sequential monte carlo algorithms, or particle filters, are Bayesian filtering algorithms, which propagate in time a discrete and random approximation of the a posteriori distribution of interest. Such algorithms are ...
详细信息
sequential monte carlo algorithms, or particle filters, are Bayesian filtering algorithms, which propagate in time a discrete and random approximation of the a posteriori distribution of interest. Such algorithms are based on importance sampling with a bootstrap resampling step, which aims at struggling against weight degeneracy. However, in some situations (informative measurements, high-dimensional model), the resampling step can prove inefficient. In this paper, we revisit the fundamental resampling mechanism, which leads us back to Rubin's static resampling mechanism. We propose an alternative rejuvenation scheme in which the resampled particles share the same marginal distribution as in the classical setup, but are now independent. This set of independent particles provides a new alternative to compute a moment of the target distribution and the resulting estimate is analyzed through a CLT. We next adapt our results to the dynamic case and propose a particle filtering algorithm based on independent resampling. This algorithm can be seen as a particular auxiliary particle filter algorithm with a relevant choice of the first-stage weights and instrumental distributions. Finally, we validate our results via simulations, which carefully take into account the computational budget.
Parallel and distributed computing technologies offer great potential for speed-up of montecarloalgorithms. However, in the development of most existing algorithms it has been implicitly assumed that implementation ...
详细信息
Among sequentialmontecarlo methods, sampling importance resampling (SIR) algorithms are based on importance sampling and on some (resampling-based) rejuvenation algorithm that aims at fighting against weight degener...
详细信息
Among sequentialmontecarlo methods, sampling importance resampling (SIR) algorithms are based on importance sampling and on some (resampling-based) rejuvenation algorithm that aims at fighting against weight degeneracy. However, this mechanism tends to be insufficient when applied to informative or high-dimensional models. In this letter, we revisit the rejuvenation mechanism and propose a class of parameterized SIR-based solutions that enable us to adjust the tradeoff between computational cost and statistical performances.
Localization of a bronchoscope and estimation of its motion is a core component for constructing a bronchoscopic navigation system that can guide physicians to perform any bronchoscopic interventions such as the trans...
详细信息
ISBN:
(纸本)9780819489654
Localization of a bronchoscope and estimation of its motion is a core component for constructing a bronchoscopic navigation system that can guide physicians to perform any bronchoscopic interventions such as the transbronchial lung biopsy (TBLB) and the transbronchial needle aspiration (TBNA). To overcome the limitations of current methods, e. g., image registration (IR) and electromagnetic (EM) localizers, this study develops a new external tracking technique on the basis of an optical mouse (OM) sensor and IR augmented by sequentialmontecarlo (SMC) sampling (here called IR-SMC). We first construct an external tracking model by an OM sensor that is uded to directly measure the bronchoscope movement information including the insertion depth and the rotation of the viewing direction of the bronchoscope. To utilize OM sensor measurements, we employed IR with SMC sampling to determine the bronchoscopic camera motion parameters. The proposed method was validated on a dynamic phantom. Experimental results demonstrate that our constructed external tracking prototype is a perspective means to estimate the bronchoscope motion, compared to the start-of-the-art, especially for image-based methods, improving the tracking performance by 17.7% successfully processed video images.
暂无评论