This paper considers the problem of direction-ofarrival (DOA) estimation for multiple uncorrelated plane waves incident on so-called "fully augmentable" sparse linear arrays. In situations where a decision is made on the number of existing signal sources (m m m) prior to the estimation stage, we investigate the conditions under which DOA estimation accuracy is effective (in the maximum-likelihood sense). In the case where m m m is less than the number of antenna sensors (M M M), a new approach called "MUSIC-maximum-entropy equalization" is proposed to improve DOA estimation performance in the "preasymptotic region" of finite sample size (N N N) and signal-tonoise ratio. A full-sized positive definite (p.d.) Toeplitz matrix is constructed from the M M M 2 2 2M M M direct data covariance matrix, and then, alternating projections are applied to find a p.d. Toeplitz matrix with m m m-variate signal eigensubspace ("signal subspace truncation"). When m m m M M M, Cramér-Rao bound analysis suggests that the minimal useful sample size N N N is rather large, even for arbitrarily strong signals. It is demonstrated that the well-known direct augmentation approach (DAA) cannot approach the accuracy of the corresponding Cramér-Rao bound, even asymptotically (as N N N ! ! !1 1 1) and, therefore, needs to be improved. We present a new estimation method whereby signal subspace truncation of the DAA augmented matrix is used for initialization and is followed by a local maximum-likelihood optimization routine. The accuracy of this method is demonstrated to be asymptotically optimal for the various superior scenarios (m m m M M M) presented.

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