Research On Direction-of-Arrival Estimation Algorithm Based On Nested Array

Recently, designing sparse array structure and studying signals processing algorithms based on sparse array have been received more and more attention. Nested array is a new kind of sparse array system by nesting two or more uniform linear arrays. Compared with traditional uniform linear arrays, the nested array with the same number of physical sensors is capable of providing enlarged array aperture size and more degrees of freedom. So it has more processing sources capacity, stronger resolution ability and higher estimation accuracy.Therefore, nested array is a hot topic in array signal processing due to these advantages. This article focuses on the high resolution narrowband signals direction of arrival (DOA)estimation algorithms and wideband signals DOA estimation algorithms using a two level nested array.Due to traditional narrowband signals DOA estimation methods can not resolve the number of sources more than the number of physical sensors. In this paper, we use Khatri-Rao product vectorizing the arrays received data vector covariance matrix to extend the array aperture and improve the degrees of freedom of the whole array. The SS-MUSIC algorithm based on a two level nested array is capable of using the increased degrees of freedom and achieving the number of signals DOA estimation more than the number of physical sensors.On this basis, for the SS-MUSIC algorithm has low resolution of the closely signals, we improve the SS-MUSIC algorithm and the weighted subspace projection method (WSPM) is proposed. This method deals the covariance matrix with noise reduction to reduce the influence of noise, and uses the reciprocal of large eigenvalues weighting signals subspace to taking advantage of signals subspace information. Simulation results are given to verify higher resolution of the proposed method.Finally, considering the currently processing underdetermined wideband signals DOA estimation algorithms based on uniform linear arrays needing estimated angles, and the estimated angles deviation have a great impact on the performance of the algorithms and need a large amount of computations. We apply the two level nested array processing the wideband signals DOA estimation and propose a new method named reconstruction of Toeplitz subspace (RTS). Firstly, using Khatri-Rao product vectorizing each frequency point of the received data covariance matrix vector to obtain a virtual array received data vector. Then we deal the newly received data vector with dimension reduction. Using the elements of dimensionality reduction received data vector to build up a Toeplitz matrix. This matrix contains the whole signals DOA information. So this method can resolve the number of wideband signals more than physical sensors and have a good estimation performance.