Theory and Algorithms for Feedback Particle Filter

反馈粒子滤波器的理论和算法

• 批准号: 1761622
• 负责人: Prashant Mehta
• 金额: $ 37.8万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1761622&HistoricalAwards=false
• 关键词: Theory; Algorithms; Feedback; Particle; Filter

Finding accurate solutions for complex optimization problems and other related challenging mathematical problems is very important in many engineering applications. Some example applications include: target tracking and surveillance where multiple sensor measurements are used to track targets, air traffic management to track airplanes, weather surveillance to track hurricanes, ground mapping, geophysical surveys, remote sensing, autonomous navigation, and robotics. State-of-the-art solution approaches to these problems include the Kalman filter algorithm and its many extensions. However, in practice, such approaches can yield inaccurate and erroneous solutions because of technical issues related to complexity in dynamics and uncertainty. In the past decade, a new class of algorithmic solution approaches to these problems has emerged referred to as the "Feedback Particle Filter". The Feedback Particle Filter can better handle the technical issues related to such complex dynamics and uncertainty. This research will advance the theoretical development and verification of the Feedback Particle Filter algorithm, and lay the groundwork for software tools that will be useful in tracking applications noted above. The project also includes several educational initiatives that seek to engage undergraduate students in entrepreneurship. A major objective of the research concerns the development of optimal control formulations of the feedback particle filter based on optimal transportation theory and mean-field games formalisms. The theoretical research is closely integrated with the work on computational algorithms. The algorithmic objectives pertain to numerical solution of the Poisson equation, convergence analysis of the particle system with finitely many particles, and comparisons with importance sampling-based algorithms. The deliverables include efficient numerical schemes which will be implemented and demonstrated in software.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在许多工程应用中，为复杂的优化问题和其他相关的具有挑战性的数学问题找到准确的解决方案非常重要。一些示例应用包括：目标跟踪和监视（使用多个传感器测量来跟踪目标）、空中交通管理来跟踪飞机、天气监视来跟踪飓风、地面测绘、地球物理调查、遥感、自主导航和机器人技术。这些问题的最先进的解决方法包括卡尔曼滤波器算法及其许多扩展。 然而，在实践中，由于与动态复杂性和不确定性相关的技术问题，这种方法可能会产生不准确和错误的解决方案。 在过去的十年中，出现了解决这些问题的一类新的算法解决方案，称为“反馈粒子滤波器”。 反馈粒子滤波器可以更好地处理与此类复杂动态和不确定性相关的技术问题。 这项研究将推进反馈粒子滤波器算法的理论开发和验证，并为可用于上述跟踪应用的软件工具奠定基础。该项目还包括几项旨在让本科生参与创业的教育举措。 该研究的一个主要目标涉及基于最优传输理论和平均场博弈形式主义的反馈粒子滤波器的最优控制公式的开发。 理论研究与计算算法工作紧密结合。 算法目标涉及泊松方程的数值求解、有限多个粒子的粒子系统的收敛分析以及与基于重要性采样的算法的比较。 可交付成果包括将在软件中实施和演示的高效数值方案。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Optimal Transportation Methods in Nonlinear Filtering

非线性滤波中的最优传输方法

• DOI: 10.1109/mcs.2021.3076391
• 发表时间: 2021
• 期刊: IEEE Control Systems
• 作者: Taghvaei, Amirhossein;Mehta, Prashant G.
• 通讯作者: Mehta, Prashant G.

What is the Lagrangian for Nonlinear Filtering?

• DOI: 10.1109/cdc40024.2019.9030206
• 发表时间: 2019-03
• 期刊: 2019 IEEE 58th Conference on Decision and Control (CDC)
• 作者: J. W. Kim;P. Mehta;Sean P. Meyn

Diffusion Map-based Algorithm for Gain Function Approximation in the Feedback Particle Filter

反馈粒子滤波器中基于扩散图的增益函数逼近算法

• DOI: 10.1137/19m124513x
• 发表时间: 2020
• 期刊: SIAM/ASA Journal on Uncertainty Quantification
• 作者: Taghvaei, Amirhossein;Mehta, Prashant G.;Meyn, Sean P.
• 通讯作者: Meyn, Sean P.

A Dual Characterization of Observability for Stochastic Systems

随机系统可观测性的双重表征

• DOI: 10.1016/j.ifacol.2021.06.126
• 发表时间: 2021
• 期刊: IFAC-PapersOnLine
• 作者: Kim, Jin Won;Mehta, Prashant G.
• 通讯作者: Mehta, Prashant G.

Feedback particle filter for collective inference

用于集体推理的反馈粒子过滤器

• DOI: 10.3934/fods.2021018
• 发表时间: 2021
• 期刊: Foundations of Data Science
• 影响因子: 2.3
• 作者: Kim, Jin-Won;Taghvaei, Amirhossein;Chen, Yongxin;Mehta, Prashant G.
• 通讯作者: Mehta, Prashant G.