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Special Issue "Sensors and Sensing Technologies for Indoor Positioning and Indoor Navigation"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 May 2020).

Special Issue Editors

Dr. Francesco Potortì
Website
Guest Editor
Institute of Information Science and Technologies (ISTI), National Research Council (CNR), Pisa, Italy
Interests: communications protocols; communications systems; RSS-based indoor localization; evaluation of indoor localization systems
Dr. Filippo Palumbo
Website SciProfiles
Guest Editor
Institute of Information Science and Technologies (ISTI), National Research Council (CNR), Pisa, Italy
Interests: pervasive computing; ambient intelligence; ambient assisted living; indoor localization; pattern recognition
Special Issues and Collections in MDPI journals
Dr. Antonino Crivello
Website
Guest Editor
Institute of Information Science and Technologies (ISTI), National Research Council (CNR), Pisa, Italy
Interests: cyber-physical systems; ambient intelligence; ambient assisted living; indoor localization and positioning; machine learning

Special Issue Information

Dear Colleagues,

The last ten years have seen enormous technical progress in the field of indoor positioning and indoor navigation; yet, in contrast with outdoor well-established GNSS solutions, no technology exists that is cheap and accurate enough for the general market. The potential applications of indoor localization are all-encompassing, from home to wide public areas, from IoT and personal devices to surveillance and crowd behavior applications, and from casual use to mission-critical systems.

This Special Issue encourages authors, from academia and industry, to submit new research results about innovations for indoor positioning and navigation. The Special Issue topics include but are not limited to:

  • Location-based services and applications;
  • Benchmarking, assessment, evaluation, standards;
  • User requirements;
  • UI, indoor maps, and 3D building models;
  • Human motion monitoring and modeling;
  • Robotics and UAV;
  • Indoor navigation and tracking methods;
  • Self-contained sensors;
  • Wearable and multisensor systems;
  • Privacy and security for ILS.

Dr. Francesco Potortì
Dr. Filippo Palumbo
Dr. Antonino Crivello
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (24 papers)

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Open AccessArticle
STEPS: An Indoor Navigation Framework for Mobile Devices
Sensors 2020, 20(14), 3929; https://doi.org/10.3390/s20143929 - 15 Jul 2020
Abstract
This paper presents a vision-based navigation system designed for indoor localization. The suggested framework works as a standalone 3 D positioning system by fusing a sophisticated optical-flow pedometry with map constrains using an advanced particle filter. The presented method requires no personal calibration [...] Read more.
This paper presents a vision-based navigation system designed for indoor localization. The suggested framework works as a standalone 3 D positioning system by fusing a sophisticated optical-flow pedometry with map constrains using an advanced particle filter. The presented method requires no personal calibration and works on standard smartphones with relatively low energy consumption. Field experiments on Android smartphones show that the expected 3 D error is about 1–2 m in most real-life scenarios. Full article
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Open AccessArticle
SVM+KF Target Tracking Strategy Using the Signal Strength in Wireless Sensor Networks
Sensors 2020, 20(14), 3832; https://doi.org/10.3390/s20143832 - 09 Jul 2020
Abstract
Target Tracking (TT) is a fundamental application of wireless sensor networks. TT based on received signal strength indication (RSSI) is by far the cheapest and simplest approach, but suffers from a low stability and precision owing to multiple paths, occlusions, and decalibration effects. [...] Read more.
Target Tracking (TT) is a fundamental application of wireless sensor networks. TT based on received signal strength indication (RSSI) is by far the cheapest and simplest approach, but suffers from a low stability and precision owing to multiple paths, occlusions, and decalibration effects. To address this problem, we propose an innovative TT algorithm, known as the SVM+KF method, which combines the support vector machine (SVM) and an improved Kalman filter (KF). We first use the SVM to obtain an initial estimate of the target’s position based on the RSSI. This enhances the ability of our algorithm to process nonlinear data. We then apply an improved KF to modify this estimated position. Our improved KF adds the threshold value of the innovation update in the traditional KF. This value changes dynamically according to the target speed and network parameters to ensure the stability of the results. Simulations and real experiments in different scenarios demonstrate that our algorithm provides a superior tracking accuracy and stability compared to similar algorithms. Full article
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Open AccessArticle
RNN-Aided Human Velocity Estimation from a Single IMU
Sensors 2020, 20(13), 3656; https://doi.org/10.3390/s20133656 - 29 Jun 2020
Abstract
Pedestrian Dead Reckoning (PDR) uses inertial measurement units (IMUs) and combines velocity and orientation estimates to determine a position. The estimation of the velocity is still challenging, as the integration of noisy acceleration and angular speed signals over a long period of time [...] Read more.
