TY - GEN
T1 - Adaptive Gaussian mixture-probability hypothesis density based multi sensor multi-target tracking
AU - Shinde, Chinmay
AU - Das, Kaushik
AU - Lima, Rolif
AU - Vankadari, Madhu Babu
AU - Kumar, Swagat
PY - 2019/6/1
Y1 - 2019/6/1
N2 - This paper addresses a novel multiple target tracking (MTT) problem in a decentralized sensors network (DSN) framework. The algorithm jointly estimates the number of targets and the states of the targets from a noisy measurement in the presence of data association uncertainty and missed detection. The standard GM-PHD filters estimate the multi-targets in a cluttered environment with an assumption that the target birth intensity is known or homogeneous. It results in inefficient tracking for new, occluded or missed targets. The issue is addressed by the proposed adaptive Gaussian birth components based estimation. A method based on covariance intersection fusion is proposed to address inter-sensor target data association.
AB - This paper addresses a novel multiple target tracking (MTT) problem in a decentralized sensors network (DSN) framework. The algorithm jointly estimates the number of targets and the states of the targets from a noisy measurement in the presence of data association uncertainty and missed detection. The standard GM-PHD filters estimate the multi-targets in a cluttered environment with an assumption that the target birth intensity is known or homogeneous. It results in inefficient tracking for new, occluded or missed targets. The issue is addressed by the proposed adaptive Gaussian birth components based estimation. A method based on covariance intersection fusion is proposed to address inter-sensor target data association.
KW - Decentralized data association
KW - Multi-sensor systems
KW - Multi-target tracking
KW - Probability hypothesis density filter
UR - http://www.scopus.com/inward/record.url?scp=85071548393&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071548393&partnerID=8YFLogxK
U2 - 10.23919/ECC.2019.8796014
DO - 10.23919/ECC.2019.8796014
M3 - Conference proceeding (ISBN)
AN - SCOPUS:85071548393
T3 - 2019 18th European Control Conference, ECC 2019
SP - 862
EP - 868
BT - 2019 18th European Control Conference, ECC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th European Control Conference, ECC 2019
Y2 - 25 June 2019 through 28 June 2019
ER -