scholarly journals Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4515 ◽  
Author(s):  
Markus Bullmann ◽  
Toni Fetzer ◽  
Frank Ebner ◽  
Markus Ebner ◽  
Frank Deinzer ◽  
...  
Keyword(s):  
Estimation Method ◽  
Indoor Positioning ◽  
Propagation Time ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Channel Bandwidth ◽  
Timing Measurement ◽  
The Difference ◽  
Error Behavior ◽  
Received Signal Strength Indication

With the addition of the Fine Timing Measurement (FTM) protocol in IEEE 802.11-2016, a promising sensor for smartphone-based indoor positioning systems was introduced. FTM enables a Wi-Fi device to estimate the distance to a second device based on the propagation time of the signal. Recently, FTM has gotten more attention from the scientific community as more compatible devices become available. Due to the claimed robustness and accuracy, FTM is a promising addition to the often used Received Signal Strength Indication (RSSI). In this work, we evaluate FTM on the 2.4 GHz band with 20 MHz channel bandwidth in the context of realistic indoor positioning scenarios. For this purpose, we deploy a least-squares estimation method, a probabilistic positioning approach and a simplistic particle filter implementation. Each method is evaluated using FTM and RSSI separately to show the difference of the techniques. Our results show that, although FTM achieves smaller positioning errors compared to RSSI, its error behavior is similar to RSSI. Furthermore, we demonstrate that an empirically optimized correction value for FTM is required to account for the environment. This correction value can reduce the positioning error significantly.

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 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.

Research on an Adaptive Algorithm for Indoor Bluetooth Positioning

2018 ◽  
Vol 32 (06) ◽  
pp. 1854014 ◽  
Author(s):  
Heng Luo ◽  
Xiaobo Niu ◽  
Junchen Li ◽  
Jianping Chen ◽  
Youmin Zou ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Indoor Positioning ◽  
Relative Difference ◽  
Adaptive Model ◽  
Received Signal Strength Indicator ◽  
Positioning Systems ◽  
Coefficient Optimization ◽  
Performance Deterioration ◽  
The Difference ◽  
Environment Experiment

Building structure and other factors lead to the performance deterioration of global postioning system (GPS) positioning systems indoors. An adaptive model for Bluetooth-based indoor positioning is proposed in this paper, targeting at the complex indoor environment, to improve the performance of Bluetooth-oriented indoor positioning systems. More accurate Received Signal Strength Indicator (RSSI) calibration which is optimized via Gaussian filtering, together with the environment-dependent attenuation coefficient optimization, results in a more precise hybrid model in the complicated indoor environment. Experiment results show that the difference between the estimated results and the measured samples is less than 0.25[Formula: see text]m as the target node and reference node is less than 1.5[Formula: see text]m far from each other. As the distance increases to more than 1.5[Formula: see text]m, the relative difference between the estimated values and the measured ones decreases to 7.8% at most, satisfying the requirements for indoor positioning applications.

scholarly journals Improved RSSI Positioning Algorithm for Coal Mine Underground Locomotive

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Bin Ge ◽  
Kai Wang ◽  
Jianghong Han ◽  
Bao Zhao
Keyword(s):  
Coal Mine ◽  
Gaussian Model ◽  
Correction Method ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Positioning Errors ◽  
Weighted Model ◽  
The Difference ◽  
Positioning Algorithm ◽  
Received Signal Strength Indication

Aiming at the large positioning errors of traditional coal mine underground locomotive, an improved received signal strength indication (RSSI) positioning algorithm for coal mine underground locomotive was proposed. The RSSI value fluctuates heavily due to the poor environment of coal mine underground. The nodes with larger RSSI value corrected by Gaussian-weighted model were selected as beacon nodes. In order to reduce the positioning error further, the estimated positions of the locomotives were corrected by the weighted distance correction method. The difference between actual position and estimated position of beacon node was regarded as the positioning error and was given a corresponding weight. The results of simulation show that the positioning accuracy of Gaussian-weighted model is better than statistical average model and Gaussian model and it has a high positioning accuracy after correcting positioning error correction. In the 10 m of communication range, positioning error can be maintained at 0.5 m.

scholarly journals Relativistic positioning: including the influence of the gravitational action of the Sun and the Moon and the Earth’s oblateness on Galileo satellites

2021 ◽  
Vol 366 (7) ◽  
Author(s):  
Neus Puchades Colmenero ◽  
José Vicente Arnau Córdoba ◽  
Màrius Josep Fullana i Alfonso
Keyword(s):  
Space Time ◽  
Satellite Orbits ◽  
The Sun ◽  
The Moon ◽  
Schwarzschild Metric ◽  
Positioning Systems ◽  
Positioning Errors ◽  
The Earth ◽  
Realistic Description ◽  
The Difference

