Grid-based Correlation Localization Method in Mixed Line-of-Sight/Non-Line-of-Sight Environments

2015 ◽  
Vol 9 (1) ◽  
Keyword(s):  
Line Of Sight ◽  
Localization Method ◽  
Mixed Line ◽  
Grid Based ◽  
Non Line Of Sight

Improved D/TA and Information Fusion Based on HMM Indoor Localization

2013 ◽  
Vol 373-375 ◽  
pp. 916-921 ◽  
Author(s):  
Jing Yu Ru ◽  
Cheng Dong Wu ◽  
Yun Zhou Zhang ◽  
Rong Fen Gong ◽  
Peng Da Liu
Keyword(s):  
Information Fusion ◽  
Indoor Localization ◽  
Hidden Markov ◽  
Transition Probability ◽  
Line Of Sight ◽  
Ml Estimation ◽  
Mixed Line ◽  
Grid Based ◽  
Accuracy And Stability ◽  
Non Line Of Sight

This paper describes an efficient Bayesian framework for localization based on Ultra-wide Bandwidth (UWB) system. Approximate grid-based method based on the Hidden Markov Model (HMM) is an effective method to estimate the position of the Moving Terminal (MT) with the mixed line-of-sight/non-line-of-sight (LOS/NLOS) situation. This article proposes an algorithm by modifying the Position Transition Probability (PTP) according to the practical dynamic model and uses the information fusion effectively. We compare the Maximum Likelihood (ML) estimation with Detection/Tracking Algorithm (D/TA) estimation and its improved algorithm by simulation, in which the localization to an identical trajectory has been tested. The results of the analysis show that the proposed method has better accuracy and stability.

Cooperative Improved Target Localization in harsh Environments using Direction of Arrival

2016 ◽  
Vol 3 (2) ◽  
pp. 420 ◽  
Author(s):  
Alireza Safaie ◽  
Reza Shahbazian ◽  
Seyed Ali Ghorashi
Keyword(s):  
Direction Of Arrival ◽  
Target Localization ◽  
Line Of Sight ◽  
Target Node ◽  
Performance Simulation ◽  
Localization Accuracy ◽  
Localization Method ◽  
Cooperative Methods ◽  
Mixed Line ◽  
Non Line Of Sight

<p>Target localization is an important issue for many applications in wireless sensor networks. However, it is rather difficult to maintain the localization accuracy in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments as NLOS propagation leads to larger error than what LOS does. In this paper, we propose a new target localization method in mixed environments where NLOS is dominant and only one base node might be in LOS toward target. We use the cooperation between receiver nodes and the direction of arrival (DOA) of received signals to estimate the target’s location. The proposed cooperative target localization method tries to identify a base node that has LOS with respect to target node and use the LOS information for precise positioning of target node. We simulate the proposed method to analyze its performance. Simulation results confirm that our proposed method improves the localization accuracy on average by 20 percent in comparison with traditional cooperative methods.</p>

scholarly journals A Novel Non-Line-of-Sight Indoor Localization Method for Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yan Wang ◽  
Xuehan Wu ◽  
Long Cheng
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Measurement Model ◽  
Residual Analysis ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Indoor Environments ◽  
Localization Method ◽  
Non Line Of Sight

The localization technology is the essential requirement of constructing a smart building and smart city. It is one of the most important technologies for wireless sensor networks (WSNs). However, when WSNs are deployed in harsh indoor environments, obstacles can result in non-line-of-sight (NLOS) propagation. In addition, NLOS propagation can seriously reduce localization accuracy. In this paper, we propose a NLOS localization method based on residual analysis to reduce the influence of NLOS error. The time of arrival (TOA) measurement model is used to estimate the distance. Then, the NLOS measurement is identified through the residual analysis method. Finally, this paper uses the LOS measurements to establish the localization objective function and proposes the particle swarm optimization with a constriction factor (PSO-C) method to compute the position of an unknown node. Simulation results show that the proposed method not only effectively identifies the LOS/NLOS propagation condition but also reduces the influence of NLOS error.

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