TY - GEN
T1 - A Heavy-Tailed Noise Tolerant Labeled Multi-Bernoulli Filter
AU - Zhang, Wanying
AU - Yang, Feng
AU - Liang, Yan
AU - Liu, Zhentao
N1 - Publisher Copyright:
© 2018 ISIF
PY - 2018/9/5
Y1 - 2018/9/5
N2 - The well-known labeled multi-Bernoulli (LMB) filter for multi-target tracking in clutters works well only under the Gaussian noise assumptions. Since this Gaussian assumption can hardly hold in practice, we present the problem of the LMB with heavy-tailed non-Gaussian measurement noise. Through modeling the measurement noise as Student's t distribution, a heavy-tailed measurement noise tolerant LMB (TLMB) is derived in the framework of variational Bayesian inference for the joint estimation of the target state together with the unknown scale matrix and degree of freedom (dof) of the Student's t distribution. Simulations on multi-target tracking in clutter with unreliable sensor demonstrate the effectiveness and superiority of the proposed TLMB.
AB - The well-known labeled multi-Bernoulli (LMB) filter for multi-target tracking in clutters works well only under the Gaussian noise assumptions. Since this Gaussian assumption can hardly hold in practice, we present the problem of the LMB with heavy-tailed non-Gaussian measurement noise. Through modeling the measurement noise as Student's t distribution, a heavy-tailed measurement noise tolerant LMB (TLMB) is derived in the framework of variational Bayesian inference for the joint estimation of the target state together with the unknown scale matrix and degree of freedom (dof) of the Student's t distribution. Simulations on multi-target tracking in clutter with unreliable sensor demonstrate the effectiveness and superiority of the proposed TLMB.
KW - heavy-tailed measurement noise
KW - labeled multi-Bernoulli filter
KW - multi-target tracking
KW - S-tudent's distribution
KW - variational Bayesian
UR - http://www.scopus.com/inward/record.url?scp=85054103686&partnerID=8YFLogxK
U2 - 10.23919/ICIF.2018.8455322
DO - 10.23919/ICIF.2018.8455322
M3 - 会议稿件
AN - SCOPUS:85054103686
SN - 9780996452762
T3 - 2018 21st International Conference on Information Fusion, FUSION 2018
SP - 2461
EP - 2467
BT - 2018 21st International Conference on Information Fusion, FUSION 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st International Conference on Information Fusion, FUSION 2018
Y2 - 10 July 2018 through 13 July 2018
ER -