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Special Issue "Sensors Localization in Indoor Wireless Networks"

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

Deadline for manuscript submissions: closed (13 July 2020).

Special Issue Editors

Dr. Paul Honeine
E-Mail Website
Guest Editor
LITIS Lab, Université de Rouen Normandie, 76000 Rouen, France
Interests: machine learning and pattern recognition for signal and image processing; localization and tracking in wireless sensor networks; collaborative information processing; distributed algorithms
Special Issues and Collections in MDPI journals
Dr. Farah Mourad-Chehade
E-Mail Website
Guest Editor
Charles Delaunay Institute, University of Technology of Troyes, 10300 Troyes, France
Interests: artificial intelligence; data mining; pattern recognition; wireless sensor networks; sensors localization; biomedical signal processing; prediction and diagnoses of pathologies
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Wireless Sensor Networks (WSNs) have shown high potential for various applications in the environmental, military, civil, biomedical, industrial, and other fields. Many of these applications are location-based, leading to an increasing demand for accurate localization algorithms. While localization in outdoor environments mainly uses GPS signals (or, more generally, a global navigation satellite system GNSS), indoor localization is very challenging under noise, non-stationnarity, cost, and energy constraints.

This Special Issue aims to highlight advances in all aspects of indoor sensors localization and, more genreally, of GNSS-free localization. Up-to-date reviews and original works are both accepted in this issue. Topics include, but are not limited to:

  • Range-based indoor localization
  • Range-free indoor localization
  • Ranging technologies
  • Griding- or zoning-based localization
  • Target tracking and sequential localization
  • Sensors deployement for coverage and accuracy optimization
  • Machine learning algorithms
  • Evidential computations
  • Collaborative signal processing for localization
  • Adaptive algorithms for localization
  • Applications

Dr. Paul Honeine
Dr. Farah Mourad-Chehade
Guest Editors

Manuscript Submission Information

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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 2200 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

  • Localization
  • Signal processing
  • Data fusion
  • Wireless sensor networks
  • Machine learning
  • Collaborative algorithms
  • Adaptive algorithms

Published Papers (18 papers)