Pedestrian Dead Reckoning (PDR) uses inertial measurement units (IMUs) and combines velocity and orientation estimates to determine a position. The estimation of the velocity is still challenging, as the integration of noisy acceleration and angular speed signals over a long period of time causes large drifts. Classic approaches to estimate the velocity optimize for specific applications, sensor positions, and types of movement and require extensive parameter tuning. Our novel hybrid filter combines a convolutional neural network (CNN) and a bidirectional recurrent neural network (BLSTM) (that extract spatial features from the sensor signals and track their temporal relationships) with a linear Kalman filter (LKF) that improves the velocity estimates. Our experiments show the robustness against different movement states and changes in orientation, even in highly dynamic situations. We compare the new architecture with conventional, machine, and deep learning methods and show that from a single non-calibrated IMU, our novel architecture outperforms the state-of-the-art in terms of velocity (≤0.16 m/s) and traveled distance (≤3 m/km). It also generalizes well to different and varying movement speeds and provides accurate and precise velocity estimates. Full article
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Open AccessArticle
Characterization of an Ultrasonic Local Positioning System for 3D Measurements
Sensors 2020, 20(10), 2794; https://doi.org/10.3390/s20102794 - 14 May 2020
Cited by 1
Abstract
Indoor location and positioning systems (ILPS) are used to locate and track people, as well as mobile and/or connected targets, such as robots or smartphones, not only inside buildings with a lack of global navigation satellite systems (GNSS) signals but also in constrained [...] Read more.
Indoor location and positioning systems (ILPS) are used to locate and track people, as well as mobile and/or connected targets, such as robots or smartphones, not only inside buildings with a lack of global navigation satellite systems (GNSS) signals but also in constrained outdoor situations with reduced coverage. Indoor positioning applications and their interest are growing in certain environments, such as commercial centers, airports, hospitals or factories. Several sensory technologies have already been applied to indoor positioning systems, where ultrasounds are a common solution due to its low cost and simplicity. This work proposes a 3D ultrasonic local positioning system (ULPS), based on a set of three asynchronous ultrasonic beacon units, capable of transmitting coded signals independently, and on a 3D mobile receiver prototype. The proposal is based on the aforementioned beacon unit, which consists of five ultrasonic transmitters oriented towards the same coverage area and has already been proven in 2D positioning by applying hyperbolic trilateration. Since there are three beacon units available, the final position is obtained by merging the partial results from each unit, implementing a minimum likelihood estimation (MLE) fusion algorithm. The approach has been characterized, and experimentally verified, trying to maximize the coverage zone, at least for typical sizes in most common public rooms and halls. The proposal has achieved a positioning accuracy below decimeters for 90% of the cases in the zone where the three ultrasonic beacon units are available, whereas these accuracies can degrade above decimeters according to whether the coverage from one or more beacon units is missing. The experimental workspace covers a large volume, where tests have been carried out at points placed in two different horizontal planes. Full article
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Open AccessArticle
Robust VLC Beacon Identification for Indoor Camera-Based Localization Systems
Sensors 2020, 20(9), 2522; https://doi.org/10.3390/s20092522 - 29 Apr 2020
Cited by 1
Abstract
Several industrial indoor positioning systems utilize LEDs as beacons and cameras as sensors: The LED beacons transmit their identity, using various means of visible light communication (VLC) techniques. To avoid flickering effects, the transmission frequency is usually much higher than the sampling frequency [...] Read more.
Several industrial indoor positioning systems utilize LEDs as beacons and cameras as sensors: The LED beacons transmit their identity, using various means of visible light communication (VLC) techniques. To avoid flickering effects, the transmission frequency is usually much higher than the sampling frequency of ordinary cameras, thus undersampling occurs. In this paper, a potential problem of undersampled protocols is highlighted: If the transmitter and receiver are not synchronized, the frequency slip between the transmitter and receiver clocks will periodically cause a burst of potential decoding errors. If the frequency slip is small (i.e., good-quality clocks are used in both the transmitter and the receiver), the time between bursts of errors is longer but at the same time the length of the bursts are also longer. An error analysis is provided as a function of protocol parameters and various error sources. Based on the results the robust-undersampled phase-shift on-off keying (UPSOOK) protocol is introduced, which guarantees the correct operation even in the presence of clock inaccuracies, as well as other error sources such as sensor noise, jitter, camera saturation, without the utilization of any error correcting codes. The properties of the proposed robust-UPSOOK protocol are demonstrated using simulations and measurements. Full article
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Open AccessArticle
State Observability through Prior Knowledge: Analysis of the Height Map Prior for Track Cycling
Sensors 2020, 20(9), 2438; https://doi.org/10.3390/s20092438 - 25 Apr 2020
Abstract
Inertial navigation systems suffer from unbounded errors in the position and orientation estimates. This drift can be corrected by applying prior knowledge, instead of using exteroceptive sensors. We want to show that the use of prior knowledge can yield full observability of the [...] Read more.
