Group target tracking using game theory

2001 ◽  
Author(s):  
Firooz A. Sadjadi ◽  
Wolfgang Kober
Keyword(s):  
Game Theory ◽  
Target Tracking ◽  
Group Target

scholarly journals Study on group target tracking to counter swarms of drones

2020 ◽  
Author(s):  
Louis Guerlin ◽  
Benjamin Pannetier ◽  
Michèle Rombaut ◽  
Maxime Derome
Keyword(s):  
Target Tracking ◽  
Group Target

scholarly journals Tracking Maneuvering Group Target with Extension Predicted and Best Model Augmentation Method Adapted

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Linhai Gan ◽  
Gang Wang
Keyword(s):  
Target Tracking ◽  
Random Matrix ◽  
Poor Performance ◽  
Group Extension ◽  
New Approach ◽  
Basic Model ◽  
True Motion ◽  
Motion Modes ◽  
Model Set ◽  
Group Target

The random matrix (RM) method is widely applied for group target tracking. The assumption that the group extension keeps invariant in conventional RM method is not yet valid, as the orientation of the group varies rapidly while it is maneuvering; thus, a new approach with group extension predicted is derived here. To match the group maneuvering, a best model augmentation (BMA) method is introduced. The existing BMA method uses a fixed basic model set, which may lead to a poor performance when it could not ensure basic coverage of true motion modes. Here, a maneuvering group target tracking algorithm is proposed, where the group extension prediction and the BMA adaption are exploited. The performance of the proposed algorithm will be illustrated by simulation.

Labeled particle unresolved target PHD filter for multiple group target tracking

2015 ◽  
Author(s):  
Yunxiang Li Yunxiang Li ◽  
Huaitie Xiao Huaitie Xiao ◽  
Hao Wu Hao Wu ◽  
Rui Hu Rui Hu ◽  
Qiang Fu Qiang Fu
Keyword(s):  
Target Tracking ◽  
Multiple Group ◽  
Group Target

scholarly journals Multi-Robot Coordination Using Switching of Methods for Deriving Equilibrium in Game Theory

1970 ◽  
Vol 8 (2) ◽  
pp. 174-181
Author(s):  
Wataru Inujima ◽  
Kazushi Nakano ◽  
Shu Hosokawa
Keyword(s):  
Game Theory ◽  
Nash Equilibrium ◽  
Target Tracking ◽  
Autonomous Robots ◽  
Stackelberg Equilibrium ◽  
Mathematical Tool ◽  
Control Performance ◽  
Game Situation ◽  
The Game Theory ◽  
Action Decision

The study of a Multi-Agent System using multiple autonomous robots has recently attracted much attention. With the problem of target a tracking as a typical case study, multiple autonomous robots decide their own actions to achieve the whole task which is tracking target. Each autonomous robot’s action influences each other. So, an action decision in coordination with other robots and the environment is needed to achieve the whole task effectively. The game theory is a major mathematical tool for realizing a coordinated action decision. The game theory mathematically deals with a multiagent environment influencing each other as a game situation. The conventional methods model one of the target tracking as a n-person general-sum game, and the use of the non-cooperative Nash equilibrium theory in non-cooperative games and the semi-cooperative Stackelberg equilibrium. The semicooperative Stackelberg equilibrium may obtain better control performance than the non-cooperative Nash equilibrium, but requires the communication among robots. In this study, we propose switching of methods in the equilibrium derivation both from the non-cooperative Nash equilibrium and the semicooperative Stackelberg equilibrium in a coordination algorithm for the target tracking. In the simulation, our proposed method achieves coordination with less connections than the method using the semicooperative Stackelberg equilibrium at all times. Furthermore, the proposed method shows better control performance than the non-cooperative Nash equilibrium.

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