Abstract
In a multisensor target tracking application running on a shared network, at what bit rates should the sensors send their measurements to the tracking fusion center? Clearly, the sensors cannot use arbitrary rates in a shared network, and a standard network rate control algorithm may not provide rates amenable to effective target tracking. For Kalman filter-based multisensor target tracking, we derive a utility function that captures the tracking quality of service as a function of the sensor bit rates. We incorporate this utility function into a network rate resource allocation framework, deriving a distributed rate control algorithm for a shared network that is suitable for current best effort packet networks, such as the Internet. In simulation studies, the new rate control algorithm engenders significantly better tracking performance than a standard rate control method, while the ordinary data transfer flows continue to effectively operate while using their standard rate control methods.
| Original language | English |
|---|---|
| Pages (from-to) | 348-362 |
| Number of pages | 15 |
| Journal | IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics |
| Volume | 39 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jan 1 2009 |
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Keywords
- Convex optimization
- Data fusion
- Kalman filtering (KF)
- Network utility maximization (NUM)
- Networked signal processing
- Target tracking
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Computer Science Applications
- Human-Computer Interaction
- Information Systems
- Software
Cite this
Transmission rate allocation in multisensor target tracking over a shared network. / Ranasingha, M. Chamara; Murthi, Manohar; Premaratne, Kamal; Fan, Xingzhe.
In: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 39, No. 2, 01.01.2009, p. 348-362.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Transmission rate allocation in multisensor target tracking over a shared network
AU - Ranasingha, M. Chamara
AU - Murthi, Manohar
AU - Premaratne, Kamal
AU - Fan, Xingzhe
PY - 2009/1/1
Y1 - 2009/1/1
N2 - In a multisensor target tracking application running on a shared network, at what bit rates should the sensors send their measurements to the tracking fusion center? Clearly, the sensors cannot use arbitrary rates in a shared network, and a standard network rate control algorithm may not provide rates amenable to effective target tracking. For Kalman filter-based multisensor target tracking, we derive a utility function that captures the tracking quality of service as a function of the sensor bit rates. We incorporate this utility function into a network rate resource allocation framework, deriving a distributed rate control algorithm for a shared network that is suitable for current best effort packet networks, such as the Internet. In simulation studies, the new rate control algorithm engenders significantly better tracking performance than a standard rate control method, while the ordinary data transfer flows continue to effectively operate while using their standard rate control methods.
AB - In a multisensor target tracking application running on a shared network, at what bit rates should the sensors send their measurements to the tracking fusion center? Clearly, the sensors cannot use arbitrary rates in a shared network, and a standard network rate control algorithm may not provide rates amenable to effective target tracking. For Kalman filter-based multisensor target tracking, we derive a utility function that captures the tracking quality of service as a function of the sensor bit rates. We incorporate this utility function into a network rate resource allocation framework, deriving a distributed rate control algorithm for a shared network that is suitable for current best effort packet networks, such as the Internet. In simulation studies, the new rate control algorithm engenders significantly better tracking performance than a standard rate control method, while the ordinary data transfer flows continue to effectively operate while using their standard rate control methods.
KW - Convex optimization
KW - Data fusion
KW - Kalman filtering (KF)
KW - Network utility maximization (NUM)
KW - Networked signal processing
KW - Target tracking
UR - http://www.scopus.com/inward/record.url?scp=64049090888&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=64049090888&partnerID=8YFLogxK
U2 - 10.1109/TSMCB.2008.2005123
DO - 10.1109/TSMCB.2008.2005123
M3 - Article
C2 - 19095557
AN - SCOPUS:64049090888
VL - 39
SP - 348
EP - 362
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
SN - 1083-4419
IS - 2
ER -