scholarly journals An Improved Compressive Sensing and Received Signal Strength-Based Target Localization Algorithm with Unknown Target Population for Wireless Local Area Networks

Sensors ◽  
2017 ◽  
Vol 17 (6) ◽  
pp. 1246 ◽  
Author(s):  
Jun Yan ◽  
Kegen Yu ◽  
Ruizhi Chen ◽  
Liang Chen
Keyword(s):  
Compressive Sensing ◽  
Local Area Networks ◽  
Signal Strength ◽  
Target Population ◽  
Local Area ◽  
Received Signal Strength ◽  
Wireless Local Area Networks ◽  
Target Localization ◽  
Localization Algorithm ◽  
Strength Based

Received-Signal-Strength-Based Indoor Positioning Using Compressive Sensing

2012 ◽  
Vol 11 (12) ◽  
pp. 1983-1993 ◽  
Author(s):  
Chen Feng ◽  
Wain Sy Anthea Au ◽  
Shahrokh Valaee ◽  
Zhenhui Tan
Keyword(s):  
Compressive Sensing ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Strength Based

scholarly journals Indoor Positioning in Wireless Local Area Networks with Online Path-Loss Parameter Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Luigi Bruno ◽  
Paolo Addesso ◽  
Rocco Restaino
Keyword(s):  
Working Conditions ◽  
Sequential Monte Carlo ◽  
Local Area Networks ◽  
Simulated Data ◽  
Computer Experiments ◽  
Local Area ◽  
Wireless Local Area Networks ◽  
Location Based Services ◽  
Indoor Environments ◽  
Localization Algorithm

Location based services are gathering an even wider interest also in indoor environments and urban canyons, where satellite systems like GPS are no longer accurate. A much addressed solution for estimating the user position exploits the received signal strengths (RSS) in wireless local area networks (WLANs), which are very common nowadays. However, the performances of RSS based location systems are still unsatisfactory for many applications, due to the difficult modeling of the propagation channel, whose features are affected by severe changes. In this paper we propose a localization algorithm which takes into account the nonstationarity of the working conditions by estimating and tracking the key parameters of RSS propagation. It is based on a Sequential Monte Carlo realization of the optimal Bayesian estimation scheme, whose functioning is improved by exploiting the Rao-Blackwellization rationale. Two key statistical models for RSS characterization are deeply analyzed, by presenting effective implementations of the proposed scheme and by assessing the positioning accuracy by extensive computer experiments. Many different working conditions are analyzed by simulated data and corroborated through the validation in a real world scenario.

scholarly journals Received signal strength–based target localization under spatially correlated shadowing via convex optimization relaxation

2018 ◽  
Vol 14 (6) ◽  
pp. 155014771878366 ◽  
Author(s):  
Shengming Chang ◽  
Youming Li ◽  
Hui Wang ◽  
Gang Wang
Keyword(s):  
Convex Optimization ◽  
Weighted Least Squares ◽  
Equivalent Form ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Target Localization ◽  
Correlated Shadowing ◽  
Spatially Correlated ◽  
Strength Based ◽  
Log Normal

Received signal strength–based target localization methods normally employ radio propagation path loss model, in which the log-normal shadowing noise is generally assumed to follow a zero-mean Gaussian distribution and is uncorrelated. In this article, however, we represent the simplified additive noise by the spatially correlated log-normal shadowing noise. We propose a new convex localization estimator in wireless sensor networks by using received signal strength measurements under spatially correlated shadowing environment. First, we derive a new non-convex estimator based on weighted least squares criterion. Second, by using the equivalence of norm, the derived estimator can be reformulated as its equivalent form which has no logarithm in the objective function. Then, the new estimator is relaxed by applying efficient convex relaxation that is based on second-order cone programming and semi-definite programming technique. Finally, the convex optimization problem can be efficiently solved by a standard interior-point method, thus to obtain the globally optimal solution. Simulation results show that the proposed estimator solves the localization problem efficiently and is close to Cramer–Rao lower bound compared with the state-of-the-art approach under correlated shadowing environment.

scholarly journals Adaptive multi-task compressive sensing for localisation in wireless local area networks

2014 ◽  
Vol 8 (10) ◽  
pp. 1736-1744 ◽  
Author(s):  
Rongpeng Li ◽  
Honggang Zhang ◽  
Jacques Palicot ◽  
Zhifeng Zhao ◽  
Yuan Zhang
Keyword(s):  
Compressive Sensing ◽  
Local Area Networks ◽  
Local Area ◽  
Wireless Local Area Networks
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