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Special Issue "Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring"

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

Deadline for manuscript submissions: 30 September 2017

Special Issue Editors

Guest Editor
Prof. Dr. Jesús Ureña

Department of Electronics, School of Engineering, University of Alcala, Campus Universitario s/n, 28805 Alcala de Henares, Madrid, Spain
Website | E-Mail
Interests: ultrasonic signal processing; Local Positioning Systems (LPSs); mobile robots;, electronic control, tracking and navigation; daily live monitoring; algorithm implementation on software and hardware
Guest Editor
Dr. Álvaro Hernández Alonso

Department of Electronics, School of Engineering, University of Alcala, Campus Universitario s/n, E-28805 Alcala de Henares, Madrid, Spain
E-Mail
Interests: ultrasonic sensory systems; Local Positioning Systems (LPSs); embedded systems; electronic design
Guest Editor
Dr. Juan Jesús García Domínguez

Department of Electronics, School of Engineering, University of Alcala, Campus Universitario s/n, 28805 Alcala de Henares, Madrid, Spain
E-Mail
Interests: ^local positioning systems; pedestrian dead reckoning; daily live monitoring; body sensor networks

Special Issue Information

Dear Colleagues,

Indoor localization has become a key issue for emerging location-based applications (LBA) in many activity areas. Different technologies and strategies, sometimes including their fusion, compete and/or collaborate to provide a solution to the indoor localization problem. Most times, the selection of a certain system depends on the final application that imposes different constraints, such as accuracy, granularity, coverage area, ease of deployment, calibration and reconfiguration, cost, etc. Large-scale deployment of such location systems needs a network support for different aspects: availability and accessibility, configurability and scalability, privacy, security, etc. Definitely, this strategic topic will lead to technological innovations that have a great impact on the daily activities of people in the coming years, in areas such as health and independent living, home and building automation, leisure, security, etc.

This Special Issue is devoted to new research results and developments in the area of sensors and technologies for Indoor Localization Systems (ILS), which include RF, IR, ultrasonic, magnetic, optical, inertial or other particular systems, as well as the positioning strategies and algorithms, including sensor fusion and networking. As the final application may play an important role from the very initial stages of the system design and deployment, we are also interested in ILS applications, for instance in object tracking, navigation of robots or people, and activity monitoring.

Prof. Dr. Jesús Ureña
Dr. Álvaro Hernández Alonso
Dr. Juan Jesús García Domínguez
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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.

Keywords

  • Sensors and technologies for Indoor Localization Systems (ILS)
  • Positioning strategies and algorithms; sensor fusion and networking.
  • System deployment and maintenance
  • Mobile Robot Navigation with ILS
  • Object tracking
  • People assistance and activity monitoring
  • Other ILS applications

Published Papers (21 papers)

