Track level fusion with an estimation of maximum bound of unknown correlation

Bakr, Muhammad Abu; Lee, Sukhan

doi:10.1109/iccais.2016.7822431

Cited by 5 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the fact that, the minimization criteria of CI mainly focus on the uncertainty of the estimate, rather than the values of the estimates , thereby, the optimal weight is independent of the values of , which may lead to certain disadvantages. To improve the accuracy, a set-theoretic criterion [55], was proposed to find the analytic center [39] (or the Chebyshev center [6]) of the solution set , i.e. (Here, is the potential function of , with the squared Mahalanobis distance .…”

Section: More Accurate Covariance Intersectionmentioning

confidence: 99%

Information Fusion over Network Dynamics with Unknown Correlations: An Overview

Yang

2023

IJNDI

View full text Add to dashboard Cite

Survey/review study Information Fusion over Network Dynamics with Unknown Correlations: An Overview Wangyan Li 1, and Fuwen Yang 2,* 1 College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China 2 Griffth School of Engineering, Griffth University, Gold Coast Campus, QLD 4222, Australia * Correspondence: fuwen.yang@griffth.edu.au Received: 24 October 2022 Accepted: 22 November 2022 Published: Abstract: Unknown correlations (UCs) generally exist in a wide spectrum of practical multi-source information fusion problems, and thereby, their corresponding fusion problems have become one of the most important topics in information fusion domain. During the past three decades, the research on this topic has been growing rapidly and extensively, and, as a result, various important advances have been reported. In this overview, we intend to summarize the culmination of years of development in the field of information fusion under UCs as a roadmap. First, the potential reasons leading to UCs are investigated. According to the unknown nature of correlations, we further divide UCs into two categories, i.e., fully UCs, and partially UCs. For each category, the corresponding fusion methods are reviewed. Next, this roadmap witnesses the recent development of information fusion under UCs in a distributed way thanks to the popularity of distributed sensing technology. In particular, the distributed fusion techniques based on consensus, diffusion, and multi-object tracking strategies for UCs are examined. Finally, some future perspectives on information fusion under UCs are pointed out.

show abstract

Section: More Accurate Covariance Intersectionmentioning

confidence: 99%

Information Fusion over Network Dynamics with Unknown Correlations: An Overview

Yang

2023

IJNDI

View full text Add to dashboard Cite

show abstract

“…In the case of scalar-valued estimates, the cross-correlation can be computed as,

where (17) is a function of known individual covariances and a correlation coefficient

in the range [−1, 1]. Based on the correlation model (17) an analytic analysis of the BC formula is carried out to give an exact solution for fusion under unknown correlation [ 29 ]. A closed-form equation for scalar-valued fusion and an approximate solution for vector valued fusion based on a uniformly distributed correlation coefficient is proposed in Reference [ 30 ].…”

Section: Fusion Under Unknown Correlationmentioning

confidence: 99%

“…Furthermore, a conservative fusion solution is also obtained under the assumption of a uniform distribution of correlation coefficient

. In Reference [ 29 ], the correlation model (18) was used in BC formula to analytically estimate the maximum bounds of the unknown correlation in track-to-track fusion. The multisensor estimation problem with the assumption of norm-bounded cross-correlation is studied in [ 88 ], where the worst-case fused MSE is minimized for all feasible cross-covariances.…”

Section: Fusion Under Unknown Correlationmentioning

confidence: 99%

“…Various methods have been proposed to cope with the problem of fusion under unknown correlations in a distributed architecture. Depending on the way that unknown cross-correlations are handled, these methods can be categorized into three groups, including (1) Data Decorrelation, where the input data sources are decorrelated before fusion based on the measurements reconstruction [ 25 , 26 ] or explicit elimination of double counting [ 27 , 28 ]; (2) Modeling Correlation, where fused solutions are obtained based on some knowledge and modeling of the unknown correlation [ 29 , 30 , 31 , 32 ]; and (3) Ellipsoidal Methods (EM), under the assumption of bounded cross-correlation, these methods attempt to provide a suboptimal but consistent fused solution by approximating the intersection among individual data sources without any knowledge of cross-correlation. The EM include, Covariance Intersection Method (CI) and its derivatives [ 33 , 34 , 35 ], Largest Ellipsoid Method (LE) [ 36 ], Internal Ellipsoidal Approximation (IEA) [ 37 , 38 ] and Ellipsoidal Intersection Method (EI) [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency

Bakr

Lee

2017

Sensors

Self Cite

View full text Add to dashboard Cite

The paradigm of multisensor data fusion has been evolved from a centralized architecture to a decentralized or distributed architecture along with the advancement in sensor and communication technologies. These days, distributed state estimation and data fusion has been widely explored in diverse fields of engineering and control due to its superior performance over the centralized one in terms of flexibility, robustness to failure and cost effectiveness in infrastructure and communication. However, distributed multisensor data fusion is not without technical challenges to overcome: namely, dealing with cross-correlation and inconsistency among state estimates and sensor data. In this paper, we review the key theories and methodologies of distributed multisensor data fusion available to date with a specific focus on handling unknown correlation and data inconsistency. We aim at providing readers with a unifying view out of individual theories and methodologies by presenting a formal analysis of their implications. Finally, several directions of future research are highlighted.

show abstract

“…The maximum covariance is obtained for the correlation coefficient

ρ_{\max} = \sqrt{\min false(P_{1}, P_{2} false) / \max false(P_{1}, P_{2} false)}

From (14), the correlation coefficient

ρ_{\max}

provides us the bound of cross‐covariance

P_{12}

between the two data sources as

P_{12}^{U} = ({, \begin{matrix} 1em4pt \\ P_{1} & if P_{1} < P_{2} \\ P_{2} & if P_{2} < P_{1} \end{matrix})

Thus, the maximum bound of cross‐covariance is the minimum of the two data sources covariance, that is,

\min ()(, P_{1}, P_{2})

. The bounded cross‐covariance can then be used in (8) and (9) to obtain the fused mean and covariance under unknown correlation [38]. The fused covariance comes out to be