16 August 2001 Complexity reducing algorithm for near optimal fusion (CRANOF) with application to tracking and information fusion
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Conditional probability logics (CPL's), such as Adams', while producing many satisfactory results, do not agree with commonsense reasoning for a number of key entailment schemes, including transitivity and contraposition. Also, CPL's and bayesian techniques, often: (1) use restrictive independence/simplification assumptions; (2) lack a rationale behind choice of prior distribution; (3) require highly complex implementation calculations; (4) introduce ad hoc techniques. To address the above difficulties, a new CPL is being developed: CRANOF - Complexity Reducing Algorithm for Near Optimal Fusion -based upon three factors: (i) second order probability logic (SOPL), i.e., probability of probabilities within a bayesian framework; (ii) justified use of Dirichlet family priors, based on an extension of Lukacs' characterization theorem; and (iii) replacement of the theoretical optimal solution by a near optimal one where the complexity of computations is reduced significantly. A fundamental application of CRANOF to correlation and tracking is provided here through a generic example in a form similar to transitivity: two track histories are to be merged or left alone, based upon observed kinematic and non-kinematic attribute information and conditional probabilities connecting the observed data to the degrees of matching of attributes, as well as relating the matching of prescribed groups of attributes from each track history to the correlation level between the histories.
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D. Bamber, D. Bamber, I. R. Goodman, I. R. Goodman, William C. Torrez, William C. Torrez, H. T. Nguyen, H. T. Nguyen, "Complexity reducing algorithm for near optimal fusion (CRANOF) with application to tracking and information fusion", Proc. SPIE 4380, Signal Processing, Sensor Fusion, and Target Recognition X, (16 August 2001); doi: 10.1117/12.436955; https://doi.org/10.1117/12.436955

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