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Research

Article
Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios
Sensors 2020, 20(16), 4515; https://doi.org/10.3390/s20164515 - 12 Aug 2020
Cited by 6 | Viewed by 943
Abstract
With the addition of the Fine Timing Measurement (FTM) protocol in IEEE 802.11-2016, a promising sensor for smartphone-based indoor positioning systems was introduced. FTM enables a Wi-Fi device to estimate the distance to a second device based on the propagation time of the [...] Read more.
With the addition of the Fine Timing Measurement (FTM) protocol in IEEE 802.11-2016, a promising sensor for smartphone-based indoor positioning systems was introduced. FTM enables a Wi-Fi device to estimate the distance to a second device based on the propagation time of the signal. Recently, FTM has gotten more attention from the scientific community as more compatible devices become available. Due to the claimed robustness and accuracy, FTM is a promising addition to the often used Received Signal Strength Indication (RSSI). In this work, we evaluate FTM on the 2.4 GHz band with 20 MHz channel bandwidth in the context of realistic indoor positioning scenarios. For this purpose, we deploy a least-squares estimation method, a probabilistic positioning approach and a simplistic particle filter implementation. Each method is evaluated using FTM and RSSI separately to show the difference of the techniques. Our results show that, although FTM achieves smaller positioning errors compared to RSSI, its error behavior is similar to RSSI. Furthermore, we demonstrate that an empirically optimized correction value for FTM is required to account for the environment. This correction value can reduce the positioning error significantly. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
Foreign Object Intrusion Detection on Metro Track Using Commodity WiFi Devices with the Fast Phase Calibration Algorithm
Sensors 2020, 20(12), 3446; https://doi.org/10.3390/s20123446 - 18 Jun 2020
Cited by 1 | Viewed by 746
Abstract
With continuous development in the scales of cities, the role of the metro in urban transportation is becoming more and more important. When running at a high speed, the safety of the train in the tunnel is significantly affected by any foreign objects. [...] Read more.
With continuous development in the scales of cities, the role of the metro in urban transportation is becoming more and more important. When running at a high speed, the safety of the train in the tunnel is significantly affected by any foreign objects. To address this problem, we propose a foreign object intrusion detection method based on WiFi technology, which uses radio frequency (RF) signals to sense environmental changes and is suitable for lightless tunnel environments. Firstly, based on extensive experiments, the abnormal phase offset between the RF chains of the WiFi network card and its offset law was observed. Based on this observation, a fast phase calibration method is proposed. This method only needs the azimuth information between the transmitter and the receiver to calibrate the the phase offset rapidly through the compensation of the channel state information (CSI) data. The time complexity of the algorithm is lower than the existing algorithm. Secondly, a method combining the MUSIC algorithm and static clutter suppression is proposed. This method utilizes the incoherence of the dynamic reflection signal to improve the efficiency of foreign object detection and localization in the tunnel with a strong multipath effect. Finally, experiments were conducted using Intel 5300 NIC in the indoor environment that was close to the tunnel environment. The performance of the detection probability and localization accuracy of the proposed method is tested. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
Improved Bluetooth Low Energy Sensor Detection for Indoor Localization Services
Sensors 2020, 20(8), 2336; https://doi.org/10.3390/s20082336 - 20 Apr 2020
Cited by 9 | Viewed by 1116
Abstract
Advancements in protocols, computing paradigms, and electronics have enabled the development of wireless sensor networks (WSNs) with high potential for various location-based applications in different fields. One of the most important topics in WSNs is the localization in environments with sensor nodes being [...] Read more.
Advancements in protocols, computing paradigms, and electronics have enabled the development of wireless sensor networks (WSNs) with high potential for various location-based applications in different fields. One of the most important topics in WSNs is the localization in environments with sensor nodes being scattered randomly over a region. Localization techniques are often challenged by localization latency, efficient energy consumption, accuracy, environmental factors, and others. The objective of this study was to improve the technique for detecting the nearest Bluetooth Low Energy sensor, which would enable the development of more efficient mobile applications for location advertising at fairs, exhibitions, and museums. The technique proposed in this study was based on the iBeacon protocol, and it was tested in a controlled room with three environmental settings regarding the density of obstacles, as well as in a real-world setting at the Expo Museum at Postojna in Slovenia. The results of several independent measures, conducted in the controlled room and in the real-world environment, showed that the proposed algorithm outperformed the standard algorithm, especially in the environments with a medium or high densities of obstacles. The results of this study can be used for the more effective planning of placing beacons in space and for optimizing the algorithms for detecting transmitters in mobile location-based applications that provide users with contextual information based on their current location. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
