A Magnetic Ranging-Aided Dead-Reckoning Positioning System for Pedestrian Applications

Pasku, Valter; Angelis, Alessio De; Moschitta, Antonio; Carbone, Paolo; Nilsson, John-Olof; Dwivedi, Satyam; Händel, Peter

doi:10.1109/tim.2017.2649918

Cited by 29 publications

(28 citation statements)

References 23 publications

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“…Obviously, works have reported in literature which achieve fingerprinting accuracy in the sub-meter-range (e.g. UWB [4]- [6], MFI [7], [8]) however they require nonoff-the-shelf and usually expensive equipment.…”

Section: Discussionmentioning

confidence: 99%

“…Such techniques include Ultra-Wideband (UWB) [4]- [6] or Magnetic Field Intensity (MFI) [7], [8] technologies etc. Because of this, fingerprint-based positioning [9]- [11] which became a popular topic of research over the last decades, still remains an active area of research mainly because of its simplicity and its potential to be applicable using off-the-shelf technologies.…”

Section: Introductionmentioning

confidence: 99%

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Multidevice Map-Constrained Fingerprint-Based Indoor Positioning Using 3-D Ray Tracing

Raspopoulos

2018

IEEE Trans. Instrum. Meas.

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Abstract-This paper studies the use of deterministic channel modelling through 3D Ray Tracing (RT) for constructing deviceindependent radiomaps for Wi-Fi RSSI-based fingerprinting indoor positioning, applicable to different devices. Device heterogeneity constitutes a limitation in fingerprint-based approaches and also constructing radiomaps through extensive in-situ measurement campaigns is laborious and time-consuming even with a single device let alone the need for radiomaps constructed using multiple different devices. This work tackles both challenges through the use of 3D RT for radiomap generation in conjunction with data calibration using a small set of device-specific measurements to make the radiomap device-independent. The efficiency of this approach is evaluated using simulations and measurements in terms of the time spent to generate the radiomap, the amount of device-specific data required for calibration and in terms of the achievable positioning accuracy. Potential accuracy improvements in the RT-based indoor positioning processes are further investigated, by studying the use of map constraints into the algorithm in the form of a-priori probabilities. In this approach, a Route Probability Factor (RPF), which reflects the likelihood of a user being in various locations inside the environment is used. The outcome of the evaluation process which includes a study of different RPF distributions, indicates the validity of the approach, demonstrated by a reduction in the positioning error for various devices. The versatility of this approach is also demonstrated for different scenarios, different devices and by considering different device-handling conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multidevice Map-Constrained Fingerprint-Based Indoor Positioning Using 3-D Ray Tracing

Raspopoulos

2018

IEEE Trans. Instrum. Meas.

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“…Furthermore, the irrelevant motion of the pedestrian can degrade the performance of the positioning system. Consequently, to rectify the pedestrian location, many sophisticated and complex DR approaches have been studied [ 20 , 21 ]. Compared to these works, our DR method can locate the pedestrian using only the heading information and the peak value of the acceleration without complex process.…”

Section: Related Workmentioning

confidence: 99%

Indoor Pedestrian Localization Using iBeacon and Improved Kalman Filter

Sung

Lee

Kim

2018

Sensors

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The reliable and accurate indoor pedestrian positioning is one of the biggest challenges for location-based systems and applications. Most pedestrian positioning systems have drift error and large bias due to low-cost inertial sensors and random motions of human being, as well as unpredictable and time-varying radio-frequency (RF) signals used for position determination. To solve this problem, many indoor positioning approaches that integrate the user’s motion estimated by dead reckoning (DR) method and the location data obtained by RSS fingerprinting through Bayesian filter, such as the Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), have recently been proposed to achieve higher positioning accuracy in indoor environments. Among Bayesian filtering methods, PF is the most popular integrating approach and can provide the best localization performance. However, since PF uses a large number of particles for the high performance, it can lead to considerable computational cost. This paper presents an indoor positioning system implemented on a smartphone, which uses simple dead reckoning (DR), RSS fingerprinting using iBeacon and machine learning scheme, and improved KF. The core of the system is the enhanced KF called a sigma-point Kalman particle filter (SKPF), which localize the user leveraging both the unscented transform of UKF and the weighting method of PF. The SKPF algorithm proposed in this study is used to provide the enhanced positioning accuracy by fusing positional data obtained from both DR and fingerprinting with uncertainty. The SKPF algorithm can achieve better positioning accuracy than KF and UKF and comparable performance compared to PF, and it can provide higher computational efficiency compared with PF. iBeacon in our positioning system is used for energy-efficient localization and RSS fingerprinting. We aim to design the localization scheme that can realize the high positioning accuracy, computational efficiency, and energy efficiency through the SKPF and iBeacon indoors. Empirical experiments in real environments show that the use of the SKPF algorithm and iBeacon in our indoor localization scheme can achieve very satisfactory performance in terms of localization accuracy, computational cost, and energy efficiency.

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“…However, the magnetic field suffers from severe interference in indoor environment, resulting in extra measurement errors. In Reference [ 19 ], a magnetic positioning system (MPS) was adopted to aid the dead-reckoning inertial navigation system (DR-INS). The range information provided by the MPS is used as the measurement fed into EKF to correct the position of the DR-INS.…”

Section: Introductionmentioning

confidence: 99%

“…This method can provide bounded position errors of 1–2 m over significantly long periods of time up to 45 min. The shortcoming of the method in Reference [ 19 ] is that the magnetic field generating coils must be installed in advance before positioning can be performed. Moreover, this method might be not suitable for multi floor application due to the assumption that the field-generating coil and the receiving node lie in the same plane.…”

Section: Introductionmentioning

confidence: 99%

Use of Magnetic Field for Mitigating Gyroscope Errors for Indoor Pedestrian Positioning

Song

et al. 2018

Sensors

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In the field of indoor pedestrian positioning, the improved Quasi-Static magnetic Field (iQSF) method has been proposed to estimate gyroscope biases in magnetically perturbed environments. However, this method is only effective when a person walks along straight-line paths. For other curved or more complex path patterns, the iQSF method would fail to detect the quasi-static magnetic field. To address this issue, a novel approach is developed for quasi-static magnetic field detection in foot-mounted Inertial Navigation System. The proposed method detects the quasi-static magnetic field using the rate of change in differences between the magnetically derived heading and the heading derived from gyroscope. In addition, to eliminate the distortions caused by system platforms and shoes, a magnetometer calibration method is developed and the calibration is transformed from three-dimensional to two-dimensional coordinate according to the motion model of a pedestrian. The experimental results demonstrate that the proposed method can provide superior performance in suppressing the heading errors with the comparison to iQSF method.

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A Magnetic Ranging-Aided Dead-Reckoning Positioning System for Pedestrian Applications

Cited by 29 publications

References 23 publications

Multidevice Map-Constrained Fingerprint-Based Indoor Positioning Using 3-D Ray Tracing

Multidevice Map-Constrained Fingerprint-Based Indoor Positioning Using 3-D Ray Tracing

Indoor Pedestrian Localization Using iBeacon and Improved Kalman Filter

Use of Magnetic Field for Mitigating Gyroscope Errors for Indoor Pedestrian Positioning

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