scholarly journals Optical Boundaries for LED-Based Indoor Positioning System

Computation ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 7 ◽  
Author(s):  
Olaoluwa Popoola ◽  
Sinan Sinanović ◽  
Wasiu Popoola ◽  
Roberto Ramirez-Iniguez
Keyword(s):  
Mathematical Model ◽  
Indoor Positioning ◽  
Experimental Measurements ◽  
Positioning System ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Light Emitting ◽  
Positioning Systems ◽  
Indoor Positioning System ◽  
Positioning Time

Overlap of footprints of light emitting diodes (LEDs) increases the positioning accuracy of wearable LED indoor positioning systems (IPS) but such an approach assumes that the footprint boundaries are defined. In this work, we develop a mathematical model for defining the footprint boundaries of an LED in terms of a threshold angle instead of the conventional half or full angle. To show the effect of the threshold angle, we compare how overlaps and receiver tilts affect the performance of an LED-based IPS when the optical boundary is defined at the threshold angle and at the full angle. Using experimental measurements, simulations, and theoretical analysis, the effect of the defined threshold angle is estimated. The results show that the positional time when using the newly defined threshold angle is 12 times shorter than the time when the full angle is used. When the effect of tilt is considered, the threshold angle time is 22 times shorter than the full angle positioning time. Regarding accuracy, it is shown in this work that a positioning error as low as 230 mm can be obtained. Consequently, while the IPS gives a very low positioning error, a defined threshold angle reduces delays in an overlap-based LED IPS.

scholarly journals Lora RTT Ranging Characterization and Indoor Positioning System

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qiang Liu ◽  
XiuJun Bai ◽  
Xingli Gan ◽  
Shan Yang
Keyword(s):  
Estimation Method ◽  
Ground Truth ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Cumulative Distribution ◽  
Positioning System ◽  
Positioning Error ◽  
Positioning Systems ◽  
Ground Truth Location ◽  
Indoor Positioning System

In recent years, indoor positioning systems (IPS) are increasingly very important for a smart factory, and the Lora positioning system based on round-trip time (RTT) has been developed. This paper introduces the ranging characterization, RTT measurement, and position estimation method. In particular, a particle filter localization method-aided Lora pseudorange fitting correction is designed to solve the problem of indoor positioning; the cumulative distribution function (CDF) criteria are used to measure the quality of the estimated location in comparison to the ground truth location; when the positioning error on the x -axis threshold is 0.2 m and 0.6 m, the CDF with pseudorange correction is 61% and 99%, which are higher than the 32% and 85% without pseudorange correction. When the positioning error on the y -axis threshold is 0.2 m and 0.6 m, the CDF with pseudorange correction is 71% and 99.9%, which are higher than the 52% and 94.8% without pseudorange correction.

scholarly journals High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo
Keyword(s):  
High Precision ◽  
Distance Estimation ◽  
Sampling Period ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Location Determination ◽  
Indoor Positioning System

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

scholarly journals Sensing Framework for the Internet of Actors in the Value Co-Creation Process with a Beacon-Attachable Indoor Positioning System

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 83
Author(s):  
Keiichi Zempo ◽  
Taiga Arai ◽  
Takuya Aoki ◽  
Yukihiko Okada
Keyword(s):  
Radio Wave ◽  
Indoor Positioning ◽  
Measurement Range ◽  
Pulse Interval ◽  
Retail Stores ◽  
Positioning System ◽  
Radio Waves ◽  
Positioning Systems ◽  
Environment Recognition ◽  
Indoor Positioning System