An Efficient NLOS Errors Mitigation Algorithm for TOA-Based Localization
Sensors 2020, 20(5), 1403; https://doi.org/10.3390/s20051403 - 04 Mar 2020
Viewed by 854
Abstract
In time-of-arrival (TOA) localization systems, errors caused by non-line-of-sight (NLOS) signal propagation could significantly degrade the location accuracy. Existing works on NLOS error mitigation commonly assume that NLOS error statistics or the TOA measurement noise variances are known. Such information is generally unavailable [...] Read more.
In time-of-arrival (TOA) localization systems, errors caused by non-line-of-sight (NLOS) signal propagation could significantly degrade the location accuracy. Existing works on NLOS error mitigation commonly assume that NLOS error statistics or the TOA measurement noise variances are known. Such information is generally unavailable in practice. The goal of this paper is to develop an NLOS error mitigation scheme without requiring such information. The core of the proposed algorithm is a constrained least-squares optimization, which is converted into a semidefinite programming (SDP) problem that can be easily solved by using the CVX toolbox. This scheme is then extended for cooperative source localization. Additionally, its performance is better than existing schemes for most of the scenarios, which will be validated via extensive simulation. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
WiFi-Based Driver’s Activity Monitoring with Efficient Computation of Radio-Image Features
Sensors 2020, 20(5), 1381; https://doi.org/10.3390/s20051381 - 03 Mar 2020
Cited by 1 | Viewed by 1002
Abstract
Driver distraction and fatigue are among the leading contributing factors in various fatal accidents. Driver activity monitoring can effectively reduce the number of roadway accidents. Besides the traditional methods that rely on camera or wearable devices, wireless technology for driver’s activity monitoring has [...] Read more.
Driver distraction and fatigue are among the leading contributing factors in various fatal accidents. Driver activity monitoring can effectively reduce the number of roadway accidents. Besides the traditional methods that rely on camera or wearable devices, wireless technology for driver’s activity monitoring has emerged with remarkable attention. With substantial progress in WiFi-based device-free localization and activity recognition, radio-image features have achieved better recognition performance using the proficiency of image descriptors. The major drawback of image features is computational complexity, which increases exponentially, with the growth of irrelevant information in an image. It is still unresolved how to choose appropriate radio-image features to alleviate the expensive computational burden. This paper explores a computational efficient wireless technique that could recognize the attentive and inattentive status of a driver leveraging Channel State Information (CSI) of WiFi signals. In this novel research work, we demonstrate an efficient scheme to extract the representative features from the discriminant components of radio-images to reduce the computational cost with significant improvement in recognition accuracy. Specifically, we addressed the problem of the computational burden by efficacious use of Gabor filters with gray level statistical features. The presented low-cost solution requires neither sophisticated camera support to capture images nor any special hardware to carry with the user. This novel framework is evaluated in terms of activity recognition accuracy. To ensure the reliability of the suggested scheme, we analyzed the results by adopting different evaluation metrics. Experimental results show that the presented prototype outperforms the traditional methods with an average recognition accuracy of 93.1 % in promising application scenarios. This ubiquitous model leads to improve the system performance significantly for the diverse scale of applications. In the realm of intelligent vehicles and assisted driving systems, the proposed wireless solution can effectively characterize the driving maneuvers, primary tasks, driver distraction, and fatigue by exploiting radio-image descriptors. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
Received Signal Strength-Based Indoor Localization Using Hierarchical Classification
Sensors 2020, 20(4), 1067; https://doi.org/10.3390/s20041067 - 15 Feb 2020
Cited by 10 | Viewed by 1084
Abstract
Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. Wi-Fi received signal strength (RSS)-based indoor localization techniques have attracted [...] Read more.
Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. Wi-Fi received signal strength (RSS)-based indoor localization techniques have attracted extensive attentions because Wi-Fi access points (APs) are widely deployed and we can obtain the Wi-Fi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classification-based method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved K-Means clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the K-Nearest Neighbor (KNN) algorithm and support vector machine (SVM) with the one-versus-one strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in real-world environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
Applying Movement Constraints to BLE RSSI-Based Indoor Positioning for Extracting Valid Semantic Trajectories
Sensors 2020, 20(2), 527; https://doi.org/10.3390/s20020527 - 17 Jan 2020
Cited by 7 | Viewed by 1280
Abstract
Indoor positioning techniques, owing to received signal strength indicator (RSSI)-based sensors, can provide useful trajectory-based services. These services include user movement analytics, next-to-visit recommendation, and hotspot detection. However, the value of RSSI is often disturbed due to obstacles in indoor environment, such as [...] Read more.
Indoor positioning techniques, owing to received signal strength indicator (RSSI)-based sensors, can provide useful trajectory-based services. These services include user movement analytics, next-to-visit recommendation, and hotspot detection. However, the value of RSSI is often disturbed due to obstacles in indoor environment, such as doors, walls, and furnitures. Therefore, many indoor positioning techniques still extract an invalid trajectory from the disturbed RSSI. An invalid trajectory contains distant or impossible consecutive positions within a short time, which is unlikely in a real-world scenario. In this study, we enhanced indoor positioning techniques with movement constraints on BLE (Bluetooth Low Energy) RSSI data to prevent an invalid semantic indoor trajectory. The movement constraints ensure that a predicted semantic position cannot be far apart from the previous position. Furthermore, we can extend any indoor positioning technique using these movement constraints. We conducted comprehensive experimental studies on real BLE RSSI datasets from various indoor environment scenarios. The experimental results demonstrated that the proposed approach effectively extracts valid indoor semantic trajectories from the RSSI data. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
An Evidential Framework for Localization of Sensors in Indoor Environments
Sensors 2020, 20(1), 318; https://doi.org/10.3390/s20010318 - 06 Jan 2020
Cited by 2 | Viewed by 1140
Abstract
Indoor localization has several applications ranging from people tracking and indoor navigation, to autonomous robot navigation and asset tracking. We tackle the problem as a zoning localization where the objective is to determine the zone where the mobile sensor resides at any instant. [...] Read more.
Indoor localization has several applications ranging from people tracking and indoor navigation, to autonomous robot navigation and asset tracking. We tackle the problem as a zoning localization where the objective is to determine the zone where the mobile sensor resides at any instant. The decision-making process in localization systems relies on data coming from multiple sensors. The data retrieved from these sensors require robust fusion approaches to be processed. One of these approaches is the belief functions theory (BFT), also called the Dempster–Shafer theory. This theory deals with uncertainty and imprecision with a theoretically attractive evidential reasoning framework. This paper investigates the usage of the BFT to define an evidence framework for estimating the most probable sensor’s zone. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
A Robust PDR/UWB Integrated Indoor Localization Approach for Pedestrians in Harsh Environments
Sensors 2020, 20(1), 193; https://doi.org/10.3390/s20010193 - 29 Dec 2019
Cited by 4 | Viewed by 1047
Abstract
Wireless sensor networks (WSNs) and the Internet of Things (IoT) have been widely used in industrial, construction, and other fields. In recent years, demands for pedestrian localization have been increasing rapidly. In most cases, these applications work in harsh indoor environments, which have [...] Read more.
Wireless sensor networks (WSNs) and the Internet of Things (IoT) have been widely used in industrial, construction, and other fields. In recent years, demands for pedestrian localization have been increasing rapidly. In most cases, these applications work in harsh indoor environments, which have posed many challenges in achieving high-precision localization. Ultra-wide band (UWB)-based localization systems and pedestrian dead reckoning (PDR) algorithms are popular. However, both have their own advantages and disadvantages, and both exhibit a poor performance in harsh environments. UWB-based localization algorithms can be seriously interfered by non-line-of-sight (NLoS) propagation, and PDR algorithms display a cumulative error. For ensuring the accuracy of indoor localization in harsh environments, a hybrid localization approach is proposed in this paper. Firstly, UWB signals cannot penetrate obstacles in most cases, and traditional algorithms for improving the accuracy by NLoS identification and mitigation cannot work in this situation. Therefore, in this study, we focus on integrating a PDR and UWB-based localization algorithm according to the UWB communication status. Secondly, we propose an adaptive PDR algorithm. UWB technology can provide high-precision location results in line-of-sight (LoS) propagation. Based on these, we can train the parameters of the PDR algorithm for every pedestrian, to improve the accuracy. Finally, we implement this hybrid localization approach in a hardware platform and experiment with it in an environment similar to industry or construction. The experimental results show a better accuracy than traditional UWB and PDR approaches in harsh environments. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning Systems
