SICD: Novel Single-Access-Point Indoor Localization Based on CSI-MIMO with Dimensionality Reduction.

Yunwei Zhang, Weigang Wang, Chendong Xu, Jie Qin, Shujuan Yu, Yun Zhang

Abstract read

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Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Yunwei ZhangCollege of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.ORCID 0000-0003-1412-3282

Weigang WangCollege of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Chendong XuCollege of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Jie QinCollege of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Shujuan YuCollege of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Yun ZhangCollege of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Funding

National Natural Science Foundation of China No.61771257National Natural Science Foundation of China No. 61871232open research fund of the National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology No. KFJJ20200103Postgraduate Research &Practice Innovation Program of Jiangsu Province No. SJCX19_0275

6 · The paper itself

Abstract

With the rise of location-based services and the rapidly growing requirements related to their applications, indoor localization based on channel state information-multiple-input multiple-output (CSI-MIMO) has become an important research topic. However, indoor localization based on CSI-MIMO has some disadvantages, including noise and high data dimensions. To overcome the above drawbacks, we proposed a novel method of indoor localization based on CSI-MIMO, named SICD. For SICD, a novel localization fingerprint was first designed which can reflect the time-frequency and space-frequency characteristics of CSI-MIMO under a single access point (AP). To reduce the redundancy in the data of CSI-MIMO amplitude, we developed a data dimensionality reduction algorithm. Moreover, by leveraging a log-normal distribution, we calculated the conditional probability of the naive Bayes classifier, which was used to predict the moving object's location. Compared with other state-of-the-art methods, the results of the experiment confirm that the SICD effectively improves localization accuracy.

Indexed as

channel state informationdimensionality reductionindoor localizationMIMOnaive Bayes

Identifiers

PMID33668436

DOI10.3390/s21041325

PMCPMC7918435

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