Earticle

초록

영어

In this paper, we propose an algorithm to locate an object with unknown coordinates based on the positive semi-definite programming in the wireless sensor networks, assuming that the squared error of the measured distance follows Gaussian distribution. We first obtain the estimator of the object location based on the maximum likelihood criterion; then considering that the estimator is a non-convex function with respect to the measured distances between the object and the anchors with known coordinates, we transform the non-convex optimization to convex one by the positive semi-definite relaxation; and finally we take the optimal solution of the convex optimization as the estimated value of the object location. Simulations results show that our algorithm is superior to the R-LS algorithm regardless of whether the object is located within the convex hull composed of the anchors.

목차

Abstract
 1. Introduction
 2. Distance Model
 3. Semi-definite Programming
  3.1. Relaxation of the Positive Semi-definite Programming
  3.2. SeDuMi Formatting
 4. Simulations and Analysis
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Shengdong Xie [ School of Information Science and Engineering, Southeast University, Computer and Software Institute, Nanjing University of Information Science & Technology ]
• Jin Wang [ Computer and Software Institute, Nanjing University of Information Science & Technology ]
• Aiqun Hu [ School of Information Science and Engineering, Southeast University ]
• Yunli Gu [ Computer and Software Institute, Nanjing University of Information Science & Technology ]
• Jiang Xu [ Computer and Software Institute, Nanjing University of Information Science & Technology ]
• Mingsheng Zhang [ Oxbridge College, Kunming University of Science and Technology Yunnan Kunming ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605