Infra-free indoor positioning using only smartphone sensors

Iwase, Tatsuya; Shibasaki, Ryosuke

doi:10.1109/ipin.2013.6817864

Cited by 18 publications

(15 citation statements)

References 3 publications

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“…For pedestrian navigation applications, the research [18] integrates propagation-model-based WiFi positioning solution with the sensor data from smartphones. Meanwhile, the research [17] uses WiFi propagation model, PDR, and multiperson collaboration to provide a positioning solution that has an accuracy of 5m (RMS). It has the worst navigation performance.…”

Section: Resultsmentioning

confidence: 99%

“…Several researches have been conducted for the integration of WiFi and MEMS sensors [13][14][15][16][17]. The research [13] proposes a pedestrian navigation algorithm based on the integration of WiFi and foot-mounted inertial sensors.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The limitation of this work is that ToF is not supported by current WiFi chipsets. [17] improves the navigation performance of the integration of low-cost sensors and WiFi in smartphones by realizing cooperative positioning among multiple pedestrians. The proposed method introduces linkage structures to simplify trajectories of pedestrians.…”

Section: Literature Reviewmentioning

confidence: 99%

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Tightly-Coupled Integration of WiFi and MEMS Sensors on Handheld Devices for Indoor Pedestrian Navigation

2016

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The need for indoor pedestrian navigators is quickly increasing in various applications over the last few years. However, indoor navigation still faces many challenges and practical issues, such as the need for special hardware designs and complicated infrastructure requirements. This paper originally proposes a pedestrian navigator based on tightly-coupled (TC) integration of low-cost MEMS (micro-electromechanical systems) sensors and WiFi for handheld devices. Two other approaches are proposed in this paper to enhance the navigation performance: 1) The use of MEMS solution based on PDR/INS (pedestrian dead reckoning / inertial navigation system) integration; 2) The use of motion constraints, such as non-holonomic constraints (NHC), zero velocity update (ZUPT), and zero angular rate update (ZARU) for the MEMS solution. There are two main contributions in this paper: (1) TC fusion of WiFi, INS, and PDR for pedestrian navigation using an EKF (Extended Kalman Filter); and (2) Better heading estimation using PDR and INS integration to remove the gyro noise which occurs when only vertical gyroscope is used. The performance of the proposed navigation algorithms has been extensively verified through field tests in indoor environments.Experiment results showed that the average RMS position error of the proposed TC integration solution was 3.47m in three trajectories, which is 0.01% of INS, 10.38% of PDR, 32.11% of the developed MEMS solution, and 64.58% of the loosely-coupled (LC) integration. The proposed TC integrated navigation system can work well in the environment with sparse deployment of WiFi access points (APs).

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Tightly-Coupled Integration of WiFi and MEMS Sensors on Handheld Devices for Indoor Pedestrian Navigation

2016

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“…Iwase at al. [29] proposed a solution to reduce the accumulative positioning error through cooperative positioning among multiple pedestrians using smartphone and pedestrian dead reckoning. Their proposed method introduces linkage structures to simplify the trajectories of pedestrians.…”

Section: Related Workmentioning

confidence: 99%

Improving RF Fingerprinting Methods by Means of D2D Communication Protocol

2019

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Radio Frequency (RF) fingerprinting is widely applied for indoor positioning due to the existing Wi-Fi infrastructure present in most indoor spaces (home, work, leisure, among others) and the widespread usage of smartphones everywhere. It corresponds to a simple idea, the signal signature in a location tends to be stable over the time. Therefore, with the signals received from multiple APs, a unique fingerprint can be created. However, the Wi-Fi signal is affected by many factors which degrade the positioning error range to around a few meters. This paper introduces a collaborative method based on device-to-device (D2D) communication to improve the positioning accuracy using only fingerprinting and the direct communication to nearby devices. The results presented in this paper show that the positioning error can be reduced around 44% by considering D2D communication in the operational stage without adding new infrastructure for fingerprinting or complex resource-consuming filters. Moreover, the presence of very large errors is significantly reduced when the collaborative positioning based on D2D is available.

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“…Cooperative localization is similar to relative localization but also includes the possibility of some anchors nodes somewhere in the network as well as the potential for fusing additional data from onboard sensors (available in a vehicle or a smartphone) to help localize a node. In [31], pedestrian dead reckoning (PDR) with a smartphone is combined with Wi-Fi RSSI ranging where RSSI is used to determine when two smartphones are near to each other. In this way, the RSSI ranging can help correct heading inaccuracies from PDR.…”

Section: Cooperative Localizationmentioning

confidence: 99%

Radio Frequency-Based Indoor Localization in Ad-Hoc Networks

Golestanian¹,

Siva²,

Poellabauer³

2017

Ad Hoc Networks

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The increasing importance of location-aware computing and context-dependent information has led to a growing interest in low-cost indoor positioning with submeter accuracy. Localization algorithms can be classified into range-based and range-free techniques. Additionally, localization algorithms are heavily influenced by the technology and network architecture utilized. Availability, cost, reliability and accuracy of localization are the most important parameters when selecting a localization method. In this chapter, we introduce basic localization techniques, discuss how they are implemented with radio frequency devices and then characterize the localization techniques based on the network architecture, utilized technologies and application of localization. We then investigate and address localization in indoor environments where the absence of global positioning system (GPS) and the presence of unique radio propagation properties make this problem one of the most challenging topics of localization in wireless networks. In particular, we study and review the previous work for indoor localization based on radio frequency (RF) signaling (like Bluetooth-based localization) to illustrate localization challenges and how some of them can be overcome.

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Infra-free indoor positioning using only smartphone sensors

Cited by 18 publications

References 3 publications

Tightly-Coupled Integration of WiFi and MEMS Sensors on Handheld Devices for Indoor Pedestrian Navigation

Tightly-Coupled Integration of WiFi and MEMS Sensors on Handheld Devices for Indoor Pedestrian Navigation

Improving RF Fingerprinting Methods by Means of D2D Communication Protocol

Radio Frequency-Based Indoor Localization in Ad-Hoc Networks

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