Compound time‐frequency domain method for estimating parameters of uniform‐sampling polynomial‐phase signals on the entire identifiable region. Issue 7 (1st September 2016)

• Record Type: Journal Article
• Authors: Deng, Zhenmiao; Xu, Rongrong; Zhang, Yixiong; Ye, Yishan
• Is Part Of: IET signal processing. Volume 10:Issue 7(2016)
• Page Start: 743
• Page End: 751
• Publication Date: 2016-09-01
• Subjects: signal sampling -- least squares approximations -- time‐frequency analysis -- iterative methods -- Monte Carlo methods -- maximum likelihood estimation -- AWGN -- polynomial approximation; polynomial‐phase signals -- PPS -- additive white Gaussian noise -- AWGN -- algebraic number theory -- least squares unwrapping estimator -- LSU estimator -- amplitude‐weighted phase‐based estimator -- time domain maximum likelihood estimator -- Cramér–Rao lower bound -- CRLB -- iterative compound time‐frequency domain parameter estimation method -- coarse estimation step -- fine estimation step -- discrete polynomial phase transform -- AWPE estimator -- Monte–Carlo simulations -- computational complexity -- threshold signal‐to‐noise ratio; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2015.0361
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : Parameter estimation of polynomial‐phase signals (PPSs) observed in additive white Gaussian noise (AWGN) is addressed. Most of the existing estimators cannot work on a fully identifiable region. Using the algebraic number theory, McKilliam et al . proposed a least squares unwrapping (LSU) estimator, which can operate on the entire identifiable region. However, its computational load may be large, especially when the number of samples is large. In this study, the authors first extend the amplitude‐weighted phase‐based estimator (AWPE) for sinusoidal and chirp signals to PPSs and derive a time domain maximum likelihood estimator. The performance is analysed and compared with the Cramér–Rao lower bound (CRLB). Then, the authors propose an iterative compound time‐frequency domain parameter estimation method, which includes a coarse estimation step and a fine estimation step conducted by the discrete polynomial phase transform and AWPE estimator, respectively. Monte–Carlo simulations show that the proposed method can work on the entire identifiable region and that it outperforms the existing state‐of‐the‐art estimators. Its computational complexity is considerably lower than that of the LSU estimator, while its threshold signal‐to‐noise ratio is a few decibels higher than that of the LSU estimator.