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28 pages, 9075 KiB

Open AccessArticle

LocSpeck: A Collaborative and Distributed Positioning System for Asymmetric Nodes Based on UWB Ad-Hoc Network and Wi-Fi Fingerprinting

by Mostafa Sakr, Andrea Masiero and Naser El-Sheimy

Sensors 2020, 20(1), 78; https://doi.org/10.3390/s20010078 - 21 Dec 2019

Cited by 21 | Viewed by 4040

Abstract

This paper presents LocSpeck, a collaborative and distributed indoor positioning system for dynamic nodes connected using an ad-hoc network, based on inter-node relative range measurements and Wi-Fi fingerprinting. The proposed system operates using peer-to-peer range measurements and does not need ultra-wideband (UWB) fixed [...] Read more.

This paper presents LocSpeck, a collaborative and distributed indoor positioning system for dynamic nodes connected using an ad-hoc network, based on inter-node relative range measurements and Wi-Fi fingerprinting. The proposed system operates using peer-to-peer range measurements and does not need ultra-wideband (UWB) fixed anchor, nor it needs a predefined network topology. The nodes could be asymmetric in terms of the available sensors onboard, the computational resources, and the power capacity. This asymmetry adversely affects the positioning performance of the weaker nodes. Collaboration between different nodes is achieved through a distributed estimator without the need of a single centralized computing element. The ranging measurement component of the system is based on the DW1000 UWB transceiver chip from Decawave, which is attached to a set of smartphones equipped with asymmetric sensors. The distributed positioning filter fuses, locally on each node, the relative range measurements, the reading from the internal sensors, and the Wi-Fi received signal strength indicator (RSSI) readings to obtain an estimate of the position of each node. The described system does not depend on fixed UWB anchors and supports online addition and removal of nodes and dynamic node role assignment, either as an anchor or as a rover. The performance of the system is evaluated by real-world test scenarios using a set of four smartphones navigating an indoor environment on foot. The performance is compared to that of a commercial UWB-based system. The results presented in this paper show that weak mobile nodes, in terms of available positioning sensors, can benefit from collaboration with other nearby nodes. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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22 pages, 1370 KiB

Open AccessArticle

Three-Dimensional Empirical AoA Localization Technique for Indoor Applications

by Abdallah Alma’aitah, Baha’ Alsaify and Raed Bani-Hani

Sensors 2019, 19(24), 5544; https://doi.org/10.3390/s19245544 - 15 Dec 2019

Cited by 12 | Viewed by 3956

Abstract

Small and pervasive devices have been increasingly used to identify and track objects automatically. Consequently, several low-cost localization schemes have been proposed in the literature based on angle of arrival (AoA), time difference of arrival (TDoA), received signal strength indicator (RSSI) or their [...] Read more.

Small and pervasive devices have been increasingly used to identify and track objects automatically. Consequently, several low-cost localization schemes have been proposed in the literature based on angle of arrival (AoA), time difference of arrival (TDoA), received signal strength indicator (RSSI) or their combinations. In this paper, we propose a three-dimensional empirical AoA localization (TDEAL) technique for battery-powered devices. The proposed technique processes the AoA measurements at fixed reader nodes to estimate the locations of the tags. The proposed technique provides localization accuracy that mitigates non-linear empirical errors in AoA measurements. We utilize two omni-directional antenna arrays at each fixed reader node to estimate the location vector. With multiple location estimations from different fixed reader nodes, each estimated location is assigned a weight that is inversely proportional to the AoA phase-difference error. Furthermore, the actual AoA parabolic formula of the location is approximated to a cone to simplify the location calculation process. The proposed localization technique has a low hardware cost, low computational requirements, and precise location estimates. Based on the performance evaluation, significant location accuracy is achieved by TDEAL; where, for instance, an average error margin of less than 13 cm is achieved using 10 readers in an area of

10 m \times 10 m

. TDEAL can be utilized to provide reference points when integrated with a relative (e.g., inertial navigation systems) localization systems. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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15 pages, 3899 KiB

Open AccessArticle

On-line Smoothing and Error Modelling for Integration of GNSS and Visual Odometry

by Thanh Trung Duong, Kai-Wei Chiang and Dinh Thuan Le

Sensors 2019, 19(23), 5259; https://doi.org/10.3390/s19235259 - 29 Nov 2019

Cited by 8 | Viewed by 3002

Abstract

Global navigation satellite systems (GNSSs) are commonly used for navigation and mapping applications. However, in GNSS-hostile environments, where the GNSS signal is noisy or blocked, the navigation information provided by a GNSS is inaccurate or unavailable. To overcome these issues, this study proposed [...] Read more.

Global navigation satellite systems (GNSSs) are commonly used for navigation and mapping applications. However, in GNSS-hostile environments, where the GNSS signal is noisy or blocked, the navigation information provided by a GNSS is inaccurate or unavailable. To overcome these issues, this study proposed a real-time visual odometry (VO)/GNSS integrated navigation system. An on-line smoothing method based on the extended Kalman filter (EKF) and the Rauch-Tung-Striebel (RTS) smoother was proposed. VO error modelling was also proposed to estimate the VO error and compensate the incoming measurements. Field tests were performed in various GNSS-hostile environments, including under a tree canopy and an urban area. An analysis of the test results indicates that with the EKF used for data fusion, the root-mean-square error (RMSE) of the three-dimensional position is about 80 times lower than that of the VO-only solution. The on-line smoothing and error modelling made the results more accurate, allowing seamless on-line navigation information. The efficiency of the proposed methods in terms of cost and accuracy compared to the conventional inertial navigation system (INS)/GNSS integrated system was demonstrated. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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19 pages, 5238 KiB

Open AccessArticle

Tag Localization with Asynchronous Inertial-Based Shifting and Trilateration

by Abdallah Y. Alma’aitah, Lobna M. Eslim and Hossam S. Hassanein

Sensors 2019, 19(23), 5204; https://doi.org/10.3390/s19235204 - 27 Nov 2019

Cited by 1 | Viewed by 2129

Abstract

Personal Area Networks (PAN) are key topologies in pervasive Internet of Things (IoT) localization applications. In the numerous object localization techniques, centralization and synchronization between the elements are assumed. In this paper, we leverage crowdsourcing from multiple fixed and mobile elements to enhance [...] Read more.

