Additive Change Detection in Nonlinear Systems With Unknown Change Parameters
نویسندگان
چکیده
منابع مشابه
Change Detection in Partially Observed Nonlinear Dynamic Systems with Unknown Change Parameters (with Proofs)
We study the change detection problem in partially observed nonlinear dynamic systems. We assume that the change parameters are unknown and the change could be gradual (slow) or sudden (drastic). For most nonlinear systems, no finite dimensional filters exist and approximation filtering methods like the Particle Filter are used. Even when change parameters are unknown, drastic changes can be de...
متن کاملAdaptive Quickest Change Detection with Unknown Parameters
In this report quickest detection of an abrupt distribution change with unknown time varying parameters is considered. A novel adaptive approach, Adaptive CUSUM Test, is proposed to tackle this problem, which is shown to outperform the celebrated Parallel CUSUM Test. Performance is evaluated through theoretical analysis and numerical simulations.
متن کاملOnline Change Detection in Exponential Families with Unknown Parameters
This paper studies online change detection in exponential families when both the parameters before and after change are unknown. We follow a standard statistical approach to sequential change detection with generalized likelihood ratio test statistics. We interpret these statistics within the framework of information geometry, hence providing a unified view of change detection for many common s...
متن کاملMulti-Chart Detection Procedure for Bayesian Quickest Change-Point Detection with Unknown Post-Change Parameters
In this paper, the problem of quickly detecting an abrupt change on a stochastic process under Bayesian framework is considered. Different from the classic Bayesian quickest change-point detection problem, this paper considers the case where there is uncertainty about the post-change distribution. Specifically, the observer only knows that the post-change distribution belongs to a parametric di...
متن کاملSlow and Drastic Change Detection in General HMMs Using Particle Filters with Unknown Change Parameters
We study the change detection problem in general HMMs, when change parameters are unknown and the change could be gradual (slow) or sudden (drastic). Drastic changes can be detected easily using the increase in tracking error or the negative log of the observation likelihood conditioned on past observations (OL). But slow changes usually get missed. We propose a statistic for slow change detect...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2007
ISSN: 1053-587X
DOI: 10.1109/tsp.2006.887111