TY - GEN
T1 - Distributed mean-field-type filter for vehicle tracking
AU - Gao, Jian
AU - Tembine, Hamidou
N1 - Publisher Copyright:
© 2017 American Automatic Control Council (AACC).
PY - 2017/6/29
Y1 - 2017/6/29
N2 - Particle filter is an effective tool for vehicle tracking. However, we need to maintain a large number of particles to keep a reasonable tracking accuracy for multi-target tracking in large scale state space. This paper proposes a new distributed mean-field-type filter to handle those noisy, partial-observed and high-dimensional data. The state space is decomposed and the particles are deployed locally and updated independently in the simplified subspaces. The filtering framework contains four operations: sampling, prediction, decomposition and correction. A mean-field term is included in the system dynamic so that the prediction is based on the previous state as well as its statistic distribution, which is estimated by a multi-frame learning procedure. The experiment on real data shows that our approach can achieve accurate tracking results with a small number of particles.
AB - Particle filter is an effective tool for vehicle tracking. However, we need to maintain a large number of particles to keep a reasonable tracking accuracy for multi-target tracking in large scale state space. This paper proposes a new distributed mean-field-type filter to handle those noisy, partial-observed and high-dimensional data. The state space is decomposed and the particles are deployed locally and updated independently in the simplified subspaces. The filtering framework contains four operations: sampling, prediction, decomposition and correction. A mean-field term is included in the system dynamic so that the prediction is based on the previous state as well as its statistic distribution, which is estimated by a multi-frame learning procedure. The experiment on real data shows that our approach can achieve accurate tracking results with a small number of particles.
UR - http://www.scopus.com/inward/record.url?scp=85027059323&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027059323&partnerID=8YFLogxK
U2 - 10.23919/ACC.2017.7963641
DO - 10.23919/ACC.2017.7963641
M3 - Conference contribution
AN - SCOPUS:85027059323
T3 - Proceedings of the American Control Conference
SP - 4454
EP - 4459
BT - 2017 American Control Conference, ACC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 American Control Conference, ACC 2017
Y2 - 24 May 2017 through 26 May 2017
ER -