Parameter estimation of periodic frequency modulated signal based on cyclic spectrum density and its application on micro-Doppler signatures extraction

This paper focus on parameter estimation of periodic frequency modulated (PFM) signals, which can be observed from periodic phenomena in nature. For example, radar returns scattered from a walking person. In this work, a method for estimating the period of the PFM signal is proposed, which combines time-frequency distribution (TFD) and cyclic spectrum density (CSD). TFD is used to transform the nonstationary PFM signal into a stationary one with the same period while CSD can estimate the period of a signal in low signal to noise (SNR) ratio case. The properties of TFD and CSD enable the proposed algorithm to extract period of the PFM signal with various modulation forms, and moreover, with a high precision in the low SNR case. Experiments with both synthetic data and measured data confirm the effectiveness of the proposed method.