基于文化双量子粒子群优化的RFID 3D-LANDMARC定位算法

发布时间：2018-06-21 21:18

本文选题：三维定位 + LANDMARC　；参考：《电子测量与仪器学报》2017年06期

【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。
[Abstract]:Radio Frequency Identification (RFID) 3D location is the main technology of indoor positioning, and the existing RFID 3D location is mainly based on Landmarc algorithm. Aiming at the problems of low localization accuracy and poor adaptability in the traditional LANDMARC localization algorithm, an RFID 3D-LANDMARC localization algorithm based on cultural double quantum particle swarm optimization (CDQPSO) optimization is proposed. The algorithm uses the advantage of BP neural network in data fitting to preprocess the collected signal and study the wireless signal transmission loss model in order to improve the localization accuracy of LandMARC algorithm. Then, the advantage of CDQPSO in global search and optimization is introduced to solve the model and solve the adaptive problem of LANDMARC localization algorithm. The experimental results show that the positioning error of the proposed algorithm is less than 0.56 m, and the accuracy and adaptability of the proposed algorithm are significantly improved compared with the basic LANDMARC algorithm and the particle swarm optimization (Land-MARC) algorithm. Moreover, it can overcome the shortcoming of slow convergence speed of particle swarm optimization algorithm.
【作者单位】：合肥工业大学电气与自动化工程学院;德州农工大学;
【基金】：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
【分类号】：TP391.44

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