Wi-Fi positioning: System considerations and device calibration

Vaupel, Thorsten; Seitz, Jochen; Kiefer, Frederic; Haimerl, Stephan; Thielecke, Jörn

doi:10.1109/ipin.2010.5646207

Cited by 50 publications

(27 citation statements)

References 6 publications

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“…Haeberlen et al [17] proposed the use of linear mapping for transforming signal strength samples from one device to match the ones of another device, however Park et al [18] claim that linear transformation alone does not solve the problem and therefore they combine it with a Kernel estimation using a wide Kernel width to further reduce the positioning error. In [37], Vaupel et al carry out anechoic chamber measurements to obtain the signal strength offsets between different devices and then apply those offsets to calibrate the radiomap according to the device used. This assumes the availability of an anechoic chamber and it limits the applicability of this approach.…”

Section: Device Heterogeneity In Fingerprint Positioningmentioning

confidence: 99%

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: Device Heterogeneity In Fingerprint Positioningmentioning

confidence: 99%

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

Raspopoulos

2018

IEEE Trans. Instrum. Meas.

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“…A common assumption about the RSSI coming from multiple APs is that the signals are distributed as multivariate Gaussians. It has however been reported (di Flora and Hermersdorf, 2008;Vauper et al, 2010) that this is not always the case: the signal can be multimodal, different recording devices can measure quite different distributions at the same location and simple changes in antenna orientation can impact the RSSI by 10dBm (Curran et al, 2011). In their experiments, Mirowski et al (2011) reported that the RSSI of an immobile receptor can be distributed in a bimodal way and that they oscillate between two extreme values distant by as much as 10dB (see Figure 1).…”

Section: Introductionmentioning

confidence: 97%

Probability kernel regression for WiFi localisation

Mirowski¹,

Whiting²,

Steck³

et al. 2012

Journal of Location Based Services

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Various methods have been developed for indoor localisation using WLAN signals. Algorithms that fingerprint the Received Signal Strength Indication (RSSI) of WiFi for different locations can achieve tracking accuracies of the order of a few meters. RSSI fingerprinting suffers though from two main limitations: first, as the signal environment changes, so does the fingerprint database, which requires regular updates; second, it has been reported that, in practice, certain devices record more complex (e.g bimodal) distributions of WiFi signals, precluding algorithms based on the mean RSSI. Mirowski et al. (2011) have recently introduced a simple methodology that takes into account the full distribution for computing similarities among fingerprints using Kullback-Leibler divergence, and then performs localisation through kernel regression. Their algorithm provides a natural way of smoothing over time and motion trajectories and can be applied directly to histograms of WiFi connections to access points, ignoring RSSI distributions, hence removing the need for fingerprint recalibration. It has been shown to outperform nearest neighbours or Kalman and particle filters, achieving up to 1m accuracy in office environments. In this paper, we focus on the relevance of Gaussian or non-Gaussian distributions for modeling RSSI distributions by considering additional probabilistic kernels for comparing Gaussian distributions and by evaluating them on three contrasting datasets. We discuss their limitations and formulate how the KL-divergence kernel regression algorithm bridges the gap with other WiFi localisation algorithms, notably Bayesian networks, SVMs and K nearest neighbours. Finally, we revisit the assumptions on the fingerprint maps and overview practical WiFi localisation software implementation.

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“…For instance Ultra Wide Band (UWB) is broadly used for medical applications because it allows to reach even millimetre accuracy (Mahfouz et al, 2008), while other magnetic-based systems allow to reach decimetre level positioning accuracy (Storms et al, 2010). Distances between object and source could be of 0-15 m. Radio Frequency Identification (RFID) (Zhou and Liu, 2007) or Wi-Fi (Vaupel et al, 2010) can potentially provide high accuracy resolutions too, but that accuracy is highly dependent on the number and the spacing among the installed tags. The same considerations can be extended also to the methods based on fingerprints approaches too.…”

Section: Introductionmentioning

confidence: 99%

Use of Assisted Photogrammetry for Indoor and Outdoor Navigation Purposes

Pagliari

Cazzaniga

Pinto

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Nowadays, devices and applications that require navigation solutions are continuously growing. For instance, consider the increasing demand of mapping information or the development of applications based on users' location. In some case it could be sufficient an approximate solution (e.g. at room level), but in the large amount of cases a better solution is required. The navigation problem has been solved from a long time using Global Navigation Satellite System (GNSS). However, it can be unless in obstructed areas, such as in urban areas or inside buildings. An interesting low cost solution is photogrammetry, assisted using additional information to scale the photogrammetric problem and recovering a solution also in critical situation for imagebased methods (e.g. poor textured surfaces). In this paper, the use of assisted photogrammetry has been tested for both outdoor and indoor scenarios. Outdoor navigation problem has been faced developing a positioning system with Ground Control Points extracted from urban maps as constrain and tie points automatically extracted from the images acquired during the survey. The proposed approach has been tested under different scenarios, recovering the followed trajectory with an accuracy of 0.20 m. For indoor navigation a solution has been thought to integrate the data delivered by Microsoft Kinect, by identifying interesting features on the RGB images and re-projecting them on the point clouds generated from the delivered depth maps. Then, these points have been used to estimate the rotation matrix between subsequent point clouds and, consequently, to recover the trajectory with few centimeters of error.

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Wi-Fi positioning: System considerations and device calibration

Cited by 50 publications

References 6 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

Probability kernel regression for WiFi localisation

Use of Assisted Photogrammetry for Indoor and Outdoor Navigation Purposes

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