Matrix computations (3rd ed.)
SIAM Journal on Scientific Computing
Asymptotical analysis of MUSIC and ESPRIT frequency estimates
ICASSP '93 Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04
Linear Regression With a Sparse Parameter Vector
IEEE Transactions on Signal Processing
Prewhitening for rank-deficient noise in subspace methods for noise reduction
IEEE Transactions on Signal Processing - Part I
Projection approximation subspace tracking
IEEE Transactions on Signal Processing
The probability of a subspace swap in the SVD
IEEE Transactions on Signal Processing
Unitary ESPRIT: how to obtain increased estimation accuracy with areduced computational burden
IEEE Transactions on Signal Processing
Asymptotic MAP criteria for model selection
IEEE Transactions on Signal Processing
Fast, rank adaptive subspace tracking and applications
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Analysis of the performance and sensitivity ofeigendecomposition-based detectors
IEEE Transactions on Signal Processing
On the behavior of information theoretic criteria for model orderselection
IEEE Transactions on Signal Processing
A new perturbation analysis for signal enumeration in rotational invariance techniques
IEEE Transactions on Signal Processing
Joint High-Resolution Fundamental Frequency and Order Estimation
IEEE Transactions on Audio, Speech, and Language Processing
A robust and computationally efficient subspace-based fundamental frequency estimator
IEEE Transactions on Audio, Speech, and Language Processing
Entropy-based subspace separation for multiple frequency estimation
Digital Signal Processing
Efficient source enumeration for accurate direction-of-arrival estimation in threshold region
Digital Signal Processing
| Hi-index | 0.00 |
We consider the problem of determining the order of a parametric model from a noisy signal based on the geometry of the space. More specifically, we do this using the nontrivial angles between the candidate signal subspace model and the noise subspace. The proposed principle is closely related to the subspace orthogonality property known from the MUSIC algorithm, and we study its properties and compare it to other related measures. For the problem of estimating the number of complex sinusoids in white noise, a computationally efficient implementation exists, and this problem is therefore considered in detail. In computer simulations, we compare the proposed method to various well-known methods for order estimation. These show that the proposed method outperforms the other previously published subspace methods and that it is more robust to the noise being colored than the previously published methods.