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Editorial

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4 pages, 169 KiB

Open AccessEditorial

Indoor Positioning and Navigation

by Simon Tomažič

Sensors 2021, 21(14), 4793; https://doi.org/10.3390/s21144793 - 14 Jul 2021

Cited by 5 | Viewed by 2867

Abstract

Recently, the social and commercial interest in location-based services (LBS) has been increasing significantly [...] Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

Research

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23 pages, 7165 KiB

Open AccessArticle

Indoor Positioning System Based on Global Positioning System Signals with Down- and Up-Converters in 433 MHz ISM Band

by Abdulkadir Uzun, Firas Abdul Ghani, Amir Mohsen Ahmadi Najafabadi, Hüsnü Yenigün and İbrahim Tekin

Sensors 2021, 21(13), 4338; https://doi.org/10.3390/s21134338 - 25 Jun 2021

Cited by 6 | Viewed by 2793

Abstract

In this paper, an indoor positioning system using Global Positioning System (GPS) signals in the 433 MHz Industrial Scientific Medical (ISM) band is proposed, and an experimental demonstration of how the proposed system operates under both line-of-sight and non-line-of-sight conditions on a building [...] Read more.

In this paper, an indoor positioning system using Global Positioning System (GPS) signals in the 433 MHz Industrial Scientific Medical (ISM) band is proposed, and an experimental demonstration of how the proposed system operates under both line-of-sight and non-line-of-sight conditions on a building floor is presented. The proposed method is based on down-converting (DC) repeaters and an up-converting (UC) receiver. The down-conversion is deployed to avoid the restrictions on the use of Global Navigation Satellite Systems (GNSS) repeaters, to achieve higher output power, and to expose the GPS signals to lower path loss. The repeaters receive outdoor GPS signals at 1575.42 MHz (L1 band), down-convert them to the 433 MHz ISM band, then amplify and retransmit them to the indoor environment. The front end up-converter is combined with an off-the-shelf GPS receiver. When GPS signals at 433 MHz are received by the up-converting receiver, it then amplifies and up-converts these signals back to the L1 frequency. Subsequently, the off-the-shelf GPS receiver calculates the pseudo-ranges. The raw data are then sent from the receiver over a 2.4 GHz Wi-Fi link to a remote computer for data processing and indoor position estimation. Each repeater also has an attenuator to adjust its amplification level so that each repeater transmits almost equal signal levels in order to prevent jamming of the off-the-shelf GPS receiver. Experimental results demonstrate that the indoor position of a receiver can be found with sub-meter accuracy under both line-of-sight and non-line-of-sight conditions. The estimated position was found to be 54 and 98 cm away from the real position, while the 50% circular error probable (CEP) of the collected samples showed a radius of 3.3 and 4 m, respectively, for line-of-sight and non-line-of-sight cases. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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21 pages, 5278 KiB

Open AccessArticle

Calibration-Free Single-Anchor Indoor Localization Using an ESPAR Antenna

by Mateusz Groth, Krzysztof Nyka and Lukasz Kulas

Sensors 2021, 21(10), 3431; https://doi.org/10.3390/s21103431 - 14 May 2021

Cited by 8 | Viewed by 2950

Abstract

In this paper, we present a novel, low-cost approach to indoor localization that is capable of performing localization processes in real indoor environments and does not require calibration or recalibration procedures. To this end, we propose a single-anchor architecture and design based on [...] Read more.

In this paper, we present a novel, low-cost approach to indoor localization that is capable of performing localization processes in real indoor environments and does not require calibration or recalibration procedures. To this end, we propose a single-anchor architecture and design based on an electronically steerable parasitic array radiator (ESPAR) antenna and Nordic Semiconductor nRF52840 utilizing Bluetooth Low Energy (BLE) protocol. The proposed algorithm relies on received signal strength (RSS) values measured by the receiver equipped with the ESPAR antenna for every considered antenna radiation pattern. The calibration-free concept is achieved by using inexpensive BLE nodes installed in known positions on the walls of the test room and acting as reference nodes for the positioning algorithm. Measurements performed in the indoor environment show that the proposed approach can successfully provide positioning results better than those previously reported for single-anchor ESPAR antenna localization systems employing the classical fingerprinting method and relying on time-consuming calibration procedures. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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20 pages, 4447 KiB

