Detection for binary transmission in a mixture of Gaussian noise and impulsive noise modeled as an alpha-stable process

We compare a recently proposed multivariate spline based on mixed partial derivatives with two other standard splines for the scattered data smoothing problem. The splines are defined as the minimiser of a penalised least squares functional. The penalties are based on partial differential operators, and are integrated using the finite element method. We compare three methods to two problems: to remove the mixture of Gaussian and impulsive noise from an image, and to recover a continuous function from a set of noisy observations.

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Signal detection in incompletely characterized impulsive noise modeled as a stable process

Proceedings of MILCOM '94 ◽

10.1109/milcom.1994.473929 ◽

2002 ◽

Author(s):

G.A. Tsihrintzis ◽

C.L. Nikias

Keyword(s):

Signal Detection ◽

Impulsive Noise ◽

Stable Process

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A Robust Diffusion Minimum Kernel Risk-Sensitive Loss Algorithm over Multitask Sensor Networks

Sensors ◽

10.3390/s19102339 ◽

2019 ◽

Vol 19 (10) ◽

pp. 2339 ◽

Cited By ~ 6

Author(s):

Xinyu Li ◽

Qing Shi ◽

Shuangyi Xiao ◽

Shukai Duan ◽

Feng Chen

Keyword(s):

Sensor Networks ◽

Gaussian Noise ◽

Impulsive Noise ◽

Estimation Algorithm ◽

Distributed Estimation ◽

Superior Performance ◽

Mean Square ◽

Unknown Parameters ◽

Risk Sensitive ◽

The Impact

Distributed estimation over sensor networks has attracted much attention due to its various applications. The mean-square error (MSE) criterion is one of the most popular cost functions used in distributed estimation, which achieves its optimality only under Gaussian noise. However, impulsive noise also widely exists in real-world sensor networks. Thus, the distributed estimation algorithm based on the minimum kernel risk-sensitive loss (MKRSL) criterion is proposed in this paper to deal with non-Gaussian noise, particularly for impulsive noise. Furthermore, multiple tasks estimation problems in sensor networks are considered. Differing from a conventional single-task, the unknown parameters (tasks) can be different for different nodes in the multitask problem. Another important issue we focus on is the impact of the task similarity among nodes on multitask estimation performance. Besides, the performance of mean and mean square are analyzed theoretically. Simulation results verify a superior performance of the proposed algorithm compared with other related algorithms.

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The synchronization of coupled stochastic systems driven by symmetric α-stable process and Brownian motion

Filomat ◽

10.2298/fil1806219a ◽

2018 ◽

Vol 32 (6) ◽

pp. 2219-2245

Author(s):

Shahad Al-Azzawi ◽

Jicheng Liu ◽

Xianming Liu

Keyword(s):

Dynamical System ◽

Mathematical Model ◽

Brownian Motion ◽

Gaussian Noise ◽

Stochastic Systems ◽

Stable Process ◽

Symmetric Stable Process ◽

And Brownian Motion ◽

Coupled Dynamical System ◽

Non Gaussian

The synchronization of stochastic differential equations (SDEs) driven by symmetric ?-stable process and Brownian Motion is investigated in pathwise sense. This coupled dynamical system is a new mathematical model, where one of the systems is driven by Gaussian noise, another one is driven by non- Gaussian noise. In this paper, we prove that the synchronization still persists for this coupled dynamical system. Examples and simulations are given.

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A Novel Adaptive State Detector-Based Post-Filtering Active Control Algorithm for Gaussian Noise Environment with Impulsive Interference

Applied Sciences ◽

10.3390/app9061176 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1176 ◽

Cited By ~ 1

Author(s):

Wenzhao Zhu ◽

Shengguo Shi ◽

Lei Luo ◽

Jinwei Sun

Keyword(s):

Active Control ◽

Gaussian Noise ◽

Control Algorithm ◽

Noise Control ◽

Impulsive Noise ◽

Active Noise Control ◽

Noise Environment ◽

Fast Tracking ◽

Impulsive Interference ◽

State Detector

For Gaussian noise with random or periodic impulsive interference, the conventional active noise control (ANC) methods with finite second-order moments may fail to converge. Furthermore, the intensity of impulsive noise typically varies over time in the actual application, which also decreases the performance of conventional active impulsive noise control methods. To address these problems, a novel adaptive state detector based post-filtering active control algorithm is proposed. In this work, information entropy with adaptive kernel size is first introduced into the cost function of a post-filtering algorithm to improve its tracking. To enhance the robust performance of adaptive filters when impulsive interference happens, a recursive optimal threshold selecting method is also developed and analyzed by statistical theories. Simulations show that the new method has fast tracking ability in non-impulsive noise environment and keeps robust when impulsive interference happens. It also works well for the impulsive noise of different degrees. Experiment results confirm the effectiveness of the proposed algorithm.

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