Multiple Power Path Loss Fingerprinting for Sensor-Based Indoor Localization

Duan, Yaoxin; Lam, Kam-Yiu; Lee, Victor; Nie, Wendi; Li, Hao; Ng, Joseph Kee‐Yin

doi:10.1109/lsens.2017.2726181

Cited by 7 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where λ j i donates the jth expression of λ i from equation (12). Typically, ( 13) is a quadratic programming problem.…”

Section: B Visual Fingerprint Relocation Methodsmentioning

confidence: 99%

“…The method of updating a WiFi radio map can be summarized into three categories. One is predicting the Recieved Signal Strength Indication (RSSI) fingerprint by the particular radio propagation model, [10]- [12] can be classified as examples of this type. The other one is leveraging the deep learning framework for generating the renewed radio fingerprint by training the large amount of time-varying RSSI, Signal Noise Ratio (SNR), or Channel State Information (CSI), such as [13]- [15].…”

Section: A Background and Significancementioning

confidence: 99%

See 1 more Smart Citation

A Visual Fingerprint Update Algorithm Based On Crowdsourced Localization And Deep Learning For Smart IoV

Yin

Sun

2021

Preprint

View full text Add to dashboard Cite

Recently, the deep learning and vision-based technologies has shown their great significance for the prospective development of smart Internet of Vehicle (IoV). When the smart vehicle enters the indoor parking of a shopping mall, the vision-based localization technology can provide reliable parking service. As known, the vision-based technique relies on a visual map without a change in the position of the reference object. Although, some researchers have proposed a few automatic visual fingerprinting (AVF) methods, which are aiming at reducing the cost of building the visual map database. However, the AVF method still costs too much under such situation, since it is impossible to determine the specific location of the displaced object. In view of the smart IoV and the development of deep learning approach, we propose a crowdsourcing and deep learning based algorithm for solving the problem in this paper. Firstly, we propose a Region-based Fully Convolutional Network (R-FCN) based method with the feedback of crowdsourced images to locate the specific displaced object in the visual map database. Secondly, we propose a method based on quadratic programming (QP) for solving the translation vector of the displaced objects, which finally solves the problem of updating the visual map database. The simulation results show that our method can provide a higher detection sensitivity and correction accuracy as well as the relocation results. It means that our proposed algorithm outperforms the compared one, which is verified by both synthetic and real data simulation.

show abstract

“…where λ j i donates the jth expression of λ i from equation (12). Typically, ( 13) is a quadratic programming problem.…”

Section: B Visual Fingerprint Relocation Methodsmentioning

confidence: 99%

Section: A Background and Significancementioning

confidence: 99%

A Visual Fingerprint Update Algorithm Based On Crowdsourced Localization And Deep Learning For Smart IoV

Yin

Sun

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…There are other factors such as the effects of the non-linear amplifiers of the BLE devices, the antenna gain variation of the signal transmitters and different kinds of antennae used by various vendors [27]. But their effect is of constant proportion in comparison to the effects of fast fading noise [28], the change in transmission power levels [29] and interference level of multi-paths [23,24]. Regardless of the gain, antennae and device heterogeneity, the random variation persists in every BLE signal mainly due to its low energy characteristic.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision

Qureshi

Umair

Hancke

2019

Sensors

View full text Add to dashboard Cite

Bluetooth Low Energy (BLE) based Wireless Indoor Localization System (WILS) with high localization accuracy and high localization precision is a key requirement in enabling the Internet of Things (IoT) in today’s applications. In this paper, we investigated the effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels. This issue is not often discussed as most of the works on localization algorithms use the highest power levels but has important practical implications for energy efficiency, e.g., if a designer would like to trade-off localization performance and node lifetime. To analyze the impact, we used the established trilateration based localization model with two methods i.e., Centroid Approximation (CA) and Minimum Mean Square Error (MMSE). We observed that trilateration based localization with MMSE method outperforms the CA method. We further investigated the use of two filters i.e., Low Pass Filter (LPF) and Kalman Filter (KF) and evaluated their effects in terms of mitigating the random variations from BLE signal. In comparison to non-filter based approach, we observed a great improvement in localization accuracy and localization precision with a filter-based approach. Furthermore, in comparison to LPF based trilateration localization with CA, the performance of a KF based trilateration localization with MMSE is far better. An average of 1 m improvement in localization accuracy and approximately 50% improvement in localization precision is observed by using KF in trilateration based localization model with the MMSE method. In conclusion, with KF in trilateration based localization model with MMSE method effectively eliminates random variations in BLE RSS with multiple transmission power levels and thus results in a BLE based WILS with high accuracy and high precision.

show abstract

“…The method of updating a WiFi radio map can be summarized into three categories. One is predicting the Received Signal Strength Indication (RSSI) fingerprint by the particular radio propagation model, [15][16][17] can be classified as examples of this type. The other one is leveraging the deep learning framework for generating the renewed radio fingerprint by training the large amount of time-varying RSSI, Signal Noise Ratio (SNR), or Channel State Information (CSI), such as [18][19][20].…”

mentioning

confidence: 99%

A visual fingerprint update algorithm based on crowdsourced localization and deep learning for smart IoV

Yin

Sun

et al. 2021

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Recently, deep learning and vision-based technologies have shown their great significance for the prospective development of smart Internet of Vehicle (IoV). When the smart vehicle enters the indoor parking of a shopping mall, the vision-based localization technology can provide reliable parking service. As known, the vision-based technique relies on a visual map without a change in the position of the reference object. Although, some researchers have proposed a few automatic visual fingerprinting (AVF) methods, which are aiming at reducing the cost of building the visual map database. However, the AVF method still costs too much under such a situation, since it is impossible to determine the specific location of the displaced object. Given the smart IoV and the development of deep learning approach, we propose an algorithm for solving the problem based on crowdsourcing and deep learning in this paper. Firstly, we propose a Region-based Fully Convolutional Network (R-FCN) based method with the feedback of crowdsourced images to locate the specific displaced object in the visual map database. Secondly, we propose a method based on quadratic programming (QP) for solving the translation vector of the displaced objects, which finally solves the problem of updating the visual map database. The simulation results show that our method can provide a higher detection sensitivity and correction accuracy as well as the relocation results. It means that our proposed algorithm outperforms the compared one, which is verified by both synthetic and real data simulation.

show abstract

Multiple Power Path Loss Fingerprinting for Sensor-Based Indoor Localization

Cited by 7 publications

References 16 publications

A Visual Fingerprint Update Algorithm Based On Crowdsourced Localization And Deep Learning For Smart IoV

A Visual Fingerprint Update Algorithm Based On Crowdsourced Localization And Deep Learning For Smart IoV

Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision

A visual fingerprint update algorithm based on crowdsourced localization and deep learning for smart IoV

Contact Info

Product

Resources

About