Joint Angle-Doppler Estimation for Passive Radar Based on Compressed Sensing

In this paper, a fast target angle and Doppler joint estimation method based on compressed sensing is proposed for passive radar. The joint estimation of multi-dimensional parameters enables improved resolution without introducing additional matching overhead. However, the dimension of the over-complete dictionary of multi-dimensional parameters is too large, resulting in a large computational complexity. In the proposed method, following segmented pulse compression, a multi-measurement vector model is constructed for the angle dimension, and then, a sparse representation model is formulated for the Doppler dimension to obtain the Doppler frequency of the targets. Additionally, the reconstruction algorithms are improved by using regularization operation. Simulated and experimental results demonstrate that the proposed method can accurately estimate both angle and Doppler parameters, while reducing the error term, reducing the dictionary dimension and achieving shorter running time.