Inertial navigation systems suffer from unbounded errors in the position and orientation estimates. This drift can be corrected by applying prior knowledge, instead of using exteroceptive sensors. We want to show that the use of prior knowledge can yield full observability of the position and orientation. A previous study showed that track cyclers can be tracked drift-free with an IMU as the only sensor and the knowledge that the bike drives on the track. In this paper, we analyze the observability of the pose in the experiment we conducted. Furthermore, we improve the pose estimation of the previous study. The observability is analyzed by testing the weak observability criterion with a Jacobian rank test. The improved estimator is presented and evaluated on a dataset with three 60-round trials (10 km each). The average RMS is 1.08 m and the estimate is drift-free. The observability analysis reveals that the system can gain complete observability in the curves and observability of the orientation on the straight parts of the race track. Full article
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Open AccessArticle
Communication Aspects of Visible Light Positioning (VLP) Systems Using a Quadrature Angular Diversity Aperture (QADA) Receiver
Sensors 2020, 20(7), 1977; https://doi.org/10.3390/s20071977 - 01 Apr 2020
Abstract
Visible light positioning (VLP) is a promising indoor localization system in which light emitting diode (LED) luminaires are used as positioning beacons. Data communication is an essential aspect of any VLP system, as each luminaire must transmit information about its own location to [...] Read more.
Visible light positioning (VLP) is a promising indoor localization system in which light emitting diode (LED) luminaires are used as positioning beacons. Data communication is an essential aspect of any VLP system, as each luminaire must transmit information about its own location to the receiver. The quadrature angular diversity aperture (QADA) is a new receiver designed specifically for VLP systems using angle-of-arrival estimation. Previous QADA research has focused only on positioning and assumed error-free communication. In this paper, we investigate, via simulations and experiment, the actual communication characteristics of a VLP system that uses a QADA receiver. We calculate the signal-to-noise ratio and bit-error-rates for a range of scenarios and demonstrate the impact of the dimensions of the receiver. We show that reliable communication is assured in typical operating scenarios, proving that communication will not be a limiting factor when using QADA in VLP systems. Full article
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Open AccessArticle
Analysis of Multiple-Access Discrimination Techniques for the Development of a PSD-Based VLP System
Sensors 2020, 20(6), 1717; https://doi.org/10.3390/s20061717 - 19 Mar 2020
Cited by 2
Abstract
There are several technologies and techniques available when developing indoor positioning systems (IPS). Recently, the development of positioning systems based on optical signals has aroused great interest, mainly those using visible light from the lighting infrastructure. In this work, we analyze which techniques [...] Read more.
There are several technologies and techniques available when developing indoor positioning systems (IPS). Recently, the development of positioning systems based on optical signals has aroused great interest, mainly those using visible light from the lighting infrastructure. In this work, we analyze which techniques give better results to lay the foundations for the development of a Visible Light Positioning system (VLP). Working only with a receiver, it is analyzed what the result of determining the position of different emitters is when they emit simultaneously and without any synchronism. The results obtained by Frequency Division Multiple Access (FDMA) (with digital bandpass filters, I/Q demodulation, and FFT) and Code Division Multiple Access (CDMA) are compared. The interference between signals when emitted simultaneously from multiple emitters is analyzed as well as the errors they cause and how these effects can be mitigated. As a result of the research, the advantages and disadvantages using different multiple-access determination techniques are determined. In addition, advantages and disadvantages of using FDMA and CDMA techniques as well as hardware requirements that make one more feasible than the other are presented. The system behavior, in terms of errors, is established using FDMA and different configurations such as: I/Q, RMS, or FFT. The work also determines the error rates that can be obtained with the different FDMA and CDMA configurations, considering different error scenarios and integration time. Synthetic emulations and empirical tests were performed, which concluded that IPS systems based on optical signals and PSD sensors can achieve very high measurement accuracies and a high measurement rate. Obtained positioning errors in a room of 3 m height are less than 1 cm when working in noisy environments. Full article
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Open AccessArticle
Doubling the Accuracy of Indoor Positioning: Frequency Diversity
Sensors 2020, 20(5), 1489; https://doi.org/10.3390/s20051489 - 09 Mar 2020
Cited by 1
Abstract
Determination of indoor position based on fine time measurement (FTM) of the round trip time (RTT) of a signal between an initiator (smartphone) and a responder (Wi-Fi access point) enables a number of applications. However, the accuracy currently attainable—standard deviations of 1–2 m [...] Read more.
