4,742 research outputs found
Collaborative sparse regression using spatially correlated supports - Application to hyperspectral unmixing
This paper presents a new Bayesian collaborative sparse regression method for
linear unmixing of hyperspectral images. Our contribution is twofold; first, we
propose a new Bayesian model for structured sparse regression in which the
supports of the sparse abundance vectors are a priori spatially correlated
across pixels (i.e., materials are spatially organised rather than randomly
distributed at a pixel level). This prior information is encoded in the model
through a truncated multivariate Ising Markov random field, which also takes
into consideration the facts that pixels cannot be empty (i.e, there is at
least one material present in each pixel), and that different materials may
exhibit different degrees of spatial regularity. Secondly, we propose an
advanced Markov chain Monte Carlo algorithm to estimate the posterior
probabilities that materials are present or absent in each pixel, and,
conditionally to the maximum marginal a posteriori configuration of the
support, compute the MMSE estimates of the abundance vectors. A remarkable
property of this algorithm is that it self-adjusts the values of the parameters
of the Markov random field, thus relieving practitioners from setting
regularisation parameters by cross-validation. The performance of the proposed
methodology is finally demonstrated through a series of experiments with
synthetic and real data and comparisons with other algorithms from the
literature
Robust Linear Spectral Unmixing using Anomaly Detection
This paper presents a Bayesian algorithm for linear spectral unmixing of
hyperspectral images that accounts for anomalies present in the data. The model
proposed assumes that the pixel reflectances are linear mixtures of unknown
endmembers, corrupted by an additional nonlinear term modelling anomalies and
additive Gaussian noise. A Markov random field is used for anomaly detection
based on the spatial and spectral structures of the anomalies. This allows
outliers to be identified in particular regions and wavelengths of the data
cube. A Bayesian algorithm is proposed to estimate the parameters involved in
the model yielding a joint linear unmixing and anomaly detection algorithm.
Simulations conducted with synthetic and real hyperspectral images demonstrate
the accuracy of the proposed unmixing and outlier detection strategy for the
analysis of hyperspectral images
Estimation of phase noise in oscillators with colored noise sources
In this letter we study the design of algorithms for estimation of phase
noise (PN) with colored noise sources. A soft-input maximum a posteriori PN
estimator and a modified soft-input extended Kalman smoother are proposed. The
performance of the proposed algorithms are compared against those studied in
the literature, in terms of mean square error of PN estimation, and symbol
error rate of the considered communication system. The comparisons show that
considerable performance gains can be achieved by designing estimators that
employ correct knowledge of the PN statistics
A Hybrid Approach to Joint Estimation of Channel and Antenna impedance
This paper considers a hybrid approach to joint estimation of channel
information and antenna impedance, for single-input, single-output channels.
Based on observation of training sequences via synchronously switched load at
the receiver, we derive joint maximum a posteriori and maximum-likelihood
(MAP/ML) estimators for channel and impedance over multiple packets. We
investigate important properties of these estimators, e.g., bias and
efficiency. We also explore the performance of these estimators through
numerical examples.Comment: 6 pages, two columns, 6 figures. References update
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