| 引用本文: | 刘嘉伟,毛文宇,鲁华祥.一种面向室内环境变动的人员目标无源定位算法[J].哈尔滨工业大学学报,2021,53(8):39.DOI:10.11918/202005081 |
| LIU Jiawei,MAO Wenyu,LU Huaxiang.Device-free indoor localization algorithm for changing environment[J].Journal of Harbin Institute of Technology,2021,53(8):39.DOI:10.11918/202005081 |
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| 一种面向室内环境变动的人员目标无源定位算法 |
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刘嘉伟1,2,毛文宇1,2,鲁华祥1,2,3,4
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(1.中国科学院大学,北京 100089; 2.中国科学院 半导体研究所,北京 100083; 3.中国科学院 脑科学与智能技术卓越 创新中心,上海 200031; 4.半导体神经网络智能感知与计算技术北京市重点实验室(中国科学院),北京 100083)
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| 摘要: |
| 现有的基于接收信号强度（RSS）的人员目标无源室内定位算法在定位环境变动的情况下难以兼顾人工工作量、时间消耗和定位准确率。针对这个问题,本文提出了基于迁移聚类和坐标融合的变分自编码器(FusVAE)的室内环境变动下人员目标无源定位算法。在环境变动后,采集少量无标签RSS样本,然后使用本文提出的基于度量学习的半监督模糊C均值聚类（SFCMML）对其进行精确聚类和标签标注,对原有的定位模型进行重训练,只需很小的人工和时间代价就可以使原定位模型在新环境下也具有较高的定位准确率。同时,针对变动后环境下采集RSS样本较少的问题,本文提出了基于坐标融合的变分自编码器（FusVAE）,对新环境下的RSS样本进行数据增强,丰富了RSS样本的数量和质量,提高了定位模型的泛化能力。实验结果表明,在环境变动的情况下,本文提出的算法的平均定位准确率可达88.6%,和同领域同类型算法相比,具有较高的定位精度和较好的环境变动适应性,更适用于变动环境下的人员目标无源室内定位问题。 |
| 关键词: 无源室内定位 RSS 机器学习 迁移学习 变分自编码器 |
| DOI：10.11918/202005081 |
| 分类号:TP391 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(61701473,U19A2080,U1936106) |
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| Device-free indoor localization algorithm for changing environment |
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LIU Jiawei1,2,MAO Wenyu1,2,LU Huaxiang1,2,3,4
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(1.University of Chinese Academy of Sciences, Beijing 100089, China; 2. Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China; 3. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; 4. Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Lab (Chinese Academy of Sciences), Beijing 100083, China)
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| Abstract: |
| The existing human target device-free indoor localization algorithm based on received signal strength (RSS) is difficult to give consideration to artificial workload, time consumption, and positioning accuracy under the circumstance of environment changes. In view of this problem, this paper proposes a device-free localization algorithm based on transfer clustering and fusion variational auto-encoder (FusVAE) in changing indoor environment. After environment changes, a small amount of RSS samples without labels were collected. Then, a semi-supervised fuzzy C-means clustering based on metric learning (SFCMML) was proposed to accurately cluster and label the samples, and the original model was retrained, where only a small amount of artificial work and process time was required to achieve a high localization accuracy for the original model in the new environment. In addition, aiming at the problem that the RSS samples collected in the new environment were in small quantity, the FusVAE was constructed based on coordinate fusion to generate RSS samples in the new environment for data enhancement, which could enrich the quantity and quality of the RSS samples, improve the generalization ability of the model, and enhance the positioning accuracy. Experimental results show that under the circumstance of environment changes, the average positioning accuracy of the proposed algorithm reached 88.6%. Compared with the algorithms of the same type in the same field, the proposed algorithm had higher positioning accuracy and better environmental adaptability, which is more applicable to device-free indoor localization in changing environment. |
| Key words: device-free localization RSS machine learning transfer learning VAE |
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