.png){kind=icon}
.png){kind=icon}
Return
Cubic phase function: A simple solution to polynomial phase signal analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2016.12.027.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This article provides an overview of the cubic phase function (CPF) as a tool proposed for both parametric and nonparametric estimation of the frequency modulated (FM) and in particular polynomial phase signals (PPS). This simple tool motivated small revolution in this field with numerous extensions and applications. We are describing the CPF and compare some of its extensions for both one-dimensional and two-dimensional signals. The comparisons are performed in terms of accuracy (measured with signal-to-noise (SNR) threshold and mean-squared error (MSE)) and computational complexity. Also, we review the CPF and related transforms applications.
Keywords:
Frequency modulated signals
Time-frequency analysis
Polynomial phase signals
Parameter estimation
Cubic phase function
Chirp-rate
Instantaneous frequency
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.9K
Citations:
1.7W