A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors

Yu, Yue; Chen, Ruizhi; Chen, Liang; Guo, Guangyi; Ye, Feng; Liu, Zuoya

doi:10.3390/rs11050504

Cited by 73 publications

(60 citation statements)

References 42 publications

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“…Yu et al [15] developed a complete indoor positioning system based on an FTM ranging model combined with a robust dead reckoning algorithm using an Unscented Kalman filter to fuse the sensors. Their system achieved a positioning error within 2 m. An interesting approach is presented by Choi et al [16], where unsupervised machine learning techniques are applied to trilateration to adaptively calibrate the range measurements.…”

Section: Related Workmentioning

confidence: 99%

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

Bullmann

Fetzer

Ebner

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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Section: Related Workmentioning

confidence: 99%

“…For the probabilistic positioning method, we use a grid search to find the mode of the densities (11) and (15). Analogous to the least-squares estimation, a position estimate is computed every 500 ms. Again, one measurement from at least three APs needs to be available in the current time interval.…”

Section: Positioning Performancementioning

confidence: 99%

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

Bullmann

Fetzer

Ebner

et al. 2020

Sensors

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“…Determining position accurately indoors, where GPS is not reliable, has many potential applications and has been of interest for some time [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] (we use the terms "position" and "location" interchangeable). One of the latest entries in this effort is fine time measurement (FTM) of round trip time (RTT) as specified in the 2016 update of the IEEE 802.11 Wi-Fi standard (also referred to as IEEE 802.11mc) reference [9,10,12,13,16].…”

Section: Overviewmentioning

confidence: 99%

Doubling the Accuracy of Indoor Positioning: Frequency Diversity

Horn¹

2020

Sensors

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Determination of indoor position based on fine time measurement (FTM) of the round trip time (RTT) of a signal between an initiator (smartphone) and a responder (Wi-Fi access point) enables a number of applications. However, the accuracy currently attainable-standard deviations of 1-2 m in distance measurement under favorable circumstances-limits the range of possible applications. An emergency worker, for example, may not be able to unequivocally determine on which floor someone in need of help is in a multi-story building. The error in position depends on several factors, including the bandwidth of the RF signal, delay of the signal due to the high relative permittivity of construction materials, and the geometry-dependent "noise gain" of position determination. Errors in distance measurements have unusal properties that are exposed here. Improvements in accuracy depend on understanding all of these error sources. This paper introduces "frequency diversity," a method for doubling the accuracy of indoor position determination using weighted averages of measurements with uncorrelated errors obtained in different channels. The properties of this method are verified experimentally with a range of responders. Finally, different ways of using the distance measurements to determine indoor position are discussed and the Bayesian grid update method shown to be more useful than others, given the non-Gaussian nature of the measurement errors.

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“…Therefore, related scientific researches begin to increase. Yue et al [37] presented a real-time Wi-Fi ranging model that can effectively eliminate the ranging errors caused by clock deviations, non-line-of-sight (NLOS) and multipath propagation. The unscented Kalman filter (UKF) was finally utilized to fuse the robust dead reckoning and Wi-Fi FTM.…”

Section: Related Workmentioning

confidence: 99%

“…However, there are many factors that can affect the Wi-Fi ranging process, such as the time delay of hardware, nonlight-of-sight (NLOS) error and multipath propagation [37]. Taking all these errors items into account, the traditional ranging model is expressed as follows:…”

Section: B Wifi Ftm Ranging Positioning Using Smartphones 1) Theoretmentioning

confidence: 99%

Indoor Positioning Tightly Coupled Wi-Fi FTM Ranging and PDR Based on the Extended Kalman Filter for Smartphones

Sun

Wang

et al. 2020

IEEE Access

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The smartphone-based Wi-Fi fine time measurement (FTM) technique has provided a new approach for Wi-Fi-based indoor location on mobile phones since the 2018 release of the Google Android Pie system, which supports the IEEE 802.11-2016 protocol and can directly measure the distance between the initiator and the responder. This paper studies in detail the properties of mobile phone Wi-Fi ranging and positioning performance. Considering non-line-of-sight (NLOS) error identification, a real-time ranging error compensation model based on the least-squares (LS) method and an adaptive Wi-Fi FTM positioning algorithm (AWFP) utilizing the weighted least-squares (WLS) method are devised. To improve accuracy, a new tightly coupled fusion positioning algorithm integrating Wi-Fi FTM and built-in mobile phone sensors based on the extended Kalman filter (EKF) is constructed. The experimental results show that the ranging precision and Wi-Fi positioning accuracy are improved. Based on the high-precision Wi-Fi ranging and positioning results, the final location accuracy of the proposed fusion method is 0.98 m, and the root-meansquare error (RMSE) is 1.10 m, which are better than those of the PDR, Wi-Fi FTM and loosely coupled PDR/Wi-Fi FTM integration based on the EKF. INDEX TERMS Indoor positioning, Wi-Fi FTM, pedestrian dead reckoning, tightly coupled, ekf.

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A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors

Cited by 73 publications

References 42 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

Doubling the Accuracy of Indoor Positioning: Frequency Diversity

Indoor Positioning Tightly Coupled Wi-Fi FTM Ranging and PDR Based on the Extended Kalman Filter for Smartphones

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