In this work, we focus on a recent algorithm [Z. Ying and B. P. Ng, "MUSIC-like DOA Estaimation Without Estimating the Number of Sources," IEEE Trans. Signal Process., vol. 58, no. 3, pp. 1668-1676, 2010], which is remarked to have multiple signal classification (MUSIC)-like performance without requiring to segregate the signal and noise subspaces. The optimization problem solved by this algorithm in each look direction is analyzed to obtain insights into the working principle of the algorithm. Besides showing the similarity between this algorithm and the MUSIC algorithm, its distinction from the Capon's estimator is also highlighted. The bounds for the sole parameter embedded within the optimization problem is also discussed. Simulation results evaluate the performance of the technique in comparison with the MUSIC algorithm.

When the number of sources is inaccurately estimated, it is well-known that the conventional subspace-based super-resolution direction-of-arrival (DOA) estimation techniques provide inconsistent spatial spectrum, and hence the DOA estimates. In this work, we present a novel technique which provides resolution capability comparable with that of the super-resolution techniques. While the working principle of the proposed technique is similar to that of the minimum-norm algorithm, the algorithm is insensitive to the estimated model-order. Simulation studies show that the proposed technique is advantageous over the use of subspace-based techniques with the number of sources estimated by well-known model order estimation techniques.

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