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This paper focuses on a distributed image fusion filtering algorithm and fusion formulas for time delayed multiple pixels received from multiple sensors (cameras). Since local cross-covariances between images are important values to implement fusion formulas, we present exact formulas for cross-covariances which are a vital factor for calculating matrix weights in image processing. Subsequent analysis of the proposed fusion algorithm is presented through a typical example demonstrating the effectiveness of the proposed fusion algorithm.

Abstract-In this paper, two robust fusion algorithms for a linear system with observation uncertainty are proposed. The first algorithm is based on the classical median function and the second one uses relative distances between local estimates and their median value. In the view of estimation accuracy, the proposed fusion algorithms can be robust against uncertainty measurements since median can avoid extremely big or small values. This fact is verified from comparative analysis using numerical examples.

In this paper, distributed fusion filtering problem in multisensory dynamic system is considered. The approximation scheme for calculation of cross-covariances is presented, which are based on a correlation coefficient in steady-state. In addition, a new limiting cross-covariance method is also proposed, which gives the opportunity to save calculation times, and is computationally useful to implement the fusion filters in a real-time application. Numerical examples demonstrating the effectiveness of the proposed algorithm are presented.

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