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

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2339 ◽  
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.

scholarly journals Recursive Minimum Complex Kernel Risk-Sensitive Loss Algorithm

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 902 ◽  
Author(s):  
Guobing Qian ◽  
Dan Luo ◽  
Shiyuan Wang
Keyword(s):  
Impulsive Noise ◽  
Recursive Least Squares ◽  
Complex Domain ◽  
Superior Performance ◽  
Mean Square ◽  
Kernel Space ◽  
Risk Sensitive ◽  
Simulation Results ◽  
Performance Surface ◽  
Complex Valued

The maximum complex correntropy criterion (MCCC) has been extended to complex domain for dealing with complex-valued data in the presence of impulsive noise. Compared with the correntropy based loss, a kernel risk-sensitive loss (KRSL) defined in kernel space has demonstrated a superior performance surface in the complex domain. However, there is no report regarding the recursive KRSL algorithm in the complex domain. Therefore, in this paper we propose a recursive complex KRSL algorithm called the recursive minimum complex kernel risk-sensitive loss (RMCKRSL). In addition, we analyze its stability and obtain the theoretical value of the excess mean square error (EMSE), which are both supported by simulations. Simulation results verify that the proposed RMCKRSL out-performs the MCCC, generalized MCCC (GMCCC), and traditional recursive least squares (RLS).

scholarly journals An Empirical Evaluation of the Parameters of Trilateral Filter for Noise Removal Implementation on Gaussian and Impulsive Noise

2017 ◽  
Vol 11 (2) ◽  
pp. 190-202
Author(s):  
Vorapoj Patanavijit ◽  
Kornkamol Thakulsukanant
Keyword(s):  
Gaussian Noise ◽  
Impulsive Noise ◽  
Median Filter ◽  
Power Level ◽  
Empirical Evaluation ◽  
Poor Performance ◽  
Bilateral Filter ◽  
Noise Removal ◽  
Superior Performance ◽  
Trilateral Filter

Since 1998, the Bilateral filter (BF) is worldwide accepted for its performance in practical point of view under Gaussian noise however the Bilateral filter has a poor performance for impulsive noise. Based on the combining of the Rank-Ordered Absolute Differences (ROAD) detection technique and the Bilateral filter for automatically reducing or persecuting of impulsive and Gaussian noise, this Trilateral filter (TF) has been proposed by Roman Garnett et al. since 2005 but the Trilateral filter efficiency is rest absolutely on spatial, radiometric, ROAD and joint impulsivity variance. Hence, this paper computationally determines the optimized values of the spatial, radiometric, ROAD and joint impulsivity variance of the Trilateral filter (TF) for maximum performance. In the experiment, nine noisy standard images (Girl-Tiffany, Pepper, Baboon, House, Resolution, Lena, Airplain, Mobile and Pentagon) under both five power-level Gaussian noise setting and five density impulsive noise setting, are used for estimating optimized parameters of Trilateral filter and for demonstrating the its overall performance, which is compared with classical noise removal techniques such as median filter, linear smoothing filter and Bilateral filter (BF). From the noise removal results of empirically experiments with the highest PSNR criterion, the trilateral filter with the optimized parameters has the superior performance because the ROAD variance and joint impulsivity variance can be statistically analyzed and estimated for each experimental case.

scholarly journals Distributed Estimation in Periodically Switching Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Jie Niu ◽  
Ya Zhang
Keyword(s):  
Sensor Networks ◽  
Estimation Error ◽  
Shortest Paths ◽  
Estimation Algorithm ◽  
Distributed Estimation ◽  
Time Period ◽  
Time Varying Networks ◽  
Weight Design ◽  
Error System ◽  
The Stability

This paper studies the distributed estimation problem of sensor networks, in which each node is periodically sensing and broadcasting in order. A consensus estimation algorithm is applied, and a weight design approach is proposed. The weights are designed based on an adjusting parameter and the nodes’ lengths of their shortest paths to the target node. By introducing a (T+2)-partite graph of the time-varying networks over a time period [0,T] and studying the relationships between the product of the time-sequence estimation error system matrices and the sequences of edges in the (T+2)-partite graph, a sufficient condition in terms of the observer gain and the adjusting parameter for the stability of the estimation error system is proposed. A simulation example is given to illustrate the results.

Sign in / Sign up

Export Citation Format

Share Document