Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Localization Method for Multiple Robots Based on Bayesian Inference in Cognitive Radio Networks

인지 무선 네트워크에서의 베이지안 추론 기반 다중로봇 위치 추정 기법 연구

  • Kim, Donggu (School of Electrical Engineering, Korea Advanced Institute of Science and Technology(KAIST)) ;
  • Park, Joongoo (School of Electronics Engineering, Kyungpook National University)
  • 김동구 (한국과학기술원 전기 및 전자공학부) ;
  • 박준구 (경북대학교 IT대학 전자공학부)
  • Received : 2015.11.19
  • Accepted : 2016.01.04
  • Published : 2016.02.01
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Abstract

In this paper, a localization method for multiple robots based on Bayesian inference is proposed when multiple robots adopting multi-RAT (Radio Access Technology) communications exist in cognitive radio networks. Multiple robots are separately defined by primary and secondary users as in conventional mobile communications system. In addition, the heterogeneous spectrum environment is considered in this paper. To improve the performance of localization for multiple robots, a realistic multiple primary user distribution is explained by using the probabilistic graphical model, and then we introduce the Gibbs sampler strategy based on Bayesian inference. In addition, the secondary user selection minimizing the value of GDOP (Geometric Dilution of Precision) is also proposed in order to overcome the limitations of localization accuracy with Gibbs sampling. Via the simulation results, we can show that the proposed localization method based on GDOP enhances the accuracy of localization for multiple robots. Furthermore, it can also be verified from the simulation results that localization performance is significantly improved with increasing number of observation samples when the GDOP is considered.

Keywords

References

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