Sensors 2019, 19(24), 5438; https://doi.org/10.3390/s19245438 - 10 Dec 2019
Cited by 7 | Viewed by 1063
Abstract
Indoor positioning systems based on radio frequency inherently present multipath-related phenomena. This causes ranging systems such as ultra-wideband (UWB) to lose accuracy when detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will [...] Read more.
Indoor positioning systems based on radio frequency inherently present multipath-related phenomena. This causes ranging systems such as ultra-wideband (UWB) to lose accuracy when detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will face critical errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques applied to a previous classification and mitigation of the propagation effects. For this purpose, real-world cross-scenarios are considered, where the data extracted from low-cost UWB devices for training the algorithms come from a scenario different from that considered for the test. The experimental results reveal that machine learning (ML) techniques are suitable for detecting non-line-of-sight (NLOS) ranging values in this situation. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
Multiple Simultaneous Ranging in IR-UWB Networks
Sensors 2019, 19(24), 5415; https://doi.org/10.3390/s19245415 - 09 Dec 2019
Cited by 3 | Viewed by 972
Abstract
Growth in the applications of wireless devices and the need for seamless solutions to location-based services has motivated extensive research efforts to address wireless indoor localization networks. Existing works provide range-based localization using ultra-wideband technology, focusing on reducing the inaccuracy in range estimation [...] Read more.
Growth in the applications of wireless devices and the need for seamless solutions to location-based services has motivated extensive research efforts to address wireless indoor localization networks. Existing works provide range-based localization using ultra-wideband technology, focusing on reducing the inaccuracy in range estimation due to clock offsets between different devices. This is generally achieved via signal message exchange between devices, which can lead to network congestion when the number of users is large. To address the problem of range estimation with limited signal messages, this paper proposes multiple simultaneous ranging methods based on a property of time difference of reception of two packets transmitted from different sources in impulse-radio ultra-wideband (IR-UWB) networks. The proposed method maintains similar robustness to the clock offsets while significantly reducing the air time occupancy when compared with the best existing ranging methods. Experimental evaluation of ranging in a line-of-sight environment shows that the proposed method enables accurate ranging with minimal air time occupancy. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
A New Multiple Hypothesis Tracker Integrated with Detection Processing
Sensors 2019, 19(23), 5278; https://doi.org/10.3390/s19235278 - 30 Nov 2019
Cited by 3 | Viewed by 925
Abstract
In extant radar signal processing systems, detection and tracking are carried out independently, and detected measurements are utilized as inputs to the tracking procedure. Therefore, the tracking performance is highly associated with detection accuracy, and this performance may severely degrade when detections include [...] Read more.
In extant radar signal processing systems, detection and tracking are carried out independently, and detected measurements are utilized as inputs to the tracking procedure. Therefore, the tracking performance is highly associated with detection accuracy, and this performance may severely degrade when detections include a mass of false alarms and missed-targets errors, especially in dense clutter or closely-spaced trajectories scenarios. To deal with this issue, this paper proposes a novel method for integrating the multiple hypothesis tracker with detection processing. Specifically, the detector acquires an adaptive detection threshold from the output of the multiple hypothesis tracker algorithm, and then the obtained detection threshold is employed to compute the score function and sequential probability ratio test threshold for the data association and track estimation tasks. A comparative analysis of three tracking algorithms in a clutter dense scenario, including the proposed method, the multiple hypothesis tracker, and the global nearest neighbor algorithm, is conducted. Simulation results demonstrate that the proposed multiple hypothesis tracker integrated with detection processing method outperforms both the standard multiple hypothesis tracker algorithm and the global nearest neighbor algorithm in terms of tracking accuracy. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