To evaluate and improve the value of a service, it is important to measure not only the outcomes, but also the process of the service. Value co-creation (VCC) is not limited to outcomes, especially in interpersonal services based on interactions between actors. In this paper, a sensing framework for a VCC process in retail stores is proposed by improving an environment recognition based indoor positioning system with high positioning performance in a metal shelf environment. The conventional indoor positioning systems use radio waves; therefore, errors are caused by reflection, absorption, and interference from metal shelves. An improvement in positioning performance was achieved in the proposed method by using an IR (infrared) slit and IR light, which avoids such errors. The system was designed to recognize many and unspecified people based on the environment recognition method that the receivers had installed, in the service environment. In addition, sensor networking was also conducted by adding a function to transmit payload and identification simultaneously to the beacons that were attached to positioning objects. The effectiveness of the proposed method was verified by installing it not only in an experimental environment with ideal conditions, but posteriorly, the system was tested in real conditions, in a retail store. In our experimental setup, in a comparison with equal element numbers, positioning identification was possible within an error of 96.2 mm in a static environment in contrast to the radio wave based method where an average positioning error of approximately 648 mm was measured using the radio wave based method (Bluetooth low-energy fingerprinting technique). Moreover, when multiple beacons were used simultaneously in our system within the measurement range of one receiver, the appropriate setting of the pulse interval and jitter rate was implemented by simulation. Additionally, it was confirmed that, in a real scenario, it is possible to measure the changes in movement and positional relationships between people. This result shows the feasibility of measuring and evaluating the VCC process in retail stores, although it was difficult to measure the interaction between actors.

scholarly journals Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression

2019 ◽  
Vol 9 (6) ◽  
pp. 1048 ◽  
Author(s):  
Huy Tran ◽  
Cheolkeun Ha
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Noise Reduction ◽  
Indoor Positioning ◽  
Computational Time ◽  
Dual Function ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Machine Learning Classification

Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy.

scholarly journals An Indoor AC Magnetic Positioning System

2021 ◽  
Author(s):  
Paolo Carbone ◽  
Guido De Angelis ◽  
Valter Pasku ◽  
Alessio De Angelis ◽  
Marco Dionigi ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Indoor Positioning ◽  
Experimental Results ◽  
System Level ◽  
Line Of Sight ◽  
Positioning System ◽  
Positioning Error ◽  
Geometric Configurations ◽  
Indoor Positioning System ◽  
Non Line Of Sight

<div><div><div><p>This paper describes the design and realization of a Magnetic Indoor Positioning System. The system is entirely realized using off-the-shelf components and is based on inductive coupling between resonating coils. Both system-level architecture and realization details are described along with experimental results. The realized system exhibits a maximum positioning error of less than 10 cm in an indoor environment over a 3×3 m2 area. Extensive experiments in larger areas, in non-line-of-sight conditions, and in unfavorable geometric configurations, show sub-meter accuracy, thus validating the robustness of the system with respect to other existing solutions.</p></div></div></div>

scholarly journals An Indoor Positioning System Using Multiple Methods and Tools

2018 ◽  
Vol 4 (1) ◽  
pp. 11-22
Author(s):  
Michael Adeyeye Oshin ◽  
Nobaene Sehloho
Keyword(s):  
Wireless Mesh Network ◽  
Ad Hoc ◽  
Indoor Positioning ◽  
Mesh Network ◽  
Positioning System ◽  
Tracking Systems ◽  
Positioning Systems ◽  
Wireless Mesh ◽  
Positioning Method ◽  
Indoor Positioning System

With many different studies showing a growing demand for the development of indoor positioning systems, numerous positioning and tracking methods and tools are available for which can be used for mobile devices. Therefore, an interest is more on development of indoor positioning and tracking systems that are accurate and effective. Presented and proposed in this work, is an indoor positioning system. As opposed to an Ad-hoc Positioning System (APS), it uses a Wireless Mesh Network (WMN). The system makes use of an already existing Wi-Fi infrastructure technology. Moreover, the approach tests the positioning of a node with its neighbours in a mesh network using multi-hopping functionality. The positioning measurements used were the ICMP echos, RSSI and RTS/CTS requests and responses. The positioning method used was the trilateral technique, in combination with the idea of the fingerprinting method. Through research and experimentation, this study developed a system which shows potential as a positioning system with an error of about 2 m to 3 m. The hybridisation of the method proves an enhancement in the system though improvements are still required.

scholarly journals Mobile Robot Indoor Positioning System Based on K-ELM

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Haixia Wang ◽  
Junliang Li ◽  
Wei Cui ◽  
Xiao Lu ◽  
Zhiguo Zhang ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Positioning Algorithm ◽  
Indoor Positioning System

