Improved Location Accuracy Using Least Square Distance Estimation for Circular Positioning Technique Based on Stationary Signal-Strength-Difference Measurements

2006 ◽  
Author(s):  
Bo-chieh Liu ◽  
Ken-huang Lin
Keyword(s):  
Distance Estimation ◽  
Signal Strength ◽  
Least Square ◽  
Location Accuracy ◽  
Positioning Technique ◽  
Strength Difference

scholarly journals Indoor 3-D Localization Based on Received Signal Strength Difference and Factor Graph for Unknown Radio Transmitter

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 338 ◽  
Author(s):  
Liyang Zhang ◽  
Taihang Du ◽  
Chundong Jiang
Keyword(s):  
Measurement Errors ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Least Square ◽  
Practical Significance ◽  
Factor Graph ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Radio Transmitter ◽  
Strength Difference

Accurate localization of the radio transmitter is an important work in radio management. Previous research is more focused on two-dimensional (2-D) scenarios, but the localization of an unknown radio transmitter under three-dimensional (3-D) scenarios has more practical significance. In this paper, we propose a novel 3-D localization algorithm with received signal strength difference (RSSD) information and factor graph (FG), which is suitable for both line-of-sight (LOS) and non-line-of-sight (NLOS) condition. Considering the stochastic properties of measurement errors caused by the indoor environment, RSSD measurements are processed with mean and variance in the form of Gaussian distribution in the FG framework. A new 3-D RSSD-based FG model is constructed with the relationship between RSSD and location coordinates by local linearization technique. The soft-information computation and iterative process of the proposed model are derived by using the sum-product algorithm. In addition, the impacts of different grid distances and number of signal receivers on positioning accuracy are explored. Finally, the performance of our proposed approach is experimentally evaluated in a real scenario. The results show that the positioning performance of the proposed algorithm is not only superior to the k-nearest neighbors (kNN) algorithm and least square (LS) algorithm, but also it can achieve a mean localization error as low as 1.15 m. Our proposed scheme provides a good solution for the accurate detection of an unknown radio transmitter under indoor 3-D space and has a good application prospect.

scholarly journals Evaluation of Technology-Supported Distance Measuring to Ensure Safe Aircraft Boarding during COVID-19 Pandemic

2020 ◽  
Vol 12 (20) ◽  
pp. 8724
Author(s):  
Paul Schwarzbach ◽  
Julia Engelbrecht ◽  
Albrecht Michler ◽  
Michael Schultz ◽  
Oliver Michler
Keyword(s):  
Three Dimensional ◽  
Virus Transmission ◽  
Distance Estimation ◽  
Signal Strength ◽  
Air Transport ◽  
Aircraft Cabins ◽  
Social Distancing ◽  
Simulation Based ◽  
Cabin Environment

With the rise of COVID-19, the sustainability of air transport is a major challenge, as there is limited space in aircraft cabins, resulting in a higher risk of virus transmission. In order to detect possible chains of infection, technology-supported apps are used for social distancing. These COVID-19 applications are based on the display of the received signal strength for distance estimation, which is strongly influenced by the spreading environment due to the signal multipath reception. Therefore, we evaluate the applicability of technology-based social distancing methods in an aircraft cabin environment using a radio propagation simulation based on a three-dimensional aircraft model. We demonstrate the susceptibility to errors of the conventional COVID-19 distance estimation, which can lead to large errors in the determination of distances and to the impracticability of traditional tracing approaches during passenger boarding/deboarding. In the context of the future connected cabin, a robust distance measurement must be implemented to ensure safe travel. Finally, our results can be transferred to similar fields of application, e.g., trains or public transport.

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