Exploring the Laplace Prior in Radio Tomographic Imaging with Sparse Bayesian Learning towards the Robustness to Multipath Fading
Sensors 2019, 19(23), 5126; https://doi.org/10.3390/s19235126 - 22 Nov 2019
Cited by 4 | Viewed by 948
Abstract
Radio tomographic imaging (RTI) is a technology for target localization by using radio frequency (RF) sensors in a wireless network. The change of the attenuation field caused by the target is represented by a shadowing image, which is then used to estimate the [...] Read more.
Radio tomographic imaging (RTI) is a technology for target localization by using radio frequency (RF) sensors in a wireless network. The change of the attenuation field caused by the target is represented by a shadowing image, which is then used to estimate the target’s position. The shadowing image can be reconstructed from the variation of the received signal strength (RSS) in the wireless network. However, due to the interference from multi-path fading, not all the RSS variations are reliable. If the unreliable RSS variations are used for image reconstruction, some artifacts will appear in the shadowing image, which may cause the target’s position being wrongly estimated. Due to the sparse property of the shadowing image, sparse Bayesian learning (SBL) can be employed for signal reconstruction. Aiming at enhancing the robustness to multipath fading, this paper explores the Laplace prior to characterize the shadowing image under the framework of SBL. Bayesian modeling, Bayesian inference and the fast algorithm are presented to achieve the maximum-a-posterior (MAP) solution. Finally, imaging, localization and tracking experiments from three different scenarios are conducted to validate the robustness to multipath fading. Meanwhile, the improved computational efficiency of using Laplace prior is validated in the localization-time experiment as well. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
A Localization and Tracking Approach in NLOS Environment Based on Distance and Angle Probability Model
Sensors 2019, 19(20), 4438; https://doi.org/10.3390/s19204438 - 14 Oct 2019
Cited by 3 | Viewed by 752
Abstract
In this paper, an optimization algorithm is presented based on a distance and angle probability model for indoor non-line-of-sight (NLOS) environments. By utilizing the sampling information, a distance and angle probability model is proposed so as to identify the NLOS propagation. Based on [...] Read more.
In this paper, an optimization algorithm is presented based on a distance and angle probability model for indoor non-line-of-sight (NLOS) environments. By utilizing the sampling information, a distance and angle probability model is proposed so as to identify the NLOS propagation. Based on the established model, the maximum likelihood estimation (MLE) method is employed to reduce the error of distance in the NLOS propagation. In order to reduce the computational complexity, a modified Monte Carlo method is applied to search the optimal position of the target. Moreover, the extended Kalman filtering (EKF) algorithm is introduced to achieve localization. The simulation and experimental results show the effectiveness of the proposed algorithm in the improvement of localization accuracy. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
A Computationally Efficient Labeled Multi-Bernoulli Smoother for Multi-Target Tracking
Sensors 2019, 19(19), 4226; https://doi.org/10.3390/s19194226 - 28 Sep 2019
Cited by 3 | Viewed by 916
Abstract
A forward–backward labeled multi-Bernoulli (LMB) smoother is proposed for multi-target tracking. The proposed smoother consists of two components corresponding to forward LMB filtering and backward LMB smoothing, respectively. The former is the standard LMB filter and the latter is proved to be closed [...] Read more.
A forward–backward labeled multi-Bernoulli (LMB) smoother is proposed for multi-target tracking. The proposed smoother consists of two components corresponding to forward LMB filtering and backward LMB smoothing, respectively. The former is the standard LMB filter and the latter is proved to be closed under LMB prior. It is also shown that the proposed LMB smoother can improve both the cardinality estimation and the state estimation, and the major computational complexity is linear with the number of targets. Implementation based on the Sequential Monte Carlo method in a representative scenario has demonstrated the effectiveness and computational efficiency of the proposed smoother in comparison to existing approaches. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
RSS Indoor Localization Based on a Single Access Point
Sensors 2019, 19(17), 3711; https://doi.org/10.3390/s19173711 - 27 Aug 2019
Cited by 5 | Viewed by 1022
Abstract
This research work investigates how RSS information fusion from a single, multi-antenna access point (AP) can be used to perform device localization in indoor RSS based localization systems. The proposed approach demonstrates that different RSS values can be obtained by carefully modifying each [...] Read more.
