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The uniform white noise assumption is one of the basic assumptions in most of the existing directional-of-arrival (DOA) estimation methods. In many applications, however, the non-uniform white noise model is more adequate. Then the noise variances at different sensors have to be also estimated as nuisance parameters while estimating DOAs. In this letter, different from the existing iterative methods that address the problem of non-uniform noise, a non-iterative two-phase subspace-based DOA estimation method is proposed. The first phase of the method is based on estimating the noise subspace via eigendecomposition (ED) of some properly designed matrix and it avoids estimating the noise covariance matrix. In the second phase, the results achieved in the first phase are used to estimate the noise covariance matrix, followed by estimating the noise subspace via generalized ED. Since the proposed method estimates DOAs in a non-iterative manner, it is computationally more efficient and has no convergence issues as compared to the existing methods. Simulation results demonstrate better performance of the proposed method as compared to other existing state-of-the-art methods. Index TermsArray processing, direction-of-arrival (DOA) estimation, subspace based methods, nonuniform noise, spectral analysis.

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In some applications, the signals received by an array are a mixture of signals emitted by both far-field and near-field sources. This study develops a new cumulant-based multiple signal classification (MUSIC) algorithm for source localisation using a new structural sparse array for scenarios where both far-field and near-field sources coexist. The key feature of this algorithm is that it utilises fourth-order cumulants to compute the virtual covariance matrix and constructs a new special cumulant matrix to acquire the largest number of virtual sensors and the largest array aperture for a given number of sensors. The authors provide a geometric proof to justify the utilisation of the proposed sparse linear array and compute the effective aperture of the array. The proposed algorithm increases resolution ability, direction of arrival (DOA) and range estimation accuracy, and the number of sources to be localised. Moreover, the new method has the main advantage that it does not use the information of all sensors; so that it provides somewhat low computational complexity while it uses many actual and virtual sensors. Monte Carlo simulations are provided to demonstrate the effectiveness of the proposed method.