TY - GEN
T1 - Robust multi-target tracking in RF tomographic network
AU - Liu, Heng
AU - Ni, Yaping
AU - Wang, Zhenghuan
AU - Xu, Shengxin
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/29
Y1 - 2015/9/29
N2 - Radio tomographic imaging (RTI) is a promising technique which allows localizing and tracking targets carrying no electronic devices. It utilizes the attenuation of wireless links to generate images of the change in the propagation field. Objects that obstruct the wireless signals in the field will lead to bright blobs in RTI image. For multi-target tracking, we employ clustering to obtain cluster observations to assign to targets. However, the blob corresponding to a target may be divided into several clusters in the process of clustering. The phenomenon is called over-clustering, i.e., there will be several cluster observations originated from the same target. Global nearest neighbor (GNN) which is popular in data association is optimal only under the assumption that only one cluster is originated from a target. However over-clustering will reduce the multi-target tracking performance of GNN. In this paper, the joint probabilistic data association (JPDA) method which is robust to over-clustering is proposed to improve the multi-target tracking performance when over-clustering is present. Real experiments are conducted in a monitored region surrounded by 20 RF sensors. When over-clustering is present, the experimental results show that the minimum tracking error of JPDA and GNN is 0.24m and 0.37m, respectively.
AB - Radio tomographic imaging (RTI) is a promising technique which allows localizing and tracking targets carrying no electronic devices. It utilizes the attenuation of wireless links to generate images of the change in the propagation field. Objects that obstruct the wireless signals in the field will lead to bright blobs in RTI image. For multi-target tracking, we employ clustering to obtain cluster observations to assign to targets. However, the blob corresponding to a target may be divided into several clusters in the process of clustering. The phenomenon is called over-clustering, i.e., there will be several cluster observations originated from the same target. Global nearest neighbor (GNN) which is popular in data association is optimal only under the assumption that only one cluster is originated from a target. However over-clustering will reduce the multi-target tracking performance of GNN. In this paper, the joint probabilistic data association (JPDA) method which is robust to over-clustering is proposed to improve the multi-target tracking performance when over-clustering is present. Real experiments are conducted in a monitored region surrounded by 20 RF sensors. When over-clustering is present, the experimental results show that the minimum tracking error of JPDA and GNN is 0.24m and 0.37m, respectively.
KW - joint probabilistic data association (JPDA)
KW - multi-target tracking
KW - over-clustering
KW - radio tomographic imaging (RTI)
KW - received signal strength (RSS)
UR - http://www.scopus.com/inward/record.url?scp=84958628373&partnerID=8YFLogxK
U2 - 10.1109/ICEIEC.2015.7284497
DO - 10.1109/ICEIEC.2015.7284497
M3 - Conference contribution
AN - SCOPUS:84958628373
T3 - ICEIEC 2015 - Proceedings of 2015 IEEE 5th International Conference on Electronics Information and Emergency Communication
SP - 99
EP - 103
BT - ICEIEC 2015 - Proceedings of 2015 IEEE 5th International Conference on Electronics Information and Emergency Communication
A2 - Tam, Vincent
A2 - Wei, Zhu
A2 - Wenzheng, Li
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2015
Y2 - 14 May 2015 through 16 May 2015
ER -