The accuracy comparison of multisensor covariance intersection fuser and three weighting fusers

Deng, Zili; Zhang, Peng; Qi, Wenjuan; Gao, Yuan; Liu, Jinfang

doi:10.1016/j.inffus.2012.05.005

Cited by 118 publications

(66 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its main principle is to use multiple sensors (homogeneous or heterogeneous) to measure the same object so as to obtain the multi-source information of the object, further integrate the information in order to obtain a better understanding of the observed object. Because of the universality and particularity for the descriptor system in the structure, and it makes the study of state estimation problem of generalized systems is far behind the traditional system [6] [ 10] . The research about multi-sensor information fusion for descriptor system is much less.…”

Section: Introductionmentioning

confidence: 99%

Multisensor Information Fusion State Estimator for Descriptor and Non Descriptor Systems

Li¹,

Zhao²,

Hao³

et al. 2015

IJCA

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Multisensor Information Fusion State Estimator for Descriptor and Non Descriptor Systems

Li¹,

Zhao²,

Hao³

et al. 2015

IJCA

View full text Add to dashboard Cite

show abstract

“…To overcome the limitation, a covariance intersection (CI) fusion algorithm is presented in [4] for a multi-sensor system with unknown cross covariance. The CI fuser is derived as a convex combination of two local estimates and its consistency is rigorously proved in [5,6] for arbitrary weighting coefficients satisfying ∑ = 1 .…”

Section: Introductionmentioning

confidence: 99%

Weighted Kullback-Leibler average-based distributed filtering algorithm

Chang

Zhou

2015

SPIE Proceedings

View full text Add to dashboard Cite

This paper considers a distributed filtering problem over a multi-sensor network in which the correlation of local estimation errors is unknown. Recently, this problem was studied by G. Battistelli [1] by developing a data fusion rule to calculate the weighted Kullback-Leibler average of local estimates with consensus algorithms for distributed averaging, where the weighted Kullback-Leibler average is defined as an averaged probability density function to minimize the sum of weighted Kullback-Leibler divergences from the original probability density functions. In this paper, we extends those earlier results by relaxing the prior assumption that all sensors share the same degree of confidence. Furthermore, a novel consensus-based distributed weighting coefficients selection scheme is developed to improve the fusion accuracy, where the weight associated with each sensor is adjusted based on the local estimation error covariance and the ones received from neighboring sensors, so that larger weight values will be assigned to a sensor with higher degree of confidence.Finally, a Monte-Carlo simulation with a 2D tracking system validates the effectiveness of the proposed distributed filtering algorithm.

show abstract

“…Compared with the centralized fuser, the distributed fuser can reduce the calculation burden and are more flexible and reliable, based on linear minimum variance rules, the weighting distributed fusion filtering has three weighting fusion algorithms which are fusers weighted by matrices, scalars and diagonal matrices. Unified fusion rules for the optimal linear estimation fusion and several distributed weighting state fusers are presented in [1][2][3], all the distributed fusion estimations need to calculate the crosscovariance of local estimates. However, in many theoretical and application problems, the cross-covariances are unknown or the computing of the cross-covariances is very difficult.…”

Section: Introductionmentioning

confidence: 99%

Several Weighting Fusion Kalman Predictors with Colored Measurement Noises and their Accuracy Comparison

Deng

2013

2013 Fifth International Conference on Measuring Technology and Mechatronics Automation

View full text Add to dashboard Cite

For two-sensor system with colored measurement noises, based on classical Kalman filtering, a covariance intersection (CI) fusion steady-state Kalman predictor without cross-covariance is presented. Under the linear unbiased minimum variance criterion, the three fusion Kalman predictors weighted by matrices, diagonal matrices and scalars are also presented respectively. Their accuracy relations are proved. The accuracy of CI fuser is higher than that of each local Kalman predictor, and lower than that of optimal fuser weighted by matrices. They can be considered as a new information fusion state observer or a new intelligent sensor. The geometric interpretation of the accuracy relations is given. A Monte-Carlo simulation example verifies the theoretical accuracy relations.

show abstract

The accuracy comparison of multisensor covariance intersection fuser and three weighting fusers

Cited by 118 publications

References 29 publications

Multisensor Information Fusion State Estimator for Descriptor and Non Descriptor Systems

Multisensor Information Fusion State Estimator for Descriptor and Non Descriptor Systems

Weighted Kullback-Leibler average-based distributed filtering algorithm

Several Weighting Fusion Kalman Predictors with Colored Measurement Noises and their Accuracy Comparison

Contact Info

Product

Resources

About