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Journal ArticleDOI

WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable Devices

16 Jun 2015-Micromachines (MDPI AG)-Vol. 6, Iss: 6, pp 747-764
TL;DR: The results supported that the WiFi-aided MM algorithm provided more reliable solutions than both WiFi and MM in the areas that have poor WiFi signal distribution or indistinctive magnetic-gradient features.
Abstract: This paper presents a WiFi-aided magnetic matching (MM) algorithm for indoor pedestrian navigation with consumer portable devices. This algorithm reduces both the mismatching rate (i.e., the rate of matching to an incorrect point that is more than 20 m away from the true value) and computational load of MM by using WiFi positioning solutions to limit the MM search space. Walking tests with Samsung Galaxy S3 and S4 smartphones in two different indoor environments (i.e., Environment #1 with abundant WiFi APs and significant magnetic features, and Environment #2 with less WiFi and magnetic information) were conducted to evaluate the proposed algorithm. It was found that WiFi fingerprinting accuracy is related to the signal distributions. MM provided results with small fluctuations but had a significant mismatch rate; when aided by WiFi, MM’s robustness was significantly improved. The outcome of this research indicates that WiFi and MM have complementary characteristics as the former is a point-by-point matching approach and the latter is based on profile-matching. Furthermore, performance improvement through integrating WiFi and MM depends on the environment (e.g., the signal distributions of magnetic intensity and WiFi RSS): In Environment #1 tests, WiFi-aided MM and WiFi provided similar results; in Environment #2 tests, the former was approximately 41.6% better. Our results supported that the WiFi-aided MM algorithm provided more reliable solutions than both WiFi and MM in the areas that have poor WiFi signal distribution or indistinctive magnetic-gradient features.

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Citations
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Journal ArticleDOI
TL;DR: The results shown in this paper demonstrate that researchers and developers should take into account the conclusions arisen in this work in order to improve the accuracy of their systems.
Abstract: We introduce a study in depth of distance/similarity measures for indoor location.Alternative measures provide better results than commonly used Euclidean distance.Choosing an appropriate non-linear representation is crucial for intensity values.Very low intensity values are representative and they should not be filtered.All the experiments are validated with a public database, so they are reproducible. Recent advances in indoor positioning systems led to a business interest in those applications and services where a precise localization is crucial. Wi-Fi fingerprinting based on machine learning and expert systems are commonly used in the literature. They compare a current fingerprint to a database of fingerprints, and then return the most similar one/ones according to: 1) a distance function, 2) a data representation method for received signal strength values, and 3) a thresholding strategy. However, most of the previous works simply use the Euclidean distance with the raw unprocessed data. There is not any previous work that studies which is the best distance function, which is the best way of representing the data and which is the effect of applying thresholding. In this paper, we present a comprehensive study using 51 distance metrics, 4 alternatives to represent the raw data (2 of them proposed by us), a thresholding based on the RSS values and the public UJIIndoorLoc database. The results shown in this paper demonstrate that researchers and developers should take into account the conclusions arisen in this work in order to improve the accuracy of their systems. The IPSs based on k-NN are improved by just selecting the appropriate configuration (mainly distance function and data representation). In the best case, 13-NN with Sorensen distance and the powed data representation, the error in determining the place (building and floor) has been reduced in more than a 50% and the positioning accuracy has been increased in 1.7 m with respect to the 1-NN with Euclidean distance and raw data commonly used in the literature. Moreover, our experiments also demonstrate that thresholding should not be applied in multi-building and multi-floor environments.

212 citations


Cites methods or result from "WiFi-Aided Magnetic Matching for In..."

  • ...Li et al. (2015) combines Wi-Fi fingerprinting (as in (Cheng et al., 2014)) and magnetic matching to enhance the accuracy of the positioning systems. k-NN and Euclidean distance are the basis of many modern IPS, even though RADAR was introduced in 2000 There are some exceptions where several…...

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  • ...…to a relative reduction of 48.21% wrong-building & wrong-floor errors) compared to the traditional approach commonly used in the literature (e.g., Marques et al. (2012); Farshad et al. (2013); Campos et al. (2014); Yu et al. (2014); Zhuang et al. (2014); Li et al. (2015), among many others)....

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  • ...A combination of technologies has also been used (Martı́ & Marı́n, 2011; Baniukevic et al., 2013; Li et al., 2015)....

