A Novel Fingerprint Localization Algorithm Based on Modified Channel State Information Using Kalman Filter

Wang, Jingjing; Park, Joon Goo

doi:10.1007/s42835-020-00441-6

Cited by 10 publications

(2 citation statements)

References 22 publications

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“…The 802.11 protocol's physical layer (PHY) [8] serves as the link between the MAC and wireless media. The value of each component in the channel gain matrix H can be used to express CSI [9]. The multipath effect of a wireless channel can be described by the term "channel impulse response" (CIR).…”

Section: B Csi-based Localizationmentioning

confidence: 99%

Fingerprinting-based Indoor Localization in a 3x3 Meter Grid Using OFDM Signals at Sub-6 GHz

Kaur,

Tan,

et al. 2024

Preprint

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Accurately determining the indoor location of mobile devices has garnered great interest due to its significant challenge in locating sources due to non-line-of-sight propagation and multipath effects. To address this challenge, This paper proposes a new approach to indoor positioning that utilises channel state information (CSI) and machine learning (ML) techniques to improve Accuracy. The proposed method extracts subcarrier amplitude and phase differences from CSI data to create fingerprints, which are then clustered to identify the number of groups of data and split into two sub-databases using a threshold. The ML algorithms and network architecture are used to train both sub-databases of fingerprints. Experiments conducted in a standard indoor environment demonstrate the effectiveness of the proposed method.

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Section: B Csi-based Localizationmentioning

confidence: 99%

Fingerprinting-based Indoor Localization in a 3x3 Meter Grid Using OFDM Signals at Sub-6 GHz

Kaur,

Tan,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The physical layer (PHY) of 802.11 protocol [ 17 ] is the interface between MAC and wireless media. CSI can be represented by the value of each element in the channel gain matrix H [ 22 , 23 ]. Channel impulse response (CIR) can describe the multi-path effect of wireless channel.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Indoor Positioning Algorithm Based on Fingerprint Using Fine-Grained CSI and RSSI Measurements of IEEE 802.11n WLAN

Wang

Park

2021

Sensors

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Received signal strength indication (RSSI) obtained by Medium Access Control (MAC) layer is widely used in range-based and fingerprint location systems due to its low cost and low complexity. However, RSS is affected by noise signals and multi-path, and its positioning performance is not stable. In recent years, many commercial WiFi devices support the acquisition of physical layer channel state information (CSI). CSI is an index that can characterize the signal characteristics with more fine granularity than RSS. Compared with RSS, CSI can avoid the effects of multi-path and noise by analyzing the characteristics of multi-channel sub-carriers. To improve the indoor location accuracy and algorithm efficiency, this paper proposes a hybrid fingerprint location technology based on RSS and CSI. In the off-line phase, to overcome the problems of low positioning accuracy and fingerprint drift caused by signal instability, a methodology based on the Kalman filter and a Gaussian function is proposed to preprocess the RSSI value and CSI amplitude value, and the improved CSI phase is incorporated after the linear transformation. The mutation and noisy data are then effectively eliminated, and the accurate and smoother outputs of the RSSI and CSI values can be achieved. Then, the accurate hybrid fingerprint database is established after dimensionality reduction of the obtained high-dimensional data values. The weighted k-nearest neighbor (WKNN) algorithm is applied to reduce the complexity of the algorithm during the online positioning stage, and the accurate indoor positioning algorithm is accomplished. Experimental results show that the proposed algorithm exhibits good performance on anti-noise ability, fusion positioning accuracy, and real-time filtering. Compared with CSI-MIMO, FIFS, and RSSI-based methods, the proposed fusion correction method has higher positioning accuracy and smaller positioning error.

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Fingerprinting‐Based Indoor Localization in a 3 × 3 Meter Grid Using OFDM Signals at Sub‐6 GHz

Kaur,

Tan,

Khan

et al. 2024

Applied AI Letters

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Accurately determining the indoor location of mobile devices has garnered significant interest due to the complex challenges posed by non‐line‐of‐sight (NLOS) propagation and multipath effects. To address this challenge, this paper proposes a new approach to indoor positioning that utilises channel state information (CSI) and machine learning (ML) techniques to improve accuracy. The proposed method extracts the amplitude and phase differences of the subcarriers from the CSI data to create fingerprints. ML algorithms and network architecture are utilised to train the CSI data from two antennas, in the form of phase and amplitude. Experiments conducted in a standard indoor environment demonstrate the effectiveness of the proposed method.

show abstract

A Novel Fingerprint Localization Algorithm Based on Modified Channel State Information Using Kalman Filter

Cited by 10 publications

References 22 publications

Fingerprinting-based Indoor Localization in a 3x3 Meter Grid Using OFDM Signals at Sub-6 GHz

Fingerprinting-based Indoor Localization in a 3x3 Meter Grid Using OFDM Signals at Sub-6 GHz

An Enhanced Indoor Positioning Algorithm Based on Fingerprint Using Fine-Grained CSI and RSSI Measurements of IEEE 802.11n WLAN

Fingerprinting‐Based Indoor Localization in a 3 × 3 Meter Grid Using OFDM Signals at Sub‐6 GHz

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