In this contribution, we explore further possibilities for statistical robustification of the
traditional-space based time-of-arrival location estimator under impulsive noise conditions …

This paper considers the problem of locating a target in 2D from range measurements
containing outliers. Assuming the number of outliers is known, we formulate the problem of …

For handling unreliable datasets contaminated by the non-line-of-sight (NLOS) bias errors,
this correspondence statistically robustifies the traditional least squares type hybrid received …

This contribution considers the problem of robust target localization using the possibly
unreliable hybrid received signal strength and time-of-arrival measurements, in the Internet …

Real-world network applications must cope with failing nodes, malicious attacks, or nodes
facing corrupted data—data classified as outliers. Our work addresses these concerns in the …

This article considers the problem of source localization (SL) using possibly unreliable time-
of-arrival (TOA) based range measurements. Adopting the strategy of statistical …

The purpose of this study is to put forward a feature extraction and pattern recognition
method for the flow noise signal of natural gas pipelines in view of the complex situation …

The traditional least squares (LS) and gradient descent (GD) algorithms can estimate the
parameters of the regression models. They can be inefficient when the models have …

Locating a target is key in many applications, namely in high-stakes real-world scenarios,
like detecting humans or obstacles in vehicular networks. In scenarios where precise …

Localization is a fundamental enabler technology for many applications, like vehicular
networks, IoT, and even medicine. While Global Navigation Satellite Systems solutions offer …