Earticle

초록

영어

Due to the wide application of range-based location algorithm for received signal strength, and according to the requirements of high accuracy and low power cost in the location algorithm for WSNs, in this paper, a Bayesian optimization RSSI and an indoor location algorithm for ILS were introduced by setting RRS ranging as location framework. Firstly, through analyzing the RSSI-based ranging model, an indoor location model was introduced. Secondly, in view of the influence on RSSI value caused by the indoor environment,the Bayesian probabilistic model was adopted to process the RSSI measured value and to screen out the "big probability" of RSSI value. Thirdly, Obtaining accurate measured data by estimating distance using method of minimum mean square error. Finally, Estimating the node location using least square method, and according to the TelosB node of Telos Series produced by company Crossbow, the ranging experiment can be designed and thus groups of experimental data were obtained and analyzed..The experimental results showed that the proposed location project greatly increased the location accuracy and decreased the computation complexity, and has obviously more advantage of running time over other location projects.

목차

Abstract
 1. Introduction
 2. Proposed Algorithm
  2.1 Location Model
  2.2. MMSE-based Ranging Program
  2.3. IL- based Location
  2.4. Indoor Optimization RSSI Location Algorithm based on Bayesian Probabilistic Model
 3. Experiment and Analysis
  3.1. Experimental Environment and Parameter Settings
  3.2. Experiment Results and Performance Analysis of Algorithm
 4. Conclusion
 References

키워드

저자

• Liu ZhongPeng [ Department of Information Technology, Baoding University, Baoding, China ]
• Liu LiJuan [ Department of Information Science and Technology, Agricultural University of Hebei, Baoding, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605