An efficient algorithm for two-dimensional frequency estimation
Multidimensional Systems and Signal Processing
One-Dimensional MODE Algorithm for Two-Dimensional FrequencyEstimation
Multidimensional Systems and Signal Processing
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
DOA estimation by fourth-order cumulants in unknown noise environments
ICASSP '93 Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04
SIAM Journal on Matrix Analysis and Applications
Multidimensional rank reduction estimator for parametric MIMO channel models
EURASIP Journal on Applied Signal Processing
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Tensor algebra and multidimensional harmonic retrieval in signal processing for MIMO radar
IEEE Transactions on Signal Processing
Training sequence optimization in MIMO systems with colored noise
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume I
Almost sure identifiability of constant modulus multidimensional harmonic retrieval
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part II
Prewhitening for rank-deficient noise in subspace methods for noise reduction
IEEE Transactions on Signal Processing - Part I
Low-rank detection of multichannel Gaussian signals using blockmatrix approximation
IEEE Transactions on Signal Processing
Robust direction-of-arrival estimation in non-Gaussian noise
IEEE Transactions on Signal Processing
Maximum-likelihood array processing in non-Gaussian noise with Gaussian mixtures
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Iterative and sequential algorithms for multisensor signalenhancement
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Structured least squares to improve the performance of ESPRIT-typealgorithms
IEEE Transactions on Signal Processing
On Estimation of Covariance Matrices With Kronecker Product Structure
IEEE Transactions on Signal Processing
Almost-sure identifiability of multidimensional harmonic retrieval
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part I
Adaptive image restoration using a generalized Gaussian model for unknown noise
IEEE Transactions on Image Processing
Hi-index | 0.08 |
Parameter estimation of multidimensional data in the presence of colored noise or interference with a Kronecker product covariance structure, which appears in electroencephalogram/magnetoencephalogram and multiple-input multiple-output applications, is addressed. In order to improve the accuracy of the multidimensional subspace-based estimation techniques designed for white noise, prewhitening algorithms are devised by exploiting the Kronecker structure of the noise covariance matrix. We first contribute to the development of the multidimensional prewhitening (MD-PWT) scheme which assumes that noise-only measurements are available. By applying prewhitening sequentially along various dimensions using the corresponding correlation factors estimated from the noise-only measurements, the MD-PWT significantly improves the performance of the closed-form parallel factor decomposition based parameter estimator (CFP-PE) with a small number of noise-only snapshots. When noise-only measurements are unavailable, an iterative joint estimation of noise and signal parameters and prewhitening algorithm is proposed by iteratively applying the MD-PWT and CFP-PE. Adaptive convergence thresholds are designed as the stopping conditions such that the optimal number of iterations is automatically determined. Simulation results show that the iterative scheme performs nearly the same as the MD-PWT with noise statistics, in all scenarios except for a special one of intermediate signal-to-noise ratios and high noise correlation levels.