Personal Area Networks (PAN) are key topologies in pervasive Internet of Things (IoT) localization applications. In the numerous object localization techniques, centralization and synchronization between the elements are assumed. In this paper, we leverage crowdsourcing from multiple fixed and mobile elements to enhance object localization. A cooperative crowdsourcing scheme is proposed to localize mobile low power tags using distributed and mobile/fixed readers for GPS assisted environments (i.e., outdoor) and fixed readers for indoors. We propose Inertial-Based Shifting and Trilateration (IBST) technique to provide an accurate reckoning of the absolute location of mobile tags. The novelty in our technique is its capability to estimate tag locations even when the tag is not covered by three readers to perform trilateration. In addition, IBST provides scalability since no processing is required by the low power tags. IBST technique is validated through extensive simulations using MATLAB. Simulation results show that IBST consistently estimates location, while other indoor localization solutions fail to provide such estimates as the state-of-the-art techniques require localization data to be available simultaneously to provide location estimation. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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17 pages, 1152 KiB

Open AccessArticle

Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

by Alwin Poulose and Dong Seog Han

Sensors 2019, 19(23), 5084; https://doi.org/10.3390/s19235084 - 21 Nov 2019

Cited by 66 | Viewed by 8069

Abstract

Smartphone camera or inertial measurement unit (IMU) sensor-based systems can be independently used to provide accurate indoor positioning results. However, the accuracy of an IMU-based localization system depends on the magnitude of sensor errors that are caused by external electromagnetic noise or sensor [...] Read more.

Smartphone camera or inertial measurement unit (IMU) sensor-based systems can be independently used to provide accurate indoor positioning results. However, the accuracy of an IMU-based localization system depends on the magnitude of sensor errors that are caused by external electromagnetic noise or sensor drifts. Smartphone camera based positioning systems depend on the experimental floor map and the camera poses. The challenge in smartphone camera-based localization is that accuracy depends on the rapidness of changes in the user’s direction. In order to minimize the positioning errors in both the smartphone camera and IMU-based localization systems, we propose hybrid systems that combine both the camera-based and IMU sensor-based approaches for indoor localization. In this paper, an indoor experiment scenario is designed to analyse the performance of the IMU-based localization system, smartphone camera-based localization system and the proposed hybrid indoor localization system. The experiment results demonstrate the effectiveness of the proposed hybrid system and the results show that the proposed hybrid system exhibits significant position accuracy when compared to the IMU and smartphone camera-based localization systems. The performance of the proposed hybrid system is analysed in terms of average localization error and probability distributions of localization errors. The experiment results show that the proposed oriented fast rotated binary robust independent elementary features (BRIEF)-simultaneous localization and mapping (ORB-SLAM) with the IMU sensor hybrid system shows a mean localization error of 0.1398 m and the proposed simultaneous localization and mapping by fusion of keypoints and squared planar markers (UcoSLAM) with IMU sensor-based hybrid system has a 0.0690 m mean localization error and are compared with the individual localization systems in terms of mean error, maximum error, minimum error and standard deviation of error. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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26 pages, 5665 KiB

Open AccessArticle

Design of a Low-Cost Indoor Navigation System for Food Delivery Robot Based on Multi-Sensor Information Fusion

by Yunlong Sun, Lianwu Guan, Zhanyuan Chang, Chuanjiang Li and Yanbin Gao

Sensors 2019, 19(22), 4980; https://doi.org/10.3390/s19224980 - 15 Nov 2019

Cited by 31 | Viewed by 8019

Abstract

As the restaurant industry is facing labor shortage issues, the use of meal delivery robots instead of waiters/waitresses not only allows the customers to experience the impact of robot technology but also benefits the restaurant business financially by reducing labor costs. Most existing [...] Read more.

As the restaurant industry is facing labor shortage issues, the use of meal delivery robots instead of waiters/waitresses not only allows the customers to experience the impact of robot technology but also benefits the restaurant business financially by reducing labor costs. Most existing meal delivery robots employ magnetic navigation technologies, which require magnetic strip installation and changes to the restaurant decor. Once the moving path is changed, the magnetic strips need to be re-laid. This study proposes multisource information fusion, i.e., the fusion of ultra-wide band positioning technology with an odometer and a low-cost gyroscope accelerometer, to achieve the positioning of a non-rail meal delivery robot with navigation. By using a low-cost electronic compass and gyroscope accelerometer, the delivery robot can move along a fixed orbit in a flexible and cost-effective manner with steering control. Ultra-wide band (UWB) and track estimation algorithm are combined by extended Kalman filter (EKF), and the positioning error after fusion is about 15 cm, which is accepted by restaurants. In summary, the proposed approach has some potential for commercial applications. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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15 pages, 3528 KiB

Open AccessArticle

A Multi-Sensor Based Roadheader Positioning Model and Arbitrary Tunnel Cross Section Automatic Cutting

by Changqing Yan, Wenxiao Zhao and Xinming Lu

Sensors 2019, 19(22), 4955; https://doi.org/10.3390/s19224955 - 14 Nov 2019

Cited by 14 | Viewed by 2368

Abstract

Autonomous posture detection and self-localization of roadheaders is the key to automatic tunneling and roadheader robotization. In this paper, a multi-sensor based positioning method, involving an inertial system for altitude angles measurement, total station for coordinate measurement, and sensors for measuring the real-time [...] Read more.

