distribution of the local posterior. The effectiveness and robustness of the proposed methods are verified through simulations which have demonstrated the superiority of arithmetic average fusion method over covariance intersection fusion and augmented measurement fusion.
KW - arithmetic average fusion
KW - heavy-tailed noise
KW - heterogeneous distribution fusion
KW - Multi-source information fusion
KW - Student's t filter
UR - http://www.scopus.com/inward/record.url?scp=105002251606&partnerID=8YFLogxK
U2 - 10.1109/SWC62898.2024.00288
DO - 10.1109/SWC62898.2024.00288
M3 - 会议稿件
AN - SCOPUS:105002251606
T3 - Proceedings - 2024 IEEE Smart World Congress, SWC 2024 - 2024 IEEE Ubiquitous Intelligence and Computing, Autonomous and Trusted Computing, Digital Twin, Metaverse, Privacy Computing and Data Security, Scalable Computing and Communications
SP - 1878
EP - 1885
BT - Proceedings - 2024 IEEE Smart World Congress, SWC 2024 - 2024 IEEE Ubiquitous Intelligence and Computing, Autonomous and Trusted Computing, Digital Twin, Metaverse, Privacy Computing and Data Security, Scalable Computing and Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE Smart World Congress, SWC 2024
Y2 - 2 December 2024 through 7 December 2024
ER -