| 袁莉芬,张悦,何怡刚,吕密.基于文化双量子粒子群优化的RFID 3D-LANDMARC定位算法[J].电子测量与仪器学报,2017,31(6):934-941 |
| 基于文化双量子粒子群优化的RFID 3D-LANDMARC定位算法 |
| RFID 3D-LANDMARC localization algorithm based on cultural double quantum particle swarm optimization |
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| DOI：10.13382/j.jemi.2017.06.017 |
| 中文关键词: 三维定位 LANDMARC 文化双量子粒子群 BP神经网络 |
| 英文关键词:three dimensional localization algorithm LANDMARC CDQPSO BP neural network |
| 基金项目:国家杰出青年科学基金（50925727）、国家重点研发计划“重大科学仪器设备开发”（2016YFF0102200）、国家自然科学基金（61102035，51577046）、国家自然科学基金重点项目(51637004)、中国博士后特别项目（2015T80651）、中国博士后面上项目（2014M5517）、合肥工业大学春华计划（2014HGCH0012）资助项目 |
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| Author | Institution |
| Yuan Lifen | School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230000, China |
| Zhang Yue | School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230000, China |
| He Yigang | School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230000, China |
| Lv Mi | Texas A&M University, College Station TX 77843, USA |
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| 中文摘要: |
| 射频识别技术（RFID）三维定位是目前室内定位的主要技术，现有的RFID三维定位主要基于LANDMARC定位算法。针对传统的LANDMARC定位算法存在定位精度低、自适应性差的问题，提出一种基于文化双量子粒子群（CDQPSO）优化的RFID 3D LANDMARC定位算法。该算法首先使用BP神经网络在数据拟合方面的优势对采集信号进行预处理，研究无线信号传输损耗模型，以提升LANDMARC算法的定位精度；然后引入CDQPSO算法在全局搜索与寻优方面的技术优势，求解模型，解决LANDMARC定位算法的自适应问题。实验研究表明，所提算法定位误差在0.56 m以下的标签达到 75%，与基本LANDMARC算法和粒子群优化LANDMARC算法相比，定位精度和适应性均得到显著提高，而且能克服粒子群算法收敛速度慢的缺点。 |
| 英文摘要: |
| The RFID three dimensional localization algorithm is the main technology of indoor localization. Aiming at the problems of low location accuracy and poor adaptability in the traditional LANDMARC localization algorithm, a RFID 3D LANDMARC localization algorithm based on the cultural double quantum particle swarm optimization is proposed. Firstly, the advantages of the BP neural network in data fitting is used to preprocess the acquired signal and the wireless signal transmission loss model is studied to improve localization accuracy of LANDMARC algorithm. With the purpose of solving the adaptive problem existed in LANDMARC localization algorithm, the cultural double quantum particle swarm optimization (CDQPSO) algorithm is introduced, which has the technology advantages in global search and optimization to solve the localization model. The experimental results show that the proposed algorithm improves the localization accuracy and adaptability significantly, compared with the basic LANDMARC algorithm and particle swarm optimization LANDMARC algorithm, with the localization error of 75% of tested label is less than 0.56 m, and it can overcome the shortcoming of slow convergence existed in particle swarm optimization. |
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