Autonomous posture detection and self-localization of roadheaders is the key to automatic tunneling and roadheader robotization. In this paper, a multi-sensor based positioning method, involving an inertial system for altitude angles measurement, total station for coordinate measurement, and sensors for measuring the real-time length of the hydraulic cylinder is presented for roadheader position measurement and posture detection. Based on this method, a positioning model for roadheader and cutter positioning is developed. Additionally, flexible trajectory planning methods are provided for automatic cutting. Based on the positioning model and the trajectory planning methods, an automatic cutting procedure is proposed and applied in practical tunneling. The experimental results verify the high accuracy and efficiency of both the positioning method and the model. Furthermore, it is indicated that arbitrary shapes can be generated automatically and precisely according to the planned trajectory, employing the automatic cutting procedure. Therefore, unmanned tunneling can be realized by employing the proposed automatic cutting process. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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21 pages, 15119 KiB

Open AccessArticle

Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles

by Mohamed Elsheikh, Walid Abdelfatah, Aboelmagd Noureldin, Umar Iqbal and Michael Korenberg

Sensors 2019, 19(22), 4896; https://doi.org/10.3390/s19224896 - 09 Nov 2019

Cited by 32 | Viewed by 4037 | Correction

Abstract

The last decade has witnessed a growing demand for precise positioning in many applications including car navigation. Navigating automated land vehicles requires at least sub-meter level positioning accuracy with the lowest possible cost. The Global Navigation Satellite System (GNSS) Single-Frequency Precise Point Positioning [...] Read more.

The last decade has witnessed a growing demand for precise positioning in many applications including car navigation. Navigating automated land vehicles requires at least sub-meter level positioning accuracy with the lowest possible cost. The Global Navigation Satellite System (GNSS) Single-Frequency Precise Point Positioning (SF-PPP) is capable of achieving sub-meter level accuracy in benign GNSS conditions using low-cost GNSS receivers. However, SF-PPP alone cannot be employed for land vehicles due to frequent signal degradation and blockage. In this paper, real-time SF-PPP is integrated with a low-cost consumer-grade Inertial Navigation System (INS) to provide a continuous and precise navigation solution. The PPP accuracy and the applied estimation algorithm contributed to reducing the effects of INS errors. The system was evaluated through two road tests which included open-sky, suburban, momentary outages, and complete GNSS outage conditions. The results showed that the developed PPP/INS system maintained horizontal sub-meter Root Mean Square (RMS) accuracy in open-sky and suburban environments. Moreover, the PPP/INS system could provide a continuous real-time positioning solution within the lane the vehicle is moving in. This lane-level accuracy was preserved even when passing under bridges and overpasses on the road. The developed PPP/INS system is expected to benefit low-cost precise land vehicle navigation applications including level 2 of vehicle automation which comprises services such as lane departure warning and lane-keeping assistance. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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22 pages, 4997 KiB

Open AccessFeature PaperArticle

Particle Imaging Velocimetry Gyroscope

by Ahmed A. Youssef and Naser El-Sheimy

Sensors 2019, 19(21), 4734; https://doi.org/10.3390/s19214734 - 31 Oct 2019

Cited by 2 | Viewed by 3586

Abstract

Inertial measurement units (IMUs) are typically classified as per the performance of the gyroscopes within each system. Consequently, it is critical for a system to have a low bias instability to have better performance. Nonetheless, there is no IMU available commercially that does [...] Read more.

Inertial measurement units (IMUs) are typically classified as per the performance of the gyroscopes within each system. Consequently, it is critical for a system to have a low bias instability to have better performance. Nonetheless, there is no IMU available commercially that does not actually suffer from bias-instability, even for the navigation grade IMUs. This paper introduces the proposition of a novel fluid-based gyroscope, which is referred to hereafter as a particle imaging velocimetry gyroscope (PIVG). The main advantages of the PIVG include being nearly drift-free, a high signal-to-noise ratio (SNR) in comparison to commercially available high-end gyroscopes, and its low cost. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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28 pages, 6771 KiB

Open AccessArticle

Direct Wideband Coherent Localization by Distributed Antenna Arrays

by Nenad Vukmirović, Miljko Erić, Miloš Janjić and Petar M. Djurić

Sensors 2019, 19(20), 4582; https://doi.org/10.3390/s19204582 - 21 Oct 2019

Cited by 10 | Viewed by 3688

Abstract

We address wideband direct coherent localization of a radio transmitter by a distributed antenna array in a multipath scenario with spatially-coherent line-of-sight (LoS) signal components. Such a signal scenario is realistic in small cells, especially indoors in the mmWave range. The system model [...] Read more.

We address wideband direct coherent localization of a radio transmitter by a distributed antenna array in a multipath scenario with spatially-coherent line-of-sight (LoS) signal components. Such a signal scenario is realistic in small cells, especially indoors in the mmWave range. The system model considers collocated time and phase synchronized receiving front-ends with antennas distributed in 3D space at known locations connected to the front-ends via calibrated coaxial cables or analog radio frequency over fiber links. The signal model assumes spherical wavefronts. We propose two ML-type algorithms (for known and unknown transmitter waveforms) and a subspace-based SCM-MUSIC algorithm for wideband direct coherent position estimation. We demonstrate the performance of the methods by Monte Carlo simulations. The results show that even in multipath environments, it is possible to achieve localization accuracy that is much better (by two to three orders of magnitude) than the carrier wavelength. They also suggest that the methods that do not exploit knowledge of the waveform have mean-squared errors approaching the Cramér–Rao bound. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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16 pages, 5454 KiB

Open AccessArticle

Odometer Velocity and Acceleration Estimation Based on Tracking Differentiator Filter for 3D-Reduced Inertial Sensor System

by Qing Zhang, Lianwu Guan and Dexin Xu

Sensors 2019, 19(20), 4501; https://doi.org/10.3390/s19204501 - 17 Oct 2019

Cited by 7 | Viewed by 4337

Abstract

Velocity information from the odometer is the key information in a reduced inertial sensor system (RISS), and is prone to noise corruption. In order to improve the navigation accuracy and reliability of a 3D RISS, a method based on a tracking differentiator (TD) [...] Read more.

