Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (9): 2465-2469.

### Indoor positioning algorithm with dynamic environment attenuation based on particle filtering

1. 1. Faculty of Software, Fujian Normal University, Fuzhou Fujian 350117, China;
2. Fujian Provincial University Engineering Research Center of Big Data Analysis and Application, Fuzhou Fujian 350117, China
• Received:2015-04-20 Revised:2015-05-28 Online:2015-09-10 Published:2015-09-17

### 基于动态环境衰减的粒子滤波室内定位算法

1. 1. 福建师范大学 软件学院, 福州 350117;
2. 大数据分析与应用福建省高校工程研究中心, 福州 350117
• 通讯作者: 肖如良(1966-),男,湖南娄底人,教授,博士,CCF会员,主要研究方向:算法设计与分析、软件工程,xiaoruliang@fjnu.edu.cn
• 作者简介:李奕诺(1989-),男,河南驻马店人,硕士研究生,主要研究方向:机器学习;倪友聪(1975-),男,安徽合肥人,副教授,博士,主要研究方向:软件工程;苏小敏(1994-),女,安徽亳州人,主要研究方向:算法设计;杜欣(1979-),女,新疆石河子人,副教授,博士,主要研究方向:算法设计与分析;蔡声镇(1954-),男,福建泉州人,教授,主要研究方向:嵌入式系统。
• 基金资助:
教育部规划基金资助项目(11YJA860028);福建省科技计划重大项目(2011H6006)。

Abstract: Due to the problem that the nodes having the same distance but different position in the complex environment, brings shortage to accuracy and stability of indoor positioning, a new indoor positioning algorithm with Dynamic Environment Attenuation Factor (DEAF) was proposed. This algorithm built a DEAF model and redefined the way to assume the value. In this algorithm, particle filtering method was firstly used to smooth the Received Signal Strength Indication (RSSI); then, the DEAF model was used to calculate the estimation distance of the node; finally, the trilateration was used to get the position of the target node. Comparative experiments had been done using several filtering models, and the results show that this dynamic environment attenuation factor model combined with particle filtering can resolve the problem of the environment difference very well. This algorithm reduces the mean error to about 0.68 m, and the result has higher positioning accuracy and good stability.

CLC Number: