A Refined Estimator of Multicomponent Third-Order Polynomial Phase Signals

GuoJian OU; ShiZhong YANG; JianXun DENG; QingPing JIANG; TianQi ZHANG

doi:10.1587/transcom.2015EBP3131

A Refined Estimator of Multicomponent Third-Order Polynomial Phase Signals

GuoJian OU, ShiZhong YANG, JianXun DENG, QingPing JIANG, TianQi ZHANG

Full Text Views

0

Cite this

Summary :

This paper describes a fast and effective algorithm for refining the parameter estimates of multicomponent third-order polynomial phase signals (PPSs). The efficiency of the proposed algorithm is accompanied by lower signal-to-noise ratio (SNR) threshold, and computational complexity. A two-step procedure is used to estimate the parameters of multicomponent third-order PPSs. In the first step, an initial estimate for the phase parameters can be obtained by using fast Fourier transformation (FFT), k-means algorithm and three time positions. In the second step, these initial estimates are refined by a simple moving average filter and singular value decomposition (SVD). The SNR threshold of the proposed algorithm is lower than those of the non-linear least square (NLS) method and the estimation refinement method even though it uses a simple moving average filter. In addition, the proposed method is characterized by significantly lower complexity than computationally intensive NLS methods. Simulations confirm the effectiveness of the proposed method.

Publication: IEICE TRANSACTIONS on Communications Vol.E99-B No.1 pp.143-151

Publication Date: 2016/01/01

Publicized

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2015EBP3131

Type of Manuscript: PAPER

Category: Fundamental Theories for Communications

Authors

GuoJian OU
  Chongqing University,Chongqing College of Electronic Engineering
ShiZhong YANG
  Chongqing University
JianXun DENG
  Chongqing College of Electronic Engineering
QingPing JIANG
  Chongqing University
TianQi ZHANG
  Chongqing University of Posts and Telecommunications

Keyword

multicomponent third-order polynomial phase signals, fast Fourier transformation (FFT), moving average filter, k-means algorithm, singular value decomposition (SVD)

Cite this

Copy

GuoJian OU, ShiZhong YANG, JianXun DENG, QingPing JIANG, TianQi ZHANG, "A Refined Estimator of Multicomponent Third-Order Polynomial Phase Signals" in IEICE TRANSACTIONS on Communications, vol. E99-B, no. 1, pp. 143-151, January 2016, doi: 10.1587/transcom.2015EBP3131.
Abstract: This paper describes a fast and effective algorithm for refining the parameter estimates of multicomponent third-order polynomial phase signals (PPSs). The efficiency of the proposed algorithm is accompanied by lower signal-to-noise ratio (SNR) threshold, and computational complexity. A two-step procedure is used to estimate the parameters of multicomponent third-order PPSs. In the first step, an initial estimate for the phase parameters can be obtained by using fast Fourier transformation (FFT), k-means algorithm and three time positions. In the second step, these initial estimates are refined by a simple moving average filter and singular value decomposition (SVD). The SNR threshold of the proposed algorithm is lower than those of the non-linear least square (NLS) method and the estimation refinement method even though it uses a simple moving average filter. In addition, the proposed method is characterized by significantly lower complexity than computationally intensive NLS methods. Simulations confirm the effectiveness of the proposed method.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2015EBP3131/_p

Copy

@ARTICLE{e99-b_1_143,
author={GuoJian OU, ShiZhong YANG, JianXun DENG, QingPing JIANG, TianQi ZHANG, },
journal={IEICE TRANSACTIONS on Communications},
title={A Refined Estimator of Multicomponent Third-Order Polynomial Phase Signals},
year={2016},
volume={E99-B},
number={1},
pages={143-151},
abstract={This paper describes a fast and effective algorithm for refining the parameter estimates of multicomponent third-order polynomial phase signals (PPSs). The efficiency of the proposed algorithm is accompanied by lower signal-to-noise ratio (SNR) threshold, and computational complexity. A two-step procedure is used to estimate the parameters of multicomponent third-order PPSs. In the first step, an initial estimate for the phase parameters can be obtained by using fast Fourier transformation (FFT), k-means algorithm and three time positions. In the second step, these initial estimates are refined by a simple moving average filter and singular value decomposition (SVD). The SNR threshold of the proposed algorithm is lower than those of the non-linear least square (NLS) method and the estimation refinement method even though it uses a simple moving average filter. In addition, the proposed method is characterized by significantly lower complexity than computationally intensive NLS methods. Simulations confirm the effectiveness of the proposed method.},
keywords={},
doi={10.1587/transcom.2015EBP3131},
ISSN={1745-1345},
month={January},}

Copy

TY - JOUR
TI - A Refined Estimator of Multicomponent Third-Order Polynomial Phase Signals
T2 - IEICE TRANSACTIONS on Communications
SP - 143
EP - 151
AU - GuoJian OU
AU - ShiZhong YANG
AU - JianXun DENG
AU - QingPing JIANG
AU - TianQi ZHANG
PY - 2016
DO - 10.1587/transcom.2015EBP3131
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E99-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2016
AB - This paper describes a fast and effective algorithm for refining the parameter estimates of multicomponent third-order polynomial phase signals (PPSs). The efficiency of the proposed algorithm is accompanied by lower signal-to-noise ratio (SNR) threshold, and computational complexity. A two-step procedure is used to estimate the parameters of multicomponent third-order PPSs. In the first step, an initial estimate for the phase parameters can be obtained by using fast Fourier transformation (FFT), k-means algorithm and three time positions. In the second step, these initial estimates are refined by a simple moving average filter and singular value decomposition (SVD). The SNR threshold of the proposed algorithm is lower than those of the non-linear least square (NLS) method and the estimation refinement method even though it uses a simple moving average filter. In addition, the proposed method is characterized by significantly lower complexity than computationally intensive NLS methods. Simulations confirm the effectiveness of the proposed method.
ER -