Velocity information from the odometer is the key information in a reduced inertial sensor system (RISS), and is prone to noise corruption. In order to improve the navigation accuracy and reliability of a 3D RISS, a method based on a tracking differentiator (TD) filter was proposed to track odometer velocity and acceleration. With the TD filter, an input signal and its differential signal are estimated fast and accurately to avoid the noise amplification that is brought by the conventional differential method. The TD filter does not depend on an object model, and has less computational complexity. Moreover, the filter phase lag is decreased by the prediction process with the differential signal of the TD filter. In this study, the numerical simulation experiments indicate that the TD filter can achieve a better performance on random noises and outliers than traditional numerical differentiation. The effectiveness of the TD filter on a 3D RISS is demonstrated using a group of offline data that were obtained from an actual vehicle experiment. We conclude that the TD filter can not only quickly and correctly filter velocity and estimate acceleration from the odometer velocity for a 3D RISS, but can also improve the reliability of the 3D RISS. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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23 pages, 4379 KiB

Open AccessArticle

An Unconventional Multiple Low-Cost IMU and GPS-Integrated Kinematic Positioning and Navigation Method Based on Singer Model

by Minghong Zhu, Fei Yu and Shu Xiao

Sensors 2019, 19(19), 4274; https://doi.org/10.3390/s19194274 - 02 Oct 2019

Cited by 3 | Viewed by 3559

Abstract

To release the strong dependence of the conventional inertial navigation mechanization on the a priori low-cost inertial measurement unit (IMU) error model, this research applies an unconventional multi-sensor integration strategy to integrate multiple low-cost IMUs and a global positioning system (GPS) for mass-market [...] Read more.

To release the strong dependence of the conventional inertial navigation mechanization on the a priori low-cost inertial measurement unit (IMU) error model, this research applies an unconventional multi-sensor integration strategy to integrate multiple low-cost IMUs and a global positioning system (GPS) for mass-market automotive applications. The unconventional integration strategy utilizes a basic three-dimensional (3D) kinematic trajectory model as the system model to directly estimate navigational parameters, and it allows the measurements from all of the sensors independently participating in measurement updates. However, the less complex kinematic model cannot realize smooth transitions between different motion statuses for the road vehicle with acceleration maneuvers. In this manuscript, we establish a more practical 3D kinematic trajectory model based on a “current” statistical Singer acceleration model to realize smooth transitions for the maneuvering vehicle. In addition, taking advantage of the unconventional strategy, we individually model the systematic errors of each IMU and the measurements of all sensors, in contrast to most existing approaches that adopt the common-mode errors for different sensors of the same design. A real dataset involving a GPS and multiple IMUs is processed to validate the success of the proposed algorithm model under the unconventional integration strategy. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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10 pages, 1207 KiB

Open AccessArticle

Space State Representation Corrections as an Aid in Pseudolite Positioning

by Jacek Rapiński and Dariusz Tomaszewski

Sensors 2019, 19(19), 4158; https://doi.org/10.3390/s19194158 - 25 Sep 2019

Cited by 1 | Viewed by 2023

Abstract

In the presented study, the authors deal with the problem of transmission of pseudolite coordinates to the receiver. Nowadays, there is no uniquely specified method that would provide data about the position of the pseudolite to the GNSS receiver. There is also no [...] Read more.

In the presented study, the authors deal with the problem of transmission of pseudolite coordinates to the receiver. Nowadays, there is no uniquely specified method that would provide data about the position of the pseudolite to the GNSS receiver. There is also no technical standard that defines the explicit way of performing such transmission. Solutions presented in the literature are usually tailored to the described system, which is then suited to the specific situation. The article shows that the universal methods, involving the modification of transmitted broadcast ephemeris data, cannot be universally used. The modifications could not have been introduced due to the low resolution of the quantities that are transmitted in the ephemeris data, in relation to the values that would have to be sent by the pseudolite. To overcome the implementation problems, the authors propose two solutions. The first solution presented is the modification of the RTCM SSR frame. This approach allows replacing one of the existing satellites in space with the pseudolite, while the second method involves the use of new RTCM frame for sending the pseudolite position. Finally, a numerical example of the proposed solutions is presented. At the end of the manuscript, their advantages and implementation possibilities are discussed. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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31 pages, 14033 KiB

Open AccessArticle

A Qualitative Analysis of a USB Camera for AGV Control

by Diogo Puppim de Oliveira, Wallace Pereira Neves dos Reis and Orides Morandin Junior

Sensors 2019, 19(19), 4111; https://doi.org/10.3390/s19194111 - 23 Sep 2019

Cited by 9 | Viewed by 7388

Abstract

The increasing use of Automated Guided Vehicles (AGV) in the industry points to a search for better techniques and technologies to adapt to market requirements. Proper position control and movement give an AGV greater movement accuracy and greater lateral oscillations stability and vibration. [...] Read more.

The increasing use of Automated Guided Vehicles (AGV) in the industry points to a search for better techniques and technologies to adapt to market requirements. Proper position control and movement give an AGV greater movement accuracy and greater lateral oscillations stability and vibration. It leads to smaller corridors and leaner plants, to more relaxed shipment devices, and to greater safety in the transport of fragile loads, for instance. AGV control techniques are not new, but new sensors’ applications are possible, such as USB cameras. In this sense, it is necessary to ensure the sensor is adequate to control system requirements. This work addresses AGVs driven by passive floor demarcations. It presents a qualitative analysis of a USB camera as sensors for AGV control, not yet a common industrial application. We performed the experiments with a small AGV prototype on an eight-shaped lane, varying both camera parameters and AGV parameters, such as linear speed. The AGV uses a USB camera with different image processing settings—different morphological filters structuring elements shapes and sizes, and three different image resolutions—to analyze the factors that affect line detection and control processing. This paper’s main contribution is a qualitative and quantitative analysis for the different sensor configurations. In addition, it discusses the influence sources on camera image as a position sensor. Furthermore, the experiments confirm sensor pertinence for the proposed control system. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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16 pages, 2991 KiB

