Novel parameter estimation of high-order polynomial phase signals using group delay

2021 ◽  
Vol 183 ◽  
pp. 108011
Author(s):  
Xiaodong Jiang ◽  
Siliang Wu ◽  
Yuan Chen
Keyword(s):  
Parameter Estimation ◽  
Group Delay ◽  
High Order ◽  
Polynomial Phase ◽  
Polynomial Phase Signals ◽  
Order Polynomial

A new method for parameter estimation of high-order polynomial-phase signals

2018 ◽  
Vol 142 ◽  
pp. 212-222 ◽  
Author(s):  
Runqing Cao ◽  
Ming Li ◽  
Lei Zuo ◽  
Zeyu Wang ◽  
Yunlong Lu
Keyword(s):  
Parameter Estimation ◽  
High Order ◽  
New Method ◽  
Polynomial Phase ◽  
Polynomial Phase Signals ◽  
Order Polynomial

scholarly journals A Novel Parameter Estimation Method Based on LSU-EKF for Polynomial Phase Signal

2017 ◽  
Author(s):  
Yi-xiong Zhang ◽  
Hua-wei Xu ◽  
Rong-rong Xu ◽  
Zhen-miao Deng ◽  
Cheng-Fu Yang
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Kalman Filter ◽  
Estimation Method ◽  
Computation Complexity ◽  
Parameter Estimation Problem ◽  
Performance Loss ◽  
Polynomial Phase ◽  
Polynomial Phase Signals ◽  
Phase Signal

The parameter estimation problem for polynomial phase signals (PPSs) arises in a number of fields, including radar, sonar, biology, etc. In this paper, a fast algorithm of parameter estimation for monocomponent PPS is considered. We propose the so-called LSU-EKF estimator, which combines the least squares unwrapping (LSU) estimator and the extended Kalman filter (EKF). First, the coarse estimates of the parameters of PPS are obtained by the LSU estimator using a small number of samples. Subsequently, these coarse estimates are used to initial the EKF. Monte-Carlo simulations show that the computation complexity of the LSU-EKF estimator is much less than that of the LSU estimator, with little performance loss. Similar to the LSU estimator, the proposed algorithm is able to work over the entire identifiable region. Moreover, in the EKF stage, the accurate estimated results can be output point-by-point, which is useful in real applications.

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