Communicating Is Crowdsourcing: Wi-Fi Indoor Localization with CSI-Based Speed Estimation

Jiang, Zhuang De; Xi, Wei; Li, Xiangyang; Tang, Shaojie; Zhao, Ji Feng; Han, Jae-Sook; Zhao, Kun; Wang, Zhi; Xiao, Bo

doi:10.1007/s11390-014-1452-7

Cited by 65 publications

(35 citation statements)

References 48 publications

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“…Seminal work exploited the ubiquitous WiFi signals to recognize gestures via micro-Doppler effects [Pu et al 2013] and estimate walking speed by PHY layer information [Jiang et al 2014]. Future research is envisioned to incorporate inertial sensors and wireless modules on smartphones synergically to derive mobility information pervasively and nonintrusively.…”

Section: Discussionmentioning

confidence: 99%

Mobility Increases Localizability

et al. 2015

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Wireless indoor positioning has been extensively studied for the past 2 decades and continuously attracted growing research efforts in mobile computing context. As the integration of multiple inertial sensors (e.g., accelerometer, gyroscope, and magnetometer) to nowadays smartphones in recent years, human-centric mobility sensing is emerging and coming into vogue. Mobility information, as a new dimension in addition to wireless signals, can benefit localization in a number of ways, since location and mobility are by nature related in the physical world. In this article, we survey this new trend of mobility enhancing smartphonebased indoor localization. Specifically, we first study how to measure human mobility: what types of sensors we can use and what types of mobility information we can acquire. Next, we discuss how mobility assists localization with respect to enhancing location accuracy, decreasing deployment cost, and enriching location context. Moreover, considering the quality and cost of smartphone built-in sensors, handling measurement errors is essential and accordingly investigated. Combining existing work and our own working experiences, we emphasize the principles and conduct comparative study of the mainstream technologies. Finally, we conclude this survey by addressing future research directions and opportunities in this new and largely open area. . 2015. Mobility increases localizability: A survey on wireless indoor localization using inertial sensors.

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Section: Discussionmentioning

confidence: 99%

Mobility Increases Localizability

et al. 2015

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“…Additionally, CSI has been investigated for fingerprinting methods and velocity estimation. In [9], the authors provided a network-based indoor tracking system, which estimates the velocity of a mobile target from CSI and locates the target by fingerprinting based on CSI.…”

Section: Related Workmentioning

confidence: 99%

Fine-grained indoor tracking by fusing inertial sensor and physical layer information in WLANs

Acuna

Zhao

et al. 2016

2016 IEEE International Conference on Communications (ICC)

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Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as a fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range-based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranges and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1.3m for mean accuracy and 2.2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.

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“…For completeness it should be added that there are recent developments in using channel state information (CSI) [29,30,31,32] instead of RSS. This has shown promise for wide bandwidth signals such as new generations of Wi-Fi, LTE and UWB with large bandwidths.…”

Section: Fisher Information Formulationmentioning

confidence: 99%

Assessment of Receiver Signal Strength Sensing for Location Estimation Based on Fisher Information

Nielsen

2016

Sensors

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Currently there is almost ubiquitous availability of wireless signaling for data communications within commercial building complexes resulting in receiver signal strength (RSS) observables that are typically sufficient for generating viable location estimates of mobile wireless devices. However, while RSS observables are generally plentiful, achieving an accurate estimation of location is difficult due to several factors affecting the electromagnetic coupling between the mobile antenna and the building access points that are not modeled and hence contribute to the overall estimation uncertainty. Such uncertainty is typically mitigated with a moderate redundancy of RSS sensor observations in combination with other constraints imposed on the mobile trajectory. In this paper, the Fisher Information (FI) of a set of RSS sensor observations in the context of variables related to the mobile location is developed. This provides a practical method of determining the potential location accuracy for the given set of wireless signals available. Furthermore, the information value of individual RSS measurements can be quantified and the RSS observables weighted accordingly in estimation combining algorithms. The practical utility of using FI in this context was demonstrated experimentally with an extensive set of RSS measurements recorded in an office complex. The resulting deviation of the mobile location estimation based on application of weighted likelihood processing to the experimental RSS data was shown to agree closely with the Cramer Rao bound determined from the FI analysis.

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Communicating Is Crowdsourcing: Wi-Fi Indoor Localization with CSI-Based Speed Estimation

Cited by 65 publications

References 48 publications

Mobility Increases Localizability

Mobility Increases Localizability

Fine-grained indoor tracking by fusing inertial sensor and physical layer information in WLANs

Assessment of Receiver Signal Strength Sensing for Location Estimation Based on Fisher Information

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