Determination of indoor position based on fine time measurement (FTM) of the round trip time (RTT) of a signal between an initiator (smartphone) and a responder (Wi-Fi access point) enables a number of applications. However, the accuracy currently attainable—standard deviations of 1–2 m in distance measurement under favorable circumstances—limits the range of possible applications. An emergency worker, for example, may not be able to unequivocally determine on which floor someone in need of help is in a multi-story building. The error in position depends on several factors, including the bandwidth of the RF signal, delay of the signal due to the high relative permittivity of construction materials, and the geometry-dependent “noise gain” of position determination. Errors in distance measurements have unusal properties that are exposed here. Improvements in accuracy depend on understanding all of these error sources. This paper introduces “frequency diversity,” a method for doubling the accuracy of indoor position determination using weighted averages of measurements with uncorrelated errors obtained in different channels. The properties of this method are verified experimentally with a range of responders. Finally, different ways of using the distance measurements to determine indoor position are discussed and the Bayesian grid update method shown to be more useful than others, given the non-Gaussian nature of the measurement errors. Full article
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Open AccessArticle
Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence
Sensors 2020, 20(5), 1350; https://doi.org/10.3390/s20051350 - 29 Feb 2020
Cited by 3
Abstract
One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human [...] Read more.
One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human body shadowing among other effects. In order to mitigate these effects, the paper proposes and implements a dynamic Artificial Intelligence (AI) model that uses the three different BLE advertising channels to detect human body shadowing and compensate the RSSI values accordingly. An experiment in an indoor office environment is conducted. 70% of the observations are randomly selected and used for training and the remaining 30% are used to evaluate the algorithm. The results show that the AI model can properly detect and significantly compensate RSSI values for a dynamic blockage caused by a human body. This can significantly improve the RSSI-based ranges and the corresponding positioning accuracies. Full article
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Open AccessArticle
A Novel Position and Orientation Sensor for Indoor Navigation Based on Linear CCDs
Sensors 2020, 20(3), 748; https://doi.org/10.3390/s20030748 - 29 Jan 2020
Abstract
The position and orientation of a mobile agent, such as robot or drone, etc., should be estimated in a timely way during operation in the structured indoor environment, so as to ensure the security and efficiency of task execution. Concerning the problem that [...] Read more.
The position and orientation of a mobile agent, such as robot or drone, etc., should be estimated in a timely way during operation in the structured indoor environment, so as to ensure the security and efficiency of task execution. Concerning the problem that the position and orientation are often estimated separately by different kinds of sensors in the off-the-shelf methods, we design a novel position orientation sensor (POS). The POS consists of four pairs of linear charge-coupled devices (CCDs) and cylindrical lenses, which can estimate the 3D coordinate of the anchor in the POS’s field of view. After detecting at least three anchors in its field of vision sequentially, the Rodrigues coordinate transformation algorithm is utilized to estimate the position and orientation of POS simultaneously. Meanwhile, the position and orientation are estimated at the receiver side. Hence there is no privacy concern associated with this system. The architecture of the proposed POS is symmetrical and redundant, even if one of the linear CCDs or cylindrical lens malfunctions, the whole system could still work normally. The proposed method is cost-effective and easily extends to a wide range. The numerical simulation demonstrates the feasibility and high accuracy of the proposed method, and it outperforms the off-the-shelf methods. Full article
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Open AccessArticle
Collaborative Smartphone-Based User Positioning in a Multiple-User Context Using Wireless Technologies
Sensors 2020, 20(2), 405; https://doi.org/10.3390/s20020405 - 10 Jan 2020
Abstract
For the localization of multiple users, Bluetooth data from the smartphone is able to complement Wi-Fi-based methods with additional information, by providing an approximation of the relative distances between users. In practice, both positions provided by Wi-Fi data and relative distance provided by [...] Read more.
For the localization of multiple users, Bluetooth data from the smartphone is able to complement Wi-Fi-based methods with additional information, by providing an approximation of the relative distances between users. In practice, both positions provided by Wi-Fi data and relative distance provided by Bluetooth data are subject to a certain degree of noise due to the uncertainty of radio propagation in complex indoor environments. In this study, we propose and evaluate two approaches, namely Non-temporal and Temporal ones, of collaborative positioning to combine these two cohabiting technologies to improve the tracking performance. In the Non-temporal approach, our model establishes an error observation function in a specific interval of the Bluetooth and Wi-Fi output. It is then able to reduce the positioning error by looking for ways to minimize the error function. The Temporal approach employs an extended error model that takes into account the time component between users’ movements. For performance evaluation, several multi-user scenarios in an indoor environment are set up. Results show that for certain scenarios, the proposed approaches attain over 40% of improvement in terms of average accuracy. Full article
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Open AccessArticle
Hybrid Wireless Fingerprint Indoor Localization Method Based on a Convolutional Neural Network
Sensors 2019, 19(20), 4597; https://doi.org/10.3390/s19204597 - 22 Oct 2019
Cited by 2
Abstract
In the indoor location field, the quality of received-signal-strength-indicator (RSSI) fingerprints plays a key role in the performance of indoor location services. However, changes in an indoor environment may lead to the decline of location accuracy. This paper presents a localization method employing [...] Read more.
