Computationally Efficient Adaptive Spline Filters for Nonlinear State Estimation

用于非线性状态估计的计算高效的自适应样条滤波器

• 批准号: 250256-2012
• 负责人: Kirubarajan, Thia
• 金额: $ 2.4万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=547561
• 关键词: Computationally; Efficient; Adaptive; Spline; Filters

The problem of nonlinear/non-Gaussian filtering has generated significant interest in the literature due to the inherent nonlinearity in most practical systems. The nonlinearity in state estimation problems may arise due to its presence in the state-to-measurement equation or in the evolution of the state itself. The presence of multiple objects further complicates the problem by adding data association to the mix. The optimal nonlinear state estimator consists of the computation of the conditional (posterior) pdf of the multitarget state given all the measurements available up to the current time. Optimal multitarget nonlinear filtering is in general a non-tractable problem, not just because of computational complexity but also due to the multimodal nature of multitarget pdf.

由于大多数实际系统固有的非线性，非线性/非高斯滤波问题引起了文献的极大兴趣。状态估计问题中的非线性可能由于其存在于状态测量方程或状态本身的演化中而出现。多个对象的存在通过添加数据关联使问题进一步复杂化。最优非线性状态估计器包括计算多目标状态的条件（后验）pdf，其中给出了截至当前时间可用的所有测量值。最优多目标非线性滤波通常是一个难以处理的问题，不仅因为计算复杂性，还因为多目标 pdf 的多模态性质。