Open AccessArticle

Genetic Algorithm Approach to the 3D Node Localization in TDOA Systems

by Javier Díez-González, Rubén Álvarez, David González-Bárcena, Lidia Sánchez-González, Manuel Castejón-Limas and Hilde Perez

Sensors 2019, 19(18), 3880; https://doi.org/10.3390/s19183880 - 09 Sep 2019

Cited by 40 | Viewed by 4333

Abstract

Positioning asynchronous architectures based on time measurements are reaching growing importance in Local Positioning Systems (LPS). These architectures have special relevance in precision applications and indoor/outdoor navigation of automatic vehicles such as Automatic Ground Vehicles (AGVs) and Unmanned Aerial Vehicles (UAVs). The positioning [...] Read more.

Positioning asynchronous architectures based on time measurements are reaching growing importance in Local Positioning Systems (LPS). These architectures have special relevance in precision applications and indoor/outdoor navigation of automatic vehicles such as Automatic Ground Vehicles (AGVs) and Unmanned Aerial Vehicles (UAVs). The positioning error of these systems is conditioned by the algorithms used in the position calculation, the quality of the time measurements, and the sensor deployment of the signal receivers. Once the algorithms have been defined and the method to compute the time measurements has been selected, the only design criteria of the LPS is the distribution of the sensors in the three-dimensional space. This problem has proved to be NP-hard, and therefore a heuristic solution to the problem is recommended. In this paper, a genetic algorithm with the flexibility to be adapted to different scenarios and ground modelings is proposed. This algorithm is used to determine the best node localization in order to reduce the Cramér-Rao Lower Bound (CRLB) with a heteroscedastic noise consideration in each sensor of an Asynchronous Time Difference of Arrival (A-TDOA) architecture. The methodology proposed allows for the optimization of the 3D sensor deployment of a passive A-TDOA architecture, including ground modeling flexibility and heteroscedastic noise consideration with sequential iterations, and reducing the spatial discretization to achieve better results. Results show that optimization with 15% of elitism and a Tournament 3 selection strategy offers the best maximization for the algorithm. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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20 pages, 11267 KiB

Open AccessArticle

Multi-Sensor Fusion Approach for Improving Map-Based Indoor Pedestrian Localization

by Hsiang-Yun Huang, Chia-Yeh Hsieh, Kai-Chun Liu, Hui-Chun Cheng, Steen J. Hsu and Chia-Tai Chan

Sensors 2019, 19(17), 3786; https://doi.org/10.3390/s19173786 - 31 Aug 2019

Cited by 12 | Viewed by 4546

Abstract

The interior space of large-scale buildings, such as hospitals, with a variety of departments, is so complicated that people may easily lose their way while visiting. Difficulties in wayfinding can cause stress, anxiety, frustration and safety issues to patients and families. An indoor [...] Read more.

The interior space of large-scale buildings, such as hospitals, with a variety of departments, is so complicated that people may easily lose their way while visiting. Difficulties in wayfinding can cause stress, anxiety, frustration and safety issues to patients and families. An indoor navigation system including route planning and localization is utilized to guide people from one place to another. The localization of moving subjects is a critical-function component in an indoor navigation system. Pedestrian dead reckoning (PDR) is a technology that is widely employed for localization due to the advantage of being independent of infrastructure. To improve the accuracy of the localization system, combining different technologies is one of the solutions. In this study, a multi-sensor fusion approach is proposed to improve the accuracy of the PDR system by utilizing a light sensor, Bluetooth and map information. These simple mechanisms are applied to deal with the issue of accumulative error by identifying edge and sub-edge information from both Bluetooth and the light sensor. Overall, the accumulative error of the proposed multi-sensor fusion approach is below 65 cm in different cases of light arrangement. Compared to inertial sensor-based PDR system, the proposed multi-sensor fusion approach can improve 90% of the localization accuracy in an environment with an appropriate density of ceiling-mounted lamps. The results demonstrate that the proposed approach can improve the localization accuracy by utilizing multi-sensor data and fulfill the feasibility requirements of localization in an indoor navigation system. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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28 pages, 21557 KiB

Open AccessArticle

A Bio-Inspired Polarization Sensor with High Outdoor Accuracy and Central-Symmetry Calibration Method with Integrating Sphere

by Yinlong Wang, Jinkui Chu, Ran Zhang, Jinshan Li, Xiaoqing Guo and Muyin Lin

Sensors 2019, 19(16), 3448; https://doi.org/10.3390/s19163448 - 07 Aug 2019

Cited by 18 | Viewed by 3294

Abstract

A bio-inspired polarization sensor with lenses for navigation was evaluated in this study. Two new calibration methods are introduced, referred to as “central-symmetry calibration” (with an integrating sphere) and “noncontinuous calibration”. A comparison between the indoor calibration results obtained from different calibration methods [...] Read more.

