ABSTRACT This paper focuses on the least-squares linear fusion filter design for discrete-time stochastic signals from multisensor measurements perturbed not only by additive noise, but also by different uncertainties that can be comprehensively modeled by random parameter matrices. The additive noises from the different sensors are assumed to be cross-correlated at the same time step and correlated with the signal at the same and subsequent time steps. A covariance-based approach is used to derive easily implementable recursive filtering algorithms under the centralized, distributed and sequential fusion architectures. Although centralized and sequential estimators both have the same accuracy, the evaluation of their computational complexity reveals that the sequential filter can provide a significant reduction of computational cost over the centralized one. The accuracy of the proposed fusion filters is explored by a simulation example, where observation matrices with random parameters are used to describe different kinds of sensor uncertainties.

中文翻译：

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基于传感器噪声和信号之间相关性的不确定输出的集中、分布式和顺序融合估计

摘要 本文重点介绍了来自多传感器测量的离散时间随机信号的最小二乘线性融合滤波器设计，这些信号不仅受到加性噪声的干扰，而且还受到可以通过随机参数矩阵综合建模的不同不确定性的干扰。假设来自不同传感器的附加噪声在同一时间步长互相关，并与同一时间步长和后续时间步长的信号相关。基于协方差的方法用于在集中式、分布式和顺序融合架构下导出易于实现的递归过滤算法。尽管集中式和顺序式估计器都具有相同的准确度，对其计算复杂性的评估表明，与集中式过滤器相比，顺序过滤器可以显着降低计算成本。通过模拟示例探索了所提出的融合滤波器的准确性，其中使用具有随机参数的观测矩阵来描述不同类型的传感器不确定性。