Open AccessArticle

Sensor Information Sharing Using a Producer-Consumer Algorithm on Small Vehicles

by Rodrigo Vazquez-Lopez, Juan Carlos Herrera-Lozada, Jacobo Sandoval-Gutierrez, Philipp von Bülow and Daniel Librado Martinez-Vazquez

Sensors 2021, 21(9), 3022; https://doi.org/10.3390/s21093022 - 25 Apr 2021

Cited by 4 | Viewed by 3214

Abstract

There are several tools, frameworks, and algorithms to solve information sharing from multiple tasks and robots. Some applications such as ROS, Kafka, and MAVLink cover most problems when using operating systems. However, they cannot be used for particular problems that demand optimization of [...] Read more.

There are several tools, frameworks, and algorithms to solve information sharing from multiple tasks and robots. Some applications such as ROS, Kafka, and MAVLink cover most problems when using operating systems. However, they cannot be used for particular problems that demand optimization of resources. Therefore, the objective was to design a solution to fit the resources of small vehicles. The methodology consisted of defining the group of vehicles with low performance or are not compatible with high-level known applications; design a reduced, modular, and compatible architecture; design a producer-consumer algorithm that adjusts to the simultaneous localization and communication of multiple vehicles with UWB sensors; validate the operation with an interception task. The results showed the feasibility of performing architecture for embedded systems compatible with other applications managing information through the proposed algorithm allowed to complete the interception task between two vehicles. Another result was to determine the system’s efficiency by scaling the memory size and comparing its performance. The work’s contributions show the areas of opportunity to develop architectures focusing on the optimization of robot resources and complement existing ones. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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21 pages, 2138 KiB

Open AccessArticle

An Automated Indoor Localization System for Online Bluetooth Signal Strength Modeling Using Visual-Inertial SLAM

by Simon Tomažič and Igor Škrjanc

Sensors 2021, 21(8), 2857; https://doi.org/10.3390/s21082857 - 19 Apr 2021

Cited by 17 | Viewed by 3670

Abstract

Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localization system that combines all the necessary components to realize low-cost [...] Read more.

Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localization system that combines all the necessary components to realize low-cost Bluetooth localization with the least data acquisition and network configuration overhead. The proposed system incorporates a sophisticated visual-inertial localization algorithm for a fully automated collection of Bluetooth signal strength data. A suitable collection of measurements can be quickly and easily performed, clearly defining which part of the space is not yet well covered by measurements. The obtained measurements, which can also be collected via the crowdsourcing approach, are used within a constrained nonlinear optimization algorithm. The latter is implemented on a smartphone and allows the online determination of the beacons’ locations and the construction of path loss models, which are validated in real-time using the particle swarm localization algorithm. The proposed system represents an advanced innovation as the application user can quickly find out when there are enough data collected for the expected radiolocation accuracy. In this way, radiolocation becomes much less time-consuming and labor-intensive as the configuration time is reduced by more than half. The experiment results show that the proposed system achieves a good trade-off in terms of network setup complexity and localization accuracy. The developed system for automated data acquisition and online modeling on a smartphone has proved to be very useful, as it can significantly simplify and speed up the installation of the Bluetooth network, especially in wide-area facilities. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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25 pages, 5502 KiB

Open AccessEditor’s ChoiceArticle

An Enhanced Indoor Positioning Algorithm Based on Fingerprint Using Fine-Grained CSI and RSSI Measurements of IEEE 802.11n WLAN

by **g**g Wang and Joongoo Park

Sensors 2021, 21(8), 2769; https://doi.org/10.3390/s21082769 - 14 Apr 2021

Cited by 41 | Viewed by 5154

Abstract

Received signal strength indication (RSSI) obtained by Medium Access Control (MAC) layer is widely used in range-based and fingerprint location systems due to its low cost and low complexity. However, RSS is affected by noise signals and multi-path, and its positioning performance is [...] Read more.

