Indoor Visible Light Localization Algorithm with the Optimal Optical Angle-of-Arrival Estimator

2021 ◽  
Author(s):  
Yongqing Yu ◽  
Bingcheng Zhu ◽  
Zaichen Zhang ◽  
Lei Wang ◽  
Liang Wu ◽  
...  
Keyword(s):  
Visible Light ◽  
Localization Algorithm ◽  
Angle Of Arrival ◽  
Light Localization

scholarly journals Performance Analysis of AOA-Based Localization Using the LS Approach: Explicit Expression of Mean-Squared Error

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Byung-Kwon Son ◽  
Do-Jin An ◽  
Joon-Ho Lee
Keyword(s):  
Explicit Expression ◽  
Mean Squared Error ◽  
Weighted Least Squares ◽  
Localization Algorithm ◽  
Angle Of Arrival ◽  
Squared Error ◽  
Distance Weighted ◽  
The Mean ◽  
Passive Localization ◽  
Location Estimate

In this paper, a passive localization of the emitter using noisy angle-of-arrival (AOA) measurements, called Brown DWLS (Distance Weighted Least Squares) algorithm, is considered. The accuracy of AOA-based localization is quantified by the mean-squared error. Various estimates of the AOA-localization algorithm have been derived (Doğançay and Hmam, 2008). Explicit expression of the location estimate of the previous study is used to get an analytic expression of the mean-squared error (MSE) of one of the various estimates. To validate the derived expression, we compare the MSE from the Monte Carlo simulation with the analytically derived MSE.

scholarly journals Probability Weighting Localization Algorithm Based on NLOS Identification in Wireless Network

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Shixun Wu ◽  
Shengjun Zhang ◽  
Kai Xu ◽  
Darong Huang
Keyword(s):  
Base Station ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Probability Weighting ◽  
Localization Algorithm ◽  
Angle Of Arrival ◽  
Chi Square ◽  
Home Base ◽  
Angle Measurements ◽  
Non Line Of Sight

In this paper, a localization scenario that the home base station (BS) measures time of arrival (TOA) and angle of arrival (AOA) while the neighboring BSs only measure TOA is investigated. In order to reduce the effect of non-line of sight (NLOS) propagation, the probability weighting localization algorithm based on NLOS identification is proposed. The proposed algorithm divides these range and angle measurements into different combinations. For each combination, a statistic whose distribution is chi-square in LOS propagation is constructed, and the corresponding theoretic threshold is derived to identify each combination whether it is LOS or NLOS propagation. Further, if those combinations are decided as LOS propagation, the corresponding probabilities are derived to weigh the accepted combinations. Simulation results demonstrate that our proposed algorithm can provide better performance than conventional algorithms in different NLOS environments. In addition, computational complexity of our proposed algorithm is analyzed and compared.

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