Change Detection in Nonlinear Systems and Applications in Shape Analysis

非线性系统中的变化检测及其在形状分析中的应用

• 批准号: 0725849
• 负责人: Namrata Vaswani
• 金额: --
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-15 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0725849&HistoricalAwards=false
• 关键词: Change; Detection; Nonlinear; Systems; Applications

ECCS-0725849VaswaniChange detection is important in most tracking applications, since it is rarely true that the system model is truly time-invariant. Some examples include detecting motion model changes in target tracking or positioning applications; or detecting abnormal shape changes in computer vision/biomedical image analysis applications. In all of the above, the state is not directly observed. The observation is a noise-corrupted and nonlinear function of the state. The effectiveness of particle filters for such nonlinear/non-Gaussian tracking problems is already well known. Often, the changed system model is not known, i.e. the change or abnormality is not characterized. For example, the change may be a gradual one, for a constant velocity target slowly accelerating to a higher speed, or a sudden one. The approach is based on particle filter based algorithms for ``slow" and ?sudden?, unknown parameter, change detection. Robust design strategies will be developed and tested for realistic applications.Intellectual Merit: Most existing approaches can be classified as either based on adaptive filtering ideas or based on loss-of-track detection. Adaptive filtering approaches are either expensive or unreliable to implement. Loss-of-track based approaches detect abrupt changes almost immediately. However, slow changes, which result in a small loss of track per unit time, usually take a long time to get detected, or sometimes get missed. We propose a novel approach that utilizes the fact that slow changes get partially tracked, and uses this ``tracked part of the change" for detection using particle filters.Broader Impact: The developed algorithms will impact a large number of positioning, navigation or defense applications that require target tracking; video based surveillance applications that require abnormal behavior detection; biomedical signal/image sequence analysis applications where detected abnormalities can be indicators of disease, as well as many other applications in econometrics, finance, robotics and vision. Educational initiatives will include introduction of a graduate class on Adaptive filtering and Monte Carlo methods; modification of existing undergraduate classes; and senior design projects to implement and compare different shape extraction techniques for various applications.

ECCS-0725849VASWANICHANGE检测在大多数跟踪应用程序中都很重要，因为系统模型确实是真正不变的。一些示例包括检测目标跟踪或定位应用程序中的运动模型变化；或检测计算机视觉/生物医学图像分析应用中的异常形状变化。在上述所有方面，未直接观察到状态。该观察结果是状态的噪声浪费和非线性功能。粒子过滤器对于此类非线性/非高斯跟踪问题的有效性已经众所周知。通常，更改的系统模型是未知的，即不会表征变化或异常。例如，变化可能是逐渐的变化，因为恒定速度目标缓慢加速到更高的速度或突然的速度。该方法基于基于粒子过滤的算法``缓慢''，然后？突然？，未知参数，变更检测。可靠的设计策略将被制定并用于现实应用。智能绩效：大多数现有方法可以根据适应性过滤的想法或基于基于较昂贵的损失的方法来归类为基于自适应过滤的想法或基于ADAPTIVE的损失。要么实现昂贵的损失。然而，慢速变化，导致每单位时间损失很小，通常需要很长时间才能被检测到，或者有时会被遗漏，我们提出了一种新颖的方法，该方法利用了缓慢的变化会被部分跟踪，并使用此“更改的跟踪部分”基于视频的监视应用需要异常的行为检测；生物医学信号/图像序列分析应用在检测到异常的情况下可以是疾病的指标，以及计量经济学，金融，机器人技术和视力的许多其他应用。教育计划将包括引入有关自适应过滤和蒙特卡洛方法的研究生课程；修改现有本科课程；以及高级设计项目，以实施和比较各种应用的不同形状提取技术。