PiLoT: A Precise IMU Based Localization Technique for Smart Phone Users

Chattha, Muhammad Ali; Naqvi, Ijaz Haider

doi:10.1109/vtcfall.2016.7881166

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…These methods are usually based on accelerometer signals and the data is often pre-processed by using a low-pass filter to reduce noise. Approaches in the frequency domain have also been explored and include a Short-Time Fourier Transform (STFT) [24] or a continuous/discrete wavelet transforms (CWT/DWT) [41].…”

Section: B Algorithmsmentioning

confidence: 99%

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Pedestrian Dead Reckoning With Wearable Sensors: A Systematic Review

Hou

Bergmann

2021

IEEE Sensors J.

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Pedestrian Dead Reckoning (PDR) is the process of calculating one's current location by using the previously known position, and advancing that position over time using established or estimated speeds and trajectories (or alternatively stride lengths and directions). PDR plays an important role in modern life, including tracking locations of people and objects whenever GPS is not available. Self-contained PDR systems do not require an infrastructure, thus they can be used for rapid deployment in situations such as search and rescue, disaster relief or medical emergencies. Wearable sensors are often applied in self-contained PDR, but implementation varies in terms of the number, type and location of sensors used. Many algorithms are designed for PDR in order to reduce the error or drift of the final estimate, with various levels of success. There is a lack of comparison between these different methods and this systematic review of PDR for wearable devices provides a comprehensive overview that can inform further design optimizations. The aim of this paper is to assess the quality of all available PDR literature with a focus on wearable sensors. It provides an outline of the state-of-the-art in the field by comparing the accuracy of different sensor layouts and algorithms. Further directions of research are suggested based on these results. This study also highlights the need for more standardised and robust assessment protocols to capture real-world tracking performance of PDR methods.

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Section: B Algorithmsmentioning

confidence: 99%

“…6) Data fusion: Filtering algorithms that integrate multiple observations are important in PDR systems with multiple sensors. The most commonly used algorithm among the presented studies is the Kalman Filter (KF) [41]. This is a recursive Bayesian filter, known to be an optimal filter for Gaussian linear systems.…”

Section: B Algorithmsmentioning

confidence: 99%

Pedestrian Dead Reckoning With Wearable Sensors: A Systematic Review

Hou

Bergmann

2021

IEEE Sensors J.

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“…Chattha and Naqvi [29] combined gyroscope and magnetometer sensors to propose a pedestrian dead reckoning algorithm based on smartphones. Because they all use sensors in smartphones to achieve positioning, and the data of built-in direction sensors of smartphone were prone to deviations in direction, and the PDR algorithm was prone to problems such as drift errors, some research teams have used special indoor areas (e.g., corner, etc) were set as landmarks, but the positioning accuracy in real scene was generally only room level (about 5 meters).…”

Section: Related Workmentioning

confidence: 99%

The Technology of Crowd-Sourcing Landmarks-Assisted Smartphone in Indoor Localization

et al. 2020

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In recent years, with the popularity of smartphones, mobile sensor technologies have been widely used in indoor localization. Its advantage is that data can be collected directly from smartphones without installing any other equipment. Meanwhile, it has the disadvantages of high requirements on positioning conditions and large deviations in positioning results. A technology of crowd-sourcing landmarks-assisted smartphone in indoor localization (CLS-IL) is proposed in this paper so as to solve these disadvantages. Firstly, the accelerometer, magnetometer and gyroscope sensors are used to acquire initial positioning of users. The characteristics of magnetometer and gyroscope sensors are analyzed through a series of experiments in allusion to the key problem of heading estimation errors, then corresponding data is matched for different experimental conditions. After that, the corresponding landmark database is established for experimental environment and an adaptive optimization algorithm of landmarks is proposed to correct drift errors caused by the readings of sensors influenced by surrounding environment and long positioning time excessively during traditional pedestrian dead reckoning. Experimental results show that the location accuracy of CLS-IL system is significantly improved compared with traditional technologies, which provides strong support for subsequent studies.

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