The parallelism speedup with respect to the number of processors required for the implementation of the Distributed Kalman Filter, for time varying system, where n=4 and m=1000, is plotted in Figure 4. 300000
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Distributed Kalman Filter Parallelism Speedup Time Varying system n=4, m=1000 Figure 4 8. CONCLUSIONS Centralized and distributed approaches to the solution of the discrete time estimation/filtering problem for multisensor environment were presented in this paper. The discrete time Centralized and Distributed Kalman Filters were analyzed. It was pointed out that both the discrete time Centralized and Distributed Kalman Filters calculate the same estimates, thus the filters are equivalent with respect to their behavior. The computational requirements of both filters were discussed. It was also proposed a method to a-priori (before the filters' implementation) define the Optimal Distributed Kalman Filter. The method is based on the a-priori determination of the measurements' optimum distribution in parallel processors using the criterion of minimizing the computation time. It was verified through simulation results that the proposed Optimal Distributed Kalman Filter presents high parallelism speedup. This result is very important due to the fact that, in most real-time applications, it is essential to obtain the estimate in the shortest possible time.
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