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Research

Open AccessArticle TrackCC: A Practical Wireless Indoor Localization System Based on Less-Expensive Chips
Sensors 2017, 17(6), 1391; doi:10.3390/s17061391
Received: 26 April 2017 / Revised: 9 June 2017 / Accepted: 9 June 2017 / Published: 15 June 2017
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Abstract
This paper aims at proposing a new wireless indoor localization system (ILS), called TrackCC, based on a commercial type of low-power system-on-chip (SoC), nRF24LE1. This type of chip has only l output power levels and acute fluctuation for a received minimum power level
[...] Read more.
This paper aims at proposing a new wireless indoor localization system (ILS), called TrackCC, based on a commercial type of low-power system-on-chip (SoC), nRF24LE1. This type of chip has only l output power levels and acute fluctuation for a received minimum power level in operation, which give rise to many practical challenges for designing localization algorithms. In order to address these challenges, we exploit the Markov theory to construct a ( l + 1 ) × ( l + 1 ) -sized state transition matrix to remove the fluctuation, and then propose a priority-based pattern matching algorithm to search for the most similar match in the signal map to estimate the real position of unknown nodes. The experimental results show that, compared to two existing wireless ILSs, LANDMARC and SAIL, which have meter level positioning accuracy, the proposed TrackCC can achieve the decimeter level accuracy on average in both line-of-sight (LOS) and non-line-of-sight (NLOS) senarios. Full article
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Open AccessArticle A Sequential Optimization Calibration Algorithm for Near-Field Source Localization
Sensors 2017, 17(6), 1405; doi:10.3390/s17061405
Received: 9 March 2017 / Revised: 12 June 2017 / Accepted: 13 June 2017 / Published: 15 June 2017
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Abstract
This paper considers the near-field source location problem for a nonuniform linear array (non-ULA) in the presence of sensor gain and phase errors. A sequential optimization calibration method is proposed to simultaneously estimate the gain and phase errors as well as the locations
[...] Read more.
This paper considers the near-field source location problem for a nonuniform linear array (non-ULA) in the presence of sensor gain and phase errors. A sequential optimization calibration method is proposed to simultaneously estimate the gain and phase errors as well as the locations of calibration sources involving the ranges and the azimuths by exploiting some imprecise a-priori knowledge of calibration sources. At each iteration of the proposed method, the source locations, and the gain and phase errors are obtained iteratively. Finally, at the analysis stage, we evaluate the effectiveness of the proposed technique using some numerical simulations. Results show that the proposed algorithm shares the capability to jointly estimate the source locations and the errors. Full article
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Open AccessArticle An Empirical Study of the Transmission Power Setting for Bluetooth-Based Indoor Localization Mechanisms
Sensors 2017, 17(6), 1318; doi:10.3390/s17061318
Received: 12 March 2017 / Revised: 3 June 2017 / Accepted: 5 June 2017 / Published: 7 June 2017
PDF Full-text (5571 KB) | HTML Full-text | XML Full-text
Abstract
Nowadays, there is a great interest in developing accurate wireless indoor localization mechanisms enabling the implementation of many consumer-oriented services. Among the many proposals, wireless indoor localization mechanisms based on the Received Signal Strength Indication (RSSI) are being widely explored. Most studies have
[...] Read more.
Nowadays, there is a great interest in developing accurate wireless indoor localization mechanisms enabling the implementation of many consumer-oriented services. Among the many proposals, wireless indoor localization mechanisms based on the Received Signal Strength Indication (RSSI) are being widely explored. Most studies have focused on the evaluation of the capabilities of different mobile device brands and wireless network technologies. Furthermore, different parameters and algorithms have been proposed as a means of improving the accuracy of wireless-based localization mechanisms. In this paper, we focus on the tuning of the RSSI fingerprint to be used in the implementation of a Bluetooth Low Energy 4.0 (BLE4.0) Bluetooth localization mechanism. Following a holistic approach, we start by assessing the capabilities of two Bluetooth sensor/receiver devices. We then evaluate the relevance of the RSSI fingerprint reported by each BLE4.0 beacon operating at various transmission power levels using feature selection techniques. Based on our findings, we use two classification algorithms in order to improve the setting of the transmission power levels of each of the BLE4.0 beacons. Our main findings show that our proposal can greatly improve the localization accuracy by setting a custom transmission power level for each BLE4.0 beacon. Full article
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Open AccessArticle Indoor Trajectory Tracking Scheme Based on Delaunay Triangulation and Heuristic Information in Wireless Sensor Networks
Sensors 2017, 17(6), 1275; doi:10.3390/s17061275
Received: 9 April 2017 / Revised: 19 May 2017 / Accepted: 22 May 2017 / Published: 2 June 2017
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Abstract
Object tracking and detection is one of the most significant research areas for wireless sensor networks. Existing indoor trajectory tracking schemes in wireless sensor networks are based on continuous localization and moving object data mining. Indoor trajectory tracking based on the received signal
[...] Read more.
