To improve the accuracy of maximum correntropy Kalman filter (MCKF) in wireless sensors networks (WSNs) positioning, a dynamic self-tuning (DSTMCKF) is proposed, where innovation and information WSNs are used to adjust noise covariance matrices, criterion for filter’s optimality. By dynamically adjusting DSTMCKF ensures that distribution accurate presence non-Gaussian (NGN), thus improving its ...

This paper addresses the problem of robust target localization in distributed multipleinput multiple-output (MIMO) radar using possibly outlier range measurements. To achieve robustness against outliers, we construct an objective function for MIMO target localization via the maximum correntropy criterion. To deal with such a nonconvex and nonlinear function, we apply a half-quadratic optimizati...

Automatic modulation classification (AMC) techniques have applications in a variety of wireless communication scenarios, such as adaptive systems, cognitive radio, and surveillance systems. However, a common requirement to most of the AMC techniques proposed in the literature is the use of signal preprocessing modules, which can increase the computational cost and decrease the scalability of th...

This work proposes a resilient and adaptive state estimation framework for robots operating in perceptually-degraded environments. The approach, called Adaptive Maximum Correntropy Criterion Kalman Filtering (AMCCKF), is inherently robust to corrupted measurements, such as those containing jumps or general non-Gaussian noise, able modify filter parameters online improve performance. Two separat...

Multi-label learning is a challenge problem in computer vision fields. Since annotating a multilabel instance costs greatly, multi-label classification has become a hot topic research. State-of-theart active learning methods either annotate all the relevant samples without diagnosing discriminative information in the labels or annotate only limited discriminative samples manually, that has weak...

Learning robust and invariant feature representations is always a crucial task in visual recognition and analysis. Mean square error (MSE) has been used in many feature encoding methods as a feature reconstruction criterion. However, due to the non-Gaussian noises and non-linearity structures in natural images, second order statistics like MSE are usually not sufficient to capture these informa...

Recently, inspired by correntropy, kernel risk-sensitive loss (KRSL) has emerged as a novel nonlinear similarity measure defined in kernel space, which achieves a better computing performance. After applying the KRSL to adaptive filtering, the corresponding minimum kernel risk-sensitive loss (MKRSL) algorithm has been developed accordingly. However, MKRSL as a traditional kernel adaptive filter...

In recent years, correntropy has been successfully applied to robust adaptive filtering eliminate adverse effects of impulsive noises or outliers. Correntropy is generally defined as the expectation a Gaussian kernel between two random variables. This definition reasonable when error variables symmetrically distributed around zero. For case asymmetric distribution, symmetric however inappropria...