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- W4323321454 abstract "Fusing information provided by measurements from separate sensors is a common problem in multiple scientific and engineering disciplines, one with many potential solutions that can be found in literature. However, many of these solutions employ schemes that competitively weight these information sources and could potentially bias the fused solution towards a specific piece of information. In contrast, other solution methods attempt to cooperatively fuse the available information at the cost of maintaining the appropriate lower (consistent) and upper (tight) bounds. The present work derives a fusion methodology that preserves both consistency and tightness while cooperatively fusing the information provided by two information sources A and B. Additionally, upper and lower bounds on a scaling parameter, Ω, are also derived and proved analytically to maintain consistency and tightness of the presented fusion solution. The derived solution is then compared to current state-of-the-art solution and shown to provide tighter approximations of the optimal solution while keeping the optimal solution as the ultimate lower bound." @default.
- W4323321454 created "2023-03-07" @default.
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- W4323321454 date "2023-07-01" @default.
- W4323321454 modified "2023-09-27" @default.
- W4323321454 title "Matrix decomposition approaches for mutual information approximation with applications to covariance intersection techniques" @default.
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- W4323321454 doi "https://doi.org/10.1016/j.inffus.2023.03.003" @default.
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