A Novel Parameter Estimation Method for Polynomial Phase Signals via Adaptive EKF

In this article, an efficient method for parameter estimation of polynomial phase signal (PPS) is proposed. Instead of the existing methods based on parametric ergodic search or phase differentiation, the proposed method adaptively tracks the PPS phase through extended Kalman filtering (EKF), termed as APT-EKF. First, based on the smoothness assumption of the local phase, a state-space model describing the PPS phase is constructed. Then, by solving the state-space model through EKF, the PPS phase can be tracked. Finally, the least square estimation (LSE) is performed for the inversion of PPS coefficients, and the O'Shea refinement strategy is implemented to enhance the estimation accuracy, thereby achieving the Cramer-Rao lower bound (CRLB). Compared with most existing studies, the proposed method occupies an obvious advantage in signal-to-noise ratio (SNR) threshold and computational efficiency. It is suitable for the arbitrary order PPS. Moreover, this article provides a comprehensive analysis of the parameter initialization, performance bounds, and computational complexity of the proposed method. Both simulation and experiment results are provided to demonstrate the effectiveness of the proposed method.