TY - JOUR
T1 - Partial consensus and conservative fusion of gaussian mixtures for distributed PHD fusion
AU - Li, Tiancheng
AU - Corchado, Juan M.
AU - Sun, Shudong
N1 - Publisher Copyright:
© 1965-2011 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - We propose a novel consensus notion, called 'partial consensus,' for distributed Gaussian mixture probability hypothesis density fusion based on a decentralized sensor network, in which only highly weighted Gaussian components (GCs) are exchanged and fused across neighbor sensors. It is shown that this not only gains high efficiency in both network communication and fusion computation, but also significantly compensates the effects of clutter and missed detections. Two 'conservative' mixture reduction schemes are devised for refining the combined GCs. One is given by pairwise averaging GCs between sensors based on Hungarian assignment and the other merges close GCs for trace minimal, yet, conservative covariance. The close connection of the result to the two approaches, known as covariance union and arithmetic averaging, is unveiled. Simulations based on a sensor network consisting of both linear and nonlinear sensors, have demonstrated the advantage of our approaches over the generalized covariance intersection approach.
AB - We propose a novel consensus notion, called 'partial consensus,' for distributed Gaussian mixture probability hypothesis density fusion based on a decentralized sensor network, in which only highly weighted Gaussian components (GCs) are exchanged and fused across neighbor sensors. It is shown that this not only gains high efficiency in both network communication and fusion computation, but also significantly compensates the effects of clutter and missed detections. Two 'conservative' mixture reduction schemes are devised for refining the combined GCs. One is given by pairwise averaging GCs between sensors based on Hungarian assignment and the other merges close GCs for trace minimal, yet, conservative covariance. The close connection of the result to the two approaches, known as covariance union and arithmetic averaging, is unveiled. Simulations based on a sensor network consisting of both linear and nonlinear sensors, have demonstrated the advantage of our approaches over the generalized covariance intersection approach.
KW - Cardinality consensus (CC)
KW - Covariance union (CU)
KW - Distributed tracking
KW - Gaussian mixture (GM)
KW - Mixture reduction (MR)
KW - Probability hypothesis density(PHD) filter
UR - http://www.scopus.com/inward/record.url?scp=85057180045&partnerID=8YFLogxK
U2 - 10.1109/TAES.2018.2882960
DO - 10.1109/TAES.2018.2882960
M3 - 文章
AN - SCOPUS:85057180045
SN - 0018-9251
VL - 55
SP - 2150
EP - 2163
JO - IEEE Transactions on Aerospace and Electronic Systems
JF - IEEE Transactions on Aerospace and Electronic Systems
IS - 5
M1 - 8543158
ER -