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The direction of arrival (DOA) error in dipole 3D arrays is estimated through Monte-Carlo simulations using the standard MUSIC algorithm. Novel 3D geometries are implemented which demonstrate better precision given the same lateral area. The mutual coupling effect is included by changing the search vector in the MUSIC DOA algorithm for the novel 3D geometries. A simple straight forward method is used which does not need complex electromagnetic computations. Simulations show that the proposed method can successfully account for the mutual coupling effects in two dimensional direction finding (both azimuth and elevation angle) with novel 3D array geometries. Reduced DOA estimation error is observed in the novel geometries which can be used as a method of mitigation for the mutual coupling effect. [ABSTRACT FROM AUTHOR]

Acoustic reflections in rooms are best understood and controlled when their causal surfaces have been accurately localized. Several in situ measurement techniques have been proposed over the years to pinpoint reflections via estimated directions of arrival at chosen field positions. The polar energy time curve (Polar ETC) is one approach requiring six cardioid impulse response measurements along the Cartesian axes about a point in a room. This paper presents a theoretical, numerical, and experimental investigation of its estimation errors due to simultaneous sound arrivals. Theoretical developments provide insights into their causes. Numerical examples based on the method of images explore conditions leading to the errors and likely error magnitudes. Experimental work using discrete planar reflectors in an anechoic chamber produced controlled reflections to further investigate the effects. An altazimuth-mounted laser and mirrors enabled reflection tracing and a means of measuring and comparing arrival directions to those estimated by the Polar ETC. The efforts demonstrate that the Polar ETC provides credible angular estimations for individual arrivals, but spurious results for most simultaneous arrivals. Single arrivals produce absolute errors of only a few degrees, while simultaneous arrivals of similar amplitudes produce errors ranging sporadically from a few degrees to many degrees. [ABSTRACT FROM AUTHOR]

In this paper we address the problem of off-grid direction of arrival (DOA) estimation based on sparse representations in the situation of multiple measurement vectors (MMV). A novel sparse DOA estimation method which changes MMV problem to SMV is proposed. This method uses sparse representations based on weighted eigenvectors (SRBWEV) to deal with the MMV problem. MMV problem can be changed to single measurement vector (SMV) problem by using the linear combination of eigenvectors of array covariance matrix in signal subspace as a new SMV for sparse solution calculation. So the complexity of this proposed algorithm is smaller than other DOA estimation algorithms of MMV. Meanwhile, it can overcome the limitation of the conventional sparsity-based DOA estimation approaches that the unknown directions belong to a predefined discrete angular grid, so it can further improve the DOA estimation accuracy. The modified Rife algorithm for DOA estimation (MRife-DOA) is simulated based on SRBWEV algorithm. In this proposed algorithm, the largest and sub-largest inner products between the measurement vector or its residual and the atoms in the dictionary are utilized to further modify DOA estimation according to the principle of Rife algorithm and the basic idea of coarse-to-fine estimation. Finally, simulation experiments show that the proposed algorithm is effective and can reduce the DOA estimation error caused by grid effect with lower complexity. [ABSTRACT FROM AUTHOR]

Covariance-based DoA estimation (CB-DoA) algorithms represent lower computational complexity alternatives to the traditional ESPRIT approach. This paper investigates CB-DoA using Krylov-subspace techniques (including Arnoldi's and Lanczos' updates) with respect to the resulting computational cost and estimation error performance. The proposed modifications also allow an automatic estimation of the number of sources. Computational analyses performed for the resulting CB-DoA algorithm indicate cost savings above $$60\,\%$$ in comparison with the standard CB-DoA implementation, which already represents a $$20\,\%$$ improvement upon its ESPRIT counterpart, at an equivalent mean squared error level, as verified in numerical simulations. [ABSTRACT FROM AUTHOR]

The accurate determination of the steering vector of a sensor array that corresponds to a desired signal is often hindered by uncertainties due to array imperfections, such as the presence of a direction-of-arrival (DOA) estimation error, mutual coupling, array sensor gain/phase uncertainties, and senor position perturbations. Consequently, the performance of conventional beamforming algorithms that use the nominal steering vector may be significantly degraded. A new method for recursively correcting possible deterministic errors in the estimated steering vector is proposed here. It employs the subspace principle and estimates the desired steering vector by using a convex optimization approach. We show that the solution can be obtained in closed form by using the Lagrange multiplier method. As the proposed method is based on an extended version of the conventional orthonormal PAST (OPAST) algorithm, it has low implementation complexity, and moving sources can be handled. In addition, a robust beamformer with a new error bound that uses the proposed steering vector estimate is derived by optimizing the worst case performance of the array after taking the uncertainties of the array covariance matrix into account. This gives a diagonally loaded Capon beamformer, where the loading level is related to the bound of the uncertainty in the array covariance matrix. Numerical results show that the proposed algorithm performs well, especially at high signal-to-noise ratio (SNR) and in the presence of deterministic sensor uncertainties. [ABSTRACT FROM PUBLISHER]