کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
716434 | 892221 | 2012 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A Backward-Simulation Based Rao-Blackwellized Particle Smoother for Conditionally Linear Gaussian Models
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موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مکانیک محاسباتی
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چکیده انگلیسی
In this article, we develop a new Rao-Blackwellized Monte Carlo smoothing algorithm for conditionally linear Gaussian models. The algorithm is based on the forward-filtering backward-simulation Monte Carlo smoother concept and performs the backward simulation directly in the marginal space of the non-Gaussian state component while treating the linear part analytically. Unlike the previously proposed backward-simulation based Rao-Blackwellized smoothing approaches, it does not require sampling of the Gaussian state component and is also able to overcome certain normalization problems of two-filter smoother based approaches. The performance of the algorithm is illustrated in a simulated application.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: IFAC Proceedings Volumes - Volume 45, Issue 16, July 2012, Pages 506-511
Journal: IFAC Proceedings Volumes - Volume 45, Issue 16, July 2012, Pages 506-511