Indoor Mobile Localization Using Wireless Sensor Networks (WSNs)

2020 ◽  
pp. 341-351
Author(s):  
M. Hamza Bin Waheed ◽  
Rao Naveed Bin Rais ◽  
Hassan Khan ◽  
Mukhtiar Bano ◽  
Syed Sherjeel A. Gilani
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Mobile Localization

scholarly journals A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of things

2020 ◽  
Vol 16 (9) ◽  
pp. 155014772096123
Author(s):  
Nan Hu ◽  
Chuan Lin ◽  
Fangjun Luan ◽  
Chengdong Wu ◽  
Qi Song ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Kalman Filter ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Nearest Neighbor ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Mobile Localization ◽  
M Estimation ◽  
Non Line Of Sight

As the key technology for Internet of things, wireless sensor networks have received more attentions in recent years. Mobile localization is one of the significant topics in wireless sensor networks. In wireless sensor network, non-line-of-sight propagation is a common phenomenon leading to the growing non-line-of-sight error. It is a fatal impact for the localization accuracy of the mobile target. In this article, a novel method based on the nearest neighbor variable estimation is proposed to mitigate the non-line-of-sight error. First, the linear regression model of the extended Kalman filter is used to obtain the residual of the distance measurement value. After that, the residual analysis is used to complete the identification of the measurement value state. Then, by analyzing the statistical characteristics of the non-line-of-sight residual, the nearest neighbor variable estimation is proposed to estimate the probability density function of residual. Finally, the improved M-estimation is proposed to locate the mobile robot. Experiment results prove that the accuracy and robustness of the proposed algorithm are better than other methods in the mixed line-of-sight/non-line-of-sight environment. The proposed algorithm effectively inhibits the non-line-of-sight error.

scholarly journals A Residual Analysis-Based Improved Particle Filter in Mobile Localization for Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2945 ◽  
Author(s):  
Long Cheng ◽  
Liang Feng ◽  
Yan Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Particle Filter ◽  
Residual Analysis ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Main Body ◽  
Localization Algorithm ◽  
Mobile Localization ◽  
Non Line Of Sight

Wireless sensor networks (WSNs) have become a popular research subject in recent years. With the data collected by sensors, the information of a monitored area can be easily obtained. As a main contribution of WSN localization is widely applied in many fields. However, when the propagation of signals is obstructed there will be some severe errors which are called Non-Line-of-Sight (NLOS) errors. To overcome this difficulty, we present a residual analysis-based improved particle filter (RAPF) algorithm. Because the particle filter (PF) is a powerful localization algorithm, the proposed algorithm adopts PF as its main body. The idea of residual analysis is also used in the proposed algorithm for its reliability. To test the performance of the proposed algorithm, a simulation is conducted under several conditions. The simulation results show the superiority of the proposed algorithm compared with the Kalman Filter (KF) and PF. In addition, an experiment is designed to verify the effectiveness of the proposed algorithm in an indoors environment. The localization result of the experiment also confirms the fact that the proposed algorithm can achieve a lower localization error compared with KF and PF.

scholarly journals Fast/fair mobile localization in infrastructure wireless sensor networks

2007 ◽  
Vol 11 (1) ◽  
pp. 29-40 ◽  
Author(s):  
Kiran Yedavalli ◽  
Bhaskar Krishnamachari ◽  
Lakshmi Venkatraman
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Mobile Localization

Principles of Wireless Sensor Networks

2012 ◽  
Author(s):  
Mohammad S. Obaidat ◽  
Sudip Misra
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor

A Power Optimization Method in Large-Scale Wireless Sensor Networks Using a Two-Stage Meta-Heuristic Algorithm

2014 ◽  
Vol 2 ◽  
pp. 302-305
Author(s):  
Ryohei Sato ◽  
Hidehiro Nakano ◽  
Arata Miyauchi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Heuristic Algorithm ◽  
Large Scale ◽  
Power Optimization ◽  
Optimization Method ◽  
Wireless Sensor ◽  
Two Stage
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