Parameter estimation of multicomponent polynomial-phase signals impinging on a multi-sensor array using extended Kalman filter

We address the problem of parameter estimation of multiple polynomial-phase signals (PPS) impinging on a multi-sensor array. The approach used herein is based on a state space modelization of the signal by performing an extended Kalman filtering (EKF) to estimate the state. This method exploits the spatial information provided by a sensor array resulting in considering different observations in the EKF equations. Simulation example supporting the theory is provided showing the efficiency of the resulting estimator by performance close to the Cramer-Rao lower bound (CRLB).