scholarly journals Distributed Kalman estimation with decoupled local filters

Automatica ◽  
2021 ◽  
Vol 130 ◽  
pp. 109724
Author(s):  
Damián Marelli ◽  
Tianju Sui ◽  
Minyue Fu
Keyword(s):  
2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Xuefeng Fan ◽  
Fei Liu

The paper presents a decentralized fusion strategy based on the optimal unbiased finite impulse response (OUFIR) filter for discrete systems with correlated process and measurement noise. We extend OUFIR filter to apply in the model with control inputs. Taking it as local filters, cross covariance between any two is calculated; then it is expressed to the fast iterative form. Finally based on cross covariance, optimal weights are utilized to fuse local estimates and the overall outcome is obtained. The numerical examples show that the proposed filter exhibits better robustness against temporary modeling uncertainties than the fusion Kalman filter used commonly.


2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Pengcheng Han ◽  
Junping Du ◽  
Ming Fang

Object tracking is one of the fundamental problems in computer vision, but existing efficient methods may not be suitable for spatial object tracking. Therefore, it is necessary to propose a more intelligent mathematical model. In this paper, we present an intelligent modeling method using an enhanced mean shift method based on a perceptual spatial-space generation model. We use a series of basic and composite graphic operators to complete signal perceptual transformation. The Monte Carlo contour detection method could overcome the dimensions problem of existing local filters. We also propose the enhanced mean shift method with estimation of spatial shape parameters. This method could adaptively adjust tracking areas and eliminate spatial background interference. Extensive experiments on a variety of spatial video sequences with comparison to several state-of-the-art methods demonstrate that our method could achieve reliable and accurate spatial object tracking.


1996 ◽  
Vol 2 (3) ◽  
pp. 173-194
Author(s):  
R. Edwin Hicks ◽  
Mehmet Sengun ◽  
Basil Fine

<em>Abstract</em>.—Community ecology increasingly seeks to integrate the influences of regional and historical processes with species interactions within local habitats. This broadened perspective is largely based on comparative approaches that employ “natural experiments” to identify factors shaping community structure. Because coastal rivers are separated from one another by insurmountable barriers (oceans or land), freshwater fishes are particularly well suited for comparative analyses of factors that influence fish community organization. In this chapter, we review how this comparative approach shed light on large-scale biodiversity gradients, community saturation, community convergence, density compensation, and the role of intrinsic and extrinsic factors in community dynamics. The main factors (e.g., river mouth discharge and history) empirically related to species richness of a river are well identified, and metacommunity ecology provides a fruitful conceptual framework for understanding how regional (river) species richness translates into local species richness. Much work remains to identify factors explaining differences among whole river basin assemblages with regard to ecological traits (e.g., trophic status and life history) composition and to assess whether trait-related environmental and biotic local filters act similarly over large spatial scales. One important conclusion that can be drawn from the studies reviewed here is that history cannot be neglected whatever the scale of investigation (global, river, or site). A second conclusion is that historical effects are not strong enough to blur the occurrence of qualitatively repeatable patterns of community structure over large spatial scale, which is encouraging because it suggests development of general predictive models of community structure is an attainable goal.


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