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Journal ArticleDOI
TL;DR: Two main contributions in this paper are TC fusion of WiFi, INS, and PDR for pedestrian navigation using an extended Kalman filter and better heading estimation using PDR and INS integration to remove the gyro noise that occurs when only vertical gyroscope is used.
Abstract: The need for indoor pedestrian navigators is quickly increasing in various applications over the last few years. However, indoor navigation still faces many challenges and practical issues, such as the need for special hardware designs and complicated infrastructure requirements. This paper originally proposes a pedestrian navigator based on tightly coupled (TC) integration of low-cost microelectromechanical systems (MEMS) sensors and WiFi for handheld devices. Two other approaches are proposed in this paper to enhance the navigation performance: 1) the use of MEMS solution based on pedestrian dead reckoning/inertial navigation system (PDR/INS) integration and 2) the use of motion constraints, such as non-holonomic constraints, zero velocity update, and zero angular rate update for the MEMS solution. There are two main contributions in this paper: 1) TC fusion of WiFi, INS, and PDR for pedestrian navigation using an extended Kalman filter and 2) better heading estimation using PDR and INS integration to remove the gyro noise that occurs when only vertical gyroscope is used. The performance of the proposed navigation algorithms has been extensively verified through field tests in indoor environments. The experiment results showed that the average root mean square position error of the proposed TC integration solution was 3.47 m in three trajectories, which is 0.01% of INS, 10.38% of PDR, 32.11% of the developed MEMS solution, and 64.58% of the loosely coupled integration. The proposed TC integrated navigation system can work well in the environment with sparse deployment of WiFi access points.

122 citations


Cites background from "WiFi-Aided Magnetic Matching for In..."

  • ...On the other hand, the demand for indoor navigation is quickly increasing in various applications including: health care monitoring, logistics, Location Based Services (LBS), emergency services, tourism, and people management [6]–[8]....

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Journal ArticleDOI
TL;DR: This paper proposes a dead-reckoning (DR)/WiFi fingerprinting/magnetic matching (MM) integration structure that uses off-the-shelf sensors in consumer portable devices and existing WiFi infrastructures and reduces the rate of mismatches by over 75.0% when compared with previous DR/WiFi/MM integration structures.

119 citations

Journal ArticleDOI
01 May 2018-Sensors
TL;DR: The proposed pedestrian dead reckoning algorithm based on the strap-down inertial navigation system (SINS) using the gyros, accelerometers, and magnetometers on smartphones achieves better position estimation when a pedestrian holds a smartphone in a swaying hand and step detection is unsuccessful.
Abstract: This paper proposes a pedestrian dead reckoning (PDR) algorithm based on the strap-down inertial navigation system (SINS) using the gyros, accelerometers, and magnetometers on smartphones. In addition to using a gravity vector, magnetic field vector, and quasi-static attitude, this algorithm employs a gait model and motion constraint to provide pseudo-measurements (i.e., three-dimensional velocity and two-dimensional position increment) instead of using only pseudo-velocity measurement for a more robust PDR algorithm. Several walking tests show that the advanced algorithm can maintain good position estimation compare to the existing SINS-based PDR method in the four basic smartphone positions, i.e., handheld, calling near the ear, swaying in the hand, and in a pants pocket. In addition, we analyze the navigation performance difference between the advanced algorithm and the existing gait-model-based PDR algorithm from three aspects, i.e., heading estimation, position estimation, and step detection failure, in the four basic phone positions. Test results show that the proposed algorithm achieves better position estimation when a pedestrian holds a smartphone in a swaying hand and step detection is unsuccessful.

79 citations


Cites background from "WiFi-Aided Magnetic Matching for In..."

  • ...Therefore, indoor localization technology is flourishing, and many different techniques have been designed and developed for tracking pedestrians’ positions when in indoor environments, such as Wi-Fi [2,3], Bluetooth/iBeacon [4,5], radio frequency identification (RFID) [6,7], near-field communication (NFC) [8], ultra-wideband (UWB) [9], magnetic matching [10,11], and inertial-sensor-based [12]....

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  • ...Currently, smartphone-based Wi-Fi indoor localization technology has become the most popular method due to its low cost and the worldwide availability of Wi-Fi access points in the consumer market [13,14]....

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  • ...In addition, some other absolute position measurements or distance measurements (e.g., UWB, vision, Wi-Fi, and BLE) will be used to control the position drift of C-INS....

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  • ...Wi-Fi signals, however, have poor stability in complex indoor environments, and can be blocked by the human body, as is the case with all other radio-frequency-signal-based indoor location methods....

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  • ...Thus, they are widely used to overcome the limitations of Wi-Fi signal fluctuations and blockage [3,15]....