A bio-inspired polarization sensor with lenses for navigation was evaluated in this study. Two new calibration methods are introduced, referred to as “central-symmetry calibration” (with an integrating sphere) and “noncontinuous calibration”. A comparison between the indoor calibration results obtained from different calibration methods shows that the two proposed calibration methods are more effective. The central-symmetry calibration method optimized the nonconstant calibration voltage deviations, caused by the off-axis feature of the integrating sphere, to be constant values which can be calibrated easily. The section algorithm proposed previously showed no experimental advantages until the central-symmetry calibration method was proposed. The outdoor experimental results indicated that the indoor calibration parameters did not perform very well in practice outdoor conditions. To establish the reason, four types of calibration parameters were analyzed using the replacement method. It can be concluded that three types can be easily calibrated or affect the sensor accuracy slightly. However, before the sensor is used outdoors every time, the last type must be replaced with the corresponding outdoor parameter, and the calculation needs a precise rotary table. This parameter, which is mainly affected by the spectrum of incident light, is the main factor determining the sensor accuracy. After calibration, the sensor reaches an indoor accuracy of ±0.009° and a static outdoor accuracy of ±0.05° under clear sky conditions. The dynamic outdoor experiment shows a ±0.5° heading deviation between the polarization sensor and the inertial navigation system with a ±0.06° angular accuracy. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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21 pages, 2398 KiB

Open AccessArticle

A Hybrid Sliding Window Optimizer for Tightly-Coupled Vision-Aided Inertial Navigation System

by Junxiang Jiang, Xiaoji Niu, Ruonan Guo and Jingnan Liu

Sensors 2019, 19(15), 3418; https://doi.org/10.3390/s19153418 - 04 Aug 2019

Cited by 8 | Viewed by 3689

Abstract

The fusion of visual and inertial measurements for motion tracking has become prevalent in the robotic community, due to its complementary sensing characteristics, low cost, and small space requirements. This fusion task is known as the vision-aided inertial navigation system problem. We present [...] Read more.

The fusion of visual and inertial measurements for motion tracking has become prevalent in the robotic community, due to its complementary sensing characteristics, low cost, and small space requirements. This fusion task is known as the vision-aided inertial navigation system problem. We present a novel hybrid sliding window optimizer to achieve information fusion for a tightly-coupled vision-aided inertial navigation system. It possesses the advantages of both the conditioning-based method and the prior-based method. A novel distributed marginalization method was also designed based on the multi-state constraints method with significant efficiency improvement over the traditional method. The performance of the proposed algorithm was evaluated with the publicly available EuRoC datasets and showed competitive results compared with existing algorithms. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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26 pages, 4079 KiB

Open AccessArticle

Using Step Size and Lower Limb Segment Orientation from Multiple Low-Cost Wearable Inertial/Magnetic Sensors for Pedestrian Navigation

by Chandra Tjhai and Kyle O’Keefe

Sensors 2019, 19(14), 3140; https://doi.org/10.3390/s19143140 - 17 Jul 2019

Cited by 12 | Viewed by 4845

Abstract

This paper demonstrates the use of multiple low-cost inertial/magnetic sensors as a pedestrian navigation system for indoor positioning. This research looks at the problem of pedestrian navigation in a practical manner by investigating dead-reckoning methods using low-cost sensors. This work uses the estimated [...] Read more.

This paper demonstrates the use of multiple low-cost inertial/magnetic sensors as a pedestrian navigation system for indoor positioning. This research looks at the problem of pedestrian navigation in a practical manner by investigating dead-reckoning methods using low-cost sensors. This work uses the estimated sensor orientation angles to compute the step size from the kinematics of a skeletal model. The orientations of limbs are represented by the tilt angles estimated from the inertial measurements, especially the pitch angle. In addition, different step size estimation methods are compared. A sensor data logging system is developed in order to record all motion data from every limb segment using a single platform and similar types of sensors. A skeletal model of five segments is chosen to model the forward kinematics of the lower limbs. A treadmill walk experiment with an optical motion capture system is conducted for algorithm evaluation. The mean error of the estimated orientation angles of the limbs is less than 6 degrees. The results show that the step length mean error is 3.2 cm, the left stride length mean error is 12.5 cm, and the right stride length mean error is 9 cm. The expected positioning error is less than 5% of the total distance travelled. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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16 pages, 4654 KiB

Open AccessArticle

Multiple Target Tracking Based on Multiple Hypotheses Tracking and Modified Ensemble Kalman Filter in Multi-Sensor Fusion

by Zequn Zhang, Kun Fu, Xian Sun and Wenjuan Ren

Sensors 2019, 19(14), 3118; https://doi.org/10.3390/s19143118 - 15 Jul 2019

Cited by 23 | Viewed by 4237

Abstract

In multi-sensor fusion (MSF), the integration of multi-sensor observation data with different observation errors to achieve more accurate positioning of the target has always been a research focus. In this study, a modified ensemble Kalman filter (EnKF) is presented to substitute the traditional [...] Read more.

In multi-sensor fusion (MSF), the integration of multi-sensor observation data with different observation errors to achieve more accurate positioning of the target has always been a research focus. In this study, a modified ensemble Kalman filter (EnKF) is presented to substitute the traditional Kalman filter (KF) in the multiple hypotheses tracking (MHT) to deal with the high nonlinearity that always shows up in multiple target tracking (MTT) problems. In addition, the multi-source observation data fusion is also realized by using the modified EnKF, which enables the low-precision observation data to be corrected by high-precision observation data, and the accuracy of the corrected data can be calibrated by the statistical information provided by the EnKF. Numerical studies are given to demonstrate the effectiveness of our proposed method and the results show that the MHT-EnKF method can achieve remarkable enhancement in dealing with nonlinear movement variation and positioning accuracy for MTT problems in MSF scenario. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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13 pages, 5713 KiB

Open AccessArticle

Geodesic-Based Method for Improving Matching Efficiency of Underwater Terrain Matching Navigation

by Zhaowei Li, Wei Zheng and Fan Wu

Sensors 2019, 19(12), 2709; https://doi.org/10.3390/s19122709 - 16 Jun 2019

Cited by 10 | Viewed by 3018

Abstract

In this study, we improved the matching efficiency of underwater terrain matching navigation. Firstly, a new geodesic-based method was developed by combining the law of the shortest arc in spherical geometry with the theory of the attitude control in space and maritime environments. [...] Read more.

