A Review of Indoor Positioning Systems (IPS) and Their Different Use-Cases

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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A Novel GNSS Repeater Architecture for Indoor Positioning Systems in ISM Band

2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting ◽

10.1109/ieeeconf35879.2020.9329653 ◽

2020 ◽

Author(s):

Abdulkadir Uzun ◽

Firas Abdul Ghani ◽

Husnu Yenigun ◽

Ibrahim Tekin

Keyword(s):

Indoor Positioning ◽

Positioning Systems ◽

Ism Band

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Off-line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences from IPIN 2020 Competition

IEEE Sensors Journal ◽

10.1109/jsen.2021.3083149 ◽

2021 ◽

pp. 1-1

Author(s):

Francesco Potorti ◽

Joaquin Torres-Sospedra ◽

Darwin Quezada-Gaibor ◽

Antonio Ramon Jimenez ◽

Fernando Seco ◽

...

Keyword(s):

Indoor Positioning ◽

Positioning Systems

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Evaluation of Multi-Sensor Fusion Methods for Ultrasonic Indoor Positioning

Applied Sciences ◽

10.3390/app11156805 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6805

Author(s):

Khaoula Mannay ◽

Jesús Ureña ◽

Álvaro Hernández ◽

José M. Villadangos ◽

Mohsen Machhout ◽

...

Keyword(s):

Kalman Filter ◽

Experimental Tests ◽

Indoor Positioning ◽

Location Based Services ◽

Smart Devices ◽

Final Position ◽

Positioning Systems ◽

Local Positioning System ◽

Tightly Coupled ◽

Position Estimate

Indoor positioning systems have become a feasible solution for the current development of multiple location-based services and applications. They often consist of deploying a certain set of beacons in the environment to create a coverage volume, wherein some receivers, such as robots, drones or smart devices, can move while estimating their own position. Their final accuracy and performance mainly depend on several factors: the workspace size and its nature, the technologies involved (Wi-Fi, ultrasound, light, RF), etc. This work evaluates a 3D ultrasonic local positioning system (3D-ULPS) based on three independent ULPSs installed at specific positions to cover almost all the workspace and position mobile ultrasonic receivers in the environment. Because the proposal deals with numerous ultrasonic emitters, it is possible to determine different time differences of arrival (TDOA) between them and the receiver. In that context, the selection of a suitable fusion method to merge all this information into a final position estimate is a key aspect of the proposal. A linear Kalman filter (LKF) and an adaptive Kalman filter (AKF) are proposed in that regard for a loosely coupled approach, where the positions obtained from each ULPS are merged together. On the other hand, as a tightly coupled method, an extended Kalman filter (EKF) is also applied to merge the raw measurements from all the ULPSs into a final position estimate. Simulations and experimental tests were carried out and validated both approaches, thus providing average errors in the centimetre range for the EKF version, in contrast to errors up to the meter range from the independent (not merged) ULPSs.

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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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