This research work investigates how RSS information fusion from a single, multi-antenna access point (AP) can be used to perform device localization in indoor RSS based localization systems. The proposed approach demonstrates that different RSS values can be obtained by carefully modifying each AP antenna orientation and polarization, allowing the generation of unique, low correlation fingerprints, for the area of interest. Each AP antenna can be used to generate a set of fingerprint radiomaps for different antenna orientations and/or polarization. The RSS fingerprints generated from all antennas of the single AP can be then combined to create a multi-layer fingerprint radiomap. In order to select the optimum fingerprint layers in the multilayer radiomap the proposed methodology evaluates the obtained localization accuracy, for each fingerprint radio map combination, for various well-known deterministic and probabilistic algorithms (Weighted k-Nearest-Neighbor—WKNN and Minimum Mean Square Error—MMSE). The optimum candidate multi-layer radiomap is then examined by calculating the correlation level of each fingerprint pair by using the “Tolerance Based—Normal Probability Distribution (TBNPD)” algorithm. Both steps take place during the offline phase, and it is demonstrated that this approach results in selecting the optimum multi-layer fingerprint radiomap combination. The proposed approach can be used to provide localisation services in areas served only by a single AP. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
NLOS Identification and Mitigation Using Low-Cost UWB Devices
Sensors 2019, 19(16), 3464; https://doi.org/10.3390/s19163464 - 08 Aug 2019
Cited by 20 | Viewed by 2188
Abstract
Indoor location systems based on ultra-wideband (UWB) technology have become very popular in recent years following the introduction of a number of low-cost devices on the market capable of providing accurate distance measurements. Although promising, UWB devices also suffer from the classic problems [...] Read more.
Indoor location systems based on ultra-wideband (UWB) technology have become very popular in recent years following the introduction of a number of low-cost devices on the market capable of providing accurate distance measurements. Although promising, UWB devices also suffer from the classic problems found when working in indoor scenarios, especially when there is no a clear line-of-sight (LOS) between the emitter and the receiver, causing the estimation error to increase up to several meters. In this work, machine learning (ML) techniques are employed to analyze several sets of real UWB measurements, captured in different scenarios, to try to identify the measurements facing non-line-of-sight (NLOS) propagation condition. Additionally, an ulterior process is carried out to mitigate the deviation of these measurements from the actual distance value between the devices. The results show that ML techniques are suitable to identify NLOS propagation conditions and also to mitigate the error of the estimates when there is LOS between the emitter and the receiver. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Article
Robust Least-SquareLocalization Based on Relative Angular Matrix in Wireless Sensor Networks
Sensors 2019, 19(11), 2627; https://doi.org/10.3390/s19112627 - 10 Jun 2019
Cited by 1 | Viewed by 1237
Abstract
Accurate position information plays an important role in wireless sensor networks (WSN), and cooperative positioning based on cooperation among agents is a promising methodology of providing such information. Conventional cooperative positioning algorithms, such as least squares (LS), rely on approximate position estimates obtained [...] Read more.
Accurate position information plays an important role in wireless sensor networks (WSN), and cooperative positioning based on cooperation among agents is a promising methodology of providing such information. Conventional cooperative positioning algorithms, such as least squares (LS), rely on approximate position estimates obtained from prior measurements. This paper explores the fundamental mechanism underlying the least squares algorithm’s sensitivity to the initial position selection and approaches to dealing with such sensitivity. This topic plays an essential role in cooperative positioning, as it determines whether a cooperative positioning algorithm can be implemented ubiquitously. In particular, a sufficient and unnecessary condition for the least squares cost function to be convex is found and proven. We then propose a robust algorithm for wireless sensor network positioning that transforms the cost function into a globally convex function by detecting the null space of the relative angle matrix when all the targets are located inside the convex polygon formed by its neighboring nodes. Furthermore, we advance one step further and improve the algorithm to apply it in both the time of arrival (TOA) and angle of arrival/time of arrival (AOA/TOA) scenarios. Finally, the performance of the proposed approach is quantified via simulations, and the results show that the proposed method has a high positioning accuracy and is robust in both line-of-sight (LOS) and non-line-of-sight (NLOS) positioning environments. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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