In sparsity-based optimization problems for two dimensional (2-D) direction-of-arrival (DOA) estimation using L-shaped nested arrays, one of the major issues is computational complexity. A 2-D DOA estimation algorithm is proposed based on reconsitution sparse Bayesian learning (RSBL) and cross covariance matrix decomposition. A single measurement vector (SMV) model is obtained by the difference coarray corresponding to one-dimensional nested array. Through spatial smoothing, the signal measurement vector is transformed into a multiple measurement vector (MMV) matrix. The signal matrix is separated by singular values decomposition (SVD) of the matrix. Using this method, the dimensionality of the sensing matrix and data size can be reduced. The sparse Bayesian learning algorithm is used to estimate one-dimensional angles. By using the one-dimensional angle estimations, the steering vector matrix is reconstructed. The cross covariance matrix of two dimensions is decomposed and transformed. Then the closed expression of the steering vector matrix of another dimension is derived, and the angles are estimated. Automatic pairing can be achieved in two dimensions. Through the proposed algorithm, the 2-D search problem is transformed into a one-dimensional search problem and a matrix transformation problem. Simulations show that the proposed algorithm has better angle estimation accuracy than the traditional two-dimensional direction finding algorithm at low signal-to-noise ratio and few samples.

This paper proposes and analyses an improved direction finding (DF) method that uses a rotating interferometer. The minimum sampling frequency is deduced in order to eliminate the phase ambiguity associated with a long baseline, the influence of phase imbalance of receiver is quantitatively discussed and the Root Mean Square Error (RMSE) of both bearing angle and pitch angle are also demonstrated. The theoretical analysis of the rotating interferometer is verified by simulation results, which show that it achieves better RMSE performance than the conventional method.

We propose a novel propagation measurement scheme for terrestrial TV signal indoor reception based on a virtual array technique. The system proposed in this paper carries out two-branch recording of target signals and the reference signal. By using the signal phase reference in the reference signal, we clarify the spatial propagation characteristics obtained from the two-dimensional virtual array outputs. Outdoor measurements were performed first to investigate the validity of the proposed measurement system. The results confirm its effectiveness in accurately determining the direction-of-arrival (DOA). We then investigated the propagation characteristics in an indoor environment. The angular spectrum obtained showed clear wave propagation structure. Thus, our proposed system is promising as a very accurate measurement tool for indoor propagation analysis.

This paper considers the problem of finding two-dimensional (2-D) direction of arrivals (DOAs) for coherent cyclostationary signals using a 2-D array with random position errors. To alleviate the performance degradation due to the coherence between the signals of interest (SOIs) and the random perturbation in 2-D array positions, a matrix reconstruction scheme in conjunction with an iterative algorithm is presented to reconstruct the correlation matrices related to the received array data so that the resulting correlation matrices possess the eigenstructures required for finding 2-D DOAs. Then, using the reconstructed matrices, we create a subspace orthogonal to the subspace spanned by the direction vectors of the SOIs. Therefore, the 2-D DOAs of the SOIs can be estimated based on a subspace-fitting concept and the created subspace. Finally, several simulation examples are presented for illustration and comparison.

A new algorithm, termed reduced-order Root-MUSIC, for high resolution direction finding is proposed. It requires finding all the roots of a polynomial with an order equaling twice the number of propagating signals. Some Monte Carlo simulations are used to test the effectiveness of the proposed algorithm.

In this paper, we propose a resolution enhancement method for estimating direction-of-arrival (DOA) of narrowband incoherent signals incident on a general array. The resolution of DOA algorithm is dependent on the aperture size of antenna array. But it is very impractical to increase the physical size of antenna array in real environment. We propose the method that improves resolution performance by virtually expanding the sensor spacing of original antenna array and then averaging the spatial spectrum of each virtual array which has a different aperture size. Superior resolution capabilities achieved with this method are shown by simulation results in comparison with the standard MUSIC for incoherent signals incident on a uniform circular array.

We consider the problem of estimating one- and two-dimensional direction of arrivals for arbitrary plane waves in an incoherent/coherent source environment. For the one-dimensional case, we use matrix pencil (MP) method developed by Y. Hua for signal-poles estimation. We then extend this method to estimate the two-dimensional direction of arrivals (2D-DOA), resulting in the "Extended Matrix Pencil" (EMP) method. This method can be applied successfully as much for an incoherent source environment as for a coherent source environment. To study the performance of these methods, in both cases results are compared with the "Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Techniques" (TLS-ESPRIT) and the "Spatial Smoothing-TLS-ESPRIT" (SS-TLS-ESPRIT) methods. The results show that the MP method estimates the DOA more accurately and better than the TLS-ESPRIT and the SS-TLS-ESPRIT, even with few snapshots. Simulation results show that the EMP method, presented in this paper, estimates the 2-DOA better than the other two methods used for comparison.

In this communication we propose to solve the problem of reconstruction from limited data using a statistical regularization method based on a Bayesian information criterion. The minimization of the Bayesian information criterion, which is used here as an objective index to measure the goodness of an estimate, gives the optimum value of the smoothing parameter. By doing so, we could reduce the inversion problem to a simple minimization of a one-variable nonlinear function. The application of such a technique overcomes the nonuniqueness of the solution of the ill-posed problem and all shortcomings of many iterative methods. In the light of simulation and application to real data, we propose a slight modification to the Bayesian information criterion to reconstruct the wave energy distribution at the ionospheric base from the observation of radio wave electromagnetic field on the ground. The achieved results in both the inversion problem and the wave direction finding are very promising and may support other works so far suggested the use of Bayesian methods in the inversion of ill-posed problems to benefit from the valuable information brought by the a priori knowledge.

When signal subspace techniques, such as MuSIC, are used to locate a number of incident signals, an exhaustive search of the array manifold has to be carried out. This search involves the evaluation of a single cost function at a number of points which form a grid, resulting in quantization-error effects. In this paper a new algorithm is put forward to overcome the quantization problem. The algorithm uses a number of cost functions, and stages, equal to the number of incident signals. At each stage a new cost function is evaluated in a small number of "special" directions, known as characteristic points. For an N-element array the characteristic points, which can be pre-calculated from the array manifold curvatures, partition the array manifold into N-1 regions. By using a simple gradient algorithm, only a small area of one of these regions is searched at each stage, demonstrating the potential benefits of the proposed approach.

The wave distribution functions (WDFs) have been reconstructed by means of the maximum entropy inversion to the observed spectral matrix composed of the auto- and cross-power spectra among the three field components (Bx, By and Ez) in which the exactly right-handed circular polarization is taken in the integration kernels. The purpose of this paper is to investigate the properties of wave distribution functions reconstructed for wave sources whose central polarization is somewhat deviated from right-handed circular and to study (1) the WDF's by using the right-handed circular polarization in the kernels, (2) the effect of larger deviations for the polarization of elementary plane waves consituting the wave source, (3) the WDF's based on the elliptical polarization kernels and (4) the effect of limiting the number of eigenvalues. It is then found that changing the polarization model in the integration kernels would be helpful in finding out the polarization of the actually observed signals.