47 research outputs found
Off-grid Direction of Arrival Estimation Using Sparse Bayesian Inference
Direction of arrival (DOA) estimation is a classical problem in signal
processing with many practical applications. Its research has recently been
advanced owing to the development of methods based on sparse signal
reconstruction. While these methods have shown advantages over conventional
ones, there are still difficulties in practical situations where true DOAs are
not on the discretized sampling grid. To deal with such an off-grid DOA
estimation problem, this paper studies an off-grid model that takes into
account effects of the off-grid DOAs and has a smaller modeling error. An
iterative algorithm is developed based on the off-grid model from a Bayesian
perspective while joint sparsity among different snapshots is exploited by
assuming a Laplace prior for signals at all snapshots. The new approach applies
to both single snapshot and multi-snapshot cases. Numerical simulations show
that the proposed algorithm has improved accuracy in terms of mean squared
estimation error. The algorithm can maintain high estimation accuracy even
under a very coarse sampling grid.Comment: To appear in the IEEE Trans. Signal Processing. This is a revised,
shortened version of version
Towards SAR Tomographic Inversion via Sparse Bayesian Learning
Existing SAR tomography (TomoSAR) algorithms are mostly based on an inversion
of the SAR imaging model, which are often computationally expensive. Previous
study showed perspective of using data-driven methods like KPCA to decompose
the signal and reduce the computational complexity. This paper gives a
preliminary demonstration of a new data-driven method based on sparse Bayesian
learning. Experiments on simulated data show that the proposed method
significantly outperforms KPCA methods in estimating the steering vectors of
the scatterers. This gives a perspective of data-drive approach or combining it
with model-driven approach for high precision tomographic inversion of large
areas.Comment: accepted in preliminary version for EUSAR2020 conferenc
Image Reconstruction for Multi-frequency Electromagnetic Tomography based on Multiple Measurement Vector Model
Imaging the bio-impedance distribution of a biological sample can provide
understandings about the sample's electrical properties which is an important
indicator of physiological status. This paper presents a multi-frequency
electromagnetic tomography (mfEMT) technique for biomedical imaging. The system
consists of 8 channels of gradiometer coils with adjustable sensitivity and
excitation frequency. To exploit the frequency correlation among each
measurement, we reconstruct multiple frequency data simultaneously based on the
Multiple Measurement Vector (MMV) model. The MMV problem is solved by using a
sparse Bayesian learning method that is especially effective for sparse
distribution. Both simulations and experiments have been conducted to verify
the performance of the method. Results show that by taking advantage of
multiple measurements, the proposed method is more robust to noisy data for
ill-posed problems compared to the commonly used single measurement vector
model.Comment: This is an accepted paper which has been submitted to I2MTC 2020 on
Nov. 201
On Phase Transition of Compressed Sensing in the Complex Domain
The phase transition is a performance measure of the sparsity-undersampling
tradeoff in compressed sensing (CS). This letter reports our first observation
and evaluation of an empirical phase transition of the minimization
approach to the complex valued CS (CVCS), which is positioned well above the
known phase transition of the real valued CS in the phase plane. This result
can be considered as an extension of the existing phase transition theory of
the block-sparse CS (BSCS) based on the universality argument, since the CVCS
problem does not meet the condition required by the phase transition theory of
BSCS but its observed phase transition coincides with that of BSCS. Our result
is obtained by applying the recently developed ONE-L1 algorithms to the
empirical evaluation of the phase transition of CVCS.Comment: 4 pages, 3 figure
Bayesian Sparse Fourier Representation of Off-Grid Targets
We consider the problem of estimating a finite sum of cisoids via the use of a sparsifying Fourier dictionary (problem that may be of use in many radar applications). Numerous signal sparse representation (SSR) techniques can be found in the literature regarding this problem. However, they are usually very sensitive to grid mismatch. In this paper, we present a new Bayesian model robust towards grid mismatch. Synthetic and experimental radar data are used to assess the ability of the proposed approach to robustify the SSR towards grid mismatch
Generalized Sparse Covariance-based Estimation
In this work, we extend the sparse iterative covariance-based estimator
(SPICE), by generalizing the formulation to allow for different norm
constraints on the signal and noise parameters in the covariance model. For a
given norm, the resulting extended SPICE method enjoys the same benefits as the
regular SPICE method, including being hyper-parameter free, although the choice
of norms are shown to govern the sparsity in the resulting solution.
Furthermore, we show that solving the extended SPICE method is equivalent to
solving a penalized regression problem, which provides an alternative
interpretation of the proposed method and a deeper insight on the differences
in sparsity between the extended and the original SPICE formulation. We examine
the performance of the method for different choices of norms, and compare the
results to the original SPICE method, showing the benefits of using the
extended formulation. We also provide two ways of solving the extended SPICE
method; one grid-based method, for which an efficient implementation is given,
and a gridless method for the sinusoidal case, which results in a semi-definite
programming problem