Generalized High-Order Phase Function for Parameter Estimation of Polynomial Phase Signal

2008 ◽  
Vol 56 (7) ◽  
pp. 3023-3028 ◽  
Author(s):  
Pu Wang ◽  
Igor Djurovic ◽  
Jianyu Yang
Keyword(s):  
Parameter Estimation ◽  
Phase Function ◽  
High Order ◽  
Polynomial Phase ◽  
Phase Signal

Parameter Estimation of Multicomponent Cubic Phase Signals

2013 ◽  
Vol 380-384 ◽  
pp. 3726-3729
Author(s):  
Peng Fei Tang ◽  
Bin Yuan ◽  
Qian Qiang Lin ◽  
Zeng Ping Chen
Keyword(s):  
Parameter Estimation ◽  
Numerical Simulations ◽  
Phase Function ◽  
Cubic Phase ◽  
Chirp Rate ◽  
Signal Component ◽  
Phase Signal ◽  
Cubic Phase Function

This paper presents an algorithm for estimating the parameters of multicomponent cubic phase signals. This algorithm combines the product generalized cubic phase function (PGCPF) and the product cubic phase function (PCPF) which are used to estimate the cubic phase coefficient and chirp rate of the cubic phase signal, respectively. This algorithm starts by estimating the parameters of the signal component with the strongest amplitude. Then removing the signal component whose parameters have been estimated, it proceeds to estimate the next signal component, and so on, until all of the signal components have been estimated. Numerical simulations are carried out to validate the performance of the proposed algorithm.

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.

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

Parameter Estimation of Hybrid Sinusoidal FM-Polynomial Phase Signal

2017 ◽  
Vol 24 (1) ◽  
pp. 66-70 ◽  
Author(s):  
Pu Wang ◽  
Philip V. Orlik ◽  
Kota Sadamoto ◽  
Wataru Tsujita ◽  
Fulvio Gini
Keyword(s):  
Parameter Estimation ◽  
Polynomial Phase ◽  
Phase Signal
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