Object tracking and detection is one of the most significant research areas for wireless sensor networks. Existing indoor trajectory tracking schemes in wireless sensor networks are based on continuous localization and moving object data mining. Indoor trajectory tracking based on the received signal strength indicator (RSSI) has received increased attention because it has low cost and requires no special infrastructure. However, RSSI tracking introduces uncertainty because of the inaccuracies of measurement instruments and the irregularities (unstable, multipath, diffraction) of wireless signal transmissions in indoor environments. Heuristic information includes some key factors for trajectory tracking procedures. This paper proposes a novel trajectory tracking scheme based on Delaunay triangulation and heuristic information (TTDH). In this scheme, the entire field is divided into a series of triangular regions. The common side of adjacent triangular regions is regarded as a regional boundary. Our scheme detects heuristic information related to a moving object’s trajectory, including boundaries and triangular regions. Then, the trajectory is formed by means of a dynamic time-warping position-fingerprint-matching algorithm with heuristic information constraints. Field experiments show that the average error distance of our scheme is less than 1.5 m, and that error does not accumulate among the regions. Full article
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Open AccessArticle A Map/INS/Wi-Fi Integrated System for Indoor Location-Based Service Applications
Sensors 2017, 17(6), 1272; doi:10.3390/s17061272
Received: 7 April 2017 / Revised: 24 May 2017 / Accepted: 30 May 2017 / Published: 2 June 2017
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Abstract
In this research, a new Map/INS/Wi-Fi integrated system for indoor location-based service (LBS) applications based on a cascaded Particle/Kalman filter framework structure is proposed. Two-dimension indoor map information, together with measurements from an inertial measurement unit (IMU) and Received Signal Strength Indicator (RSSI)
[...] Read more.
In this research, a new Map/INS/Wi-Fi integrated system for indoor location-based service (LBS) applications based on a cascaded Particle/Kalman filter framework structure is proposed. Two-dimension indoor map information, together with measurements from an inertial measurement unit (IMU) and Received Signal Strength Indicator (RSSI) value, are integrated for estimating positioning information. The main challenge of this research is how to make effective use of various measurements that complement each other in order to obtain an accurate, continuous, and low-cost position solution without increasing the computational burden of the system. Therefore, to eliminate the cumulative drift caused by low-cost IMU sensor errors, the ubiquitous Wi-Fi signal and non-holonomic constraints are rationally used to correct the IMU-derived navigation solution through the extended Kalman Filter (EKF). Moreover, the map-aiding method and map-matching method are innovatively combined to constrain the primary Wi-Fi/IMU-derived position through an Auxiliary Value Particle Filter (AVPF). Different sources of information are incorporated through a cascaded structure EKF/AVPF filter algorithm. Indoor tests show that the proposed method can effectively reduce the accumulation of positioning errors of a stand-alone Inertial Navigation System (INS), and provide a stable, continuous and reliable indoor location service. Full article
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Open AccessArticle A LiDAR and IMU Integrated Indoor Navigation System for UAVs and Its Application in Real-Time Pipeline Classification
Sensors 2017, 17(6), 1268; doi:10.3390/s17061268
Received: 29 March 2017 / Revised: 30 May 2017 / Accepted: 30 May 2017 / Published: 2 June 2017
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Abstract
Mapping the environment of a vehicle and localizing a vehicle within that unknown environment are complex issues. Although many approaches based on various types of sensory inputs and computational concepts have been successfully utilized for ground robot localization, there is difficulty in localizing
[...] Read more.
Mapping the environment of a vehicle and localizing a vehicle within that unknown environment are complex issues. Although many approaches based on various types of sensory inputs and computational concepts have been successfully utilized for ground robot localization, there is difficulty in localizing an unmanned aerial vehicle (UAV) due to variation in altitude and motion dynamics. This paper proposes a robust and efficient indoor mapping and localization solution for a UAV integrated with low-cost Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) sensors. Considering the advantage of the typical geometric structure of indoor environments, the planar position of UAVs can be efficiently calculated from a point-to-point scan matching algorithm using measurements from a horizontally scanning primary LiDAR. The altitude of the UAV with respect to the floor can be estimated accurately using a vertically scanning secondary LiDAR scanner, which is mounted orthogonally to the primary LiDAR. Furthermore, a Kalman filter is used to derive the 3D position by fusing primary and secondary LiDAR data. Additionally, this work presents a novel method for its application in the real-time classification of a pipeline in an indoor map by integrating the proposed navigation approach. Classification of the pipeline is based on the pipe radius estimation considering the region of interest (ROI) and the typical angle. The ROI is selected by finding the nearest neighbors of the selected seed point in the pipeline point cloud, and the typical angle is estimated with the directional histogram. Experimental results are provided to determine the feasibility of the proposed navigation system and its integration with real-time application in industrial plant engineering. Full article
Open AccessArticle An Improved Compressive Sensing and Received Signal Strength-Based Target Localization Algorithm with Unknown Target Population for Wireless Local Area Networks
Sensors 2017, 17(6), 1246; doi:10.3390/s17061246