Received signal strength indication (RSSI) obtained by Medium Access Control (MAC) layer is widely used in range-based and fingerprint location systems due to its low cost and low complexity. However, RSS is affected by noise signals and multi-path, and its positioning performance is not stable. In recent years, many commercial WiFi devices support the acquisition of physical layer channel state information (CSI). CSI is an index that can characterize the signal characteristics with more fine granularity than RSS. Compared with RSS, CSI can avoid the effects of multi-path and noise by analyzing the characteristics of multi-channel sub-carriers. To improve the indoor location accuracy and algorithm efficiency, this paper proposes a hybrid fingerprint location technology based on RSS and CSI. In the off-line phase, to overcome the problems of low positioning accuracy and fingerprint drift caused by signal instability, a methodology based on the Kalman filter and a Gaussian function is proposed to preprocess the RSSI value and CSI amplitude value, and the improved CSI phase is incorporated after the linear transformation. The mutation and noisy data are then effectively eliminated, and the accurate and smoother outputs of the RSSI and CSI values can be achieved. Then, the accurate hybrid fingerprint database is established after dimensionality reduction of the obtained high-dimensional data values. The weighted k-nearest neighbor (WKNN) algorithm is applied to reduce the complexity of the algorithm during the online positioning stage, and the accurate indoor positioning algorithm is accomplished. Experimental results show that the proposed algorithm exhibits good performance on anti-noise ability, fusion positioning accuracy, and real-time filtering. Compared with CSI-MIMO, FIFS, and RSSI-based methods, the proposed fusion correction method has higher positioning accuracy and smaller positioning error. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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17 pages, 1162 KiB

Open AccessArticle

Appearance-Based Sequential Robot Localization Using a Patchwise Approximation of a Descriptor Manifold

by Alberto Jaenal, Francisco-Angel Moreno and Javier Gonzalez-Jimenez

Sensors 2021, 21(7), 2483; https://doi.org/10.3390/s21072483 - 2 Apr 2021

Cited by 6 | Viewed by 2125

Abstract

This paper addresses appearance-based robot localization in 2D with a sparse, lightweight map of the environment composed of descriptor–pose image pairs. Based on previous research in the field, we assume that image descriptors are samples of a low-dimensional Descriptor Manifold that is locally [...] Read more.

This paper addresses appearance-based robot localization in 2D with a sparse, lightweight map of the environment composed of descriptor–pose image pairs. Based on previous research in the field, we assume that image descriptors are samples of a low-dimensional Descriptor Manifold that is locally articulated by the camera pose. We propose a piecewise approximation of the geometry of such Descriptor Manifold through a tessellation of so-called Patches of Smooth Appearance Change (PSACs), which defines our appearance map. Upon this map, the presented robot localization method applies both a Gaussian Process Particle Filter (GPPF) to perform camera tracking and a Place Recognition (PR) technique for relocalization within the most likely PSACs according to the observed descriptor. A specific Gaussian Process (GP) is trained for each PSAC to regress a Gaussian distribution over the descriptor for any particle pose lying within that PSAC. The evaluation of the observed descriptor in this distribution gives us a likelihood, which is used as the weight for the particle. Besides, we model the impact of appearance variations on image descriptors as a white noise distribution within the GP formulation, ensuring adequate operation under lighting and scene appearance changes with respect to the conditions in which the map was constructed. A series of experiments with both real and synthetic images show that our method outperforms state-of-the-art appearance-based localization methods in terms of robustness and accuracy, with median errors below 0.3 m and 6°. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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25 pages, 21577 KiB

Open AccessArticle

Calibration of Visible Light Positioning Systems with a Mobile Robot

by Robin Amsters, Eric Demeester, Nobby Stevens and Peter Slaets

Sensors 2021, 21(7), 2394; https://doi.org/10.3390/s21072394 - 30 Mar 2021

Cited by 13 | Viewed by 3257

Abstract

Most indoor positioning systems require calibration before use. Fingerprinting requires the construction of a signal strength map, while ranging systems need the coordinates of the beacons. Calibration approaches exist for positioning systems that use Wi-Fi, radio frequency identification or ultrawideband. However, few examples [...] Read more.