In the indoor location field, the quality of received-signal-strength-indicator (RSSI) fingerprints plays a key role in the performance of indoor location services. However, changes in an indoor environment may lead to the decline of location accuracy. This paper presents a localization method employing a Hybrid Wireless fingerprint (HW-fingerprint) based on a convolutional neural network (CNN). In the proposed scheme, the Ratio fingerprint was constructed by calculating the ratio of different RSSIs from important contribution access points (APs). The HW-fingerprint combined the Ratio fingerprint and the RSSI to enhance the expression of indoor environment characteristics. Moreover, a CNN architecture was constructed to learn important features from the complex HW-fingerprint for indoor locations. In the experiment, the HW-fingerprint was tested in an actual indoor scene for 15 days. Results showed that the average daily location accuracy of the K-Nearest Neighbor (KNN), Support Vector Machines (SVMs), and CNN was improved by 3.39%, 8.03% and 9.03%, respectively, when using the HW-fingerprint. In addition, the deep-learning method was 4.19% and 16.37% higher than SVM and KNN in average daily location accuracy, respectively. Full article
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Open AccessArticle
Accurate and Robust Monocular SLAM with Omnidirectional Cameras
Sensors 2019, 19(20), 4494; https://doi.org/10.3390/s19204494 - 16 Oct 2019
Cited by 1
Abstract
Simultaneous localization and mapping (SLAM) are fundamental elements for many emerging technologies, such as autonomous driving and augmented reality. For this paper, to get more information, we developed an improved monocular visual SLAM system by using omnidirectional cameras. Our method extends the ORB-SLAM [...] Read more.
Simultaneous localization and mapping (SLAM) are fundamental elements for many emerging technologies, such as autonomous driving and augmented reality. For this paper, to get more information, we developed an improved monocular visual SLAM system by using omnidirectional cameras. Our method extends the ORB-SLAM framework with the enhanced unified camera model as a projection function, which can be applied to catadioptric systems and wide-angle fisheye cameras with 195 degrees field-of-view. The proposed system can use the full area of the images even with strong distortion. For omnidirectional cameras, a map initialization method is proposed. We analytically derive the Jacobian matrices of the reprojection errors with respect to the camera pose and 3D position of points. The proposed SLAM has been extensively tested in real-world datasets. The results show positioning error is less than 0.1% in a small indoor environment and is less than 1.5% in a large environment. The results demonstrate that our method is real-time, and increases its accuracy and robustness over the normal systems based on the pinhole model. Full article
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Open AccessArticle
Novel Drift Reduction Methods in Foot-Mounted PDR System
Sensors 2019, 19(18), 3962; https://doi.org/10.3390/s19183962 - 13 Sep 2019
Cited by 2
Abstract
The zero-velocity update (ZUPT)-aided extended Kalman filter (EKF) is commonly used in the traditional inertial navigation system (INS)-based foot-mounted pedestrian dead reckoning (PDR) system, which can effectively suppress the error growth of the inertial-based pedestrian navigation systems. However, in the realistic test, the [...] Read more.
The zero-velocity update (ZUPT)-aided extended Kalman filter (EKF) is commonly used in the traditional inertial navigation system (INS)-based foot-mounted pedestrian dead reckoning (PDR) system, which can effectively suppress the error growth of the inertial-based pedestrian navigation systems. However, in the realistic test, the system still often suffers from drift, which is commonly caused by two reasons: failed detection of the stationary phase in the dynamic pedestrian gait and heading drift, which is a poorly observable variable of the ZUPT method. In this paper, firstly, in order to improve the initial heading alignment accuracy, a novel method to calibrate the PDR system’s initial absolute heading is proposed which is based on the geometric method. By using a calibration line rather than only using the heading of the starting point, the method can calibrate the initial heading of the PDR system more accurately. Secondly, for the problem of failed detection of the stationary phase in the dynamic pedestrian gait, a novel stationary phase detection method is proposed, which is based on foot motion periodicity rather than the threshold comparison principle in the traditional method. In an experiment, we found that the zero-speed state points always occur around the minimum value of the stationary detector in each gait cycle. By taking the minimum value in each gait cycle as the zero-speed state point, it can effectively reduce the failed detection of the zero-speed interval. At last, in order to reduce the heading drifts during walking over time, a new motion constraint method is exploited based on the range constraint principle. During pedestrian walking, the distance between the foot position estimates of the current moment and the previous stationary period is within the maximum stride length. Once the distance is greater than the maximum stride length, the constraint method is used to confine the current estimated foot position to the sphere of the maximum stride length relative to the previous stationary foot position. Finally, the effectiveness of all proposed methods is verified by the experiments. Full article
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Open AccessArticle
A Calibration-Free Method Based on Grey Relational Analysis for Heterogeneous Smartphones in Fingerprint-Based Indoor Positioning
Sensors 2019, 19(18), 3885; https://doi.org/10.3390/s19183885 - 09 Sep 2019
Abstract
The fingerprint method has been widely adopted in Wi-Fi indoor positioning because of its advantage in non-line-of-sight channels between access points (APs) and mobile users. However, the received signal strength (RSS) during the fingerprint positioning process generally varies due to the dissimilar hardware [...] Read more.