Received: 10 March 2017 / Revised: 18 May 2017 / Accepted: 22 May 2017 / Published: 30 May 2017
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Abstract
In this paper a two-phase compressive sensing (CS) and received signal strength (RSS)-based target localization approach is proposed to improve position accuracy by dealing with the unknown target population and the effect of grid dimensions on position error. In the coarse localization phase,
[...] Read more.
In this paper a two-phase compressive sensing (CS) and received signal strength (RSS)-based target localization approach is proposed to improve position accuracy by dealing with the unknown target population and the effect of grid dimensions on position error. In the coarse localization phase, by formulating target localization as a sparse signal recovery problem, grids with recovery vector components greater than a threshold are chosen as the candidate target grids. In the fine localization phase, by partitioning each candidate grid, the target position in a grid is iteratively refined by using the minimum residual error rule and the least-squares technique. When all the candidate target grids are iteratively partitioned and the measurement matrix is updated, the recovery vector is re-estimated. Threshold-based detection is employed again to determine the target grids and hence the target population. As a consequence, both the target population and the position estimation accuracy can be significantly improved. Simulation results demonstrate that the proposed approach achieves the best accuracy among all the algorithms compared. Full article
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Open AccessArticle Vital Sign Monitoring and Mobile Phone Usage Detection Using IR-UWB Radar for Intended Use in Car Crash Prevention
Sensors 2017, 17(6), 1240; doi:10.3390/s17061240
Received: 23 March 2017 / Revised: 20 May 2017 / Accepted: 25 May 2017 / Published: 30 May 2017
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Abstract
In order to avoid car crashes, active safety systems are becoming more and more important. Many crashes are caused due to driver drowsiness or mobile phone usage. Detecting the drowsiness of the driver is very important for the safety of a car. Monitoring
[...] Read more.
In order to avoid car crashes, active safety systems are becoming more and more important. Many crashes are caused due to driver drowsiness or mobile phone usage. Detecting the drowsiness of the driver is very important for the safety of a car. Monitoring of vital signs such as respiration rate and heart rate is important to determine the occurrence of driver drowsiness. In this paper, robust vital signs monitoring through impulse radio ultra-wideband (IR-UWB) radar is discussed. We propose a new algorithm that can estimate the vital signs even if there is motion caused by the driving activities. We analyzed the whole fast time vital detection region and found the signals at those fast time locations that have useful information related to the vital signals. We segmented those signals into sub-signals and then constructed the desired vital signal using the correlation method. In this way, the vital signs of the driver can be monitored noninvasively, which can be used by researchers to detect the drowsiness of the driver which is related to the vital signs i.e., respiration and heart rate. In addition, texting on a mobile phone during driving may cause visual, manual or cognitive distraction of the driver. In order to reduce accidents caused by a distracted driver, we proposed an algorithm that can detect perfectly a driver's mobile phone usage even if there are various motions of the driver in the car or changes in background objects. These novel techniques, which monitor vital signs associated with drowsiness and detect phone usage before a driver makes a mistake, may be very helpful in developing techniques for preventing a car crash. Full article
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Open AccessArticle LOCALI: Calibration-Free Systematic Localization Approach for Indoor Positioning
Sensors 2017, 17(6), 1213; doi:10.3390/s17061213
Received: 15 March 2017 / Revised: 20 May 2017 / Accepted: 22 May 2017 / Published: 25 May 2017
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Abstract
Recent advancements in indoor positioning systems are based on infrastructure-free solutions, aimed at improving the location accuracy in complex indoor environments without the use of specialized resources. A popular infrastructure-free solution for indoor positioning is a calibration-based positioning, commonly known as fingerprinting. Fingerprinting
[...] Read more.
Recent advancements in indoor positioning systems are based on infrastructure-free solutions, aimed at improving the location accuracy in complex indoor environments without the use of specialized resources. A popular infrastructure-free solution for indoor positioning is a calibration-based positioning, commonly known as fingerprinting. Fingerprinting solutions require extensive and error-free surveys of environments to build radio-map databases, which play a key role in position estimation. Fingerprinting also requires random updates of the database, when there are significant changes in the environment or a decrease in the accuracy. The calibration of the fingerprinting database is a time-consuming and laborious effort that prevents the extensive adoption of this technique. In this paper, we present a systematic LOCALIzation approach, “LOCALI”, for indoor positioning, which does not require a calibration database and extensive updates. The LOCALI exploits the floor plan/wall map of the environment to estimate the target position by generating radio maps by integrating path-losses over certain trajectories in complex indoor environments, where triangulation using time information or the received signal strength level is highly erroneous due to the fading effects caused by multi-path propagation or absorption by environmental elements or varying antenna alignment. Experimental results demonstrate that by using the map information and environmental parameters, a significant level of accuracy in indoor positioning can be achieved. Moreover, this process requires considerably lesser effort compared to the calibration-based techniques. Full article