In this study, we improved the matching efficiency of underwater terrain matching navigation. Firstly, a new geodesic-based method was developed by combining the law of the shortest arc in spherical geometry with the theory of the attitude control in space and maritime environments. Secondly, along a design track, the geodesic-based method helped reduce the radius of the search matching area, and improved the matching efficiency. Finally, for parameter setting, the search matching time of underwater terrain matching navigation was reduced from 9.84 s to 1.29 s (about 7.6 times), with the matching accuracy being invariable using the new geodesic-based method. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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15 pages, 10572 KiB

Open AccessArticle

Precise Point Positioning Using World’s First Dual-Frequency GPS/GALILEO Smartphone

by Abdelsatar Elmezayen and Ahmed El-Rabbany

Sensors 2019, 19(11), 2593; https://doi.org/10.3390/s19112593 - 06 Jun 2019

Cited by 69 | Viewed by 7306

Abstract

The release of the world’s first dual-frequency GPS/Galileo smartphone, Xiaomi mi 8, in 2018 provides an opportunity for high-precision positioning using ultra low-cost sensors. In this research, the GNSS precise point positioning (PPP) accuracy of the Xiaomi mi 8 smartphone is tested in [...] Read more.

The release of the world’s first dual-frequency GPS/Galileo smartphone, Xiaomi mi 8, in 2018 provides an opportunity for high-precision positioning using ultra low-cost sensors. In this research, the GNSS precise point positioning (PPP) accuracy of the Xiaomi mi 8 smartphone is tested in post-processing and real-time modes. Raw dual-frequency observations are collected over two different time windows from both of the Xiaomi mi 8 smartphone and a Trimble R9 geodetic-quality GNSS receiver using a short baseline, due to the lack of a nearby reference station to the observation site. The data sets are first processed in differential modes using Trimble business center (TBC) software in order to provide the reference positioning solution for both of the geodetic receiver and the smartphone. An in-house PPP software is then used to process the collected data in both of post-processing and real-time modes. Precise ephemeris obtained from the multi-GNSS experiment (MGEX) is used for post-processing PPP, while the new NAVCAST real-time GNSS service, Germany, is used for real-time PPP. Additionally, the real-time PPP solution is assessed in both of static and kinematic modes. It is shown that the dual-frequency GNSS smartphone is capable of achieving decimeter-level positioning accuracy, in both of post-processing and real-time PPP modes, respectively. Meter-level positioning accuracy is achieved in the kinematic mode. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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23 pages, 9583 KiB

Open AccessArticle

Floor Identification Using Magnetic Field Data with Smartphone Sensors

by Imran Ashraf, Soojung Hur, Muhammad Shafiq and Yongwan Park

Sensors 2019, 19(11), 2538; https://doi.org/10.3390/s19112538 - 03 Jun 2019

Cited by 25 | Viewed by 5346

Abstract

Floor identification plays a key role in multi-story indoor positioning and localization systems. Current floor identification systems rely primarily on Wi-Fi signals and barometric pressure data. Barometric systems require installation of additional standalone sensors to perform floor identification. Wi-Fi systems, on the other [...] Read more.

Floor identification plays a key role in multi-story indoor positioning and localization systems. Current floor identification systems rely primarily on Wi-Fi signals and barometric pressure data. Barometric systems require installation of additional standalone sensors to perform floor identification. Wi-Fi systems, on the other hand, are vulnerable to the dynamic environment and adverse effects of path loss, shadowing, and multipath fading. In this paper, we take advantage of a pervasive magnetic field to compensate for the limitations of these systems. We employ smartphone sensors to make the proposed scheme infrastructure free and cost-effective. We use smartphone magnetic sensors to identify the floors in a multi-story building with improved accuracy. Floor identification is performed with user activities of normal walking, call listening, and phone swinging. Various machine learning techniques are leveraged to identify user activities. Extensive experiments are performed to evaluate the proposed magnetic-data-based floor identification scheme. Additionally, the impact of device heterogeneity on floor identification is investigated using Samsung Galaxy S8, LG G6, and LG G7 smartphones. Research results demonstrate that the magnetic floor identification outperforms barometric and Wi-Fi-enabled floor detection techniques. A floor change module is incorporated to further enhance the accuracy of floor identification. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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19 pages, 3325 KiB

Open AccessArticle

Quaternion-Based Robust Attitude Estimation Using an Adaptive Unscented Kalman Filter

by Antônio C. B. Chiella, Bruno O. S. Teixeira and Guilherme A. S. Pereira

Sensors 2019, 19(10), 2372; https://doi.org/10.3390/s19102372 - 23 May 2019

Cited by 43 | Viewed by 5710

Abstract

This paper presents the Quaternion-based Robust Adaptive Unscented Kalman Filter (QRAUKF) for attitude estimation. The proposed methodology modifies and extends the standard UKF equations to consistently accommodate the non-Euclidean algebra of unit quaternions and to add robustness to fast and slow variations in [...] Read more.

This paper presents the Quaternion-based Robust Adaptive Unscented Kalman Filter (QRAUKF) for attitude estimation. The proposed methodology modifies and extends the standard UKF equations to consistently accommodate the non-Euclidean algebra of unit quaternions and to add robustness to fast and slow variations in the measurement uncertainty. To deal with slow time-varying perturbations in the sensors, an adaptive strategy based on covariance matching that tunes the measurement covariance matrix online is used. Additionally, an outlier detector algorithm is adopted to identify abrupt changes in the UKF innovation, thus rejecting fast perturbations. Adaptation and outlier detection make the proposed algorithm robust to fast and slow perturbations such as external magnetic field interference and linear accelerations. Comparative experimental results that use an industrial manipulator robot as ground truth suggest that our method overcomes a trusted commercial solution and other widely used open source algorithms found in the literature. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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20 pages, 1511 KiB

Open AccessArticle

A Novel FEM Based T-S Fuzzy Particle Filtering for Bearings-Only Maneuvering Target Tracking

by Xiaoli Wang, Liangqun Li and Weixin Xie

Sensors 2019, 19(9), 2208; https://doi.org/10.3390/s19092208 - 13 May 2019

Cited by 7 | Viewed by 2830

Abstract

In this paper, we propose a novel fuzzy expectation maximization (FEM) based Takagi-Sugeno (T-S) fuzzy particle filtering (FEMTS-PF) algorithm for a passive sensor system. In order to incorporate target spatial-temporal information into particle filtering, we introduce a T-S fuzzy modeling algorithm, in which [...] Read more.

