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This paper proposes a new packet dropout compensation framework for networked multi-sensor systems. It is more general, which includes the existing popular mechanisms such as the zero-input and hold-input mechanisms as the special cases. Based on the proposed compensation framework, the centralized fusion linear optimal full-order prior filters with compensators of different weighting factors are...
The optimal filtering problem for multi-sensor networked systems with unknown channel interferences and packet losses is studied. There are possible packet dropouts and unknown channel interferences during the data transmissions from the sensors to the filter. The phenomena of packet losses are described by a set of random variables satisfying Bernoulli distributions. The prediction values of the...
The optimal filtering problem is addressed for multi-rate systems with one-step auto-correlated noises. The state is updated at the highest sampling rate and the sensor has a lower sampling rate. System noise and measurement noise are one-step auto-correlated, respectively. An optimal filter in the linear minimum variance sense is proposed via an innovation analysis approach. A simulation example...
This paper is concerned with the filtering problem for discrete-time stochastic linear system with bounded multiple packet dropouts. An optimal prior filter is developed in linear unbiased minimum variance sense. Its solution depends on the recursion of a Riccati equation and a Lyapunov equation, which involves the complex computation of multiple sums by some correlated terms. To reduce the computational...
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