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Open AccessArticle A Novel Real-Time Reference Key Frame Scan Matching Method
Sensors 2017, 17(5), 1060; doi:10.3390/s17051060
Received: 18 March 2017 / Revised: 23 April 2017 / Accepted: 3 May 2017 / Published: 7 May 2017
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Abstract
Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions’ environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global
[...] Read more.
Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions’ environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global approaches. Both approaches suffer from accumulated errors and high processing time due to the iterative nature of the scan matching method. Moreover, point-to-point scan matching is prone to outlier association processes. This paper proposes a low-cost novel method for 2D real-time scan matching based on a reference key frame (RKF). RKF is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches. This algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The algorithm depends on the iterative closest point algorithm during the lack of linear features which is typically exhibited in unstructured environments. The algorithm switches back to the RKF once linear features are detected. To validate and evaluate the algorithm, the mapping performance and time consumption are compared with various algorithms in static and dynamic environments. The performance of the algorithm exhibits promising navigational, mapping results and very short computational time, that indicates the potential use of the new algorithm with real-time systems. Full article
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Open AccessArticle Orthogonal Chirp-Based Ultrasonic Positioning
Sensors 2017, 17(5), 976; doi:10.3390/s17050976
Received: 15 March 2017 / Revised: 18 April 2017 / Accepted: 24 April 2017 / Published: 27 April 2017
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Abstract
This paper presents a chirp based ultrasonic positioning system (UPS) using orthogonal chirp waveforms. In the proposed method, multiple transmitters can simultaneously transmit chirp signals, as a result, it can efficiently utilize the entire available frequency spectrum. The fundamental idea behind the proposed
[...] Read more.
This paper presents a chirp based ultrasonic positioning system (UPS) using orthogonal chirp waveforms. In the proposed method, multiple transmitters can simultaneously transmit chirp signals, as a result, it can efficiently utilize the entire available frequency spectrum. The fundamental idea behind the proposed multiple access scheme is to utilize the oversampling methodology of orthogonal frequency-division multiplexing (OFDM) modulation and orthogonality of the discrete frequency components of a chirp waveform. In addition, the proposed orthogonal chirp waveforms also have all the advantages of a classical chirp waveform. Firstly, the performance of the waveforms is investigated through correlation analysis and then, in an indoor environment, evaluated through simulations and experiments for ultrasonic (US) positioning. For an operational range of approximately 1000 mm, the positioning root-mean-square-errors (RMSEs) &90% error were 4.54 mm and 6.68 mm respectively. Full article
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Open AccessArticle Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network
Sensors 2017, 17(5), 969; doi:10.3390/s17050969
Received: 18 January 2017 / Revised: 20 April 2017 / Accepted: 21 April 2017 / Published: 27 April 2017
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Abstract
Received-signal-strength-based (RSS-based) device-free localization (DFL) is a promising technique since it is able to localize the person without attaching any electronic device. This technology requires measuring the RSS of all links in the network constituted by several radio frequency (RF) sensors. It is
[...] Read more.
Received-signal-strength-based (RSS-based) device-free localization (DFL) is a promising technique since it is able to localize the person without attaching any electronic device. This technology requires measuring the RSS of all links in the network constituted by several radio frequency (RF) sensors. It is an energy-intensive task, especially when the RF sensors work in traditional work mode, in which the sensors directly send raw RSS measurements of all links to a base station (BS). The traditional work mode is unfavorable for the power constrained RF sensors because the amount of data delivery increases dramatically as the number of sensors grows. In this paper, we propose a binary work mode in which RF sensors send the link states instead of raw RSS measurements to the BS, which remarkably reduces the amount of data delivery. Moreover, we develop two localization methods for the binary work mode which corresponds to stationary and moving target, respectively. The first localization method is formulated based on grid-based maximum likelihood (GML), which is able to achieve global optimum with low online computational complexity. The second localization method, however, uses particle filter (PF) to track the target when constant snapshots of link stats are available. Real experiments in two different kinds of environments were conducted to evaluate the proposed methods. Experimental results show that the localization and tracking performance under the binary work mode is comparable to the those in traditional work mode while the energy efficiency improves considerably. Full article
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Open AccessArticle Platform Architecture for Decentralized Positioning Systems
Sensors 2017, 17(5), 957; doi:10.3390/s17050957
Received: 13 February 2017 / Revised: 7 April 2017 / Accepted: 19 April 2017 / Published: 26 April 2017
