Secure neighbor discovery is an important element in wireless networking. Several important network functionalities such as routing and localization basically rely on correct neighbor discovery which is the problem concerned in this work. A wormhole attacker can easily tunnel and replay packets from one area of the wireless network to another one, to make the nodes in such typically far awar ar...

This paper addresses the problem of extracting a time-varying signal subspace from noisy signal measurements for direction-of-arrival (DOA) estimation and tracking. A robust adaptive method for extracting the signal subspace is developed based on robust statistics. The method is more resistant to the presence of outliers than existing adaptive methods. Robust DOA estimation and tracking are ach...

This document provides a brief proposal for “Robust Optimization & Robust Control Synthesis” reading group.

Abstract Due to the complexity of robust statistical methods, simulation studies are widely used to gain insight into the quality of these procedures. The R package simFrame is an object-oriented framework for statistical simulation with special emphasis on applications in robust statistics. Contamination is thereby modeled as a two-step process. Furthermore, the existing framework may be exten...

We present a new method for point cloud denoising. We introduce a robust smoothing operator Q(r) = r + t∗n∗, inspired in moving least squares and M-estimators robust statistics theory. Our algorithm can be seen as a generalization and improvement of the Fleishman et al algorithm for mesh denoising.

the robustness property can be added to dsc system at the expense of reducing performance, i.e., increasing the sum-rate. the aim of designing robust dsc schemes is to trade off between system robustness and compression efficiency. in this paper, after deriving an inner bound on the rate–distortion region for the quadratic gaussian mdc based rdsc system with two encoders, the structure of the r...

Robust statistics deals with deviations from ideal parametric models and their dangers for the statistical procedures derived under the assumed model. Its primary goal is the development of procedures which are still reliable and reasonably efficient under small deviations from the model, i.e. when the underlying distribution lies in a neighborhood of the assumed model. Robust statistics is the...

Kalman filter is an optimal filtering solution in certain cases, however, it is more often than not, regarded as a non-robust filter. The slight mismatch in noise statistics or process model may lead to large performance deterioration and the loss of optimality. This research paper proposes an alternative method for robust adaptive filtering concerning lack of information of noise statistics. T...