Weak Calibration of a Visible Light Positioning System Based on a Position-Sensitive Detector: Positioning Error Assessment

Reduced deployment and calibration requirements are key for scalable and cost-effective indoor positioning systems. In this work, we propose a low-complexity, weak calibration procedure for an indoor positioning system based on infrastructure lighting and a positioning-sensitive detector. The proposed calibration relies on genetic algorithms to obtain the relevant system parameters in the real positioning environment without a priori information, and requires a low number of simple measurements. The achievable performance of the proposal was assessed by direct comparison with a formal offline calibration method requiring complex dedicated infrastructure and instruments. The comparative error assessment showed that the maximum accuracy reduction compared to the significantly more costly formal calibration was below 25 mm, and the overall absolute positioning error was smaller than 35 mm with orientation errors of around 0.25°. The performance achieved with the proposed weak calibration procedure is sufficient for many indoor positioning applications and largely reduces the cost and complexity of setting up the positioning system in real environments.

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Lora RTT Ranging Characterization and Indoor Positioning System

Wireless Communications and Mobile Computing ◽

10.1155/2021/5529329 ◽

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.

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Optical Boundaries for LED-Based Indoor Positioning System

Computation ◽

10.3390/computation7010007 ◽

2019 ◽

Vol 7 (1) ◽

pp. 7 ◽

Cited By ~ 1

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.

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Accuracy and Precision Assessment of AoA-Based Indoor Positioning Systems Using Infrastructure Lighting and a Position-Sensitive Detector

Sensors ◽

10.3390/s20185359 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5359

Author(s):

Álvaro De-La-Llana-Calvo ◽

David Salido-Monzú ◽

José-Luis Lázaro-Galilea ◽

Alfredo Gardel-Vicente ◽

Ignacio Bravo-Muñoz ◽

...

Keyword(s):

Target Position ◽

Indoor Positioning ◽

Position Sensitive Detector ◽

External Information ◽

Integration Time ◽

Positioning Error ◽

Sensitive Detector ◽

Positioning Systems ◽

Accuracy And Precision ◽

Position Sensitive

Unlike GNSS-based outdoor positioning, there is no technological alternative for Indoor Positioning Systems (IPSs) that generally stands out from the others. In indoor contexts, the measurement technologies and localization strategies to be used depend strongly on the application requirements and are complementary to each other. In this work, we present an optical IPS based on a Position-Sensitive Detector (PSD) and exploiting illumination infrastructure to determine the target position by Angle of Arrival (AoA) measurements. We combine the proposed IPS with different positioning strategies depending on the number of visible emitters (one, two, or more) and available prior or additional information about the scenario and target. The accuracy and precision of the proposal is assessed experimentally for the different strategies in a 2.47 m high space covering approximately 2.2 m2, using high-end geodetic equipment to establish the reference ground truth. When the orientation of the target is known from external measurements, an average positioning error of 8.2 mm is obtained using the signal received from only one emitter. Using simultaneous observations from two emitters, an average positioning error of 9.4 mm is obtained without external information when the target movement is restricted to a plane. Conversely, if four signals are available, an average positioning error of 4.9 cm is demonstrated, yielding the complete 3D pose of the target free of any prior assumption or additional measurements. In all cases, a precision (2σ) better than 5.9 mm is achieved across the complete test space for an integration time of 10 ms. The proposed system represents a prospectively useful alternative for indoor positioning applications requiring fast and reliable cm-level accuracy with moderate cost when smart illumination infrastructure is available in the environment.

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

Sensors ◽

10.3390/s21113701 ◽

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.

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

Sensors ◽

10.3390/s21010083 ◽

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.

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Hybrid Deep Learning Model Based Indoor Positioning Using Wi-Fi RSSI Heat Maps for Autonomous Applications

Electronics ◽

10.3390/electronics10010002 ◽

2020 ◽

Vol 10 (1) ◽

pp. 2

Author(s):

Alwin Poulose ◽

Dong Seog Han

Keyword(s):

Deep Learning ◽

Signal Strength ◽

Indoor Positioning ◽

Learning Model ◽

Signal Interference ◽

Positioning System ◽

Heat Maps ◽

Positioning Systems ◽

Localization Performance ◽

Deep Learning Model

Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user’s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.

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

Applied Sciences ◽

10.3390/app9061048 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1048 ◽

Cited By ~ 8

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.

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An Indoor AC Magnetic Positioning System

10.36227/techrxiv.15066198.v1 ◽

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>

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An Indoor Positioning System Using Multiple Methods and Tools

International Journal of Information Communication Technology and Applications ◽

10.17972/ijicta20184134 ◽

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.

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A robust mobile robot indoor positioning system based on Wi-Fi

International Journal of Advanced Robotic Systems ◽

10.1177/1729881419896660 ◽

2020 ◽

Vol 17 (1) ◽

pp. 172988141989666 ◽

Cited By ~ 2

Author(s):

Wei Cui ◽

Qingde Liu ◽

Linhan Zhang ◽

Haixia Wang ◽

Xiao Lu ◽

...

Keyword(s):

Mobile Robot ◽

Principal Component ◽

Indoor Positioning ◽

Positioning System ◽

Infrared Sensors ◽

Positioning Systems ◽

Indoor Mobile Robot ◽

Robot Positioning ◽

Learning Machine ◽

Noisy Measurements

Recently, most of the existing mobile robot indoor positioning systems (IPSs) use infrared sensors, cameras, and other extra infrastructures. They usually suffer from high cost and special hardware implementation. In order to address the above problems, this article proposes a Wi-Fi-based indoor mobile robot positioning system and designs and develops a robot positioning platform based on the commercial Wi-Fi devices, such as Wi-Fi routers. Furthermore, a robust principal component analysis-based extreme learning machine algorithm is proposed to address the issue of noisy measurements in IPSs. Real-world robot indoor positioning experiments are extensively carried out and the results verify the effectiveness and superiority of the proposed system.

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