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

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.

Parameter Estimation of Polynomial Phase Signal by Unscented Kalman Filtering

2014 ◽  
Vol 644-650 ◽  
pp. 4253-4256
Author(s):  
Wan Ge Li ◽  
Jin Feng Hu ◽  
Hui Ai ◽  
Zhi Rong Lin ◽  
Ya Xuan Zhang
Keyword(s):  
Parameter Estimation ◽  
Kalman Filtering ◽  
State Space Model ◽  
Radar Signal ◽  
Signal Parameters ◽  
Polynomial Phase ◽  
Polynomial Phase Signals ◽  
Phase Signal ◽  
Stable Estimation ◽  
Core Issues

The parameter estimation of the Polynomial Phase Signals (PPS) is one of the core issues. In this paper, UKF-based algorithm is proposed to estimate the parameter of PPS embedded in Gaussian noise. The algorithm constructs an adequate state-space model to represent the PPS and the model can also be implied in real radar signal. Unscented Kalman filtering is applied to estimate the signal parameters. The method achieves the lower SNR threshold, the faster convergence speed, the higher accuracy and more stable estimation performance compared with the existing methods. Simulation also verifies the efficiency of the proposed method.

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