The fingerprint method has been widely adopted in Wi-Fi indoor positioning because of its advantage in non-line-of-sight channels between access points (APs) and mobile users. However, the received signal strength (RSS) during the fingerprint positioning process generally varies due to the dissimilar hardware configurations of heterogeneous smartphones. This difference may degrade the accuracy of fingerprint matching between fingerprint and test data. Thus, this paper puts forward a fingerprint method based on grey relational analysis (GRA) to approach the challenge of heterogeneous smartphones and to improve positioning accuracy. Initially, the grey relational coefficient (GRC) between the RSS comparability sequence of each reference point (RP) and the RSS reference sequence of the test point (TP) is calculated. Subsequently, the grey relational degree (GRD) between each RP and TP is determined on the basis of GRC, and the K most relational RPs are selected in accordance with the value of GRD. Finally, the user location is determined by weighting the K most relational RPs that correspond to the coordinates. The main advantage of this GRA method is that it does not require device calibration when handling heterogeneous smartphone problems. We further carry out extensive experiments using heterogeneous Android smartphones in an office environment to verify the positioning performance of the proposed method. Experimental results indicate that the proposed method outperforms the existing ones no matter whether heterogeneous smartphones are used. Full article
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Open AccessArticle
Time-Series Laplacian Semi-Supervised Learning for Indoor Localization
Sensors 2019, 19(18), 3867; https://doi.org/10.3390/s19183867 - 07 Sep 2019
Abstract
Machine learning-based indoor localization used to suffer from the collection, construction, and maintenance of labeled training databases for practical implementation. Semi-supervised learning methods have been developed as efficient indoor localization methods to reduce use of labeled training data. To boost the efficiency and [...] Read more.
Machine learning-based indoor localization used to suffer from the collection, construction, and maintenance of labeled training databases for practical implementation. Semi-supervised learning methods have been developed as efficient indoor localization methods to reduce use of labeled training data. To boost the efficiency and the accuracy of indoor localization, this paper proposes a new time-series semi-supervised learning algorithm. The key aspect of the developed method, which distinguishes it from conventional semi-supervised algorithms, is the use of unlabeled data. The learning algorithm finds spatio-temporal relationships in the unlabeled data, and pseudolabels are generated to compensate for the lack of labeled training data. In the next step, another balancing-optimization learning algorithm learns a positioning model. The proposed method is evaluated for estimating the location of a smartphone user by using a Wi-Fi received signal strength indicator (RSSI) measurement. The experimental results show that the developed learning algorithm outperforms some existing semi-supervised algorithms according to the variation of the number of training data and access points. Also, the proposed method is discussed in terms of why it gives better performance, by the analysis of the impact of the learning parameters. Moreover, the extended localization scheme in conjunction with a particle filter is executed to include additional information, such as a floor plan. Full article
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Open AccessArticle
Endpoints-Clipping CSI Amplitude for SVM-Based Indoor Localization
Sensors 2019, 19(17), 3689; https://doi.org/10.3390/s19173689 - 25 Aug 2019
Cited by 3
Abstract
With the wide application of Channel State Information (CSI) in the field of sensing, the accuracy of positioning accuracy of indoor fingerprint positioning is increasingly necessary. The flexibility of the CSI signals may lead to an increase in fingerprint noise and inaccurate data [...] Read more.