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Abstract
A platform architecture for positioning systems is essential for the realization of a flexible localization system, which interacts with other systems and supports various positioning technologies and algorithms. The decentralized processing of a position enables pushing the application-level knowledge into a mobile station
[...] Read more.
A platform architecture for positioning systems is essential for the realization of a flexible localization system, which interacts with other systems and supports various positioning technologies and algorithms. The decentralized processing of a position enables pushing the application-level knowledge into a mobile station and avoids the communication with a central unit such as a server or a base station. In addition, the calculation of the position on low-cost and resource-constrained devices presents a challenge due to the limited computing, storage capacity, as well as power supply. Therefore, we propose a platform architecture that enables the design of a system with the reusability of the components, extensibility (e.g., with other positioning technologies) and interoperability. Furthermore, the position is computed on a low-cost device such as a microcontroller, which simultaneously performs additional tasks such as data collecting or preprocessing based on an operating system. The platform architecture is designed, implemented and evaluated on the basis of two positioning systems: a field strength system and a time of arrival-based positioning system. Full article
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Open AccessArticle An Improved BLE Indoor Localization with Kalman-Based Fusion: An Experimental Study
Sensors 2017, 17(5), 951; doi:10.3390/s17050951
Received: 14 March 2017 / Revised: 20 April 2017 / Accepted: 20 April 2017 / Published: 26 April 2017
PDF Full-text (1204 KB) | HTML Full-text | XML Full-text
Abstract
Indoor positioning has grasped great attention in recent years. A number of efforts have been exerted to achieve high positioning accuracy. However, there exists no technology that proves its efficacy in various situations. In this paper, we propose a novel positioning method based
[...] Read more.
Indoor positioning has grasped great attention in recent years. A number of efforts have been exerted to achieve high positioning accuracy. However, there exists no technology that proves its efficacy in various situations. In this paper, we propose a novel positioning method based on fusing trilateration and dead reckoning. We employ Kalman filtering as a position fusion algorithm. Moreover, we adopt an Android device with Bluetooth Low Energy modules as the communication platform to avoid excessive energy consumption and to improve the stability of the received signal strength. To further improve the positioning accuracy, we take the environmental context information into account while generating the position fixes. Extensive experiments in a testbed are conducted to examine the performance of three approaches: trilateration, dead reckoning and the fusion method. Additionally, the influence of the knowledge of the environmental context is also examined. Finally, our proposed fusion method outperforms both trilateration and dead reckoning in terms of accuracy: experimental results show that the Kalman-based fusion, for our settings, achieves a positioning accuracy of less than one meter. Full article
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Open AccessArticle Using Dimensionality Reduction Techniques for Refining Passive Indoor Positioning Systems Based on Radio Fingerprinting
Sensors 2017, 17(4), 871; doi:10.3390/s17040871
Received: 31 January 2017 / Revised: 3 April 2017 / Accepted: 12 April 2017 / Published: 15 April 2017
PDF Full-text (3218 KB) | HTML Full-text | XML Full-text
Abstract
Indoor positioning methods based on fingerprinting and radio signals rely on the quality of the radio map. For example, for room-level classification purposes, it is required that the signal observations related to each room exhibit significant differences in their RSSI values. However, it
[...] Read more.
Indoor positioning methods based on fingerprinting and radio signals rely on the quality of the radio map. For example, for room-level classification purposes, it is required that the signal observations related to each room exhibit significant differences in their RSSI values. However, it is difficult to verify and visualize that separability since radio maps are constituted by multi-dimensional observations whose dimension is directly related to the number of access points or monitors being employed for localization purposes. In this paper, we propose a refinement cycle for passive indoor positioning systems, which is based on dimensionality reduction techniques, to evaluate the quality of a radio map. By means of these techniques and our own data representation, we have defined two different visualization methods to obtain graphical information about the quality of a particular radio map in terms of overlapping areas and outliers. That information will be useful to determine whether new monitors are required or some existing ones should be moved. We have performed an exhaustive experimental analysis based on a variety of different scenarios, some deployed by our own research group and others corresponding to a well-known existing dataset widely analyzed by the community, in order to validate our proposal. As we will show, among the different combinations of data representation methods and dimensionality reduction techniques that we discuss, we have found that there are some specific configurations that are more useful in order to perform the refinement process. Full article
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Open AccessArticle Modeling Infrared Signal Reflections to Characterize Indoor Multipath Propagation
Sensors 2017, 17(4), 847; doi:10.3390/s17040847
Received: 27 February 2017 / Revised: 3 April 2017 / Accepted: 7 April 2017 / Published: 13 April 2017
