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In this paper, an optimal distributed fusion estimation algorithm is presented for multi-sensor non-uniform sampling systems with the correlated process noise and measurement noises at the same time and network-induced packet dropouts. First, the non-uniform sampling problem is first transformed to a single-rate synchronous sampling problem. Then, local optimal filters and the filtering error cross-covariance...
This paper is concerned with the distributed fusion filtering problem for a class of asynchronous multi-rate multi-sensor systems with different packet dropout rates, where the system is described at the highest sampling rate and different sensors may have different measurement sampling rates. Firstly, the multi-rate fusion estimation problem is transformed into an equivalent single rate fusion estimation...
Using the innovation analysis approach, a novel optimal filter is presented for discrete-time ARMA (auto-regressing moving average) signals based on the white noise estimators. It is a recursive non-augmented filter. Compared with the augmented approach, it has the reduced computational cost and the same accuracy. A simulation example shows the effectiveness of the proposed algorithm.
Based on the innovation analysis approach, the estimation error cross-covariance matrices between local estimators based on any two sensors are derived for multi-sensor multi-delay systems with correlated noises. The non-augmented distributed weighted fusion optimal estimators are given based on the optimal weighted fusion estimation algorithm in the linear minimum variance sense. Compared with the...
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