With the wide application of Channel State Information (CSI) in the field of sensing, the accuracy of positioning accuracy of indoor fingerprint positioning is increasingly necessary. The flexibility of the CSI signals may lead to an increase in fingerprint noise and inaccurate data classification. This paper presents an indoor localization algorithm based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Endpoints-Clipping (EC) CSI amplitude, and Support Vector Machine (EC-SVM). In the offline phase, the CSI amplitude information collected through the three channels is combined and clipped using the EC, and then a fingerprint database is obtained. In the online phase, the SVM is used to train the data in the fingerprint database, and the corresponding relationship is found with the CSI data collected in real time to perform matching and positioning. The experimental results show that the positioning accuracy of the EC-SVM algorithm is superior to the state-of-art indoor CSI-based localization technique. Full article
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Open AccessArticle
A Fusion Localization Method based on a Robust Extended Kalman Filter and Track-Quality for Wireless Sensor Networks
Sensors 2019, 19(17), 3638; https://doi.org/10.3390/s19173638 - 21 Aug 2019
Cited by 2
Abstract
As one of the most essential technologies, wireless sensor networks (WSNs) integrate sensor technology, embedded computing technology, and modern network and communication technology, which have become research hotspots in recent years. The localization technique, one of the key techniques for WSN research, determines [...] Read more.
As one of the most essential technologies, wireless sensor networks (WSNs) integrate sensor technology, embedded computing technology, and modern network and communication technology, which have become research hotspots in recent years. The localization technique, one of the key techniques for WSN research, determines the application prospects of WSNs to a great extent. The positioning errors of wireless sensor networks are mainly caused by the non-line of sight (NLOS) propagation, occurring in complicated channel environments such as the indoor conditions. Traditional techniques such as the extended Kalman filter (EKF) perform unsatisfactorily in the case of NLOS. In contrast, the robust extended Kalman filter (REKF) acquires accurate position estimates by applying the robust techniques to the EKF in NLOS environments while losing efficiency in LOS. Therefore it is very hard to achieve high performance with a single filter in both LOS and NLOS environments. In this paper, a localization method using a robust extended Kalman filter and track-quality-based (REKF-TQ) fusion algorithm is proposed to mitigate the effect of NLOS errors. Firstly, the EKF and REKF are used in parallel to obtain the location estimates of mobile nodes. After that, we regard the position estimates as observation vectors, which can be implemented to calculate the residuals in the Kalman filter (KF) process. Then two KFs with a new observation vector and equation are used to further filter the estimates, respectively. At last, the acquired position estimates are combined by the fusion algorithm based on the track quality to get the final position vector of mobile node, which will serve as the state vector of both KFs at the next time step. Simulation results illustrate that the TQ-REKF algorithm yields better positioning accuracy than the EKF and REKF in the NLOS environment. Moreover, the proposed algorithm achieves higher accuracy than interacting multiple model algorithm (IMM) with EKF and REKF. Full article
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Open AccessArticle
A Device-Free Indoor Localization Method Using CSI with Wi-Fi Signals
Sensors 2019, 19(14), 3233; https://doi.org/10.3390/s19143233 - 23 Jul 2019
Cited by 2
Abstract
Amid the ever-accelerated development of wireless communication technology, we have become increasingly demanding for location-based service; thus, passive indoor positioning has gained widespread attention. Channel State Information (CSI), as it can provide more detailed and fine-grained information, has been followed by researchers. Existing [...] Read more.
Amid the ever-accelerated development of wireless communication technology, we have become increasingly demanding for location-based service; thus, passive indoor positioning has gained widespread attention. Channel State Information (CSI), as it can provide more detailed and fine-grained information, has been followed by researchers. Existing indoor positioning methods, however, are vulnerable to the environment and thus fail to fully reflect all the position features, due to limited accuracy of the fingerprint. As a solution, a CSI-based passive indoor positioning method was proposed, Wavelet Domain Denoising (WDD) was adopted to deal with the collected CSI amplitude, and the CSI phase information was unwound and transformed linearly in the offline phase. The post-processed amplitude and phase were taken as fingerprint data to build a fingerprint database, correlating with reference point position information. Results of experimental data analyzed under two different environments show that the present method boasts lower positioning error and higher stability than similar methods and can offer decimeter-level positioning accuracy. Full article
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Open AccessArticle
Wireless Indoor Localization Using Convolutional Neural Network and Gaussian Process Regression
Sensors 2019, 19(11), 2508; https://doi.org/10.3390/s19112508 - 31 May 2019
Cited by 3
Abstract
This paper presents a localization model employing convolutional neural network (CNN) and Gaussian process regression (GPR) based on Wi-Fi received signal strength indication (RSSI) fingerprinting data. In the proposed scheme, the CNN model is trained by a training dataset. The trained model adapts [...] Read more.
