Filtering Noisy 802.11 Time-of-Flight Ranging Measurements

Marcaletti, Andreas; Rea, Maurizio; Giustiniano, Domenico; Lenders, Vincent; Fakhreddine, Aymen

doi:10.1145/2674005.2674998

“…is the density function of the probability to observe the FTM measurement d i,m at position ρ t from (10). This joint density function allows us to assign a weight to every particle, described by its position ρ t , based on the aforementioned probabilistic positioning approach.…”

Section: Particle Filteringmentioning

confidence: 99%

“…This would render the method incomparable to the others. For FTM the distance measurement is used in (10), with σ 2 i = 3.5 for every AP, which was found empirically. In contrast to least-squares estimation, the RSSI obtained by the hardware is used directly in (13).…”

Section: Positioning Performancementioning

confidence: 99%

“…Consequently, some building elements, like concrete support walls, can add a significant delay to the propagation time, while other materials, like drywall, only add a negligible delay. However, for most indoor environments the signal propagation speed can be assumed to be constant, as the total travel distance in non-air media is usually negligible short compared to the travel distance in air [10]. Because of the direct relation to the propagation time, time-based distance measurements are assumed to be more robust against external interference and environmental changes compared to received power measurements.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios

Bullmann

¹

,

Fetzer

²

,

Ebner

³

et al. 2020

Sensors

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With the addition of the Fine Timing Measurement (FTM) protocol in IEEE 802.11-2016, a promising sensor for smartphone-based indoor positioning systems was introduced. FTM enables a Wi-Fi device to estimate the distance to a second device based on the propagation time of the signal. Recently, FTM has gotten more attention from the scientific community as more compatible devices become available. Due to the claimed robustness and accuracy, FTM is a promising addition to the often used Received Signal Strength Indication (RSSI). In this work, we evaluate FTM on the 2.4 GHz band with 20 MHz channel bandwidth in the context of realistic indoor positioning scenarios. For this purpose, we deploy a least-squares estimation method, a probabilistic positioning approach and a simplistic particle filter implementation. Each method is evaluated using FTM and RSSI separately to show the difference of the techniques. Our results show that, although FTM achieves smaller positioning errors compared to RSSI, its error behavior is similar to RSSI. Furthermore, we demonstrate that an empirically optimized correction value for FTM is required to account for the environment. This correction value can reduce the positioning error significantly.

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“…There have been several studies that resolve time-of-flight to around ten nanoseconds using the clocks of Wi-Fi cards [55,35,19,38]. Many conclude that the clocks on current Wi-Fi hardware alone cannot permit higher resolutions of time-of-flight [10,49,40].…”

Section: Related Workmentioning

confidence: 99%

Sub-Nanosecond Time of Flight on Commercial Wi-Fi Cards

VasishtDeepak

¹

,

Kumar

²

,

KatabiDina

³

2015

SIGCOMM Comput. Commun. Rev.

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Time-of-flight, i.e., the time incurred by a signal to travel from transmitter to receiver, is perhaps the most intuitive way to measure distances using wireless signals. It is used in major positioning systems such as GPS, RADAR, and SONAR. However, attempts at using time-of-flight for indoor localization have failed to deliver acceptable accuracy due to fundamental limitations in measuring time on Wi-Fi and other RF consumer technologies. While the research community has developed alternatives for RF-based indoor localization that do not require time-of-flight, those approaches have their own limitations that hamper their use in practice. In particular, many existing approaches need receivers with large antenna arrays while commercial Wi-Fi nodes have two or three antennas. Other systems require fingerprinting the environment to create signal maps. More fundamentally, none of these methods support indoor positioning between a pair of Wi-Fi devices without third party support.In this paper, we present a set of algorithms that measure the time-of-flight to sub-nanosecond accuracy on commercial Wi-Fi cards. We implement these algorithms and demonstrate a system that achieves accurate device-todevice localization, i.e. enables a pair of Wi-Fi devices to locate each other without any support from the infrastructure, not even the location of the access points.

show abstract

“…This paper is closely related to past work that measures the time-of-flight of Wi-Fi signals. There have been several studies that resolve time-of-flight to around ten nanoseconds using the clocks of Wi-Fi cards [55,35,19,38]. Many conclude that the clocks on current Wi-Fi hardware alone cannot permit higher resolutions of time-of-flight [10,49,40].…”

Section: Related Workmentioning

confidence: 99%

Sub-Nanosecond Time of Flight on Commercial Wi-Fi Cards

Vasisht

¹

,

Kumar

²

,

Katabi

³

2015

Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication

View full text Add to dashboard Cite

Time-of-flight, i.e., the time incurred by a signal to travel from transmitter to receiver, is perhaps the most intuitive way to measure distances using wireless signals. It is used in major positioning systems such as GPS, RADAR, and SONAR. However, attempts at using time-of-flight for indoor localization have failed to deliver acceptable accuracy due to fundamental limitations in measuring time on Wi-Fi and other RF consumer technologies. While the research community has developed alternatives for RF-based indoor localization that do not require time-of-flight, those approaches have their own limitations that hamper their use in practice. In particular, many existing approaches need receivers with large antenna arrays while commercial Wi-Fi nodes have two or three antennas. Other systems require fingerprinting the environment to create signal maps. More fundamentally, none of these methods support indoor positioning between a pair of Wi-Fi devices without third party support.In this paper, we present a set of algorithms that measure the time-of-flight to sub-nanosecond accuracy on commercial Wi-Fi cards. We implement these algorithms and demonstrate a system that achieves accurate device-todevice localization, i.e. enables a pair of Wi-Fi devices to locate each other without any support from the infrastructure, not even the location of the access points.

show abstract

Filtering Noisy 802.11 Time-of-Flight Ranging Measurements

Cited by 34 publications

References 12 publications

Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios

Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios

Sub-Nanosecond Time of Flight on Commercial Wi-Fi Cards

Sub-Nanosecond Time of Flight on Commercial Wi-Fi Cards

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