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Journal ArticleDOI
TL;DR: A Wi-Fi and PDR (pedestrian dead reckoning) real-time fusion scheme is proposed in this paper to perform fusing calculation by adaptively determining the dynamic noise of a filtering system according to pedestrian movement, which can effectively restrain the jumping or accumulation phenomena of wireless positioning and the PDR error accumulation problem.
Abstract: Wireless signal strength is susceptible to the phenomena of interference, jumping, and instability, which often appear in the positioning results based on Wi-Fi field strength fingerprint database technology for indoor positioning. Therefore, a Wi-Fi and PDR (pedestrian dead reckoning) real-time fusion scheme is proposed in this paper to perform fusing calculation by adaptively determining the dynamic noise of a filtering system according to pedestrian movement (straight or turning), which can effectively restrain the jumping or accumulation phenomena of wireless positioning and the PDR error accumulation problem. Wi-Fi fingerprint matching typically requires a quite high computational burden: To reduce the computational complexity of this step, the affinity propagation clustering algorithm is adopted to cluster the fingerprint database and integrate the information of the position domain and signal domain of respective points. An experiment performed in a fourth-floor corridor at the School of Environment and Spatial Informatics, China University of Mining and Technology, shows that the traverse points of the clustered positioning system decrease by 65%–80%, which greatly improves the time efficiency. In terms of positioning accuracy, the average error is 4.09 m through the Wi-Fi positioning method. However, the positioning error can be reduced to 2.32 m after integration of the PDR algorithm with the adaptive noise extended Kalman filter (EKF).

70 citations


Cites methods from "WiFi-Aided Magnetic Matching for In..."

  • ...This algorithm reduces both the mismatching rate and computational load of MM by using Wi-Fi positioning solutions to limit the MM search space [17]....

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References
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Proceedings ArticleDOI
26 Mar 2000
TL;DR: RADAR is presented, a radio-frequency (RF)-based system for locating and tracking users inside buildings that combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications.
Abstract: The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy.

8,667 citations


"WiFi-Aided Magnetic Matching for In..." refers background in this paper

  • ...WiFi fingerprinting approaches based on received signal strengths (RSS) have gained a large amount of attention, as they can provide position without any knowledge of the access point (AP) location or signal-propagation model [3]....

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Book
01 Jan 1997
TL;DR: In this paper, the physical principles of inertial navigation, the associated growth of errors and their compensation, and their application in a broad range of applications are discussed, drawing current technological developments and providing an indication of potential future trends.
Abstract: Inertial navigation is widely used for the guidance of aircraft, missiles ships and land vehicles, as well as in a number of novel applications such as surveying underground pipelines in drilling operations. This book discusses the physical principles of inertial navigation, the associated growth of errors and their compensation. It draws current technological developments, provides an indication of potential future trends and covers a broad range of applications. New chapters on MEMS (microelectromechanical systems) technology and inertial system applications are included.

2,536 citations

Book
17 Jan 2005
TL;DR: After the introduction of fast moving vehicles, and later when defensive or hostile weapons came into use, it was not sufficient to know where the platform was located but it was really vital to be aware of its momentary alignment, in a three dimensional space.
Abstract: photographing -not to mention walking in the city -plus those of us engaged with defense activities can state it is more convenient to get lost if one knows where this happ ens. Perhaps this is one of the key reasons why methods and technologies for navigation have been an area of continuing efforts and interest. After the introduction of fast moving vehicles, and later when defensive or hostile weapons came into use, it was not sufficient to know where the platform was located but it was really vital to be aware of its momentary alignment, of course , in a three dimensional space. New challenges were put to the shoulders of the navigator. When time, equipment. and location allow, navigation rel ying on external references such as radio beacons on ground or up in the space orbits are often preferred. However, such cooperative systems may not be available, or their performance is inadequat e for the short time constants of platform motion. We are thus forced to use autonomous navigation modes. It is here that inertial navigation systems have.

657 citations

Journal ArticleDOI
TL;DR: A comprehensive overview of existing indoor navigation systems is provided and the dierent techniques used for locating the user; planning a path; representing the environment; and interacting with the user are analyzed.

283 citations


Additional excerpts

  • ...Wireless positioning technologies have been applied to provide long-term absolute positions [2]....

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Proceedings ArticleDOI
Philipp Bolliger1
19 Sep 2008
TL;DR: Methods to omit the time-consuming training phase and instead incorporate a folksonomy-like approach where the users train the system while using it enable the system to expeditiously adapt to changes in the environment, caused for example by replaced access points.
Abstract: Redpin is a fingerprint-based indoor localization system designed and built to run on mobile phones. The basic principles of our system are based on known systems like Place Lab or Radar. However, with Redpin it is possible to consider the signal-strength of GSM, Bluetooth, and WiFi access points on a mobile phone. Moreover, we devised methods to omit the time-consuming training phase and instead incorporate a folksonomy-like approach where the users train the system while using it. Finally, this approach also enables the system to expeditiously adapt to changes in the environment, caused for example by replaced access points.

276 citations


"WiFi-Aided Magnetic Matching for In..." refers background in this paper

  • ...The point-by-point training can take up to several hours even for a small building [26]....

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Trending Questions (1)
How do I make my WIFI signal stable?

Our results supported that the WiFi-aided MM algorithm provided more reliable solutions than both WiFi and MM in the areas that have poor WiFi signal distribution or indistinctive magnetic-gradient features.