论文标题
多传感器次优融合学生的$ T $过滤器
Multi-sensor Suboptimal Fusion Student's $t$ Filter
论文作者
论文摘要
在存在重型过程和测量噪声的情况下，提出了一个多传感器融合学生的$ t $滤波器，用于时间序列递归估算。该方法是由信息理论优化驱动的，基于次优算术平均值（AA）融合方法扩展了单个传感器学生的$ t $ t $ kalman滤波器。为了确保计算高效的封闭形式$ t $密度递归，在局部传感器过滤和传感器间融合计算中都使用了合理的近似。总体框架适合任何面向高斯的融合方法，例如协方差交叉点（CI）。模拟证明了与经典的高斯估计器相比，提出的基于AA融合的$ t $滤波器在处理异常值方面的有效性，与CI方法和增强测量融合相比，AA融合的优势是AA融合的优势。
A multi-sensor fusion Student's $t$ filter is proposed for time-series recursive estimation in the presence of heavy-tailed process and measurement noises. Driven from an information-theoretic optimization, the approach extends the single sensor Student's $t$ Kalman filter based on the suboptimal arithmetic average (AA) fusion approach. To ensure computationally efficient, closed-form $t$ density recursion, reasonable approximation has been used in both local-sensor filtering and inter-sensor fusion calculation. The overall framework accommodates any Gaussian-oriented fusion approach such as the covariance intersection (CI). Simulation demonstrates the effectiveness of the proposed multi-sensor AA fusion-based $t$ filter in dealing with outliers as compared with the classic Gaussian estimator, and the advantage of the AA fusion in comparison with the CI approach and the augmented measurement fusion.