Mobile Robot Indoor Positioning System has wide application in the industry and home automation field. Unfortunately, existing mobile robot indoor positioning methods often suffer from poor positioning accuracy, system instability, and need for extra installation efforts. In this paper, we propose a novel positioning system which applies the centralized positioning method into the mobile robot, in which real-time positioning is achieved via interactions between ARM and computer. We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments. Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts since our system solely relies on Wi-Fi devices.

scholarly journals Rationale for the Choice of a Positioning System for Mobile Agricultural Robot Movement Controlling

2020 ◽  
Vol 14 (4) ◽  
pp. 63-70
Author(s):  
A. V. Teterev
Keyword(s):  
Information Exchange ◽  
Information Transfer ◽  
Plant Protection ◽  
Plant Protection Products ◽  
Chemical Plant ◽  
Positioning System ◽  
Positioning Error ◽  
Robotic Device ◽  
Positioning Accuracy ◽  
Positioning Systems

A correctly selected positioning system for controlling the mobile robotic means movement ensures high positioning accuracy of the robotic platform in the garden, allows to automate precise operations in the garden and systematize route planning algorithms.(Research purpose) To substantiate the rational choice of a positioning system for controlling the mobile robotic device movement.(Materials and methods) The author formulated requirements for the positioning system to perform precise operations in the garden: mechanized collection of fruits and berries, diff erentiated application of fertilizers and chemical plant protection products. The main ones were: the positioning error was no more than 5 centimetres, the stability of information transfer to the server for building traffi c maps, the movement of a robotic device along a given trajectory, equipping beacons with a mobile power source with a capacity of at least 800 milliampere-hour, information exchange between the beacon and the built-in robotic means with a microprocessor controller according to the RS-485 standard, the signal coverage area was at least 100 square meter.(Results and discussion) The six most relevant positioning systems of the following manufacturers were described: RealTrac, Rusoft CKT, Neomatic, ISBC, Avtosensor, Marvelmind. The author compared their technical and operational parameters: operating frequencies, range, data transfer interface, location accuracy and cost of ready-made kits. He showed that Marvelmind provided uninterrupted operation at frequencies of 433 and 915 megahertz with a positioning error of no more than 2 centimetres. The tests were carried out on a small robotic vehicle with the following characteristics: maximum transport speed – 30 kilometre per hour, operating weight – 500 kilograms, length 2 metres, width – 1.2 metres, height – 1.6 metres.(Conclusions) The author substantiated the choice of the most suitable and aff ordable Marvelmind positioning system and experimentally confi rmed the positioning accuracy declared by the manufacturer. When driving in a loop-free and looped turn, the positioning accuracy did not exceed 1.5 centimetres, which met the agrotechnical requirements for mechanized collection of fruits and berries, for diff erentiated application of fertilizers and chemical plant protection products

scholarly journals Practical And Robust Approach For A Neural Networks Based Indoor Positioning System Using Ultrawide Band

2021 ◽  
Author(s):  
Ryan Murari
Keyword(s):  
Neural Networks ◽  
Synthetic Data ◽  
Real Data ◽  
Signal Propagation ◽  
Indoor Positioning ◽  
Sensor Technology ◽  
Positioning System ◽  
Distance Measurements ◽  
Positioning Systems ◽  
Indoor Positioning System

With the increasing widespread of sensor technology, new solutions for indoor positioning systems are continuously being developed and with them, new services requiring accurate positioning data have seen a great rise in popularity. In this thesis, a new design technique and deployment methodology for an indoor positioning system using neural networks is proposed to offer more flexibility and simplicity in the development of such a system which is currently very context-bound. The usage of battery-powered tags implies also that systems should not require excessive power consumption and the large number of targets to position requires a method that is not only accurate but also scalable. The proposed positioning system utilizes a small “swarm” of neural networks tasked to position targets based on distance measurements from Ultrawide Band sensors and requires shorter fingerprint collection campaigns and enables more flexibility in system deployment and alterations. Instead of relying solely on real data collected on the field for the training of neural networks, synthetic data is used for an initial training phase. Together, these propositions allow flexibility in terms of adding, removing or altering positions of reference nodes and simplifies offline deployment operations of an indoor positioning system. This thesis presents a system operating in a laboratory-workshop environment capable of good positioning accuracies and maintains robust performances in poor signal propagation.

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