Clustering Non-Gaussian Data Using Mixture Estimation with Uniform Components

Nagy, Iván; Suzdaleva, Evgenia

doi:10.1007/978-3-319-78437-3_14

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…The distribution of outlier measurements could be a uniform distribution, an exponential distribution, a Rayleigh distribution, etc. Here, in this paper, we assumed the distribution of outlier measurements was a uniform distribution (considering it was convenient) in which the assumption was commonly exploited in many works [39,40,41,42]. For example, the authors in [40] constructed a deep regression model with the use of a Gaussian-uniform mixture model to deal with the outliers.…”

Section: Numerical Simulationsmentioning

confidence: 99%

A Robust, Non-Cooperative Localization Algorithm in the Presence of Outlier Measurements in Ocean Sensor Networks

Mei

Xian

et al. 2019

Sensors

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As an important means of multidimensional observation on the sea, ocean sensor networks (OSNs) could meet the needs of comprehensive information observations in large-scale and multifactor marine environments. In what concerns OSNs, accurate location information is the basis of the data sets. However, because of the multipath effect—signal shadowing by waves and unintentional or malicious attacks—outlier measurements occur frequently and inevitably, which directly degrades the localization accuracy. Therefore, increasing localization accuracy in the presence of outlier measurements is a critical issue that needs to be urgently tackled in OSNs. In this case, this paper proposed a robust, non-cooperative localization algorithm (RNLA) using received signal strength indication (RSSI) in the presence of outlier measurements in OSNs. We firstly formulated the localization problem using a log-normal shadowing model integrated with a first order Taylor series. Nevertheless, the problem was infeasible to solve, especially in the presence of outlier measurements. Hence, we then converted the localization problem into the optimization problem using squared range and weighted least square (WLS), albeit in a nonconvex form. For the sake of an accurate solution, the problem was then transformed into a generalized trust region subproblem (GTRS) combined with robust functions. Although GTRS was still a nonconvex framework, the solution could be acquired by a bisection approach. To ensure global convergence, a block prox-linear (BPL) method was incorporated with the bisection approach. In addition, we conducted the Cramer–Rao low bound (CRLB) to evaluate RNLA. Simulations were carried out over variable parameters. Numerical results showed that RNLA outperformed the other algorithms under outlier measurements, notwithstanding that the time for RNLA computation was a little bit more than others in some conditions.

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