3 research outputs found
Expectation Propagation Line Spectral Estimation
Line spectral estimation (LSE) is a fundamental problem in signal processing
fields, as it arises in various fields such as radar signal processing and
communication fields. This paper develops expectation propagation (EP) based
LSE (EPLSE) method. The proposed method automatically estimates the model
order, noise variance, and can deal with the nonlinear measurements. Numerical
experiments show the excellent performance of EPLSE
Variational Bayesian Line Spectral Estimation with Multiple Measurement Vectors
In this paper, the line spectral estimation (LSE) problem with multiple
measurement vectors (MMVs) is studied utilizing the Bayesian methods. Motivated
by the recently proposed variational line spectral estimation (VALSE) method,
we develop the multisnapshot VALSE (MVALSE) for multi snapshot scenarios, which
is especially important in array signal processing. The MVALSE shares the
advantages of the VALSE method, such as automatically estimating the model
order, noise variance, weight variance, and providing the uncertain degrees of
the frequency estimates. It is shown that the MVALSE can be viewed as applying
the VALSE with single measurement vector (SMV) to each snapshot, and combining
the intermediate data appropriately. Furthermore, the Seq-MVALSE is developed
to perform sequential estimation. Finally, numerical results are conducted to
demonstrate the effectiveness of the MVALSE method, compared to the
state-of-the-art methods in the MMVs setting
A New Atomic Norm for DOA Estimation With Gain-Phase Errors
The problem of direction of arrival (DOA) estimation has been studied for
decades as an essential technology in enabling radar, wireless communications,
and array signal processing related applications. In this paper, the DOA
estimation problem in the scenario with gain-phase errors is considered, and a
sparse model is formulated by exploiting the signal sparsity in the spatial
domain. By proposing a new atomic norm, named as GP-ANM, an optimization method
is formulated via deriving a dual norm of GP-ANM. Then, the corresponding
semidefinite program (SDP) is given to estimate the DOA efficiently, where the
SDP is obtained based on the Schur complement. Moreover, a regularization
parameter is obtained theoretically in the convex optimization problem.
Simulation results show that the proposed method outperforms the existing
methods, including the subspace-based and sparse-based methods in the scenario
with gain-phase errors.Comment: 15 pages, 16 figure