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.
Abstract-We consider the adaptive radar problem where the properties of the (nonstationary) clutter signals can be estimated using multiple observations of radar returns from a number of sufficiently homogeneous range/azimuth resolution cells. We derive a method for approximating an arbitrary Hermitian covariance matrix by a time-varying autoregressive model of order , TVAR( ), that is based on the Dym-Gohberg band-matrix extension technique which gives the unique TVAR( ) model for any nondegenerate covariance matrix. We demonstrate that the Dym-Gohberg transformation of the sample covariance matrix gives the maximum-likelihood (ML) estimate of the TVAR( ) covariance matrix. We introduce an example of TVAR( ) clutter modeling for high-frequency over-the-horizon radar that demonstrates its practical importance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.