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Abstract
In this paper, we address the fundamental problem of line spectral estimation in a Bayesian framework. We target model order and parameter estimation via variational inference in a probabilistic model in which the frequencies are continuous-valued, i.e., not restricted to a grid; and the coefficients are governed by a Bernoulli-Gaussian prior model turning model order selection into binary sequence detection. Unlike earlier works which retain only point estimates of the frequencies, we undertake a more complete Bayesian treatment by estimating the posterior probability density functions (pdfs) of the frequencies and computing expectations over them. Thus, we additionally capture and operate with the uncertainty of the frequency estimates. Aiming to maximize the model evidence, variational optimization provides analytic approximations of the posterior pdfs and also gives estimates of the additional parameters. We propose an accurate representation of the pdfs of the frequencies by mixtures of von Mises pdfs, which yields closed-form expectations. We define the algorithm VALSE in which the estimates of the pdfs and parameters are iteratively updated. VALSE is a gridless, convergent method, does not require parameter tuning, can easily include prior knowledge about the frequencies and provides approximate posterior pdfs based on which the uncertainty in line spectral estimation can be quantified. Simulation results show that accounting for the uncertainty of frequency estimates, rather than computing just point estimates, significantly improves the performance. The performance of VALSE is superior to that of state-of-the-art methods and closely approaches the Cramér-Rao bound computed for the true model order.
Original language | English |
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Journal | I E E E Transactions on Signal Processing |
Volume | 65 |
Issue number | 9 |
Pages (from-to) | 2247 - 2261 |
ISSN | 1053-587X |
DOIs | |
Publication status | Published - 1 May 2017 |
Keywords
- Line spectral estimation
- complex sinusoids
- model order selection
- Bayesian inference
- von Mises distribution
- super-resolution
- Bernoulli-Gaussian model
- sparse estimation
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Dive into the research topics of 'Variational Bayesian Inference of Line Spectra'. Together they form a unique fingerprint.Projects
- 1 Finished
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Managing Interference in Dense Wireless Networks: An Approach Inspired by the Physics of Large Systems
Badiu, M. A.
01/03/2016 → 28/02/2018
Project: Research