An INS/WiFi Indoor Localization System Based on the Weighted Least Squares.

An INS/WiFi Indoor Localization System Based on the Weighted Least Squares.
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DOI:
10.3390/s18051458
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发表时间:
2018-05-07
期刊:
Sensors (Basel, Switzerland)
影响因子:
--
通讯作者:
Shi J
Shi J
中科院分区:
其他
文献类型:
--
作者:
Chen J;Ou G;Peng A;Zheng L;Shi J

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对于智能手机室内本地化,本文提出了INS/WiFi混合定位系统。加速度和角速度用于估计步长和标题。 INS的问题在于,定位错误会随着时间而增加。使用无线电信号强度作为指纹是一种广泛使用的技术。指纹匹配的主要问题是由于噪声而导致不匹配。考虑到不同的缺点和优势,智能手机的惯性传感器和WiFi已集成到室内定位中。对于混合定位系统,使用预处理技术来增强WiFi信号质量。惯性导航系统限制了WiFi匹配的范围。提出了多维动态时间扭曲(MDTW),以计算数据库中测量信号与指纹之间的距离。提出了基于MDTW的加权最小二乘(WLS),以融合多个指纹定位结果以提高定位准确性和鲁棒性。我们使用四种模式(呼叫,悬挂,手持和口袋),在走廊,书房和图书馆堆栈室进行了步行实验。实验结果表明,混合系统的平均定位精度约为2.03 m。
For smartphone indoor localization, an INS/WiFi hybrid localization system is proposed in this paper. Acceleration and angular velocity are used to estimate step lengths and headings. The problem with INS is that positioning errors grow with time. Using radio signal strength as a fingerprint is a widely used technology. The main problem with fingerprint matching is mismatching due to noise. Taking into account the different shortcomings and advantages, inertial sensors and WiFi from smartphones are integrated into indoor positioning. For a hybrid localization system, pre-processing techniques are used to enhance the WiFi signal quality. An inertial navigation system limits the range of WiFi matching. A Multi-dimensional Dynamic Time Warping (MDTW) is proposed to calculate the distance between the measured signals and the fingerprint in the database. A MDTW-based weighted least squares (WLS) is proposed for fusing multiple fingerprint localization results to improve positioning accuracy and robustness. Using four modes (calling, dangling, handheld and pocket), we carried out walking experiments in a corridor, a study room and a library stack room. Experimental results show that average localization accuracy for the hybrid system is about 2.03 m.
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