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[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

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Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.

WiFi-SLAM Using G aussian Process Latent Variable Models

This paper provides an overview of the most significant existing methods for indoor positioning on a contemporary smartphone. The approaches include Wi-Fi and Bluetooth based positioning, magnetic field fingerprinting, map aided navigation using building floor plans, and aiding from self-contained sensors. Wi-Fi and Bluetooth based positioning methods considered in this survey are fingerprint approaches that determine a user's position using a database of radio signal strength measurements that were collected earlier at known locations. Magnetic field fingerprinting can be used in an information fusion algorithm to improve positioning. The map-matching algorithms include application of wall constraints, topological indoor maps, and building geometry for heading correction. Finally, methods for step counting, step length and direction estimation, orientation tracking, motion classification, transit mode detection, and floor change detection in multi-storey buildings are discussed.

A Survey of Selected Indoor Positioning Methods for Smartphones

Indoor wireless systems often operate under non-line-of-sight (NLOS) conditions that can cause ranging errors for location-based applications. As such, these applications could benefit greatly from NLOS identification and mitigation techniques. These techniques have been primarily investigated for ultra-wide band (UWB) systems, but little attention has been paid to WiFi systems, which are far more prevalent in practice. In this study, we address the NLOS identification and mitigation problems using multiple received signal strength (RSS) measurements from WiFi signals. Key to our approach is exploiting several statistical features of the RSS time series, which are shown to be particularly effective. We develop and compare two algorithms based on machine learning and a third based on hypothesis testing to separate LOS/NLOS measurements. Extensive experiments in various indoor environments show that our techniques can distinguish between LOS/NLOS conditions with an accuracy of around 95%. Furthermore, the presented techniques improve distance estimation accuracy by 60% as compared to state-of-the-art NLOS mitigation techniques. Finally, improvements in distance estimation accuracy of 50% are achieved even without environment-specific training data, demonstrating the practicality of our approach to real world implementations.

Non-Line-of-Sight Identification and Mitigation Using Received Signal Strength

Indoor tracking and navigation is a fundamental need for pervasive and context-aware smartphone applications. Although indoor maps are becoming increasingly available, there is no practical and reliable indoor map matching solution available at present. We present MapCraft, a novel, robust and responsive technique that is extremely computationally efficient (running in under 10 ms on an Android smartphone), does not require training in different sites, and tracks well even when presented with very noisy sensor data. Key to our approach is expressing the tracking problem as a conditional random field (CRF), a technique which has had great success in areas such as natural language processing, but has yet to be considered for indoor tracking. Unlike directed graphical models like Hidden Markov Models, CRFs capture arbitrary constraints that express how well observations support state transitions, given map constraints. Extensive experiments in multiple sites show how MapCraft outperforms state-of-the art approaches, demonstrating excellent tracking error and accurate reconstruction of tortuous trajectories with zero training effort. As proof of its robustness, we also demonstrate how it is able to accurately track the position of a user from accelerometer and magnetometer measurements only (i.e. gyro- and WiFi-free). We believe that such an energy-efficient approach will enable always-on background localisation, enabling a new era of location-aware applications to be developed.

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Lightweight map matching for indoor localisation using conditional random fields

Bluetooth low energy(BTLE) is an emerging lowpower wireless communication technology which is expected to be of great potential in the next few years. In this paper, we propose several empirical propagation models for BTLE in different conditions: indoor/outdoor, line-of-sight (LOS) / non-line-of-sight (NLOS). Then we compare the propagation characteristics between BTLE and WiFi. It is demonstrated in our experiments that BTLE propagation model can better relate RSSI to range than WiFi, which indicates that BTLE can be more accurate when used in localization scenarios. Extensive experiments in indoor environments have been conducted to explicitly compare the localization accuracy between BTLE and WiFi with nearly identical external environments and conditions. BTLE is proved to be more accurate than WiFi by around 27 percent. We also discuss various underlying reasons why BTLE outperforms WiFi in localization scenarios. We believe that such an accurate and low-cost technique will enable practical and ubiquitous indoor localization.

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Does BTLE measure up against WiFi? A comparison of indoor location performance

We address the problem of localization in vehicular ad hoc networks. Our goal is to leverage vehicle communications and smartphone sensors to improve the overall localization performance. Assuming vehicles are equipped with the IEEE 802.11p wireless interfaces, we employ a two-stage Bayesian filter to track the vehicle’s position: an unscented Kalman filter for heading estimation using smartphone inertial sensors, and a particle filter that fuses vehicle-to-vehicle signal strength measurements received from mobile anchors whose positions are uncertain, with velocity, GPS position, and map information. Our model leads to a robust localization system and is able to provide useful position information even in the absence of GPS data. We evaluate the algorithm performance using real-world measurements collected from four communicating vehicles in an urban scenario, and considering different combinations of location information sources.

/pdf/neighbor-aided-localization-in-vehicular-networks-2qc24l1tx7.pdf

Neighbor-Aided Localization in Vehicular Networks

Pedestrian dead reckoning, especially on smart-phones, is likely to play an increasingly important role in indoor tracking and navigation, due to its low cost and ability to work without any additional infrastructure. A challenge however, is that positioning, both in terms of step detection and heading estimation, must be accurate and reliable, even when the use of the device is so varied in terms of placement (e.g. handheld or in a pocket) or orientation (e.g holding the device in either portrait or landscape mode). Furthermore, the placement can vary over time as a user performs different tasks, such as making a call or carrying the device in a bag. A second challenge is to be able to distinguish between a true step and other periodic motion such as swinging an arm or tapping when the placement and orientation of the device is unknown. If this is not done correctly, then the PDR system typically overestimates the number of steps taken, leading to a significant long term error. We present a fresh approach, robust PDR (R-PDR), based on exploiting how bipedal motion impacts acquired sensor waveforms. Rather than attempting to recognize different placements through sensor data, we instead simply determine whether the motion of one or both legs impact the measurements. In addition, we formulate a set of techniques to accurately estimate the device orientation, which allows us to very accurately (typically over 99%) reject false positives. We demonstrate that regardless of device placement, we are able to detect the number of steps taken with >99.4% accuracy. R-PDR thus addresses the two main limitations facing existing PDR techniques.

Zhuoling Xiao

Papers

Non-Line-of-Sight Identification and Mitigation Using Received Signal Strength

Lightweight map matching for indoor localisation using conditional random fields

Does BTLE measure up against WiFi? A comparison of indoor location performance

Neighbor-Aided Localization in Vehicular Networks

Robust pedestrian dead reckoning (R-PDR) for arbitrary mobile device placement