A Doppler Estimation Algorithm for Radar Imaging Based on Low-Rank Embedding Theory

Doppler estimation accuracy is vital in Doppler diversity-based forward-looking imaging of the monopulse radar. Although the existing Doppler estimation methods can improve the image resolution in highly squinted regions, the accuracy of Doppler frequency estimation still needs to be refined in cases of target multiplicity and low signal-to-noise ratio (SNR). In this paper, a Doppler estimation algorithm based on matrix low-rank embedding theory is proposed, which utilizes the particular structure and decomposition properties of the Hankel-Toeplitz matrix to transform the Doppler estimation problem to an equivalent matrix rank-constrained recovery problem and combines with the Akaike information criterion (AIC) to estimate the number of targets. The numerical analysis results of point-target simulation show that the proposed algorithm can precisely estimate the Doppler frequency and complex amplitude. Notably, in the case of low SNR and target multiplicity, the accuracy is much higher than that of the traditional algorithm.