From Global Statistic to Local Statistic: Micro-Doppler Period Estimation Based on Short-Time Similarity Statistic

Micro-Doppler (m-D) period is an important feature of micro-motion targets, playing a vital role in detecting and recognizing moving targets like ground vehicles, aircraft, and space debris. Existing m-D period estimation algorithms mostly exploit the periodicity of the global radar echo signals, and extra compensation steps are needed under high-order translations. To solve that problem, this work focuses on the periodicity of the local structure of radar signals, and a short-time similarity statistic framework is introduced. It is a standard m-D period estimation procedure where various statistics can be flexibly incorporated. Under this framework three novel short-time similarity statistics – short-time ambiguity function (AF) entropy, scatter feature correlation coefficient, and normalized time-frequency (TF) Wasserstein distance are presented. Our proposed framework and specific statistics are proved to outperform conventional global statistic methods under low signal-noise ratio (SNR), high-order translations, and signal-missing scenarios through Monte Carlo simulations. The proposed methods in this work are also validated through the SAR experiment.