Most indoor positioning systems require calibration before use. Fingerprinting requires the construction of a signal strength map, while ranging systems need the coordinates of the beacons. Calibration approaches exist for positioning systems that use Wi-Fi, radio frequency identification or ultrawideband. However, few examples are available for the calibration of visible light positioning systems. Most works focused on obtaining the channel model parameters or performed a calibration based on known receiver locations. In this paper, we describe an improved procedure that uses a mobile robot for data collection and is able to obtain a map of the environment with the beacon locations and their identities. Compared to previous work, the error is almost halved. Additionally, this approach does not require prior knowledge of the number of light sources or the receiver location. We demonstrate that the system performs well under a wide range of lighting conditions and investigate the influence of parameters such as the robot trajectory, camera resolution and field of view. Finally, we also close the loop between calibration and positioning and show that our approach has similar or better accuracy than manual calibration. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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17 pages, 3159 KiB

Open AccessArticle

Towards Precise Positioning and Movement of UAVs for Near-Wall Tasks in GNSS-Denied Environments

by Félix Orjales, Javier Losada-Pita, Alejandro Paz-Lopez and Álvaro Deibe

Sensors 2021, 21(6), 2194; https://doi.org/10.3390/s21062194 - 21 Mar 2021

Cited by 4 | Viewed by 3318

Abstract

UAVs often perform tasks that require flying close to walls or structures and in environments where a satellite-based location is not possible. Flying close to solid bodies implies a higher risk of collisions, thus requiring an increase in the precision of the measurement [...] Read more.

UAVs often perform tasks that require flying close to walls or structures and in environments where a satellite-based location is not possible. Flying close to solid bodies implies a higher risk of collisions, thus requiring an increase in the precision of the measurement and control of the UAV’s position. The aerodynamic distortions generated by nearby walls or other objects are also relevant, making the control more complex and further placing demands on the positioning system. Performing wall-related tasks implies flying very close to the wall and, in some cases, even touching it. This work presents a Near-Wall Positioning System (NWPS) based on the combination of an Ultra-wideband (UWB) solution and LIDAR-based range finders. This NWPS has been developed and tested to allow precise positioning and orientation of a multirotor UAV relative to a wall when performing tasks near it. Specific position and orientation control hardware based on horizontal thrusters has also been designed, allowing the UAV to move smoothly and safely near walls. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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14 pages, 3597 KiB

Open AccessCommunication

A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks

by Zhongliang Deng, Shihao Tang, **wen Deng, Lu Yin and **grong Liu

Sensors 2021, 21(5), 1890; https://doi.org/10.3390/s21051890 - 8 Mar 2021

Cited by 13 | Viewed by 2556

Abstract

Location information is one of the basic elements of the Internet of Things (IoT), which is also an important research direction in the application of wireless sensor networks (WSNs). Aiming at addressing the TOA positioning problem in the low anchor node density deployment [...] Read more.

Location information is one of the basic elements of the Internet of Things (IoT), which is also an important research direction in the application of wireless sensor networks (WSNs). Aiming at addressing the TOA positioning problem in the low anchor node density deployment environment, the traditional cooperative localization method will reduce the positioning accuracy due to excessive redundant information. In this regard, this paper proposes a location source optimization algorithm based on fuzzy comprehensive evaluation. First, each node calculates its own time-position distribute conditional posterior Cramer-Rao lower bound (DCPCRLB) and transfers it to neighbor nodes. Then collect the DCPCRLB, distance measurement, azimuth angle and other information from neighboring nodes to form a fuzzy evaluation factor set and determine the final preferred location source after fuzzy change. The simulation results show that the method proposed in this paper has better positioning accuracy about 33.9% with the compared method in low anchor node density scenarios when the computational complexity is comparable. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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21 pages, 1913 KiB

Open AccessArticle

Real-Time Locating System in Production Management

by András Rácz-Szabó, Tamás Ruppert, László Bántay, Andreas Löcklin, László Jakab and János Abonyi

Sensors 2020, 20(23), 6766; https://doi.org/10.3390/s20236766 - 26 Nov 2020

Cited by 42 | Viewed by 7813

Abstract

Real-time monitoring and optimization of production and logistics processes significantly improve the efficiency of production systems. Advanced production management solutions require real-time information about the status of products, production, and resources. As real-time locating systems (also referred to as indoor positioning systems) can [...] Read more.

