Distributed Sensor Allocation Algorithm for Target Tracking Based on Potential Game

RAN Xiaomin; FANG Deliang

doi:10.11999/JEIT170229

Volume 39 Issue 11

Nov. 2017

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RAN Xiaomin, FANG Deliang. Distributed Sensor Allocation Algorithm for Target Tracking Based on Potential Game[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2748-2754. doi: 10.11999/JEIT170229

Citation:

RAN Xiaomin, FANG Deliang. Distributed Sensor Allocation Algorithm for Target Tracking Based on Potential Game[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2748-2754. doi: 10.11999/JEIT170229

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Distributed Sensor Allocation Algorithm for Target Tracking Based on Potential Game

doi: 10.11999/JEIT170229

RAN Xiaomin¹,
FANG Deliang¹

Funds:

The National Science and Technology Major Project of the Ministry of Science and Technology of China (2014ZX03006003)

Received Date: 2017-03-20
Rev Recd Date: 2017-05-15
Publish Date: 2017-11-19

Abstract

Abstract

Considering the limitation of energy, bandwidth, observation distance and communication distance in the Wireless Sensor Networks (WSN), a distributed sensor allocation algorithm based on potential game is proposed to solve the multi-target tracking problem. The predicted coordinate of target and Geometric Dilution Of Precision (GDOP) is used to establish the sensor allocation game modal with local information, and it is proved to be an exact potential game with at least one Nash Equilibrium point. On this basis, a parallel best response dynamic is proposed as the learning algorithm to search the Nash Equilibrium point. It is proved that the learning algorithm can help the game modal converge to a Nash Equilibrium point, and has faster convergence speed than traditional best response dynamic when sensors just communicate with local one-hop neighboring ones. In addition, a fully distributed decision makers selection mechanism is proposed on the basis of the Carrier Sense Multiple Access (CSMA), which is more satisfied with the self-organizing characteristic. The simulation results show that the proposed algorithm has great advantages in convergence speed, tracking accuracy and energy efficiency.
- Wireless Sensor Networks (WSN),
- Target tracking,
- Sensor selection,
- Potential game

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References(25)

References

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