2021
DOI: 10.3390/s21041325
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SICD: Novel Single-Access-Point Indoor Localization Based on CSI-MIMO with Dimensionality Reduction

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… Show more

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Cited by 12 publications
(7 citation statements)
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“…CBAM enhances the attention of network to important features during the learning from both the channel and spatial dimensions. To demonstrate the effectiveness of the CBAM-1DCNN model in this paper, we compared it with other models, including support vector machine [32], Naive Bayes classification algorithm [33], decision tree algorithm [34], and 1DCNN. Among them, except that 1DCNN does not contain the CBAM module, other network parameters are consistent with CBAM-1DCNN.…”
Section: The Effectiveness Of Cbam-1dcnnmentioning
confidence: 99%
“…CBAM enhances the attention of network to important features during the learning from both the channel and spatial dimensions. To demonstrate the effectiveness of the CBAM-1DCNN model in this paper, we compared it with other models, including support vector machine [32], Naive Bayes classification algorithm [33], decision tree algorithm [34], and 1DCNN. Among them, except that 1DCNN does not contain the CBAM module, other network parameters are consistent with CBAM-1DCNN.…”
Section: The Effectiveness Of Cbam-1dcnnmentioning
confidence: 99%
“…Fan et al [15] developed a machine learning approach to exploit multipath MIMO fingerprints for hierarchical localization. Zhang et al [16] proposed an indoor localization system based on probabilistic fingerprints, which is derived by the Bayes theorem. However, they are the only conventional classification methods for indoor localization and need to extract features manually.…”
Section: Introductionmentioning
confidence: 99%
“…Using a unique approach, ref. [35] introduced a dimensionality reduction algorithm for CSI-MIMO localization, incorporating locally linear embedding for regularization. This ensured the data's inherent structure was preserved even after its projection to a lower-dimensional space.…”
Section: Introductionmentioning
confidence: 99%