Loại dữ liệuHiệu năng tốt nhất Cấp Cây cao nhất của tập hợp Tất cả các cảm biến / Tất cả các nguồn trong quá khứ Tất cả các loại dữ liệuFusion Hiệu suất dữ liệu"Đầu gối của đường cong" Thiết kếChi phí / phức tạp dữ liệu FusionChọn tập hợp dữ liệu và trộn các tiêu chí | Least Complex Tree Knee-of-the-Curve Best Fusion Tree Performance Tree FIGURE Selecting data aggregation and batching criteria to achieve desired performance vs. cost. and therefore generally better decisions as well as local feedback for higher rate responses. Such tight coupling between estimation and control processes allows rapid autonomous feedback with centralized coordination commands requiring less communications bandwidth. These cascaded data fusion and resource management trees also enable the data fusion tree to be selected online via resource management Level 4 data fusion . This can occur when for example a high priority set of data arrives or when observed operating conditions differ significantly from those for which the given fusion tree was designed. In summary the data fusion tree specifies How the data can be batched . by level sensor source time and report data type and The order that the batches . fusion tree nodes are to be processed. The success of a particular fusion tree will be determined by the fidelity of the model of the data environment and of the required decision space used in developing the tree. A tree based on high-fidelity models will be more likely to associate received data effectively . sufficient to resolve state estimates that are critical to making response decisions . This is the case when the data that is batched in early fusion nodes are of sufficient accuracy and precision in common dimensions to create association hypotheses that closely reflect the actual report-to-entity causal relationships. For poorly understood or highly dynamic data environments a reduced degree of partitioning may be warranted. Alternately the performance of any fusion process can generally be improved by making the process adaptive to the estimated sensed environment. This can be accomplished by integrating the data fusion tree with a dual fan-out resource management tree to provide more accurate local feedback at higher rates as
