Distributed estimation in general directed sensor networks based on batch covariance intersection

2016 ◽  
Author(s):  
Tao Sun ◽  
Ming Xin ◽  
Bin Jia
Keyword(s):  
Sensor Networks ◽  
Distributed Estimation ◽  
Covariance Intersection

scholarly journals Distributed Asynchronous Fusion Algorithm for Sensor Networks with Packet Losses

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Tianpeng Chu ◽  
Guoqing Qi ◽  
Yinya Li ◽  
Andong Sheng
Keyword(s):  
Sensor Networks ◽  
Estimation Method ◽  
Distributed Estimation ◽  
Fusion Algorithm ◽  
Packet Losses ◽  
Packet Dropouts ◽  
Covariance Intersection ◽  
Measurement Equations ◽  
Simulation Results ◽  
Intersection Method

This paper is concerned with the problem of distributed estimation fusion over peer-to-peer asynchronous sensor networks with random packet dropouts. A distributed asynchronous fusion algorithm is proposed via the covariance intersection method. First, local estimator is developed in an optimal batch fashion by constructing augmented measurement equations. Then the fusion estimator is designed to fuse local estimates in the neighborhood. Both local estimator and fusion estimator are developed by taking into account the random packet losses. The presented estimation method improves local estimates and reduces the estimate disagreement. Simulation results validate the effectiveness of the proposed distributed asynchronous fusion algorithm.

Robust Decentralized and Distributed Estimation of a Correlated Parameter Vector in MIMO-OFDM Wireless Sensor Networks

2021 ◽  
pp. 1-1
Author(s):  
Kunwar Pritiraj Rajput ◽  
Mohammad Faisal Ahmed ◽  
Naveen K. D. Venkategowda ◽  
Aditya K. Jagannatham ◽  
Govind Sharma ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Distributed Estimation ◽  
Wireless Sensor ◽  
Parameter Vector ◽  
Mimo Ofdm

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.

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