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Abstract
The robustH∞fusion filtering problem is considered for linear time-varying uncertain systems observed by multiple sensors. A performance index function for this problem is defined as an indefinite quadratic inequality which is solved by the projection method in Krein space. On this basis, a robust centralized finite horizonH∞fusion filtering algorithm is proposed. However, this centralized fusion method is with poor real time property, as the number of sensors increases. To resolve this difficulty, within the sequential fusion framework, the performance index function is described as a set of quadratic inequalities including an indefinite quadratic inequality. And a sequential robust finite horizonH∞fusion filtering algorithm is given by solving this quadratic inequality group. Finally, two simulation examples for time-varying/time-invariant multisensor systems are exploited to demonstrate the effectiveness of the proposed methods in the respect of the real time property and filtering accuracy.
Highlights
In many advanced systems, multiple homogeneous or heterogeneous sensors are spatially distributed to provide large coverage, diverse viewing angles of the things of interest [1, 2]
We aim to investigate the robust H∞ fusion filtering method for time-varying multisensor uncertain systems
The research work in this paper mainly includes the following parts: (i) In this paper, the impacts of the parameter uncertainty and the system noises on the fusion estimate errors are expressed by an indefinite quadratic inequality, whose stationary can be given by a projection method in Krein space
Summary
Multiple homogeneous or heterogeneous sensors are spatially distributed to provide large coverage, diverse viewing angles of the things of interest [1, 2]. In [16], a centralized distributed H∞-consensus filtering method is proposed for discrete time-varying systems by solving a set of different linear matrix inequalities in each filtering period and further extended for two kinds of uncertain systems. (i) In this paper, the impacts of the parameter uncertainty and the system noises on the fusion estimate errors are expressed by an indefinite quadratic inequality, whose stationary can be given by a projection method in Krein space. On this basis, a robust centralized finite horizon H∞ fusion filtering algorithm is designed. The vectors in Hilbert space are denoted by bold face letters, such as “x(i),” and the ones in Krein space are denoted by the bold face letters with bar, such as “x(i).” ⟨A, B⟩ stands for the inner product in Krein space
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