IMU dataset for motion and device mode classification

Kasebzadeh, Parinaz; Hendeby, Gustaf; Fritsche, Carsten; Gunnarsson, Fredrik; Gustafsson, Fredrik

doi:10.1109/ipin.2017.8115956

Cited by 29 publications

(27 citation statements)

References 15 publications

(15 reference statements)

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“…To foster reproducibility and comparability in indoor positioning research, several databases have recently been made available to the public [10,[16][17][18][19][20]. More specifically, Table 1 presents public databases that we have found available on-line and which can be used to train a WiFi RSS-based IPS.…”

Section: Introductionmentioning

confidence: 99%

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Mendoza-Silva

Richter

Torres-Sospedra

et al. 2018

Data

133

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Abstract:WiFi fingerprinting, one of the most popular methods employed in indoor positioning, currently faces two major problems: lack of robustness to short and long time signal changes and difficult reproducibility of new methods presented in the relevant literature. This paper presents a WiFi RSS (Received Signal Strength) database created to foster and ease research works that address the above-mentioned two problems. A trained professional took several consecutive fingerprints while standing at specific positions and facing specific directions. The consecutive fingerprints may enable the study of short-term signals variations. The data collection spanned over 15 months, and, for each month, one type of training datasets and five types of test datasets were collected. The measurements of a dataset type (training or test) were taken at the same positions and directions every month, in order to enable the analysis of long-term signal variations. The database is provided with supporting materials and software, which give more information about the collection environment and eases the database utilization, respectively. The WiFi measurements and the supporting materials are available at the Zenodo repository under the open-source MIT license.

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Section: Introductionmentioning

confidence: 99%

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Mendoza-Silva

Richter

Torres-Sospedra

et al. 2018

Data

133

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“…These can be used on the device for positioning, but also transmitted to the network. For instance, inertial sensor measurements can be combined with the global positioning system (GPS) for classification of the user's motion modes, see (Kasebzadeh et al, 2017). Fusion of RTT and TDOA measurements is another example of this type.…”

Section: Level 3: Modality Fusion and Temporal Filteringmentioning

confidence: 99%

Time of Flight Estimation for Radio Network Positioning

Radnosrati

2020

Linköping Studies in Science and Technology. Dissertations

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Trilateration is the mathematical theory of computing the intersection of circles. These circles may be obtained by time of flight (ToF) measurements in radio systems, as well as laser, radar and sonar systems. A first purpose of this thesis is to survey recent efforts in the area and their potential for localization. The rest of the thesis then concerns selected problems in new cellular radio standards as well as fundamental challenges caused by propagation delays in the ToF measurements, which cannot travel faster than the speed of light. We denote the measurement uncertainty stemming from propagation delays for positive noise, and develop a general theory with optimal estimators for selected distributions, which can be applied to trilateration but also a much wider class of estimation problems. The first contribution concerns a narrow-band mode in the long-term evolution (LTE) standard intended for internet of things (IoT) devices. This LTE standard includes a special position reference signal sent synchronized by all base stations (BS) to all IoT devices. Each device can then compute several pair-wise time differences that correspond to hyperbolic functions. The simulation-based performance evaluation indicates that decent position accuracy can be achieved despite the narrow bandwidth of the channel. The second contribution is a study of how timing measurements in LTE can be combined. Round trip time (RTT) to the serving BS and time difference of arrival (TDOA) to the neighboring BS are used as measurements. We propose a filtering framework to deal with the existing uncertainty in the solution and evaluate with both simulated and experimental test data. The results indicate that the position accuracy is better than 40 meters 95% of the time. The third contribution is a comprehensive theory of how to estimate the signal observed in positive noise, that is, random variables with positive support. It is well known from the literature that order statistics give one order of magnitude lower estimation variance compared to the best linear unbiased estimator (BLUE). We provide a systematic survey of some common distributions with positive support, and provide derivations and summaries of estimators based on order statistics, including the BLUE one for comparison. An iterative global navigation satellite system (GNSS) localization algorithm, based on the derived estimators, is introduced to jointly estimate the receiver's position and clock bias. The fourth contribution is an extension of the third contribution to a particular approach to utilize positive noise in nonlinear models. That is, order statistics have been employed to derive estimators for a generic nonlinear model with positive noise. The proposed method further enables the estimation of the hyperparameters of the underlying noise distribution. The performance of the proposed estimator is then compared with the maximum likelihood estimator when the underlying noise follows either a uniform or exponential distribution. v The quest for pursuing a doctoral d...

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“…The PDR algorithm has become more and more common in many smartphone based applications because it is an efficient way for tracking the user based on inertial readings [28,29]. Graph optimization, as an alternative to filter based methods, has gained more and more attention over the years, and has been proven to have more accurate results than filter based methods in vision based positioning [30][31][32].…”

Section: Related Workmentioning

confidence: 99%

“…The user can carry the smartphone while at standstill, moving forward and during other irregular movements. This issue has been studied in publications such as [28]. If new steps were detected falsely, the tracking error will increase.…”

Section: Pedestrian Dead Reckoningmentioning

confidence: 99%

An Improved Pedestrian Ttracking Method Based on Wi-Fi Fingerprinting and Pedestrian Dead Reckoning

Bao

Tang

Guo

et al. 2020

Sensors

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Wi-Fi based positioning has great potential for use in indoor environments because Wi-Fi signals are near-ubiquitous in many indoor environments. With a Reference Fingerprint Map (RFM), fingerprint matching can be adopted for positioning. Much assisting information can be adopted for increasing the accuracy of Wi-Fi based positioning. One of the most adopted pieces of assisting information is the Pedestrian Dead Reckoning (PDR) information derived from inertial measurements. This is widely adopted because the inertial measurements can be acquired through a Commercial Off The Shelf (COTS) smartphone. To integrate the information of Wi-Fi fingerprinting and PDR information, many methods have adopted filters, such as Kalman filters and particle filters. A new methodology for integration of Wi-Fi fingerprinting and PDR is proposed using graph optimization in this paper. For the Wi-Fi based fingerprinting part, our method adopts the state-of-art hierarchical structure and the Penalized Logarithmic Gaussian Distance (PLGD) metric. In the integration part, a simple extended Kalman filter (EKF) is first used for integration of Wi-Fi fingerprinting and PDR results. Then, the tracking results are adopted as initial values for the optimization block, where Wi-Fi fingerprinting and PDR results are adopted to form an concentrated cost function (CCF). The CCF can be minimized with the aim of finding the optimal poses of the user with better tracking results. With both real-scenario experiments and simulations, we show that the proposed method performs better than classical Kalman filter based and particle filter based methods with both less average and maximum positioning error. Additionally, the proposed method is more robust to outliers in both Wi-Fi based and PDR based results, which is commonly seen in practical situations.

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IMU dataset for motion and device mode classification

Cited by 29 publications

References 15 publications

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Time of Flight Estimation for Radio Network Positioning

An Improved Pedestrian Ttracking Method Based on Wi-Fi Fingerprinting and Pedestrian Dead Reckoning

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