Research On Distributed Estimation Based On Robustness In Wireless Sensor Networks

The problem of parameter estimation,which is the indirect determination of the unknown parameters from measurements of other quantities,is a key issue in the signal processing field.Distributed estimation has become very popular for parameter estimation in wireless sensor networks.The objective is to enable the nodes to estimate a vector of parameters of interest in a distributed manner from the observed data.Distributed estimation schemes over adaptive networks can be mainly classified into incremental strategies,consensus strategies,and diffusion strategies.In the incremental strategies,data is processed in a cyclic fashion through the network.The consensus strategies rely on the fusion of intermediate estimates of multiple neighboring nodes.In the Diffusion strategies,information is processed at all nodes while the nodes communicate with all their neighbors to share their intermediate estimates.The diffusion strategies are particularly attractive because they are robust,flexible and fully-distributed,such as the diffusion least mean squares(DLMS)algorithm.In this paper,we focus on the diffusion estimation strategies.The performance of distributed estimation degrades severely when the signals are perturbed by non-Gaussian noise.Non-Gaussian noise may be natural,due to atmospheric phenomena,or man-made,due to either electric machinery present in the operation environment,or multipath telecommunications signals.Recently,some researchers focus on improving robustness for non-Gaussian noise of distributed estimation methods.The efforts are mainly directed at searching for a more robust cost function to replace the mean-square error(MSE)criterion,which is optimal only when the measurement noise is Gaussian.The error entropy criterion based on the minimum error entropy(MEE)method also has shown its ability to achieve more accurate estimates than mean-square error(MSE)under non-Gaussian noise.In this paper,firstly,in order to solve the problem of robust estimation of DLMS algorithm for non-Gaussian noise environment,proposed a robust diffusion estimation algorithm based on a minimum error entropy criterion with a self-adjusting step-size,which are referred to as the diffusion MEE-SAS(DMEE-SAS)algorithm.The DMEE-SAS algorithm has a fast speed of convergence and is robust against non-Gaussian noise in the measurements and better achieves the balance between the communication load and the estimated performance.Secondly,we also design an Improving DMEE-SAS algorithm by using a switching scheme between DMEE-SAS and diffusion minimum error entropy(DMEE)algorithms for a non-stationary environment,which tracks the changing estimator very effectively.The Improving DMEE-SAS algorithm can avoid the small effective step-size of the DMEE-SAS algorithm when it is close to the optimal estimator.This research has an important role in the robustness of distributed estimation algorithms.Finally,the first time to divide the wireless sensor networks into several sub-regions,combinning the diffusion strategy with the parameter estimation was carried out,and a diffusion least mean squares estimation algorithm based on sub-regions(SR-DLMS)was proposed.The proposed algorithm can reduce the communication cost without affecting the estimation performance,and shows robustness in Gaussian noise environment.This exploration has practical significance for balancing the algorithm robustness and communication cost in the distributed parameter estimation problem.