Performance analysis of subspace-based parameter estimation algorithms
Abstract
New perturbation formulas were developed for signal and orthogonal subspaces which are estimated from a noisy data matrix. These formulas are: (1) based on a finite amount of data; (2) derived under the assumption of high signal-to-noise ratio; and (3) applicable to arrays of arbitrary geometry, and they provide a common foundation for all analyses. A number of array processing algorithms were analyzed which are classified as follows: (1) Signal subspace algorithms: ESPRIT, state-space realization (including TAM), and Matrix Pencil; (2) orthogonal subspace algorithms: MUSIC and Min-Norm. Analytical variance formulas were developed for the case in which estimates are obtained by searching for the extrema of a function (used with arbitrary array geometry), as well as the case in which estimates are obtained by rooting a polynomial or finding the eigenvalues of a matrix (used with a uniform line array geometry). In addition, improvements were developed for a state-space algorithm for frequency-wavenumber (2-D) estimation. A procedure to pair individual frequency and wavenumber estimates was given, and it was also shown how a 2-D forward-backward data matrix can be used to improve the performance of the state-space approach.
- Publication:
-
Progress Report
- Pub Date:
- June 1990
- Bibcode:
- 1990uri..rept.....V
- Keywords:
-
- Algorithms;
- Electrical Engineering;
- Estimating;
- Geometry;
- Linear Arrays;
- Noise Reduction;
- Radio Frequency Interference;
- Signal Processing;
- Data Bases;
- Display Devices;
- Eigenvalues;
- Polynomials;
- Signal To Noise Ratios;
- Electronics and Electrical Engineering