Mixed sources localisation using a sparse representation of cumulant vectors

Abstract

In this study, a new mixed near-field and far-field sources localisation algorithm based on sparse signal recovery is addressed. In this scheme, two special cumulant vectors are constructed successively, the first one is used to obtain the azimuth estimations of all the incoming signals, and the second one is used to distinguish the mixed sources as well as estimate the range related to the near-field sources. The reweighted ℓ 1-norm minimisation with one iteration is utilised for sparse signal recovery. In the recovery process, the authors propose to select the regularisation parameter by a special case of 2-fold cross-validation. The simulation results demonstrate the effectiveness and the efficiency of the proposed algorithm.

Generalised two stage cumulants‐based MUSIC algorithm for passive mixed sources localisation

“…H a(γ k , ϕ); k = 1, 2, …, K, (26) where U n2 spans the noise subspace of R. Eventually, the azimuth DOA and the range of the kth source are estimated using (4):…”

“…In recent years, sparse signal representation (SSR) framework has been employed in array signal processing. In this regard, several algorithms have been presented to localise NF sources [20,21], FF sources [22][23][24][25], and mixed sources [26][27][28]. Similar to subspace-based methods, SSR-based ones exhibit some advantages such as high resolution, and robustness to the noise.…”

“…In recent years, in order to employ the merits of the HOSs and alleviate the array aperture loss, many algorithms based on HOSs [26,[29][30][31][32] have been proposed. In general, cumulants are utilised to establish virtual arrays and virtual covariance matrices whose signal subspace can be specified by the use of a virtual or an extended aperture [33][34][35].…”

DOA estimation for far-field sources in mixed signals with mutual coupling and gain-phase error array

“…In [30], based on FOC and the estimation of signal parameters via rotational invariance techniques (ES-PRIT), K Wang proposed a new localization algorithm for the mixed signals. In [31,32], two localization methods based on sparse signal reconstruction are provided by Ye and B Wang respectively; they can achieve improved accuracy and resolve signals which are close to each other. The methods above only apply to the background of only FS, but there are rare published literatures of DOA estimation for mixed signals at the background of more than one kind of array error.…”

Passive Localization of Mixed Far-Field and Near-Field Sources without Estimating the Number of Sources

“…In [ 25 – 27 ], the authors utilize the sparse signal recovery technique for mixed sources localization, which provide improved estimation accuracy. The algorithms in [ 26 , 27 ] are based on the construction of fourth order cumulant matrices and vectors, whereas the anti-diagonal elements of the second-order array covariance matrix is exploited in [ 25 ]. However, these sparse-recovery-based algorithms call for an enormous amount of computations.…”