Averaging based distributed estimation algorithm for rate-constrained sensor networks with additive quantization model

2015 ◽  
Author(s):  
Shanying Zhu ◽  
Shuai Liu ◽  
Jinming Xu ◽  
Yeng Chai Soh ◽  
Lihua Xie
Keyword(s):  
Sensor Networks ◽  
Estimation Algorithm ◽  
Distributed Estimation

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 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.

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