Real-time monitoring and optimization of production and logistics processes significantly improve the efficiency of production systems. Advanced production management solutions require real-time information about the status of products, production, and resources. As real-time locating systems (also referred to as indoor positioning systems) can enrich the available information, these systems started to gain attention in industrial environments in recent years. This paper provides a review of the possible technologies and applications related to production control and logistics, quality management, safety, and efficiency monitoring. This work also provides a workflow to clarify the steps of a typical real-time locating system project, including the cleaning, pre-processing, and analysis of the data to provide a guideline and reference for research and development of indoor positioning-based manufacturing solutions. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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17 pages, 7240 KiB

Open AccessArticle

Deep Learning-Based Positioning of Visually Impaired People in Indoor Environments

by Payal Mahida, Seyed Shahrestani and Hon Cheung

Sensors 2020, 20(21), 6238; https://doi.org/10.3390/s20216238 - 31 Oct 2020

Cited by 17 | Viewed by 3242

Abstract

Wayfinding and navigation can present substantial challenges to visually impaired (VI) people. Some of the significant aspects of these challenges arise from the difficulty of knowing the location of a moving person with enough accuracy. Positioning and localization in indoor environments require unique [...] Read more.

Wayfinding and navigation can present substantial challenges to visually impaired (VI) people. Some of the significant aspects of these challenges arise from the difficulty of knowing the location of a moving person with enough accuracy. Positioning and localization in indoor environments require unique solutions. Furthermore, positioning is one of the critical aspects of any navigation system that can assist a VI person with their independent movement. The other essential features of a typical indoor navigation system include pathfinding, obstacle avoidance, and capabilities for user interaction. This work focuses on the positioning of a VI person with enough precision for their use in indoor navigation. We aim to achieve this by utilizing only the capabilities of a typical smartphone. More specifically, our proposed approach is based on the use of the accelerometer, gyroscope, and magnetometer of a smartphone. We consider the indoor environment to be divided into microcells, with the vertex of each microcell being assigned two-dimensional local coordinates. A regression-based analysis is used to train a multilayer perceptron neural network to map the inertial sensor measurements to the coordinates of the vertex of the microcell corresponding to the position of the smartphone. In order to test our proposed solution, we used IPIN2016, a publicly-available multivariate dataset that divides the indoor environment into cells tagged with the inertial sensor data of a smartphone, in order to generate the training and validating sets. Our experiments show that our proposed approach can achieve a remarkable prediction accuracy of more than 94%, with a 0.65 m positioning error. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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20 pages, 5465 KiB

Open AccessArticle

ARBIN: Augmented Reality Based Indoor Navigation System

by Bo-Chen Huang, Jiun Hsu, Edward T.-H. Chu and Hui-Mei Wu

Sensors 2020, 20(20), 5890; https://doi.org/10.3390/s20205890 - 17 Oct 2020

Cited by 32 | Viewed by 8731

Abstract

Due to the popularity of indoor positioning technology, indoor navigation applications have been deployed in large buildings, such as hospitals, airports, and train stations, to guide visitors to their destinations. A commonly-used user interface, shown on smartphones, is a 2D floor map with [...] Read more.

Due to the popularity of indoor positioning technology, indoor navigation applications have been deployed in large buildings, such as hospitals, airports, and train stations, to guide visitors to their destinations. A commonly-used user interface, shown on smartphones, is a 2D floor map with a route to the destination. The navigation instructions, such as turn left, turn right, and go straight, pop up on the screen when users come to an intersection. However, owing to the restrictions of a 2D navigation map, users may face mental pressure and get confused while they are making a connection between the real environment and the 2D navigation map before moving forward. For this reason, we developed ARBIN, an augmented reality-based navigation system, which posts navigation instructions on the screen of real-world environments for ease of use. Thus, there is no need for users to make a connection between the navigation instructions and the real-world environment. In order to evaluate the applicability of ARBIN, a series of experiments were conducted in the outpatient area of the National Taiwan University Hospital YunLin Branch, which is nearly 1800 m², with 35 destinations and points of interests, such as a cardiovascular clinic, x-ray examination room, pharmacy, and so on. Four different types of smartphone were adopted for evaluation. Our results show that ARBIN can achieve 3 to 5 m accuracy, and provide users with correct instructions on their way to the destinations. ARBIN proved to be a practical solution for indoor navigation, especially for large buildings. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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19 pages, 4030 KiB

Open AccessArticle

Position Estimation in Corridors Along the Coupled Mode of Radiating Cables

by Olga Blaszkiewicz, Jaroslaw Sadowski and Jacek Stefanski

Sensors 2020, 20(18), 5064; https://doi.org/10.3390/s20185064 - 6 Sep 2020

Cited by 2 | Viewed by 2420

Abstract

Radiating cables are mostly used to provide radio communication in tunnels or corridors, but they can also be used to estimate the position of a mobile terminal along the cable. In this paper, a measuring receiver’s position was estimated by measuring the difference [...] Read more.