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Abstract
In this paper, we propose a model to characterize Infrared (IR) signal reflections on any kind of surface material, together with a simplified procedure to compute the model parameters. The model works within the framework of Local Positioning Systems (LPS) based on IR
[...] Read more.
In this paper, we propose a model to characterize Infrared (IR) signal reflections on any kind of surface material, together with a simplified procedure to compute the model parameters. The model works within the framework of Local Positioning Systems (LPS) based on IR signals (IR-LPS) to evaluate the behavior of transmitted signal Multipaths (MP), which are the main cause of error in IR-LPS, and makes several contributions to mitigation methods. Current methods are based on physics, optics, geometry and empirical methods, but these do not meet our requirements because of the need to apply several different restrictions and employ complex tools. We propose a simplified model based on only two reflection components, together with a method for determining the model parameters based on 12 empirical measurements that are easily performed in the real environment where the IR-LPS is being applied. Our experimental results show that the model provides a comprehensive solution to the real behavior of IR MP, yielding small errors when comparing real and modeled data (the mean error ranges from 1% to 4% depending on the environment surface materials). Other state-of-the-art methods yielded mean errors ranging from 15% to 40% in test measurements. Full article
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Open AccessArticle Hand-Based Gesture Recognition for Vehicular Applications Using IR-UWB Radar
Sensors 2017, 17(4), 833; doi:10.3390/s17040833
Received: 26 February 2017 / Revised: 28 March 2017 / Accepted: 6 April 2017 / Published: 11 April 2017
Cited by 1 | PDF Full-text (7129 KB) | HTML Full-text | XML Full-text
Abstract
Modern cars continue to offer more and more functionalities due to which they need a growing number of commands. As the driver tries to monitor the road and the graphic user interface simultaneously, his/her overall efficiency is reduced. In order to reduce the
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Modern cars continue to offer more and more functionalities due to which they need a growing number of commands. As the driver tries to monitor the road and the graphic user interface simultaneously, his/her overall efficiency is reduced. In order to reduce the visual attention necessary for monitoring, a gesture-based user interface is very important. In this paper, gesture recognition for a vehicle through impulse radio ultra-wideband (IR-UWB) radar is discussed. The gestures can be used to control different electronic devices inside a vehicle. The gestures are based on human hand and finger motion. We have implemented a real-time version using only one radar sensor. Studies on gesture recognition using IR-UWB radar have rarely been carried out, and some studies are merely simple methods using the magnitude of the reflected signal or those whose performance deteriorates largely due to changes in distance or direction. In this study, we propose a new hand-based gesture recognition algorithm that works robustly against changes in distance or direction while responding only to defined gestures by ignoring meaningless motions. We used three independent features, i.e., variance of the probability density function (pdf) of the magnitude histogram, time of arrival (TOA) variation and the frequency of the reflected signal, to classify the gestures. A data fitting method is included to differentiate between gesture signals and unintended hand or body motions. We have used the clustering technique for the classification of the gestures. Moreover, the distance information is used as an additional input parameter to the clustering algorithm, such that the recognition technique will not be vulnerable to distance change. The hand-based gesture recognition proposed in this paper would be a key technology of future automobile user interfaces. Full article
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Open AccessArticle A Novel Robust Trilateration Method Applied to Ultra-Wide Bandwidth Location Systems
Sensors 2017, 17(4), 795; doi:10.3390/s17040795
Received: 14 February 2017 / Revised: 25 March 2017 / Accepted: 31 March 2017 / Published: 7 April 2017
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Abstract
Due to the non-line-of-sight (NLOS) and multipath fading channel (MPF) of the wireless networks, the non-existence of the intersection point often occurs in the range-based localization methods, e.g., the centroid-based trilateration method. To alleviate the problem, a confidence-based intersection method which expands the
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Due to the non-line-of-sight (NLOS) and multipath fading channel (MPF) of the wireless networks, the non-existence of the intersection point often occurs in the range-based localization methods, e.g., the centroid-based trilateration method. To alleviate the problem, a confidence-based intersection method which expands the range of the circle within a certain confidence interval is proposed. In the method, the confidence interval is estimated based on the Cramér–Rao lower bound of the time of flight (TOF) measurement. Furthermore, an intersection determination method is proposed to select the intersection point with higher confidence level. The simulation and experimental results show the superiority of the proposed method in localization accuracy and robustness to noise compared to the conventional trilateration method, e.g., the centroid-based and least squares based trilateration methods. Full article
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Open AccessArticle An Improved Approach for RSSI-Based only Calibration-Free Real-Time Indoor Localization on IEEE 802.11 and 802.15.4 Wireless Networks
Sensors 2017, 17(4), 717; doi:10.3390/s17040717
