40 research outputs found
Robust M-Estimation Based Bayesian Cluster Enumeration for Real Elliptically Symmetric Distributions
Robustly determining the optimal number of clusters in a data set is an
essential factor in a wide range of applications. Cluster enumeration becomes
challenging when the true underlying structure in the observed data is
corrupted by heavy-tailed noise and outliers. Recently, Bayesian cluster
enumeration criteria have been derived by formulating cluster enumeration as
maximization of the posterior probability of candidate models. This article
generalizes robust Bayesian cluster enumeration so that it can be used with any
arbitrary Real Elliptically Symmetric (RES) distributed mixture model. Our
framework also covers the case of M-estimators that allow for mixture models,
which are decoupled from a specific probability distribution. Examples of
Huber's and Tukey's M-estimators are discussed. We derive a robust criterion
for data sets with finite sample size, and also provide an asymptotic
approximation to reduce the computational cost at large sample sizes. The
algorithms are applied to simulated and real-world data sets, including
radar-based person identification, and show a significant robustness
improvement in comparison to existing methods
Real Elliptically Skewed Distributions and Their Application to Robust Cluster Analysis
This article proposes a new class of Real Elliptically Skewed (RESK)
distributions and associated clustering algorithms that allow for integrating
robustness and skewness into a single unified cluster analysis framework.
Non-symmetrically distributed and heavy-tailed data clusters have been reported
in a variety of real-world applications. Robustness is essential because a few
outlying observations can severely obscure the cluster structure. The RESK
distributions are a generalization of the Real Elliptically Symmetric (RES)
distributions. To estimate the cluster parameters and memberships, we derive an
expectation maximization (EM) algorithm for arbitrary RESK distributions.
Special attention is given to a new robust skew-Huber M-estimator, which is
also the maximum likelihood estimator (MLE) for the skew-Huber distribution
that belongs to the RESK class. Numerical experiments on simulated and
real-world data confirm the usefulness of the proposed methods for skewed and
heavy-tailed data sets
Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization
Distributed learning paradigms, such as federated or decentralized learning,
allow a collection of agents to solve global learning and optimization problems
through limited local interactions. Most such strategies rely on a mixture of
local adaptation and aggregation steps, either among peers or at a central
fusion center. Classically, aggregation in distributed learning is based on
averaging, which is statistically efficient, but susceptible to attacks by even
a small number of malicious agents. This observation has motivated a number of
recent works, which develop robust aggregation schemes by employing robust
variations of the mean. We present a new attack based on sensitivity curve
maximization (SCM), and demonstrate that it is able to disrupt existing robust
aggregation schemes by injecting small, but effective perturbations
Radar Based Humans Localization with Compressed Sensing and Sparse Reconstruction
Localization and detection is a vital task in emergency rescue operations. Devastating natural disasters can create environments that are inaccessible or dangerous for human rescuers. Contaminated areas or buildings in danger of collapsing can be searched by rescue robots which are equipped with diverse sensors such as optical and radar sensors. In scenarios where the line of sight is blocked, e.g., by a wall, a door or heavy smoke or dust, sensors like LiDAR or cameras are not able to provide sufficient information. The usage of radar in these kinds of situations can drastically improve situational awareness and hence the likelihood of rescue. In this paper, we present a method that is used for radar imaging behind obstacles by utilizing a signal model that includes the floor reflection propagation path in addition to the direct path of the radar signal. Additionally, compressed sensing methods are presented and applied to real world radar data that was recorded by a Stepped Frequency Continuous Wave (SFCW) radar mounted on a semi-autonomous robot. The results show an improved radar image that allows the clear identification of persons behind obstacles
Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization
Distributed learning paradigms, such as federated or decentralized learning, allow a collection of agents to solve global learning and optimization problems through limited local interactions. Most such strategies rely on a mixture of local adaptation and aggregation steps, either among peers or at a central fusion center. Classically, aggregation in distributed learning is based on averaging, which is statistically efficient, but susceptible to attacks by even a small number of malicious agents. This observation has motivated a number of recent works, which develop robust aggregation schemes by employing robust variations of the mean. We present a new attack based on sensitivity curve maximization (SCM), and demonstrate that it is able to disrupt existing robust aggregation schemes by injecting small, but effective perturbations
Multi-Person Localization and Vital Sign Estimation Radar Dataset
The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infrastructures. For search and rescue missions in degraded visual conditions or non-line of sight scenarios, radar-based approaches may contribute to acquire valuable, and otherwise unavailable information.
This dataset provides the possibility to develop algorithms for, e.g., radar-based (through-wall) multi-person detection, localization, 3D direction-of-arrival estimation, breathing frequency estimation or heart beat estimation. The challenging dataset was collected using a semi-autonomous robot equipped with a commercially available through-wall radar system. The dataset is composed of 62 scenarios of various difficulty levels with up to five persons captured in different postures, angles and ranges including wooden and stone obstacles that block the radar line of sight. Ground truth data for reference locations, respiration, electrocardiogram, and acceleration signals are included