| 1. |
- Kim, Su Min, et al.
(författare)
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Sphere decoding inspired approximation method to compute the entropy of large Gaussian mixture distributions
- 2014
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Ingår i: IEEE Workshop on Statistical Signal Processing Proceedings. - 9781479949755 ; , s. 264-267
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Konferensbidrag (refereegranskat)abstract
- The computation of mutual informations of large scale systems with finite input alphabet and Gaussian noise has often prohibitive complexities. In this paper, we propose a novel approach exploiting the sphere decoding concept to bound and approximate such mutual information term with reduced complexity and good accuracy. Using Monte-Carlo simulations, the method is numerically demonstrated for the computation of the mutual information of a frequency- and time-selective channel with QAM modulation.
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| 2. |
- Kronander, Joel, 1983-, et al.
(författare)
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Backward sequential Monte Carlo for marginal smoothing
- 2014
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Ingår i: Proc. 18th Workshop on Statistical Signal Processing. - Piscataway, NJ : IEEE. - 9781479949755 ; , s. 368-371
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Konferensbidrag (refereegranskat)abstract
- In this paper we propose a new type of particle smoother with linear computational complexity. The smoother is based on running a sequential Monte Carlo sampler backward in time after an initial forward filtering pass. While this introduces dependencies among the backward trajectories we show through simulation studies that the new smoother can outperform existing forward-backward particle smoothers when targeting the marginal smoothing densities.
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| 3. |
- Kronander, Joel, 1983-, et al.
(författare)
-
Robust auxiliary particle filters using multiple importance sampling
- 2014
-
Ingår i: Proc. 18th Workshop on Statistical Signal Processing. - Piscataway, NJ : IEEE. - 9781479949755 ; , s. 268-271
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Konferensbidrag (refereegranskat)abstract
- A poor choice of importance density can have detrimental effect on the efficiency of a particle filter. While a specific choice of proposal distribution might be close to optimal for certain models, it might fail miserably for other models, possibly even leading to infinite variance. In this paper we show how mixture sampling techniques can be used to derive robust and efficient particle filters, that in general performs on par with, or better than, the best of the standard importance densities. We derive several variants of the auxiliary particle filter using both random and deterministic mixture sampling via multiple importance sampling. The resulting robust particle filters are easy to implement and require little parameter tuning.
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| 4. |
- Saha, Saikat, et al.
(författare)
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Rao-Blackwellized particle filter for Markov modulated nonlinear dynamic systems
- 2014
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Ingår i: Statistical Signal Processing (SSP), 2014 IEEE Workshop on. - : IEEE. - 9781479949755 ; , s. 272-275
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Konferensbidrag (refereegranskat)abstract
- The Markov modulated (switching) state space is an important model paradigm in statistical signal processing. In this article, we specifically consider Markov modulated nonlinear state-space models and address the online Bayesian inference problem for such models. In particular, we propose a new Rao-Blackwellized particle filter for the inference task which is our main contribution here. A detailed description of the problem and an algorithm is presented.
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