Distance Estimation From Received Signal Strength Under Log-Normal Shadowing: Bias and Variance

2009 ◽  
Vol 16 (3) ◽  
pp. 216-218 ◽  
Author(s):  
Sree Divya Chitte ◽  
Soura Dasgupta ◽  
Zhi Ding
Keyword(s):  
Distance Estimation ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Log Normal

scholarly journals Human-Induced Effects on RSS Ranging Measurements for Cooperative Positioning

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Francescantonio Della Rosa ◽  
Mauro Pelosi ◽  
Jari Nurmi
Keyword(s):  
Human Factors ◽  
Path Loss ◽  
Distance Estimation ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Experimental Investigations ◽  
Positioning Accuracy ◽  
Cooperative Positioning ◽  
Hand Grip ◽  
The Impact

We present experimental evaluations of human-induced perturbations on received-signal-strength-(RSS-) based ranging measurements for cooperative mobile positioning. To the best of our knowledge, this work is the first attempt to gain insight and understand the impact of both body loss and hand grip on the RSS for enhancing proximity measurements among neighbouring devices in cooperative scenarios. Our main contribution is represented by experimental investigations. Analysis of the errors introduced in the distance estimation using path-loss-based methods has been carried out. Moreover, the exploitation of human-induced perturbations for enhancing the final positioning accuracy through cooperative schemes has been assessed. It has been proved that the effect of cooperation is very limited if human factors are not taken into account when performing experimental activities.

scholarly journals Characterization of the Log-Normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks

2020 ◽  
Vol 9 (1) ◽  
pp. 12 ◽  
Author(s):  
José Vallet García
Keyword(s):  
Empirical Studies ◽  
Path Loss ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Wireless Sensor ◽  
Model Parameters ◽  
Normal Model ◽  
Path Loss Exponent ◽  
Localization Algorithms ◽  
Log Normal

Using the classical received signal strength (RSS)-distance log-normal model in wireless sensor network (WSN) applications poses a series of characteristic challenges derived from (a) the model’s structural limitations when it comes to explaining real observations, (b) the inherent hardware (HW) variability typically encountered in the low-cost nodes of WSNs, and (c) the inhomogeneity of the deployment environment. The main goal of this article is to better characterize how these factors impact the model parameters, an issue that has received little attention in the literature. For that matter, I qualitatively elaborate on their effects and interplay, and present the results of two quantitative empirical studies showing how much the parameters can vary depending on (a) the nodes used in the model identification and their position in the environment, and (b) the antenna directionality. I further show that the path loss exponent and the reference power can be highly correlated. In view of all this, I argue that real WSN deployments are better represented by random model parameters jointly accounting for HW and local environmental characteristics, rather than by deterministic independent ones. I further argue that taking this variability into account results in more realistic models and plausible results derived from their usage. The article contains example values of the mean and standard deviation of the model parameters, and of the correlation between the path loss exponent and the reference power. These can be used as a guideline in other studies. Given the sensitivity of localization algorithms to the proper model selection and identification demonstrated in the literature, the structural limitations of the log-normal model, the variability of its parameters and their interrelation are all relevant aspects that practitioners need to be aware of when devising optimal localization algorithms for real WSNs that rely on this popular model.

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.

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