Detecting Jamming Signal Using 2D Direction of Arrival Estimation Technique

In this letter, we use two parallel coprime arrays to localize the jamming source. The classical Two-Dimensional (2D) Direction of Arrival (DOA) estimation primarily relies on unstructured sample covariance matrix. However, estimating the covariance matrix from a finite number of observations frequently fails to perfectly reflect the Toeplitz and Hermitian structural characteristics. Thus, we propose a high-precision localizer of jamming via preprocessing the covariance matrix. First, we introduce 2-Level-Toeplitz (2LT) structure matrix for 2D coarray signal, and derive it in terms of the entries of a Toeplitz matrix. Second, we parameterize the 2LT matrix and borrow the structured covariance estimation technique to obtain the least squares close-form solution. Finally, we reconstruct the noiseless augmented covariance matrix and apply 2D DOA estimation method. Our simulations show that structured covariance matrix enhances the performances of 2D DOA estimation with sparse arrays for jamming signals. Specifically, the Root Mean Squared Error (RMSE) of our estimator consistently approaches the Cramér-Rao Bound (CRB) for different signal-to-noise ratio values and an increasing number of jamming devices.