大规模卡尔曼滤波的数据绘制
Data Sketching for Large-Scale Kalman Filtering
摘要：
在数据生成呈指数级增长的时代，通过利用全部可用信息来执行推理任务并不总是一个负担得起的选择。本文提出的方法，使跟踪大规模的动态过程，通过卡尔曼滤波器负担得起的，通过处理数量减少的数据。介绍了三种不同的方法，用于减少滤波器校正步骤中涉及的数据数量。为此，前两种方法分别采用随机投影和基于新息的删失进行降维和测量选择。第三种方法通过利用观测的顺序处理和基于信息理论度量选择一些信息更新来降低复杂性。模拟合成数据，比较所提出的方法与竞争的替代品，并证实其功效的估计精度在复杂性降低。最后，监测大型网络被认为是一个应用领域，与Kronecker图测试所提出的方法，以评估其跟踪流量矩阵和随时间变化的链路成本的效率。
英文摘要：
In an age of exponentially increasing data generation, performing inference tasks by utilizing the available information in its entirety is not always an affordable option. The present paper puts forth approaches to render tracking of large-scale dynamic processes via a Kalman filter affordable, by processing a reduced number of data. Three distinct methods are introduced for reducing the number of data involved in the correction step of the filter. Towards this goal, the first two methods employ random projections and innovation-based censoring to effect dimensionality reduction and measurement selection respectively. The third method achieves reduced complexity by leveraging sequential processing of observations and selecting a few informative updates based on an information-theoretic metric. Simulations on synthetic data, compare the proposed methods with competing alternatives, and corroborate their efficacy in terms of estimation accuracy over complexity reduction. Finally, monitoring large networks is considered as an application domain, with the proposed methods tested on Kronecker graphs to evaluate their efficiency in tracking traffic matrices and time-varying link costs.
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。
