A Fast and Efficient Data Association of Passive Sensor Tracking

Data association is one of the key and difficult problems for multisensor-multitarget tracking. The classic multidimensional assignment algorithm often uses Lagrange relaxation algorithm to solve association problem with the angle only data obtained by passive sensors in presence of clutter, false a...

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Vydané v:2010 International Conference on Intelligent Computation Technology and Automation Ročník 1; s. 88 - 91
Hlavní autori: Changning Tong, Yuesong Lin, Yunfei Guo, Yan Zuo
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.05.2010
Predmet:
Acoustic measurements
Acoustic sensors
Computer vision
data association
Intelligent sensors
Iterative algorithms
Lagrange multipliers
Lagrangian functions
Multidimensional systems
passive sensor
Sensor systems
surrogate modified sub gradient
Surveillance
Target tracking
ISBN:9781424472796, 1424472792
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Popis
Shrnutí:Data association is one of the key and difficult problems for multisensor-multitarget tracking. The classic multidimensional assignment algorithm often uses Lagrange relaxation algorithm to solve association problem with the angle only data obtained by passive sensors in presence of clutter, false alarm condition. The sub gradient is applied to update the Lagrange multipliers, but it needs to minimize all the sub problems at every iterative time to solve the dual solution in the classic algorithm. This leads to long compute time and bad real-time performance. Aimed at the problem, an improved data association algorithm based on the Lagrange relaxation algorithm is introduced in this paper. It uses the surrogate modified sub gradient to update the Lagrange multipliers. Compared with the classical algorithm, new algorithm has less compute time and higher association accuracy via simulation.
ISBN:9781424472796
1424472792
DOI:10.1109/ICICTA.2010.403