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 Performance Improvement Design of Bluetooth Low Energy-Based Wireless Indoor Positioning Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Kriangkrai Maneerat ◽  
Kamol Kaemarungsi
Keyword(s):  
Indoor Positioning ◽  
Scene Analysis ◽  
Average Error ◽  
Positioning System ◽  
Location Accuracy ◽  
Positioning Systems ◽  
Location Determination ◽  
Indoor Positioning System ◽  
Error Distance ◽  
Accuracy Performance

The systematic design of wireless indoor positioning systems can offer another essential approach to achieving the required performance objectives aside from using suitable location determination algorithms. This manuscript investigates Bluetooth Low Energy- (BLE-) based wireless indoor positioning systems and how adjusting the system design parameters can affect their location determination performance. Without placing emphasis on sophisticated location determination algorithms, this work provides guidelines for how a system designer can control the balance among multiple positioning performance metrics. For example, a balance between the number of installed reference nodes and the accuracy performance can be chosen to control deployment costs, such as the installation expense, infrastructure expense, and installation time. To demonstrate our baseline study, we compare three different designs of BLE wireless indoor positioning system that utilize location determination algorithms based on proximity, trilateration, and scene analysis. These designs are also compared over two different building sizes, which are medium and large. The design model and performance analysis data were based on our actual implementation of the hardware and software system for a BLE wireless indoor positioning system. Specifically, the received signal strength indication data were collected from our prototype reference nodes. The findings from our study indicated that a proximity-based system can only provide fair location accuracy performance (average error distance of 5 m to 7 m) making it unsuitable for applications that require high accuracy. For medium location accuracy performance (average error distance of 3 m to 5 m), the trilateration-based system can achieve the highest efficiency in terms of number of installed reference nodes over the accuracy. The trilateration-based system can reduce the number of installed reference nodes by 154% to achieve the same level of accuracy as the scene analysis-based system. For good location accuracy performance (average error distance ≤ 3 m), the scene analysis-based system yields the highest scalability performance in terms of installed reference nodes. The scene analysis-based system can reduce the number of reference nodes by 40% and 113% to achieve the same accuracy performance when compared with trilateration and proximity-based systems, respectively. Finally, the validation results from the actual installation of Bluetooth-based indoor positioning systems confirmed that our proposed framework can help the system designers to achieve the required performance goal.

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 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 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 Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5776
Author(s):  
Zhongfeng Zhang ◽  
Minjae Lee ◽  
Seungwon Choi
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Software Radio ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Training Dataset ◽  
Positioning System ◽  
Indoor Environments ◽  
Generative Adversarial Network ◽  
Indoor Positioning System

In a Wi-Fi indoor positioning system (IPS), the performance of the IPS depends on the channel state information (CSI), which is often limited due to the multipath fading effect, especially in indoor environments involving multiple non-line-of-sight propagation paths. In this paper, we propose a novel IPS utilizing trajectory CSI observed from predetermined trajectories instead of the CSI collected at each stationary location; thus, the proposed method enables all the CSI along each route to be continuously encountered in the observation. Further, by using a generative adversarial network (GAN), which helps enlarge the training dataset, the cost of trajectory CSI collection can be significantly reduced. To fully exploit the trajectory CSI’s spatial and temporal information, the proposed IPS employs a deep learning network of a one-dimensional convolutional neural network–long short-term memory (1DCNN-LSTM). The proposed IPS was hardware-implemented, where digital signal processors and a universal software radio peripheral were used as a modem and radio frequency transceiver, respectively, for both access point and mobile device of Wi-Fi. We verified that the proposed IPS based on the trajectory CSI far outperforms the state-of-the-art IPS based on the CSI collected from stationary locations through extensive experimental tests and computer simulations.

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.

Sign in / Sign up

Export Citation Format

Share Document