TY - GEN
T1 - Cardinality Balanced Multi-Target Multi-Bernoulli filter for target tracking with amplitude information
AU - Feng, Yang
AU - Wanying, Zhang
AU - Yan, Liang
AU - Yazhe, Su
AU - Xuanzheng, Yao
N1 - Publisher Copyright:
© 2016 ISIF.
PY - 2016/8/1
Y1 - 2016/8/1
N2 - The Cardinality Balanced Multi-Target Multi-Bernoulli (CBMeMBer) filter is a recursive Bayesian algorithm for estimating multiple target states with a varying target number in cluttered environment. Knowledge of the amplitude information plays a significant role in multi-target tracking and results in better tracking performance for harsh scenarios with low signal-to-ratio (SNR) or high false alarms, however, it has not been considered in CBMeMBer filter. In this paper, an improved CBMeMBer filter accommodating nonlinear measurement model, as well as unknown SNR, is proposed for radar sensors. Furthermore, the proposed filter is able to adaptively design the birth target density without the assumption that the birth density is known as a prior. Simulation results demonstrate the effectiveness and high estimation accuracy of the proposed filter over traditional CBMeMBer filter, particularly in the estimate of cardinality.
AB - The Cardinality Balanced Multi-Target Multi-Bernoulli (CBMeMBer) filter is a recursive Bayesian algorithm for estimating multiple target states with a varying target number in cluttered environment. Knowledge of the amplitude information plays a significant role in multi-target tracking and results in better tracking performance for harsh scenarios with low signal-to-ratio (SNR) or high false alarms, however, it has not been considered in CBMeMBer filter. In this paper, an improved CBMeMBer filter accommodating nonlinear measurement model, as well as unknown SNR, is proposed for radar sensors. Furthermore, the proposed filter is able to adaptively design the birth target density without the assumption that the birth density is known as a prior. Simulation results demonstrate the effectiveness and high estimation accuracy of the proposed filter over traditional CBMeMBer filter, particularly in the estimate of cardinality.
KW - amplitude information
KW - CBMeMBer filter
KW - multi-target tracking
KW - SNR
UR - http://www.scopus.com/inward/record.url?scp=84992121853&partnerID=8YFLogxK
M3 - 会议稿件
AN - SCOPUS:84992121853
T3 - FUSION 2016 - 19th International Conference on Information Fusion, Proceedings
SP - 958
EP - 964
BT - FUSION 2016 - 19th International Conference on Information Fusion, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th International Conference on Information Fusion, FUSION 2016
Y2 - 5 July 2016 through 8 July 2016
ER -