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Bộ nhớ hạn chế và vai trò quan trọng trung hạn bộ nhớ đóng trong cả haiNhư vậy đến nay chương này đã mô tả tương tự multisensor multitarget của đo lường và các mô hình chuyển động, | Average MSE x 1 estimate 1 000--- ----- -- ---- -- ---- --- --- 900- - 800- - 700- - 600- - 500- . - 400- - 300- . - 200- . - 100- _ - 0 I . r 0 1 0 20 30 40 50 60 70 80 90 1 00 Average MSE x 2 estimate 01--- ---- ---- --- ---- ---- ---- ---- ---- ----- 0 1 0 20 30 40 50 60 70 80 90 1 00 B FIGURE 12.7 Disconnected nodes. A Mean squared error in x. B Mean squared error in x. C Mean squared error in x. Mean squared errors and estimated covariances for all states in each of the four nodes. The curves for Node 1 are solid Node 2 are dashed Node 3 are dotted and Node 4 are dash-dotted. The mean squared error is the rougher of the two lines for each node. The results from the first strategy no data distribution are shown in Figure 12.7. As expected the system behaves poorly. Because each node operates in isolation only Node 1 which measures x is fully observable. The position variance increases without bound for the three remaining nodes. Similarly the velocity is observable for Nodes 1 2 and 4 but it is not observable for Node 3. The results of the second strategy all nodes are assumed independent are shown in Figure 12.8. The effect of assumed independence observations is obvious all of the estimates for all of the states in all of the nodes apart from x for Node 3 are inconsistent. This clearly illustrates the problem of double counting. Finally the results from the CI distribution scheme are shown in Figure 12.9. Unlike the other two approaches all the nodes are consistent and observable. Furthermore as the results in Table 12.2 indicate the steady-state covariances of all of the states in all of the nodes are smaller than those for case 1. In other words this example shows that this data distribution scheme successfully and usefully propagates data through an apparently degenerate data network. 2001 CRC Press LLC 13 Data Fusion in Nonlinear Systems Simon Julier IDAK Industries Jeffrey K. Uhlmann University of Missouri 13.1 Introduction 13.2 Estimation in Nonlinear .