By David L. Hall, James Llinas
If you are or are within the info fusion box - you need to HAVE THIS BOOK!!!
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Additional info for Multisensor Data Fusion, 2 Volume Set
1 Has the problem been solved before? Has the same problem appeared in a different form and is there an existing solution? Is there a related problem with similar constraints? Is there a related problem with the same unknowns? Can the problem be subdivided into parts that are easier to solve? 12 Data Fusion “Levels” Of the many ways to differentiate types of data fusion functions, the JDL model has gained the widest usage. ”2 * This is seen as equivalent to the concept of framework as used in Reference 11.
Track splitting in its various forms5 is a widely applied strategy for handling the ambiguities inherent in correlating tracks with reports from multiple targets. It is also used to minimize the effects of spurious reports when tracking a single target. Nevertheless, some serious difficulties remain. First, track splitting does not completely decompose a multiple-target tracking problem into independent single-target problems, the way the nearest-neighbor strategy was intended to function. For example, two hypothesis tracks may lock onto the trajectory of a single object.
1) pL1(xd )Λ ς (xd ) xd where pL1(xd) is the probability currently assigned to discrete state, xd , by Level 1 data fusion of observations associated with node X, and Λ(xd) is the evidence communicated to X from the tracks related to Y in a Level 2 association hypothesis. 2) d X , Y ∈ζ The factors ΛY(xd) are interpreted in terms of relational states among entities as follows. Ordered pairs of entities are hypothesized as having relational states, ri (X,Y). * Updating a track, Y, contributes information for evaluating the probability of each state, x, of a possible related entity, X.
Multisensor Data Fusion, 2 Volume Set by David L. Hall, James Llinas