Received: 31 January 2017 / Revised: 15 March 2017 / Accepted: 25 March 2017 / Published: 29 March 2017
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Abstract
Assuming a reliable and responsive spatial contextualization service is a must-have in IEEE 802.11 and 802.15.4 wireless networks, a suitable approach consists of the implementation of localization capabilities, as an additional application layer to the communication protocol stack. Considering the applicative scenario where
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Assuming a reliable and responsive spatial contextualization service is a must-have in IEEE 802.11 and 802.15.4 wireless networks, a suitable approach consists of the implementation of localization capabilities, as an additional application layer to the communication protocol stack. Considering the applicative scenario where satellite-based positioning applications are denied, such as indoor environments, and excluding data packet arrivals time measurements due to lack of time resolution, received signal strength indicator (RSSI) measurements, obtained according to IEEE 802.11 and 802.15.4 data access technologies, are the unique data sources suitable for indoor geo-referencing using COTS devices. In the existing literature, many RSSI based localization systems are introduced and experimentally validated, nevertheless they require periodic calibrations and significant information fusion from different sensors that dramatically decrease overall systems reliability and their effective availability. This motivates the work presented in this paper, which introduces an approach for an RSSI-based calibration-free and real-time indoor localization. While switched-beam array-based hardware (compliant with IEEE 802.15.4 router functionality) has already been presented by the author, the focus of this paper is the creation of an algorithmic layer for use with the pre-existing hardware capable to enable full localization and data contextualization over a standard 802.15.4 wireless sensor network using only RSSI information without the need of lengthy offline calibration phase. System validation reports the localization results in a typical indoor site, where the system has shown high accuracy, leading to a sub-metrical overall mean error and an almost 100% site coverage within 1 m localization error. Full article
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Open AccessArticle Assessing Human Activity in Elderly People Using Non-Intrusive Load Monitoring
Sensors 2017, 17(2), 351; doi:10.3390/s17020351
Received: 30 November 2016 / Revised: 21 January 2017 / Accepted: 25 January 2017 / Published: 11 February 2017
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Abstract
The ageing of the population, and their increasing wish of living independently, are motivating the development of welfare and healthcare models. Existing approaches based on the direct heath-monitoring using body sensor networks (BSN) are precise and accurate. Nonetheless, their intrusiveness causes non-acceptance. New
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The ageing of the population, and their increasing wish of living independently, are motivating the development of welfare and healthcare models. Existing approaches based on the direct heath-monitoring using body sensor networks (BSN) are precise and accurate. Nonetheless, their intrusiveness causes non-acceptance. New approaches seek the indirect monitoring through monitoring activities of daily living (ADLs), which proves to be a suitable solution. ADL monitoring systems use many heterogeneous sensors, are less intrusive, and are less expensive than BSN, however, the deployment and maintenance of wireless sensor networks (WSN) prevent them from a widespread acceptance. In this work, a novel technique to monitor the human activity, based on non-intrusive load monitoring (NILM), is presented. The proposal uses only smart meter data, which leads to minimum intrusiveness and a potential massive deployment at minimal cost. This could be the key to develop sustainable healthcare models for smart homes, capable of complying with the elderly people’ demands. This study also uses the Dempster-Shafer theory to provide a daily score of normality with regard to the regular behavior. This approach has been evaluated using real datasets and, additionally, a benchmarking against a Gaussian mixture model approach is presented. Full article
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Open AccessArticle Reliable Identification of Vehicle-Boarding Actions Based on Fuzzy Inference Syste
Sensors 2017, 17(2), 333; doi:10.3390/s17020333
Received: 31 December 2016 / Revised: 29 January 2017 / Accepted: 6 February 2017 / Published: 9 February 2017
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Abstract
Existing smartphone-based solutions to prevent distracted driving suffer from inadequate system designs that only recognize simple and clean vehicle-boarding actions, thereby failing to meet the required level of accuracy in real-life environments. In this paper, exploiting unique sensory features consistently monitored from a
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Existing smartphone-based solutions to prevent distracted driving suffer from inadequate system designs that only recognize simple and clean vehicle-boarding actions, thereby failing to meet the required level of accuracy in real-life environments. In this paper, exploiting unique sensory features consistently monitored from a broad range of complicated vehicle-boarding actions, we propose a reliable and accurate system based on fuzzy inference to classify the sides of vehicle entrancebyleveragingbuilt-insmartphonesensorsonly. Theresultsofourcomprehensiveevaluation on three vehicle types with four participants demonstrate that the proposed system achieves 91.1%∼94.0% accuracy, outperforming other methods by 26.9%∼38.4% and maintains at least 87.8 %accuracy regardless of smartphone positions and vehicle types. Full article
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