Non-Line-of-Sight Identification for Indoor Positioning Using Ultra-WideBand Radio Signals

Navigation ◽  
2013 ◽  
Vol 60 (2) ◽  
pp. 97-111 ◽  
Author(s):  
Junlin Yan ◽  
Christian C. J. M. Tiberius ◽  
Giovanni Bellusci ◽  
Gerard J. M. Janssen
Keyword(s):  
Indoor Positioning ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Radio Signals ◽  
Wideband Radio ◽  
Non Line Of Sight

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 Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1238
Author(s):  
Javier San Martín ◽  
Ainhoa Cortés ◽  
Leticia Zamora-Cadenas ◽  
Bo Joel Svensson
Keyword(s):  
Autonomous Vehicles ◽  
Research Work ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Use Case ◽  
Positioning System ◽  
Precise Positioning ◽  
Research Institutes ◽  
Simulated Environment ◽  
Non Line Of Sight

In this paper, we analyze the performance of a positioning system based on the fusion of Ultra-Wideband (UWB) ranging estimates together with odometry and inertial data from the vehicle. For carrying out this data fusion, an Extended Kalman Filter (EKF) has been used. Furthermore, a post-processing algorithm has been designed to remove the Non Line-Of-Sight (NLOS) UWB ranging estimates to further improve the accuracy of the proposed solution. This solution has been tested using both a simulated environment and a real environment. This research work is in the scope of the PRoPART European Project. The different real tests have been performed on the AstaZero proving ground using a Radio Control car (RC car) developed by RISE (Research Institutes of Sweden) as testing platform. Thus, a real time positioning solution has been achieved complying with the accuracy requirements for the PRoPART use case.

scholarly journals A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

2020 ◽  
Vol 10 (3) ◽  
pp. 956 ◽  
Author(s):  
Minghao Si ◽  
Yunjia Wang ◽  
Shenglei Xu ◽  
Meng Sun ◽  
Hongji Cao
Keyword(s):  
Numerical Experiments ◽  
New Technologies ◽  
Gaussian Model ◽  
Indoor Positioning ◽  
Least Square ◽  
Line Of Sight ◽  
Positioning Accuracy ◽  
Timing Measurement ◽  
Positioning Method ◽  
Non Line Of Sight

In recent years, many new technologies have been used in indoor positioning. In 2016, IEEE 802.11-2016 created a Wi-Fi fine timing measurement (FTM) protocol, making Wi-Fi ranging more robust and accurate, and providing meter-level positioning accuracy. However, the accuracy of positioning methods based on the new ranging technology is influenced by non-line-of-sight (NLOS) errors. To enhance the accuracy, a positioning method with LOS (line-of-sight)/NLOS identification is proposed in this paper. A Gaussian model has been established to identify NLOS signals. After identifying and discarding NLOS signals, the least square (LS) algorithm is used to calculate the location. The results of the numerical experiments indicate that our algorithm can identify and discard NLOS signals with a precision of 83.01% and a recall of 74.97%. Moreover, compared with the traditional algorithms, by all ranging results, the proposed method features more accurate and stable results for indoor positioning.

scholarly journals Improving Deep Learning-Based UWB LOS/NLOS Identification with Transfer Learning: An Empirical Approach

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1714
Author(s):  
JiWoong Park ◽  
SungChan Nam ◽  
HongBeom Choi ◽  
YoungEun Ko ◽  
Young-Bae Ko
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Training Time ◽  
Identification Scheme ◽  
Office Environments ◽  
Non Line Of Sight

This paper presents an improved ultra-wideband (UWB) line of sight (LOS)/non-line of sight (NLOS) identification scheme based on a hybrid method of deep learning and transfer learning. Previous studies have limitations, in that the classification accuracy significantly decreases in an unknown place. To solve this problem, we propose a transfer learning-based NLOS identification method for classifying the NLOS conditions of the UWB signal in an unmeasured environment. Both the multilayer perceptron and convolutional neural network (CNN) are introduced as classifiers for NLOS conditions. We evaluate the proposed scheme by conducting experiments in both measured and unmeasured environments. Channel data were measured using a Decawave EVK1000 in two similar indoor office environments. In the unmeasured environment, the existing CNN method showed an accuracy of approximately 44%, but when the proposed scheme was applied to the CNN, it showed an accuracy of up to 98%. The training time of the proposed scheme was measured to be approximately 48 times faster than that of the existing CNN. When comparing the proposed scheme with learning a new CNN in an unmeasured environment, the proposed scheme demonstrated an approximately 10% higher accuracy and approximately five times faster training time.

Modelling the Effect of Human Body around User on Signal Strength and Accuracy of Indoor Positioning

2020 ◽  
Vol 12 (7) ◽  
Author(s):  
Firdaus Firdaus ◽  
◽  
Noor Azurati Ahmad ◽  
Shamsul Sahibuddin ◽  
Rudzidatul Akmam Dziyauddin ◽  
...  
Keyword(s):  
Human Body ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Line Of Sight ◽  
Minimal Cost ◽  
The People ◽  
Indoor Positioning System ◽  
Presence Of People ◽  
Non Line Of Sight

WLAN indoor positioning system (IPS) has high accurate of position estimation and minimal cost. However, environmental conditions such as the people presence effect (PPE) greatly influence WLAN signal and it will decrease the accuracy. This research modelled the effect of people around user on signal strength and the accuracy. We have modelled the human body around user effects by proposed a general equation of decrease in signal strength as function of position, distance, and number of people. Signal strength decreased from 5 dBm to 1 dBm when people in line of sight (LOS) position, and start from 0.5 dBm to 0.3 dBm when people in non-line of sight (NLOS) position. The system accuracy decreases due to the presence of people. When the system is in NLOS case, the presence of people causes a decrease in accuracy from 33% to 57%. Then the accuracy decrease from 273% to 334% in LOS case.

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