98 research outputs found
Tensor Decomposition-based Beamspace Esprit Algorithm for Multidimensional Harmonic Retrieval
Beamspace processing is an efficient and commonly used approach in harmonic retrieval (HR). In the beamspace, measurements are obtained by linearly transforming the sensing data, thereby achieving a compromise between estimation accuracy and system complexity. Meanwhile, the widespread use of multi-sensor technology in HR has highlighted the necessity to move from a matrix (two-way) to tensor (multi-way) analysis. In this paper, we propose a beamspace tensor-ESPRIT for multidimensional HR. In our algorithm, parameter estimation and association are achieved simultaneously
Sparse Bayesian Learning Approach for Discrete Signal Reconstruction
This study addresses the problem of discrete signal reconstruction from the
perspective of sparse Bayesian learning (SBL). Generally, it is intractable to
perform the Bayesian inference with the ideal discretization prior under the
SBL framework. To overcome this challenge, we introduce a novel discretization
enforcing prior to exploit the knowledge of the discrete nature of the
signal-of-interest. By integrating the discretization enforcing prior into the
SBL framework and applying the variational Bayesian inference (VBI)
methodology, we devise an alternating update algorithm to jointly characterize
the finite alphabet feature and reconstruct the unknown signal. When the
measurement matrix is i.i.d. Gaussian per component, we further embed the
generalized approximate message passing (GAMP) into the VBI-based method, so as
to directly adopt the ideal prior and significantly reduce the computational
burden. Simulation results demonstrate substantial performance improvement of
the two proposed methods over existing schemes. Moreover, the GAMP-based
variant outperforms the VBI-based method with an i.i.d. Gaussian measurement
matrix but it fails to work for non i.i.d. Gaussian matrices.Comment: 13 pages, 7 figure
Convergence Analysis of Consensus-ADMM for General QCQP
We analyze the convergence properties of the consensus-alternating direction
method of multipliers (ADMM) for solving general quadratically constrained
quadratic programs. We prove that the augmented Lagrangian function value is
monotonically non-increasing as long as the augmented Lagrangian parameter is
chosen to be sufficiently large. Simulation results show that the augmented
Lagrangian function is bounded from below when the matrix in the quadratic term
of the objective function is positive definite. In such a case, the
consensus-ADMM is convergent.Comment: 13 pages, 5 figure
Sparse Array Beamformer Design via ADMM
In this paper, we devise a sparse array design algorithm for adaptive
beamforming. Our strategy is based on finding a sparse beamformer weight to
maximize the output signal-to-interference-plus-noise ratio (SINR). The
proposed method utilizes the alternating direction method of multipliers
(ADMM), and admits closed-form solutions at each ADMM iteration. The algorithm
convergence properties are analyzed by showing the monotonicity and boundedness
of the augmented Lagrangian function. In addition, we prove that the proposed
algorithm converges to the set of Karush-Kuhn-Tucker stationary points.
Numerical results exhibit its excellent performance, which is comparable to
that of the exhaustive search approach, slightly better than those of the
state-of-the-art solvers, including the semidefinite relaxation (SDR), its
variant (SDR-V), and the successive convex approximation (SCA) approaches, and
significantly outperforms several other sparse array design strategies, in
terms of output SINR. Moreover, the proposed ADMM algorithm outperforms the
SDR, SDR-V, and SCA methods, in terms of computational complexity.Comment: Accepted by IEEE Transactions on Signal Processin
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