In this paper, we propose a novel fuzzy expectation maximization (FEM) based Takagi-Sugeno (T-S) fuzzy particle filtering (FEMTS-PF) algorithm for a passive sensor system. In order to incorporate target spatial-temporal information into particle filtering, we introduce a T-S fuzzy modeling algorithm, in which an improved FEM approach is proposed to adaptively identify the premise parameters, and the model probability is adjusted by the premise membership functions. In the proposed FEM, the fuzzy parameter is derived by the fuzzy C-regressive model clustering method based on entropy and spatial-temporal characteristics, which can avoid the subjective influence caused by the artificial setting of the initial value when compared to the traditional FEM. Furthermore, using the proposed T-S fuzzy model, the algorithm samples particles, which can effectively reduce the particle degradation phenomenon and the parallel filtering, can realize the real-time performance of the algorithm. Finally, the results of the proposed algorithm are evaluated and compared to several existing filtering algorithms through a series of Monte Carlo simulations. The simulation results demonstrate that the proposed algorithm is more precise, robust and that it even has a faster convergence rate than the interacting multiple model unscented Kalman filter (IMMUKF), interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model Rao-Blackwellized particle filter (IMMRBPF). Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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24 pages, 1327 KiB

Open AccessArticle

Enhancing the Accuracy and Robustness of a Compressive Sensing Based Device-Free Localization by Exploiting Channel Diversity

by Dongping Yu, Yan Guo, Ning Li and Xiaoqin Yang

Sensors 2019, 19(8), 1828; https://doi.org/10.3390/s19081828 - 17 Apr 2019

Cited by 1 | Viewed by 2443

Abstract

As an emerging and promising technique, device-free localization (DFL) estimates target positions by analyzing their shadowing effects. Most existing compressive sensing (CS)-based DFL methods use the changes of received signal strength (RSS) to approximate the shadowing effects. However, in changing environments, RSS readings [...] Read more.

As an emerging and promising technique, device-free localization (DFL) estimates target positions by analyzing their shadowing effects. Most existing compressive sensing (CS)-based DFL methods use the changes of received signal strength (RSS) to approximate the shadowing effects. However, in changing environments, RSS readings are vulnerable to environmental dynamics. The deviation between runtime RSS variations and the data in a fixed dictionary can significantly deteriorate the performance of DFL. In this paper, we introduce ComDec, a novel CS-based DFL method using channel state information (CSI) to enhance localization accuracy and robustness. To exploit the channel diversity of CSI measurements, the DFL problem is formulated as a joint sparse recovery problem that recovers multiple sparse vectors with common support. To solve this problem, we develop a joint sparse recovery algorithm under the variational Bayesian inference framework. In this algorithm, dictionaries are parameterized based on the saddle surface model. To adapt to the environmental changes and different channel characteristics, dictionary parameters are modelled as tunable parameters. Simulation results verified the superior performance of ComDec as compared with other state-of-the-art CS-based DFL methods. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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15 pages, 5189 KiB

Open AccessArticle

Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor Localization

by Ahmed H. Salamah, Mohamed Tamazin, Maha A. Sharkas, Mohamed Khedr and Mohamed Mahmoud

Sensors 2019, 19(7), 1678; https://doi.org/10.3390/s19071678 - 08 Apr 2019

Cited by 11 | Viewed by 3179

Abstract

The smartphone market is rapidly spreading, coupled with several services and applications. Some of these services require the knowledge of the exact location of their handsets. The Global Positioning System (GPS) suffers from accuracy deterioration and outages in indoor environments. The Wi-Fi Fingerprinting [...] Read more.

The smartphone market is rapidly spreading, coupled with several services and applications. Some of these services require the knowledge of the exact location of their handsets. The Global Positioning System (GPS) suffers from accuracy deterioration and outages in indoor environments. The Wi-Fi Fingerprinting approach has been widely used in indoor positioning systems. In this paper, Principal Component Analysis (PCA) is utilized to improve the performance and to reduce the computation complexity of the Wi-Fi indoor localization systems based on a machine learning approach. The experimental setup and performance of the proposed method were tested in real indoor environments at a large-scale environment of 960 m² to analyze the performance of different machine learning approaches. The results show that the performance of the proposed method outperforms conventional indoor localization techniques based on machine learning techniques. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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16 pages, 8114 KiB

Open AccessArticle

Multi-Sensor Passive Localization Using Direct Position Determination with Time-Varying Delay

by Shangyu Zhang, Zhen Huang, Xuefeng Feng, Jiazhi He and Lei Shi

Sensors 2019, 19(7), 1541; https://doi.org/10.3390/s19071541 - 29 Mar 2019

Cited by 4 | Viewed by 2913

Abstract

This paper focuses on passive emitter localization using moving sensors. The increase in observation time is beneficial to improve the localization accuracy, but it could cause deterioration of the relative motion between the emitter and the sensors, especially the nonlinear motion. The common [...] Read more.

This paper focuses on passive emitter localization using moving sensors. The increase in observation time is beneficial to improve the localization accuracy, but it could cause deterioration of the relative motion between the emitter and the sensors, especially the nonlinear motion. The common localization algorithms typically have two steps: (1) parameter estimation and (2) position determination, where the parameters are assumed to be constant, and it is not applicable for long observation times. We proposed the time-varying delay-based direct position determination (DPD-TVD) method, regarding the variation in the propagation time delay during the observation time. Using one step, the proposed algorithm can obtain the emitter’s position directly from the received signals by calculating the cost function corresponding to the map grid. By better adapting to highly dynamic scenarios, the proposed algorithm can achieve better localization accuracy than that of constant parameters using one-step or two-step procedures, which is demonstrated by the simulation results. Full article

(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)

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