具有偏斜测量噪声的状态空间模型鲁棒推断
Robust Inference for State-Space Models with Skewed Measurement Noise
AI总结：
本文提出了一种针对具有偏斜和厚尾测量噪声的线性离散时间状态空间模型的滤波和平滑算法，通过变分贝叶斯方法近似后验分布，实验表明其在精度上优于传统低复杂度方法。
摘要：
本文提出了针对线性离散时间状态空间模型的滤波和平滑算法，该算法使用变分贝叶斯方法近似具有正态先验和偏斜t分布测量噪声的后验分布。在模拟的伪距定位场景中，所提出的方法在精度上优于传统低复杂度方法，滤波器的计算复杂度约为卡尔曼滤波的5到10倍。
英文摘要：
Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy-tailed measurement noise are presented. The algorithms use a variational Bayes approximation of the posterior distribution of models that have normal prior and skew-t-distributed measurement noise. The proposed filter and smoother are compared with conventional low-complexity alternatives in a simulated pseudorange positioning scenario. In the simulations the proposed methods achieve better accuracy than the alternative methods, the computational complexity of the filter being roughly 5 to 10 times that of the Kalman filter.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
