I-Corps: Commercialization of Feedback Particle Filter for Target State Estimation

I-Corps：用于目标状态估计的反馈粒子滤波器的商业化

• 批准号: 1343554
• 负责人: Prashant Mehta
• 金额: $ 5万
• 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: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1343554&HistoricalAwards=false
• 关键词: Corps; Commercialization; Feedback; Particle; Filter

The goal of the project is to further develop a commercialization a nonlinear estimation platform technology for applications involving target state estimation. Nonlinear estimation is a widely pervasive enabling technology. Some current algorithms either require specific mathematical characteristics of the underlying model or suffer from robustness issues. In applications involving target state estimation (target tracking), these approaches can lead to poor terminal accuracy and sub-optimal tracking performance. The innovation of the proposed project lies in an algorithm for nonlinear estimation that may provide more accurate results with less computational overhead. In target state estimation applications, existing solutions have difficulty tracking targets that exhibit nonlinear and uncertain motion. These algorithms are successfully able to address these problems.Increasing accuracy and speed for nonlinear estimation at lower computational cost could benefit many applications and industries. The National Defense would benefit from increased radar, laser, and satellite tracking accuracy. Financial firms could make better derivatives models and trading decisions. Applied to robotic vision, autonomous vehicles could navigate more accurately and assembly robots could spot more defects. Software tools based on these algorithms have a potential to impact a number of application areas both in military and civilian domain: Application areas of interest in the military domain include Air Moving Target Indicator (AMTI) and Ground Moving Target Indicator (GMTI) systems deployed in Intelligence, Surveillance, and Reconnaissance (ISR) operations. Civilian applications include air traffic surveillance, weather surveillance, ground mapping, geophysical surveys, remote sensing, autonomous navigation and robotics.

该项目的目标是进一步开发一个商业化的非线性估计平台技术，用于涉及目标状态估计的应用。非线性估计是一种广泛应用的使能技术。当前的一些算法要么需要底层模型的特定数学特征，要么存在鲁棒性问题。在涉及目标状态估计（目标跟踪）的应用中，这些方法可能导致较差的终端精度和次优跟踪性能。该方案的创新之处在于一种非线性估计算法，它可以用更少的计算开销提供更准确的结果。在目标状态估计应用中，现有的求解方法难以跟踪具有非线性和不确定运动的目标。这些算法成功地解决了这些问题。在较低的计算成本下提高非线性估计的精度和速度可以使许多应用和行业受益。国防将受益于雷达、激光和卫星跟踪精度的提高。金融公司可以制定更好的衍生品模型和交易决策。应用于机器人视觉，自动驾驶汽车可以更准确地导航，装配机器人可以发现更多的缺陷。基于这些算法的软件工具有可能影响军事和民用领域的许多应用领域：军事领域感兴趣的应用领域包括部署在情报、监视和侦察（ISR）行动中的空中移动目标指示器（AMTI）和地面移动目标指示器（GMTI）系统。民用应用包括空中交通监视、天气监视、地面测绘、地球物理调查、遥感、自主导航和机器人技术。