%0 Journal Article
%T Distributed Asynchronous Fusion Algorithm for Sensor Networks with Packet Losses
%A Tianpeng Chu
%A Guoqing Qi
%A Yinya Li
%A Andong Sheng
%J Discrete Dynamics in Nature and Society
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/957439
%X 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. 1. Introduction Distributed fusion and estimation problem over peer-to-peer sensor networks has attracted significant interest in the research community, because of its variety of applications, such as environmental monitoring, surveillance, and target tracking, [1¨C10]. In these applications, every sensor in the network does not only take measurements in a parallel manner but also acquires information from neighbors and processes it to get an estimate. Compared with the centralized fusion fashion, the main advantage of distributed fusion estimation is the computation burden alleviation and robustness enhancement. In general, two main approaches are presented in the literature to solve the distributed fusion and estimation problem. The first approach is the consensus approach. The consensus approach was proposed in [11, 12], where local measurements are exchanged among neighbors to get local estimates, and then by using average consensus algorithms among neighbors every sensor in the network gets the same estimate in steady-state. In order to obtain the same estimate at every sensor in the network, the consensus approach may iterate several times for each new measurement. This is highly undesired when estimating the state of dynamic systems where new measurements need to be processed in a timely manner. The second approach is the diffusion approach. The diffusion approach was presented in [13], in which the estimates of local filters are calculated individually at each sensor by using the data from the neighborhood, and then the local estimates from the neighborhood are fused locally by a convex combination. Therefore, the diffusion approach is well suited for estimating the state of dynamic systems where new measurements are being taken in real time. Though the adaptive weights for the diffusion algorithm were presented
%U http://www.hindawi.com/journals/ddns/2014/957439/