Radiating cables are mostly used to provide radio communication in tunnels or corridors, but they can also be used to estimate the position of a mobile terminal along the cable. In this paper, a measuring receiver’s position was estimated by measuring the difference in the direct signal’s reception time, which was generated by a transmitter connected to one end of the radiating cable, and the delayed signal retransmitted from another end. During tests, a relatively narrowband (23 MHz) signal was used in the unlicensed band (2.4 GHz) and 50 m long coupled mode radiating cable. The cable was installed along a corridor in the office building. Measurement results used different equipment configurations (i.e., return signal only amplified or amplified and frequency-shifted), which presented possible sources of errors. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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27 pages, 1650 KiB

Open AccessArticle

Toward Accurate Position Estimation Using Learning to Prediction Algorithm in Indoor Navigation

by Faisal Jamil, Naeem Iqbal, Shabir Ahmad and Do-Hyeun Kim

Sensors 2020, 20(16), 4410; https://doi.org/10.3390/s20164410 - 7 Aug 2020

Cited by 54 | Viewed by 8803

Abstract

Internet of Things is advancing, and the augmented role of smart navigation in automating processes is at its vanguard. Smart navigation and location tracking systems are finding increasing use in the area of the mission-critical indoor scenario, logistics, medicine, and security. A demanding [...] Read more.

Internet of Things is advancing, and the augmented role of smart navigation in automating processes is at its vanguard. Smart navigation and location tracking systems are finding increasing use in the area of the mission-critical indoor scenario, logistics, medicine, and security. A demanding emerging area is an Indoor Localization due to the increased fascination towards location-based services. Numerous inertial assessments unit-based indoor localization mechanisms have been suggested in this regard. However, these methods have many shortcomings pertaining to accuracy and consistency. In this study, we propose a novel position estimation system based on learning to the prediction model to address the above challenges. The designed system consists of two modules; learning to prediction module and position estimation using sensor fusion in an indoor environment. The prediction algorithm is attached to the learning module. Moreover, the learning module continuously controls, observes, and enhances the efficiency of the prediction algorithm by evaluating the output and taking into account the exogenous factors that may have an impact on its outcome. On top of that, we reckon a situation where the prediction algorithm can be applied to anticipate the accurate gyroscope and accelerometer reading from the noisy sensor readings. In the designed system, we consider a scenario where the learning module, based on Artificial Neural Network, and Kalman filter are used as a prediction algorithm to predict the actual accelerometer and gyroscope reading from the noisy sensor reading. Moreover, to acquire data, we use the next-generation inertial measurement unit, which contains a 3-axis accelerometer and gyroscope data. Finally, for the performance and accuracy of the proposed system, we carried out numbers of experiments, and we observed that the proposed Kalman filter with learning module performed better than the traditional Kalman filter algorithm in terms of root mean square error metric. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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20 pages, 6842 KiB

Open AccessArticle

A Method of Multiple Dynamic Objects Identification and Localization Based on Laser and RFID

by Wenpeng Fu, Ran Liu, Heng Wang, Rashid Ali, Yong** He, Zhiqiang Cao and Zhenghong Qin

Sensors 2020, 20(14), 3948; https://doi.org/10.3390/s20143948 - 16 Jul 2020

Cited by 12 | Viewed by 3264

Abstract

In an indoor environment, object identification and localization are paramount for human-object interaction. Visual or laser-based sensors can achieve the identification and localization of the object based on its appearance, but these approaches are computationally expensive and not robust against the environment with [...] Read more.

