High accuracy estimation of multi-frequency signal parameters by improved phase linear regression

Authors:
LiMin Zhu;XueMei Song;HanXiong Li;Han Ding
Affiliations:
Robotics Institute, Shanghai Jiao Tong University, Shanghai 200030, PR China;Robotics Institute, Shanghai Jiao Tong University, Shanghai 200030, PR China;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong;Robotics Institute, Shanghai Jiao Tong University, Shanghai 200030, PR China
Venue:
Signal Processing
Year:
2007

Citing 7
Cited 1

Estimating the frequency of a noisy sinusoid by linear regression

IEEE Transactions on Information Theory
An efficient method for the estimation of the frequency of a single tone in noise from the phases of discrete Fourier transforms

Signal Processing
Digital spectral analysis: with applications

Digital spectral analysis: with applications
Generalisation of the method for the estimation of the frequencies of tones in noise from the phases of discrete fourier transforms

Signal Processing
Computationally efficient estimation of sinusoidal frequency at low SNR

ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05
The statistical performance of some instantaneous frequencyestimators

IEEE Transactions on Signal Processing
Single tone parameter estimation from discrete-time observations

IEEE Transactions on Information Theory

Multi-frequency identification method in signal processing

Digital Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

An improved phase regression approach for estimating the parameters of a multi-frequency signal from discrete samples corrupted by additive noise is presented. It efficiently estimates the signal frequency and phase by linear regression on the phase spectra of segmented signal blocks, and the signal amplitude directly from the discrete-time Fourier transform of the window function. The techniques of weighted spectral lines averaging and overlapped signal segmenting are introduced to improve the estimation accuracy. The expressions of the estimator variances are derived, and shown to almost reach the Cramer-Rao bounds. Numerical simulations are given to confirm the validity of the presented approach.