By Mario A. Jordán, Jorge L. Bustamante

ISBN-10: 9533072008

ISBN-13: 9789533072005

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Conclusions In this chapter, two fusion predictors (FLP and PFF) for mixed continuous-discrete linear systems in a multisensor environment are proposed. Both of these predictors are derived by using the optimal local Kalman estimators (filters and predictors) and fusion formula. The fusion predictors represent the optimal linear combination of an arbitrary number of local Kalman estimators and each is fused by the MSE criterion. Equivalence between the two fusion predictors is established. However, the PFF algorithm is found to more significantly reduce the computational complexity, due to the fact that the PFF’s weights b(i) t k do not .

CP FLP PFF , P1,t+Δ , P1,t+Δ , and analogously for Figs. 2. The analysis of results in Figs. , FLP PFF Pi,t+Δ =Pi,t+Δ , and the MSEs of each predictor are reduced from N = 2 to N = 3 . The usage of three sensors allows to increase the accuracy of fusion predictors compared with the optimal FLP PFF CP (N=3) = Pi,t+Δ (N=3) < Pi,t+Δ (N=2). , Pi,t+Δ CP FLP PFF between optimal Pi,t+Δ and fusion MSEs Pi,t+Δ , Pi,t+Δ are small, especially for steady-state regime. 6GHz CPU and 3G RAM are reported. The CPU time for CP, FLP, and PFF are represented in Table 1.

2. Statement of problem – centralized predictor We consider a linear system described by the stochastic differential equation x t = Ft x t + G t v t , t ≥ 0 , (1) where x t ∈ ℜn is the state, v t ∈ ℜq is a zero-mean Gaussian white noise with covariance E v t vsT = Q t δ ( t-s ) , and Ft ∈ ℜn×n , Gt ∈ ℜn×q , and Qt ∈ ℜq×q . Suppose that overall discrete observations Yt k ∈ ℜm at time instants t 1 , t 2 ,... ; t k+1 >t k ≥ t 0 =0 ; m=m 1 + +m N , { } mi n ×m i mi is the local sensor observation, H(i) , and w(i) , k = 1, 2,...

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