Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 15 April 2017 / Approved: 17 April 2017 / Online: 17 April 2017 (05:26:33 CEST)

How to cite: Zhang, Y.; Xu, H.; Xu, R.; Deng, Z.; Yang, C. A Novel Parameter Estimation Method Based on LSU-EKF for Polynomial Phase Signal. Preprints 2017, 2017040091 (doi: 10.20944/preprints201704.0091.v1). Zhang, Y.; Xu, H.; Xu, R.; Deng, Z.; Yang, C. A Novel Parameter Estimation Method Based on LSU-EKF for Polynomial Phase Signal. Preprints 2017, 2017040091 (doi: 10.20944/preprints201704.0091.v1).

Abstract

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.

Subject Areas

radar; polynomial phase signal; least squares unwrapping; extended Kalman filter

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