A Robust Integration Platform of Wi-Fi RTT, RSS Signal, and MEMS-IMU for Locating Commercial Smartphone Indoors

Guo, Guangyi; Chen, Ruizhi; Ye, Feng; Liu, Zuoya; Xu, Shihao; Huang, Lixiong; Li, Zheng; Qian, Long

doi:10.1109/jiot.2022.3150958

Cited by 20 publications

(9 citation statements)

References 33 publications

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“…To avoid delays to users, it is desired to keep the filter's latency under 0.5 seconds during the targeting phase. In [15], the localization error and computation time of different numbers of particles are compared in detail. In this study, the number of particles is set to 2000 because the localization accuracy is acceptable while keeping the latency low.…”

Section: Localization Results In Positioning Phasementioning

confidence: 99%

“…In recent studies [15][16], the readings from a variety of sensors and context information such as RSSI and locationbased services can be fused with the RTT ranging results to get better positioning accuracy. However, these approaches still require a necessary number of FTMR coordinates to be known and fixed in advance (typically, 3 for two-dimensional and 4 for three-dimensional positioning, respectively).…”

Section: Related Workmentioning

confidence: 99%

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Fusion-Based Smartphone Positioning Using Unsupervised Calibration of Crowdsourced Wi-Fi FTM

Chan

Lai

et al. 2022

IEEE Access

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This paper presents a multi-source fusion smartphone localization solution using Wi-Fi Fine Time Measurement (FTM) and Pedestrian Dead Reckoning (PDR), calibrated via multi-source and unsupervised crowdsourcing. In crowdsourcing phase, user movement within the site utilizes PDR to infer their location, and this location is used to calibrate the FTM data. The multi-layer perceptron (MLP) of the ranging model is suitable for non-line-of-sight (NLOS) reception, and the ranging accuracy is improved by more than 24%. In the positioning phase, the 90 percentile error of the ranging model trained using only crowdsourced data is less than 1.37m, which is 32% smaller than the traditional weighted least squares (WLS) localization error.

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Section: Localization Results In Positioning Phasementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fusion-Based Smartphone Positioning Using Unsupervised Calibration of Crowdsourced Wi-Fi FTM

Chan

Lai

et al. 2022

IEEE Access

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“…Both the relative and absolute position observables of the integration process are optimized, which then improves the accuracy and stability of the system. Afterward, observation quality evaluation and control strategy (OQECS) is executed for the real‐time quality evaluation of each observable and is fed for filtration 75 . Subsequently, the significant importance of indoor positioning systems (IPS) over the global positioning system (GPS) is analyzed by concerning the IoT‐based context services.…”

Section: Literature Reviewmentioning

confidence: 99%

A review on evolving domains of Internet of Things: Architecture, applications, and technical challenges

Sharma,

Sangwan,

Singh

2023

Int J Communication

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SummaryThe Internet of Things (IoT) is a system that includes smart items with different sensors, advanced technologies, analytics, cloud servers, and other wireless devices that integrate and work together to create an intelligent environment that benefits end users. With its wide spectrum of applications, IoT is revolutionizing both the current and future generations of the Internet. IoT systems can be employed for broad‐ranging real applications, such as agriculture, the environment, cities, healthcare, and the industrial sector. In this paper, we briefly discuss the three‐tier architectural view of IoT, its different communication technologies, and the smart sensors. Moreover, we study various application areas of IoT such as the environmental domain, healthcare, agriculture, smart cities, and industrial, commercial, and general aspects. A critical analysis is shown for the existing schemes and techniques related to this work. Further, this paper addresses the basic context, tools and evaluation approaches, future scope, and the advantages and disadvantages of the aforestated IoT applications. A comprehensive analysis is provided for each domain along with its fundamental parameters like the quality of service (QoS), network longevity, scalability, energy efficiency, accuracy, and cost. Finally, this study highlights the technical challenges and open research problems existing in different IoT applications.

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“…In this work, an MMBB is designed to reduce these interferences and effects and calculate accurate ranges. Based on previous research on PLM [27][28][29], when the geometric range between a transmitter and receiver exceeds a certain range, the accuracy of RSSI will be difficult to reflect the change in distance. Therefore, we take 10 m as the typical working distance and deployment density of MMBB, and the upper limit of range estimation for the SLM.…”

Section: Finite Range Estimation Based On Mmbb and Segmented Linear M...mentioning

confidence: 99%

“…In our previous study [33], we compared commonly used compensation models [34][35][36][37][38] and, in this paper, we chose the empirical linear model proposed in [35] to represent the relationship between pedestrian height, stride frequency, and step length. This model was calibrated by pretraining, using real-time walking distances [27].…”

Section: Pedestrian Walking Speed Measured By Accelerometermentioning

confidence: 99%

Locating Smartphone Indoors by Using Tightly Coupling Bluetooth Ranging and Accelerometer Measurements

et al. 2022

Self Cite

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High-precision, low-cost, and wide coverage indoor positioning technology is the key to indoor and outdoor integrated location-based services, and it has broad market prospects and social value. However, achieving sub-meter level positioning accuracy in indoor environments remains a real challenge due to the blockage of indoor Global Navigation Satellite System (GNSS) signals, the complexity of indoor environments, and the unpredictability of user behavior. In this paper, we introduce a multi-module BLE broadcaster (MMBB)-based indoor positioning solution in which a tightly coupled fusion architecture is implemented on a smartphone. The solution integrates ranging measurements from multiple MMBB and the measurements of the accelerometer built into a smartphone. It becomes an instant positioning solution without any training phase by adopting a calibrated linearly segmented path loss model for ranging. We apply the pedestrian walking speed derived by the smartphone accelerometer to constrain an unscented Kalman filter method that is used to estimate the location and speed. The accuracy of the proposed method is 50% at 0.79 m and 95% at 1.6 m at in terms of horizontal error distance. Position update frequency is 10 Hz and the time to first fix is 0.1 s. The system can easily adapt a global coordinator system so that it can seamlessly work together with the GNSS to form an indoor/outdoor positioning solution.

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A Robust Integration Platform of Wi-Fi RTT, RSS Signal, and MEMS-IMU for Locating Commercial Smartphone Indoors

Cited by 20 publications

References 33 publications

Fusion-Based Smartphone Positioning Using Unsupervised Calibration of Crowdsourced Wi-Fi FTM

Fusion-Based Smartphone Positioning Using Unsupervised Calibration of Crowdsourced Wi-Fi FTM

A review on evolving domains of Internet of Things: Architecture, applications, and technical challenges

Locating Smartphone Indoors by Using Tightly Coupling Bluetooth Ranging and Accelerometer Measurements

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