Indoor Fingerprinting Localization Based on Fine-grained CSI using Principal Component Analysis

Wang, Jingjing; Wang, Xianqing; Peng, Jishen; Hwang, Jun; Park, Joon Goo

doi:10.1109/icufn49451.2021.9528612

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…In other words, the base model, which accounts for approximately 23% of the variance ratio's explainability, does not fully reflect the model's variation and thus does not fit the indoor parking system scenario. This is consistent with the finding that the less variance explained ratio by a principal component, the less likely the algorithms are to characterize the 'CSI amplitude-location' relationship, and thus fail to estimate the target position or indoor parking lots [92,93].…”

Section: Distribution Of the Temporal Variations Of Csi Amplitude Mea...supporting

confidence: 87%

Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting

Gidey

Guo

Zhong

et al. 2022

Sensors

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Indoor signals are susceptible to NLOS propagation effects, multipath effects, and a dynamic environment, posing more challenges than outdoor signals despite decades of advancements in location services. In modern Wi-Fi networks that support both MIMO and OFDM techniques, Channel State Information (CSI) is now used as an enhanced wireless channel metric replacing the Wi-Fi received signal strength (RSS) fingerprinting method. The indoor multipath effects, however, make it less robust and stable. This study proposes a positive knowledge transfer-based heterogeneous data fusion method for representing the different scenarios of temporal variations in CSI-based fingerprint measurements generated in a complex indoor environment targeting indoor parking lots, while reducing the training calibration overhead. Extensive experiments were performed with real-world scenarios of the indoor parking phenomenon. Results revealed that the proposed algorithm proved to be an efficient algorithm with consistent positioning accuracy across all potential variations. In addition to improving indoor parking location accuracy, the proposed algorithm provides computationally robust and efficient location estimates in dynamic environments. A Cramer-Rao lower bound (CRLB) analysis was also used to estimate the lower bound of the parking lot location error variance under various temporal variation scenarios. Based on analytical derivations, we prove that the lower bound of the variance of the location estimator depends on the (i) angle of the base stations, (ii) number of base stations, (iii) distance between the target and the base station, djr (iv) correlation of the measurements, ρrjai and (v) signal propagation parameters σC and γ.

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Section: Distribution Of the Temporal Variations Of Csi Amplitude Mea...supporting

confidence: 87%

Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting

Gidey

Guo

Zhong

et al. 2022

Sensors

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“…In addition, the device diversity increases the heterogeneity of the datasets [9]. Several alternatives have been proposed to reduce the complexity and dimensionality of radio maps [10], [11], [12] and computational load at the operational stage [13].…”

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confidence: 99%

Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive

Quezada-Gaibor

Klus

et al. 2023

J. Ind. Sea. Pos. Nav.

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Indoor positioning based on machine-learning (ML) models has attracted widespread interest in the last few years, given its high performance and usability. Supervised, semisupervised, and unsupervised models have thus been widely used in this field, not only to estimate the user position, but also to compress, clean, and denoise fingerprinting datasets. Some scholars have focused on developing, improving, and optimizing ML models to provide accurate solutions to the end user. This article introduces a novel method to initialize the input weights in autoencoder extreme learning machine (AE-ELM), namely factorized input data (FID), which is based on the normalized form of the orthogonal component of the input data. AE-ELM with FID weight initialization is used to efficiently reduce the radio map. Once the dimensionality of the dataset is reduced, we use k-nearest neighbors to perform the position estimation. This research work includes a comparative analysis with several traditional ways to initialize the input weights in AE-ELM, showing that FID provide a significantly better reconstruction error. Finally, we perform an assessment with 13 indoor positioning datasets collected from different buildings and in different countries. We show that the dimensionality of the datasets can be reduced more than 11 times on average, while the positioning error suffers only a small increment of 15% (on average) in comparison to the baseline.

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A Real-time Respiration Monitoring System Using WiFi-Based Radar Model

Xie

Gan

Shi

et al. 2022

2022 IEEE International Symposium on Circuits and Systems (ISCAS)

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Indoor Fingerprinting Localization Based on Fine-grained CSI using Principal Component Analysis

Cited by 5 publications

References 10 publications

Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting

Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting

Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive

A Real-time Respiration Monitoring System Using WiFi-Based Radar Model

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