In an indoor environment, object identification and localization are paramount for human-object interaction. Visual or laser-based sensors can achieve the identification and localization of the object based on its appearance, but these approaches are computationally expensive and not robust against the environment with obstacles. Radio Frequency Identification (RFID) has a unique tag ID to identify the object, but it cannot accurately locate it. Therefore, in this paper, the data of RFID and laser range finder are fused for the better identification and localization of multiple dynamic objects in an indoor environment. The main method is to use the laser range finder to estimate the radial velocities of objects in a certain environment, and match them with the object’s radial velocities estimated by the RFID phase. The method also uses a fixed time series as “sliding time window” to find the cluster with the highest similarity of each RFID tag in each window. Moreover, the Pearson correlation coefficient (PCC) is used in the update stage of the particle filter (PF) to estimate the moving path of each cluster in order to improve the accuracy in a complex environment with obstacles. The experiments were verified by a SCITOS G5 robot. The results show that this method can achieve an matching rate of 90.18% and a localization accuracy of 0.33m in an environment with the presence of obstacles. This method effectively improves the matching rate and localization accuracy of multiple objects in indoor scenes when compared to the Bray-Curtis (BC) similarity matching-based approach as well as the particle filter-based approach. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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Review

Jump to: Editorial, Research, Other

32 pages, 796 KiB

Open AccessEditor’s ChoiceReview

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

by Santosh Subedi and Jae-Young Pyun

Sensors 2020, 20(24), 7230; https://doi.org/10.3390/s20247230 - 17 Dec 2020

Cited by 71 | Viewed by 8346

Abstract

In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location [...] Read more.

In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS’s incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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Other

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15 pages, 5239 KiB

Open AccessLetter

Cooperative Localization and Time Synchronization Based on M-VMP Method

by Zhongliang Deng, Shihao Tang, Buyun Jia, Hanhua Wang, **wen Deng and **nyu Zheng

Sensors 2020, 20(21), 6315; https://doi.org/10.3390/s20216315 - 5 Nov 2020

Cited by 2 | Viewed by 1993

Abstract

Localization estimation and clock synchronization are important research directions in the application of wireless sensor networks. Aiming at the problems of low positioning accuracy and slow convergence speed in localization estimation methods based on message passing, this paper proposes a low-complexity distributed cooperative [...] Read more.

Localization estimation and clock synchronization are important research directions in the application of wireless sensor networks. Aiming at the problems of low positioning accuracy and slow convergence speed in localization estimation methods based on message passing, this paper proposes a low-complexity distributed cooperative joint estimation method suitable for dynamic networks called multi-Gaussian variational message passing (M-VMP). The proposed method constrains the message to be a multi-Gaussian function superposition form to reduce the information loss in the variational message passing algorithm (VMP). Only the mean, covariance and weight of each message need to be transmitted in the network, which reduces the computational complexity while ensuring the information completeness. The simulation results show that the proposed method is superior to the VMP algorithm in terms of position accuracy and convergence speed and is close to the sum-product algorithm over a wireless network (SPAWN) based on non-parametric belief propagation, but the computational complexity and communication load are significantly reduced. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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17 pages, 2296 KiB

Open AccessLetter

An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning

by Yong Shi, Wenzhong Shi, **ntao Liu and **anjian **ao

Sensors 2020, 20(15), 4244; https://doi.org/10.3390/s20154244 - 30 Jul 2020

Cited by 37 | Viewed by 5258

Abstract

Received signal strength indicator (RSSI)-based positioning is suitable for large-scale applications due to its advantages of low cost and high accuracy. However, it suffers from low stability because RSSI is easily blocked and easily interfered with by objects and environmental effects. Therefore, this [...] Read more.

Received signal strength indicator (RSSI)-based positioning is suitable for large-scale applications due to its advantages of low cost and high accuracy. However, it suffers from low stability because RSSI is easily blocked and easily interfered with by objects and environmental effects. Therefore, this paper proposed a tri-partition RSSI classification and its tracing algorithm as an RSSI filter. The proposed filter shows an available feature, where small test RSSI samples gain a low deviation of less than 1 dBm from a large RSSI sample collected about 10 min, and the sub-classification RSSIs conform to normal distribution when the minimum sample count is greater than 20. The proposed filter also offers several advantages compared to the mean filter, including lower variance range with an overall range of around 1 dBm, 25.9% decreased sample variance, and 65% probability of mitigating RSSI left-skewness. We experimentally confirmed the proposed filter worked in the path-loss exponent fitting and location computing, and a 4.45-fold improvement in positioning stability based on the sample standard variance, and positioning accuracy improved by 20.5% with an overall error of less than 1.46 m. Full article

(This article belongs to the Special Issue Indoor Positioning and Navigation)

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