Angle estimation based on coarray tensor completion for bistatic MIMO radar with sparse array

Currently, the parameter estimation methods for sparse array bistatic multiple-input multiple-output (MIMO) radar utilizing coarray tensors primarily focus on continuous virtual arrays, overlooking the overall potential of the entire virtual coarray. To address this limitation, a parameter estimation method based on coarray tensor completion is proposed for bistatic MIMO radar with sparse arrays. First, a coarray tensor with missing elements is constructed using the virtual difference coarray based on cross-correlation. However, this coarray tensor contains whole slices of missing elements, making it difficult to directly perform tensor completion. Therefore, the coarray tensor is reconstructed to ensure it contains no missing slices. Additionally, to perform tensor completion more effectively, the reconstructed tensor needs to maximize the dispersion-to-percentage ratio (DPR) of the missing elements. Subsequently, the tensor nuclear norm minimization problem is solved to complete the reconstructed tensor. Finally, parallel factor (PARAFAC) decomposition is applied to the completed tensor to obtain the factor matrices, which are then used to estimate the direction of departure (DOD) and direction of arrival (DOA). The proposed algorithm leverages all coarray elements, resulting in improved estimation accuracy. Simulation experiments confirm the superiority of the proposed method.