Micro-Doppler extraction from ballistic missile radar returns using time-frequency analysis

Abstract

The spinning motion of a ballistic missile (BM) induces periodic frequency modulations on the returned radar signal. This micro-Doppler modulation frequency is a stable and unique parameter in radar target recognition. This paper first establishes the radar signal model of the spinning missile during flight, and then extracts the micro-Doppler modulation frequency through analysis of the periodic structure of the resulting spectrogram (short time Fourier Transform (STFT)) -i.e., the time-frequency distribution (TFD). In order to be used for BM target recognition we implemented a data-dependent optimal window length for the STFT. We treated the resultant TFD as an image and then used the 2-D DFT/FFT for feature extraction. Experimental results illustrate the validity of the proposed method.

Micromotion Parameter Estimation of Free Rigid Targets Based on Radar Micro-Doppler

“…On the basis, we then obtain the estimate of its nutation angle from (16). Finally, the inertia ratio of the microdynamic target would be given by substituting the estimated spin rate, precession rate, and nutation angle into (17). Fig.…”

Practical implementation of the spectrogram‐inverse Radon transform based algorithm for resident space objects parameter estimation

“…its spectrum is time-variant. This effect, is the same observed in signals generated by micro-Doppler (μD) modulations and it has been widely studied by the radar community [29][30][31][32][33][34][35][36]. Typically, time-frequency transform is used to analyse the signal in a joint time-frequency domain [37][38][39][40].…”

Analysis of phase noise influence on micro‐Doppler feature extraction of vibrating target

“…Micro‐Doppler features has been widely used to recognise the targets and there have been decades of work on the extraction of micro‐Doppler features. Many studies are aimed at the m‐D effect of ballistic targets [3–6]. Most of the analysis methods are based on the time–frequency distribution method [4–9], some of them use the generalised Hough transform [6], Hilbert–Huang transform [7] and the inverse Radon transform [8].…”