AbstractUncertainties in the satellite world lines lead to dominant positioning errors. In the present work, using the approach presented in Puchades and Sáez (Astrophys. Space Sci. 352, 307–320, 2014), a new analysis of these errors is developed inside a great region surrounding Earth. This analysis is performed in the framework of the so-called Relativistic Positioning Systems (RPS). Schwarzschild metric is used to describe the satellite orbits corresponding to the Galileo Satellites Constellation. Those orbits are circular with the Earth as their centre. They are defined as the nominal orbits. The satellite orbits are not circular due to the perturbations they have and to achieve a more realistic description such perturbations need to be taken into account. In Puchades and Sáez (Astrophys. Space Sci. 352, 307–320, 2014) perturbations of the nominal orbits were statistically simulated. Using the formula from Coll et al. (Class. Quantum Gravity. 27, 065013, 2010) a user location is determined with the four satellites proper times that the user receives and with the satellite world lines. This formula can be used with any satellite description, although photons need to travel in a Minkowskian space-time. For our purposes, the computation of the photon geodesics in Minkowski space-time is sufficient as demonstrated in Puchades and Sáez (Adv. Space Res. 57, 499–508, 2016). The difference of the user position determined with the nominal and the perturbed satellite orbits is computed. This difference is defined as the U-error. Now we compute the perturbed orbits of the satellites considering a metric that takes into account the gravitational effects of the Earth, the Moon and the Sun and also the Earth oblateness. A study of the satellite orbits in this new metric is first introduced. Then we compute the U-errors comparing the positions given with the Schwarzschild metric and the metric introduced here. A Runge-Kutta method is used to solve the satellite geodesic equations. Some improvements in the computation of the U-errors using both metrics are introduced with respect to our previous works. Conclusions and perspectives are also presented.

scholarly journals Robust Trilateration Based Algorithm for Indoor Positioning Systems

2021 ◽  
Vol 47 (3) ◽  
pp. 1195-1210
Author(s):  
Simeon Pande ◽  
Kwame S Ibwe
Keyword(s):  
Indoor Environment ◽  
Path Loss ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Received Signal Strength Indicator ◽  
Position Information ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Distance Relationship ◽  
Indoor Positioning System

Abstract Indoor Positioning Systems (IPS) plays crucial roles in indoor environment items positioning used in self-navigating robots and helping hands. To obtain position information, positioning algorithms employing Received Signal Strength Indicator (RSSI) are of great benefits since they reuse the existing radio wireless infrastructures for indoor positioning. However, the changes in the indoor environment decrease the overall accuracy of the developed indoor positioning algorithms. To cope with the challenge of environmental dependency in indoor positioning, a robust algorithm using radio signal identification was developed. The algorithm uses circle expansion and reduction mechanism to achieve better RSSI-Distance relationship. The distances from RSSI-Distance relationship are used in trilateration algorithm for position estimation. Experiments were performed to compare position accuracy of the basic RSSI-Based and the proposed algorithm. Simulation results showed that proposed algorithm showed less average positioning errors by 11.2066% and 3.7279% at path loss coefficients of 3.11 and 3.21, respectively compared to the existing algorithms. Likewise, the proposed algorithm showed 2.7282% increase in positioning error when environment was changed from that of path loss coefficient 3.11 to 3.21. The existing basic algorithms show error fluctuation of 10% with the same environment changes. Keywords: Indoor Positioning System; RFID; RSSI; Trilateration

scholarly journals A Method for Improving UWB Indoor Positioning

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Zhanjun Hao ◽  
Beibei Li ◽  
Xiaochao Dang
Keyword(s):  
Estimation Method ◽  
Pulse Amplitude ◽  
Relative Displacement ◽  
Indoor Positioning ◽  
Base Stations ◽  
Ultra Wideband ◽  
Signal Interference ◽  
Positioning Errors ◽  
Wireless Environment ◽  
The Time Domain

The existing positioning methods that use received signal strength indication (RSSI) and channel state information (CSI) may suffer from multipath and shadowing in a complex wireless environment, which can result in more positioning errors. This paper proposes a method for accurate multilabel positioning in the non-line-of-sight (NLOS) environment. First, the position is roughly estimated using the orthogonal variable spreading factor (OVSF-TH) algorithm, which can automatically match the signal interference. The ultra-wideband (UWB) spectral density and pulse amplitude in the time domain are used to determine the direction of the label and enhance estimation of the mobile label direction. Then, the location of the tag is obtained by triangulation, and a coordinate-based coordinate estimation method is proposed to calculate the relative displacement of multiple tags to determine the label position. Finally, by setting up a real experimental environment, the influence of the number of base stations on the accuracy and the performance of the localization method under different circumstances are analyzed. The theoretical analysis and experimental results show that the method is simple to deploy, inexpensive, and very accurate in terms of positioning, having a clearly effective indoor positioning accuracy. Compared with other existing positioning methods, this method can achieve more accurate positioning. Moreover, it has important theoretical and practical applicability because of the reliability and accuracy of indoor positioning in an NLOS environment.

scholarly journals Characterization of Multipath Effects in Indoor Positioning Systems by AoA and PoA Based on Optical Signals

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 917 ◽  
Author(s):  
Álvaro De-La-Llana-Calvo ◽  
José-Luis Lázaro-Galilea ◽  
Alfredo Gardel-Vicente ◽  
David Rodríguez-Navarro ◽  
Ignacio Bravo-Muñoz ◽  
...  
Keyword(s):  
Phase Shift ◽  
Indoor Positioning ◽  
Optical Signal ◽  
Angle Of Arrival ◽  
Optical Signals ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Multipath Effects ◽  
Position Sensitive

In this paper, we characterize and measure the effects of the errors introduced by the multipath when obtaining the position of an agent by means of Indoor Positioning Systems (IPS) based on optical signal. These effects are characterized in Local Positioning Systems (LPSs) based on two different techniques: the first one by determining the Angle of Arrival (AoA) of the infrared signal (IR) to the detector; and the second one by working with the measurement of the Phase shift of signal Arrival from the transmitter to a receiver (PoA). We present the obtained results and conclusions, which indicate that using Position Sensitive Devices (PSD) the multipath effects for AoA have little impact on the measurement, while for PoA the positioning errors are very significant, making the system useless in many cases.

Sign in / Sign up

Export Citation Format

Share Document