Statistical analysis of wireless sensor network Gaussian range estimation errors

Statistical analysis of wireless sensor network Gaussian range estimation errors

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Wireless sensor network (WSN) localisation has attracted significant research interest. The quality of time-of-flight ranging when used as the basis of localisation, has a direct impact on precision and accuracy. Recently significant attention has been devoted to modelling and analysis of range estimation error (REE) in indoor and cluttered environments. A Gaussianity assumption for the distribution of REE is a common practice in the literature. The motivation of this study is to analyse this assumption. To scrutinise this, rather than relying on computer generated data, a real IEEE 802.15.4 compliant WSN test-bed is used for collecting ranging data, covering outdoor and indoor environments for both line-of-sight and non-line-of-sight propagation conditions. The distribution of REE is analysed using both graphical and numerical goodness-of-fit (GOF) techniques, that is, quantile–quantile plotting, empirical cumulative distribution function plotting, probability density function plotting, linear correlation coefficient (γ) test, kurtosis (K) test, skewness (S) test, Anderson–Darling (A 2) test and chi-squared (χ 2) test. The GOF statistical analysis of the experimental results suggest that REE is not Gaussian distributed. A novel means of enhancement called the range filtration algorithm (RFA) is proposed. The RFA is based on the A 2 test, it filters out the range estimates with high errors.


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