Efficient Optimized Tensor-Based Expanded Nested MIMO Radar for DOD and DOA Estimation

In this article, we employed an Expanded Nested Array (ENA) to construct the multiple input and multiple output (MIMO) radar, which efficiently avoid the mutual coupling between dense sensors compared to the Nested Array (NA) and its existing modifications. What's more, the degrees of freedom (DOFs) of ENA is larger than the conventional co-prime array (CPA), which is regarded as an effective way to reduce the mutual coupling. Next, the article appointed the method of a double-iteration Toeplitz matrix reconstruction technique to address the problem of single snapshot signal. Drawing upon this technology, we can construct a novel virtual covariance matrix with minimal computational load. Based on the covariance matrix obtained quickly, this article establishes an efficient and optimized tensor model guided by the maximum number of detectable targets to improve the parameter estimation accuracy and identification ability of methods. Numerous simulation experiments have demonstrated the effectiveness of the proposed algorithm, profit from the ingenious array layout and perfect tensor signal model.