Performance Of Direction Of Arrival Estimation Algorithms Research Articles

Direct Derivation of the Stochastic CRB of DOA Estimation for Rectilinear Sources

Several direction of arrival (DOA) estimation algorithms have been proposed to exploit the structure of rectilinear or strictly second-order noncircular signals. But until now, only the compact closed-form expressions of the corresponding deterministic Cramer–Rao bound (DCRB) have been derived because it is much easier to derive than the stochastic CRB (SCRB). As this latter bound is asymptotically achievable by the maximum likelihood estimator, while the DCRB is unattainable, it is important to have a compact closed-form expression for this SCRB to assess the performance of DOA estimation algorithms for rectilinear signals. The aim of this letter is to derive this expression directly from the Slepian–Bangs formula including in particular the case of prior knowledge of uncorrelated or coherent sources. Some properties of these SCRBs are proved and numerical illustrations are given.

SUBSPACE BASED DOA ESTIMATION TECHNIQUES

The subspace based techniques are used for Direction of Arrival (DOA) estimation in this work. The subspace based techniques are based on using the eigen structure of data covariance matrix. The subspace based techniques includes MUSIC, ROOTMUSIC, ESPRIT. The aim is to analyze the performance of DOA estimation algorithms in challenging environment, such as low signal to noise ratio, closely spaced sources. The performance of subspace based DOA estimation algorithm is done on Uniform Linear Array (ULA). Simulation result shows the effect of varying parameters will affect DOA estimation. The simulation shows that the MUSIC algorithm has better accuracy as compared to the Root-MUSIC and ESPRIT.

A DOA Estimation Algorithm without Source Number Estimation for Nonplanar Array with Arbitrary Geometry

The performances of most of the high resolution methods always depend on the estimation of the source number. In real application, when the estimated number of signals is not correct, the orthogonality between signal subspace and noise subspace can not be maintained any more. And the performance of DOA estimation algorithm will deteriorate severely. In this paper, a high resolution algorithm called m- MVM without source number estimation and eigendecomposition for Direction-Of-Arrival (DOA) estimation is proposed, which is an improvement of the Minimum Variance Method (MVM). Furthermore, it is suitable to nonplanar arrays of arbitrary geometries. Some representative computer simulations are presented to illustrate the performance comparison between different algorithms and different arrays.