This paper presents a localization model employing convolutional neural network (CNN) and Gaussian process regression (GPR) based on Wi-Fi received signal strength indication (RSSI) fingerprinting data. In the proposed scheme, the CNN model is trained by a training dataset. The trained model adapts to complex scenes with multipath effects or many access points (APs). More specifically, the pre-processing algorithm makes the RSSI vector which is formed by considerable RSSI values from different APs readable by the CNN algorithm. The trained CNN model improves the positioning performance by taking a series of RSSI vectors into account and extracting local features. In this design, however, the performance is to be further improved by applying the GPR algorithm to adjust the coordinates of target points and offset the over-fitting problem of CNN. After implementing the hybrid model, the model is experimented with a public database that was collected from a library of Jaume I University in Spain. The results show that the hybrid model has outperformed the model using k-nearest neighbor (KNN) by 61.8%. While the CNN model improves the performance by 45.8%, the GPR algorithm further enhances the localization accuracy. In addition, the paper has also experimented with the three kernel functions, all of which have been demonstrated to have positive effects on GPR. Full article
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Open AccessArticle
ES-DPR: A DOA-Based Method for Passive Localization in Indoor Environments
Sensors 2019, 19(11), 2482; https://doi.org/10.3390/s19112482 - 30 May 2019
Cited by 4
Abstract
In this paper, we propose a novel indoor passive localization approach called eigenspace-based DOA with direct-path recognition (ES-DPR), based on a DOA estimation algorithm with multiple omnidirectional antennas deployed in a uniform linear array (ULA). To address the multipath propagation interference problem in [...] Read more.
In this paper, we propose a novel indoor passive localization approach called eigenspace-based DOA with direct-path recognition (ES-DPR), based on a DOA estimation algorithm with multiple omnidirectional antennas deployed in a uniform linear array (ULA). To address the multipath propagation interference problem in the indoor environments, we utilize the azimuth and RSS estimation results, which are calculated by using the eigenspace-based DOA (ES-DOA) algorithm, in a novel style. A direct-path bearing recognition algorithm is introduced to identify the real DOA of the signal source in different indoor environments, by combining the azimuth and RSS estimation with ensemble learning methods. Numerical simulations are conducted to verify the validity and superiority of the proposed method. The results show that the proposed ES-DPR method can achieve high resolution and has strong anti-noise capability in dealing with the multipath signals, and the direct-path recognition algorithm is reliable and robust in different indoor environments, even in undetectable direct-path conditions. Full article
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Open AccessArticle
Indoor Localization within Multi-Story Buildings Using MAC and RSSI Fingerprint Vectors
Sensors 2019, 19(11), 2433; https://doi.org/10.3390/s19112433 - 28 May 2019
Cited by 1
Abstract
For existing wireless network devices and smart phones to achieve available positioning accuracy easily, fingerprint localization is widely used in indoor positioning, which depends on the differences of the Received Signal Strength Indicator (RSSI) from the Wireless Local Area Network (WLAN) in different [...] Read more.
For existing wireless network devices and smart phones to achieve available positioning accuracy easily, fingerprint localization is widely used in indoor positioning, which depends on the differences of the Received Signal Strength Indicator (RSSI) from the Wireless Local Area Network (WLAN) in different places. Currently, most researchers pay more attention to the improvement of online positioning algorithms using RSSI values, while few focus on the MAC (media access control) addresses received from the WLAN. Accordingly, we attempt to integrate MAC addresses and RSSI values simultaneously in order to realize indoor localization within multi-story buildings. A novel approach to indoor positioning within multi-story buildings is presented in this article, which includes two steps: firstly, to identify the floor using the difference of received MAC addresses in different floors; secondly, to implement further localization on the same floor. Meanwhile, clustering operation using MAC addresses as the clustering index is introduced in the online positioning phase to improve the efficiency and accuracy of indoor positioning. Experimental results show that the proposed approach can achieve not only the precise location with the horizontal accuracy of 1.8 meters, but also the floor where the receiver is located within multi-story buildings. Full article
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Review

Jump to: Research

Open AccessReview
A Meta-Review of Indoor Positioning Systems
Sensors 2019, 19(20), 4507; https://doi.org/10.3390/s19204507 - 17 Oct 2019
Cited by 10
Abstract
An accurate and reliable Indoor Positioning System (IPS) applicable to most indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and [...] Read more.
An accurate and reliable Indoor Positioning System (IPS) applicable to most indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and verifiable evaluations, leads to difficulties for appreciating the true value of most proposals. This paper provides a meta-review that performed a comprehensive compilation of 62 survey papers in the area of indoor positioning. The paper provides the reader with an introduction to IPS and the different technologies, techniques, and some methods commonly employed. The introduction is supported by consensus found in the selected surveys and referenced using them. Thus, the meta-review allows the reader to inspect the IPS current state at a glance and serve as a guide for the reader to easily find further details on each technology used in IPS. The analyses of the meta-review contributed with insights on the abundance and academic significance of published IPS proposals using the criterion of the number of citations. Moreover, 75 works are identified as relevant works in the research topic from a selection of about 4000 works cited in the analyzed surveys. Full article
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