| 基于WiFi的室内定位准确率改进算法 |
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| 引用本文： | 潘维蔚,康凯,张武雄,王海峰. 基于WiFi的室内定位准确率改进算法[J]. 计算机工程, 2020, 46(2): 207-213 |
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| 作者姓名： | 潘维蔚 康凯 张武雄 王海峰 |
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| 作者单位： | 中国科学院上海高等研究院,上海201210;中国科学院大学电子电气与通信工程学院,北京100049;上海科技大学信息科学与技术学院,上海201210;中国科学院上海高等研究院,上海201210;中国科学院上海微系统与信息技术研究所,上海200050 |
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| 基金项目： | 上海市科委科技创新计划;国家自然科学基金 |
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| 摘 要： | 针对目前无线保真定位系统在低于2 m的误差范围内累计准确率过低的问题,提出一种改进的去异常值的线性判别低维组合算法。根据无线保真信号强度数据的自身特征进行异常值去除,利用线性判别算法在低维情况下进行排列组合,对所得的若干个概率值求和,通过门限设置对在线定位阶段的新数据进行约束,以提高相邻网格定位的准确率。在真实办公环境室内相邻网格的多次不同场景下将实测数据集作为测试集进行实验,结果验证了该算法的有效性和正确性。
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| 关 键 词： | 室内定位 无线保真 信号强度 机器学习 线性判别算法 |
Improved Algorithm for Indoor Positioning Accuracy Based on WiFi |
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| PAN Weiwei,KANG Kai,ZHANG Wuxiong,WANG Haifeng. Improved Algorithm for Indoor Positioning Accuracy Based on WiFi[J]. Computer Engineering, 2020, 46(2): 207-213 |
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| Authors: | PAN Weiwei KANG Kai ZHANG Wuxiong WANG Haifeng |
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| Affiliation: | (Shanghai Advanced Research Institute,Chinese Academy of Sciences,Shanghai 201210,China;School of Electronic,Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing 100049,China;School of Information Science and Technology,ShanghaiTech University,Shanghai 201210,China;Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences,Shanghai 200050,China) |
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| Abstract: | The current Wireless Fidelity(WiFi)positioning system has the problem of low cumulative accuracy when the error range is less than two meters.Therefore,this paper proposes an improved linear discriminant low dimensional combination algorithm,in which the outlier is removed according to the characteristic of WiFi signal strength data.The linear discriminant algorithm is used to arrange combinations under low dimensional conditions,and the probability values obtained are summed up.The threshold is set as the additional constraint for the new data obtained in the online positioning phase,so as to improve the accuracy of adjacent grid positioning.The actual measured data of indoor outdoor adjacent grid in real office environment under different conditions are used as the testing dataset and the results verify the effectiveness and correctness of the proposed algorithm. |
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| Keywords: | indoor position Wireless Fidelity(WiFi) signal strength machine learning linear discriminant algorithm |
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